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"paperAbstract": "Machine-learned models are often described as \"black boxes\". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (<i>e.g.</i>, the age of an individual), others capture characteristics that could be <i>adjusted</i> (<i>e.g.</i>, the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible - assuming every instance to be a static point located in the chosen feature space. There are many circumstances however where it is important to understand <i>(i)</i> why a model outputs a certain prediction on a given instance, <i>(ii)</i> which adjustable features of that instance should be modified, and finally <i>(iii)</i> how to alter such a prediction when the mutated instance is input back to the model.\n In this paper, we present a technique that exploits the internals of a tree-based ensemble classifier to offer <i>recommendations</i> for transforming true negative instances into positively predicted ones. We demonstrate the validity of our approach using an online advertising application. First, we design a Random Forest classifier that effectively separates between two types of ads: <i>low</i> (negative) and <i>high</i> (positive) quality ads (instances). Then, we introduce an algorithm that provides recommendations that aim to transform a low quality ad (negative instance) into a high quality one (positive instance). Finally, we evaluate our approach on a subset of the active inventory of a large ad network, <i>Yahoo Gemini</i>.",
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"http://doi.acm.org/10.1145/3097983.3098039"
],
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"title": "Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking",
"venue": "KDD",
"year": 2017
},
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"name": "Trinabh Gupta"
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"name": "Henrique Fingler"
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{
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"name": "Lorenzo Alvisi"
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{
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"name": "Michael Walfish"
}
],
"doi": "10.1145/3098822.3098835",
"doiUrl": "https://doi.org/10.1145/3098822.3098835",
"entities": [
"Anti-spam techniques",
"Cryptographic protocol",
"Email",
"Email encryption",
"Encryption",
"End-to-end encryption",
"Plaintext",
"Privacy"
],
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"paperAbstract": "Emails today are often encrypted, but only between mail servers---the vast majority of emails are exposed in plaintext to the mail servers that handle them. While better than no encryption, this arrangement leaves open the possibility of attacks, privacy violations, and other disclosures. Publicly, email providers have stated that default end-to-end encryption would conflict with essential functions (spam filtering, etc.), because the latter requires analyzing email text. The goal of this paper is to demonstrate that there is no conflict. We do so by designing, implementing, and evaluating Pretzel. Starting from a cryptographic protocol that enables two parties to jointly perform a classification task without revealing their inputs to each other, Pretzel refines and adapts this protocol to the email context. Our experimental evaluation of a prototype demonstrates that email can be encrypted end-to-end and providers can compute over it, at tolerable cost: clients must devote some storage and processing, and provider overhead is roughly 5x versus the status quo.",
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"http://arxiv.org/abs/1612.04265",
"http://www.cs.nyu.edu/~mwalfish/papers/pretzel-sigcomm17.pdf",
"https://arxiv.org/pdf/1612.04265v3.pdf",
"https://arxiv.org/pdf/1612.04265v2.pdf",
"http://doi.acm.org/10.1145/3098822.3098835",
"https://arxiv.org/pdf/1612.04265v1.pdf"
],
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"title": "Pretzel: Email encryption and provider-supplied functions are compatible",
"venue": "SIGCOMM",
"year": 2017
},
"004c2345477eda977f12b4485ac24a9e41557439": {
"authors": [
{
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"name": "Aasheesh Kolli"
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{
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"name": "Vaibhav Gogte"
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{
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],
"name": "Ali G. Saidi"
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{
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"name": "Stephan Diestelhorst"
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{
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"name": "Peter M. Chen"
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{
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],
"name": "Satish Narayanasamy"
},
{
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],
"doi": "10.1145/3079856.3080229",
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"paperAbstract": "The commercial release of byte-addressable persistent memories, such as Intel/Micron 3D XPoint memory, is imminent. Ongoing research has sought mechanisms to allow programmers to implement recoverable data structures in these new main memories. Ensuring recoverability requires programmer control of the order of persistent stores; recent work proposes persistency models as an extension to memory consistency to specify such ordering. Prior work has considered persistency models at the abstraction of the instruction set architecture. Instead, we argue for extending the language-level memory model to provide guarantees on the order of persistent writes.\n We explore a taxonomy of guarantees a language-level persistency model might provide, considering both atomicity and ordering constraints on groups of persistent stores. Then, we propose and evaluate Acquire-Release Persistency (ARP), a language-level persistency model for C++11. We describe how to compile code written for ARP to a state-of-the-art ISA-level persistency model. We then consider enhancements to the ISA-level persistency model that can distinguish memory consistency constraints required for proper synchronization but unnecessary for correct recovery. With these optimizations, we show that ARP increases performance by up to 33.2% (19.8% avg.) over coding directly to the baseline ISA-level persistency model for a suite of persistent-write-intensive workloads.",
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"title": "Language-level persistency",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"paperAbstract": "Recent research has shown that applications often incorrectly implement crash consistency. We present the Crash-Consistent File System (ccfs), a file system that improves the correctness of application-level crash consistency protocols while maintaining high performance. A key idea in ccfs is the abstraction of a <i>stream</i>. Within a stream, updates are committed in program order, improving correctness; across streams, there are no ordering restrictions, enabling scheduling flexibility and high performance. We empirically demonstrate that applications running atop ccfs achieve high levels of crash consistency. Further, we show that ccfs performance under standard file-system benchmarks is excellent, in the worst case on par with the highest performing modes of Linux ext4, and in some cases notably better. Overall, we demonstrate that both application correctness and high performance can be realized in a modern file system.",
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"title": "Ambit: in-memory accelerator for bulk bitwise operations using commodity DRAM technology",
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"paperAbstract": "In today's data analytics frameworks, many users struggle to reason about the performance of their workloads. Without an understanding of what factors are most important to performance, users can't determine what configuration parameters to set and what hardware to use to optimize runtime. This paper explores a system architecture designed to make it easy for users to reason about performance bottlenecks. Rather than breaking jobs into tasks that pipeline many resources, as in today's frameworks, we propose breaking jobs into monotasks: units of work that each use a single resource. We demonstrate that explicitly separating the use of different resources simplifies reasoning about performance without sacrificing performance. Monotasks provide job completion times within 9% of Apache Spark for typical scenarios, and lead to a model for job completion time that predicts runtime under different hardware and software configurations with at most 28% error. Furthermore, separating the use of different resources allows for new optimizations to improve performance.",
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"http://kayousterhout.org/talks/2017_10_29_SOSP_Monotasks.pdf",
"http://doi.acm.org/10.1145/3132747.3132766"
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"title": "Monotasks: Architecting for Performance Clarity in Data Analytics Frameworks",
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"paperAbstract": "Exploiting the cloud storage hierarchy both within and across data-centers of different cloud providers empowers Internet applications to choose data centers (DCs) and storage services based on storage needs. However, using multiple storage services across multiple data centers brings a complex data placement problem that depends on a large number of factors including, e.g., desired goals, storage and network characteristics, and pricing policies. In addition, dynamics e.g., changing user locations and access patterns, make it impossible to determine the best data placement statically. In this paper, we present TripS, a lightweight system that considers <i>both</i> data center locations and storage tiers to determine the data placement for geo-distributed storage systems. Such systems make use of TripS by providing inputs including SLA, consistency model, fault tolerance, latency information, and cost information. With given inputs, TripS models and solves the data placement problem using mixed integer linear programming (MILP) to determine data placement. In addition, to adapt quickly to dynamics, we introduce the notion of Target Locale List (TLL), a pro-active approach to avoid expensive re-evaluation of the optimal placement. The TripS prototype is running on Wiera, a policy driven geo-distributed storage system, to show how a storage system can easily utilize TripS for data placement. We evaluate TripS/Wiera on multiple data centers of AWS and Azure. The results show that TripS/Wiera can reduce cost 14.96% ∼ 98.1% based on workloads in comparison with other works' approaches and can handle both short- and long-term dynamics to avoid SLA violations.",
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"title": "TripS: automated multi-tiered data placement in a geo-distributed cloud environment",
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"paperAbstract": "In many organizations, it is often challenging for users to find relevant data for specific tasks, since the data is usually scattered across the enterprise and often inconsistent. In fact, data scientists routinely report that the majority of their effort is spent finding, cleaning, integrating, and accessing data of interest to a task at hand. In order to decrease the \u201cgrunt work\u201d needed to facilitate the analysis of data \u201cin the wild\u201d, we present DATA CIVILIZER, an end-to-end big data management system. DATA CIVILIZER has a linkage graph computation module to build a linkage graph for the data and a data discovery module which utilizes the linkage graph to help identify data that is relevant to user tasks. It also uses the linkage graph to discover possible join paths that can then be used in a query. For the actual query execution, we use a polystore DBMS, which federates query processing across disparate systems. In addition, DATA CIVILIZER integrates data cleaning operations into query processing. Because different users need to invoke the above tasks in different orders, DATA CIVILIZER embeds a workflow engine which enables the arbitrary composition of different modules, as well as the handling of data updates. We have deployed our preliminary DATA CIVILIZER system in two institutions, MIT and Merck and describe initial positive experiences that show the system shortens the time and effort required to find, prepare, and analyze data.",
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"title": "The Data Civilizer System",
"venue": "CIDR",
"year": 2017
},
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"authors": [
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"name": "Nishanth Chandran"
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"name": "Juan A. Garay"
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"name": "Payman Mohassel"
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"name": "Satyanarayana Vusirikala"
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"doi": "10.1145/3133956.3134100",
"doiUrl": "https://doi.org/10.1145/3133956.3134100",
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"paperAbstract": "While the feasibility of constant-round and actively secure MPC has been known for over two decades, the last few years have witnessed a flurry of designs and implementations that make its deployment a palpable reality. To our knowledge, however, existing concretely efficient MPC constructions are only for up to three parties.\n In this paper we design and implement a new actively secure 5PC protocol tolerating two corruptions that requires 8 rounds of interaction, only uses fast symmetric-key operations, and incurs 60% less communication than the passively secure state-of-the-art solution from the work of Ben-Efraim, Lindell, and Omri [CCS 2016]. For example, securely evaluating the AES circuit when the parties are in different regions of the U.S. and Europe only takes 1.8s which is 2.6x faster than the passively secure 5PC in the same environment.\n Instrumental for our efficiency gains (less interaction, only symmetric key primitives) is a new 4-party primitive we call Attested OT, which in addition to Sender and Receiver involves two additional \"assistant parties\" who will attest to the respective inputs of both parties, and which might be of broader applicability in practically relevant MPC scenarios. Finally, we also show how to generalize our construction to n parties with similar efficiency properties where the corruption threshold is <i>t</i> ≈ √<i>n</i>, and propose a combinatorial problem which, if solved optimally, can yield even better corruption thresholds for the same cost.",
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"http://eprint.iacr.org/2017/519",
"http://doi.acm.org/10.1145/3133956.3134100",
"https://www.microsoft.com/en-us/research/wp-content/uploads/2017/08/ccs2017.pdf"
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"title": "Efficient, Constant-Round and Actively Secure MPC: Beyond the Three-Party Case",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Qi Alfred Chen"
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{
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"name": "Matthew Thomas"
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{
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"name": "Eric Osterweil"
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{
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"name": "Yulong Cao"
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{
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"name": "Jie You"
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{
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"name": "Zhuoqing Morley Mao"
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],
"doi": "10.1145/3133956.3134084",
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"id": "014b09f5b1872a7aa70ec233c2746ee1cb93f7cd",
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"paperAbstract": "The recent unprecedented delegation of new generic top-level domains (gTLDs) has exacerbated an existing, but fallow, problem called name collisions. One concrete exploit of such problem was discovered recently, which targets internal namespaces and enables Man in the Middle (MitM) attacks against end-user devices from anywhere on the Internet. Analysis of the underlying problem shows that it is not specific to any single service protocol, but little attention has been paid to understand the vulnerability status and the defense solution space at the service level. In this paper, we perform the first systematic study of the robustness of internal network services under name collision attacks.\n We first perform a measure study and uncover a wide spectrum of services affected by the name collision problem. We then collect their client implementations and systematically analyze their vulnerability status under name collision attacks using dynamic analysis. Out of the 48 identified exposed services, we find that nearly all (45) of them expose vulnerabilities in popular clients. To demonstrate the severity, we construct exploits and find a set of new name collision attacks with severe security implications including MitM attacks, internal or personal document leakage, malicious code injection, and credential theft. We analyze the causes, and find that the name collision problem broadly breaks common security assumptions made in today's service client software. Leveraging the insights from our analysis, we propose multiple service software level solutions, which enables the victim services to actively defend against name collision attacks.",
"pdfUrls": [
"https://acmccs.github.io/papers/p941-chenA.pdf",
"http://web.eecs.umich.edu/~alfchen/alfred_ccs17.pdf",
"http://doi.acm.org/10.1145/3133956.3134084"
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"title": "Client-side Name Collision Vulnerability in the New gTLD Era: A Systematic Study",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Gunjae Koo"
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"name": "Yunho Oh"
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{
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],
"name": "Won Woo Ro"
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{
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"name": "Murali Annavaram"
}
],
"doi": "10.1145/3079856.3080239",
"doiUrl": "https://doi.org/10.1145/3079856.3080239",
"entities": [
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"CPU cache",
"Cache (computing)",
"Graphics processing unit",
"Locality of reference",
"Simulation",
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"id": "014d28ef6ad36b22c1a4edb43c1b34bc7981b2e3",
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"journalName": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
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"paperAbstract": "Long latency of memory operation is a prominent performance bottleneck in graphics processing units (GPUs). The small data cache that must be shared across dozens of warps (a collection of threads) creates significant cache contention and premature data eviction. Prior works have recognized this problem and proposed warp throttling which reduces the number of active warps contending for cache space. In this paper we discover that individual load instructions in a warp exhibit four different types of data locality behavior: (1) data brought by a warp load instruction is used only once, which is classified as streaming data (2) data brought by a warp load is reused multiple times within the same warp, called intra-warp locality (3) data brought by a warp is reused multiple times but across different warps, called inter-warp locality (4) and some data exhibit both a mix of intra- and inter-warp locality. Furthermore, each load instruction exhibits consistently the same locality type across all warps within a GPU kernel. Based on this discovery we argue that cache management must be done using per-load locality type information, rather than applying warp-wide cache management policies. We propose Access Pattern-aware Cache Management (APCM), which dynamically detects the locality type of each load instruction by monitoring the accesses from one exemplary warp. APCM then uses the detected locality type to selectively apply cache bypassing and cache pinning of data based on load locality characterization. Using an extensive set of simulations we show that APCM improves performance of GPUs by 34% for cache sensitive applications while saving 27% of energy consumption over baseline GPU.",
"pdfUrls": [
"http://www-scf.usc.edu/~gunjaeko/pubs/Gunjae_ISCA17.pdf",
"http://doi.acm.org/10.1145/3079856.3080239"
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"title": "Access pattern-aware cache management for improving data utilization in GPU",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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],
"name": "David Lie"
},
{
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"name": "Petros Maniatis"
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],
"doi": "10.1145/3102980.3102996",
"doiUrl": "https://doi.org/10.1145/3102980.3102996",
"entities": [
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"Display resolution",
"GLIMMER",
"Personally identifiable information",
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],
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"paperAbstract": "Users today enjoy access to a wealth of services that rely on user-contributed data, such as recommendation services, prediction services, and services that help classify and interpret data. The quality of such services inescapably relies on trustworthy contributions from users. However, validating the trustworthiness of contributions may rely on privacy-sensitive contextual data about the user, such as a user's location or usage habits, creating a conflict between privacy and trust: users benefit from a higher-quality service that identifies and removes illegitimate user contributions, but, at the same time, they may be reluctant to let the service access their private information to achieve this high quality.\n We argue that this conflict can be resolved with a pragmatic Glimmer of Trust, which allows services to validate user contributions in a trustworthy way without forfeiting user privacy. We describe how trustworthy hardware such as Intel's SGX can be used on the client-side---in contrast to much recent work exploring SGX in cloud services---to realize the Glimmer architecture, and demonstrate how this realization is able to resolve the tension between privacy and trust in a variety of cases.",
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"http://arxiv.org/abs/1702.07436",
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"title": "Glimmers: Resolving the Privacy/Trust Quagmire",
"venue": "HotOS",
"year": 2017
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"name": "Matei Zaharia"
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"doi": "10.1145/3132747.3132783",
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"paperAbstract": "Private communication over the Internet remains a challenging problem. Even if messages are encrypted, it is hard to deliver them without revealing metadata about which pairs of users are communicating. Scalable anonymity systems, such as Tor, are susceptible to traffic analysis attacks that leak metadata. In contrast, the largest-scale systems with metadata privacy require passing all messages through a small number of providers, requiring a high operational cost for each provider and limiting their deployability in practice.\n This paper presents Stadium, a point-to-point messaging system that provides metadata and data privacy while scaling its work efficiently across hundreds of low-cost providers operated by different organizations. Much like Vuvuzela, the current largest-scale metadata-private system, Stadium achieves its provable guarantees through differential privacy and the addition of noisy cover traffic. The key challenge in Stadium is limiting the information revealed from the many observable traffic links of a highly distributed system, without requiring an overwhelming amount of noise. To solve this challenge, Stadium introduces techniques for distributed noise generation and differentially private routing as well as a verifiable parallel mixnet design where the servers collaboratively check that others follow the protocol. We show that Stadium can scale to support 4x more users than Vuvuzela using servers that cost an order of magnitude less to operate than Vuvuzela nodes.",
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"http://people.csail.mit.edu/nickolai/papers/tyagi-stadium-eprint.pdf",
"http://eprint.iacr.org/2016/943.pdf",
"http://eprint.iacr.org/2016/943",
"https://www.cs.cornell.edu/~tyagi/papers/stadium.pdf"
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"title": "Stadium: A Distributed Metadata-Private Messaging System",
"venue": "SOSP",
"year": 2016
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"authors": [
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"name": "Baojian Zhou"
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"name": "Adil Alim"
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"doi": "10.1109/ICDM.2017.13",
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"paperAbstract": "Detection of interesting (e.g., coherent or anomalous) clusters has been studied extensively on plain or univariate networks, with various applications. Recently, algorithms have been extended to networks with multiple attributes for each node in the real-world. In a multi-attributed network, often, a cluster of nodes is only interesting for a subset (subspace) of attributes, andthis type of clusters is called subspace clusters. However, in the current literature, few methods are capable of detecting subspace clusters, which involves concurrent feature selection and network cluster detection. These relevant methods are mostly heuristic-driven and customized for specific application scenarios. In this work, we present a generic and theoretical framework for detection of interesting subspace clusters in large multi-attributed networks. Specifically, we propose a subspace graph-structured matching pursuit algorithm, namely, SG-Pursuit, to address a broad class of such problems for different scorefunctions (e.g., coherence or anomalous functions) and topology constraints (e.g., connected subgraphs and dense subgraphs). We prove that our algorithm 1) runs in nearly-linear time on the network size and the total number of attributes and 2) enjoys rigorous guarantees (geometrical convergence rate and tight error bound) analogous to those of the state-of-the-art algorithms for sparse feature selection problems and subgraph detection problems. As a case study, we specialize SG-Pursuit to optimizea number of well-known score functions for two typical tasks, including detection of coherent dense and anomalous connected subspace clusters in real-world networks. Empirical evidence demonstrates that our proposed generic algorithm SG-Pursuit is superior over state-of-the-art methods that are designed specifically for these two tasks.",
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"http://arxiv.org/abs/1709.05246",
"https://dac.cs.vt.edu/wp-content/uploads/2017/11/a-generic-framework.pdf",
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"title": "A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
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"paperAbstract": "Troubleshooting in an infrastructure-as-a-Service (IaaS) cloud platform is an inherently difficult task because it is a multi-player as well as multi-layer environment where tenant and provider effectively share administrative duties. To address these concerns, we present our work on CloudSight in which cloud providers allow tenants greater system-wide visibility through a transparency-as-a-service abstraction. We present the design, implementation, and evaluation of CloudSight in the OpenStack cloud platform. We also develop two example applications that make use of the CloudSight abstraction and use the applications to explore real cloud problems.",
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"title": "CloudSight: A Tenant-Oriented Transparency Framework for Cross-Layer Cloud Troubleshooting",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
"year": 2017
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"name": "Natalie Stanley"
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"name": "Andrew Chi"
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"name": "Cynthia Sturton"
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"doi": "10.1145/3037697.3037734",
"doiUrl": "https://doi.org/10.1145/3037697.3037734",
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"paperAbstract": "We present a methodology for identifying security critical properties for use in the dynamic verification of a processor. Such verification has been shown to be an effective way to prevent exploits of vulnerabilities in the processor, given a meaningful set of security properties. We use known processor errata to establish an initial set of security-critical invariants of the processor. We then use machine learning to infer an additional set of invariants that are not tied to any particular, known vulnerability, yet are critical to security.\n We build a tool chain implementing the approach and evaluate it for the open-source OR1200 RISC processor. We find that our tool can identify 19 (86.4%) of the 22 manually crafted security-critical properties from prior work and generates 3 new security properties not covered in prior work.",
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"title": "Identifying Security Critical Properties for the Dynamic Verification of a Processor",
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"paperAbstract": "Fuzzing is a software testing technique that finds bugs by repeatedly injecting mutated inputs to a target program. Known to be a highly practical approach, fuzzing is gaining more popularity than ever before. Current research on fuzzing has focused on producing an input that is more likely to trigger a vulnerability.\n In this paper, we tackle another way to improve the performance of fuzzing, which is to shorten the execution time of each iteration. We observe that AFL, a state-of-the-art fuzzer, slows down by 24x because of file system contention and the scalability of fork() system call when it runs on 120 cores in parallel. Other fuzzers are expected to suffer from the same scalability bottlenecks in that they follow a similar design pattern. To improve the fuzzing performance, we design and implement three new operating primitives specialized for fuzzing that solve these performance bottlenecks and achieve scalable performance on multi-core machines. Our experiment shows that the proposed primitives speed up AFL and LibFuzzer by 6.1 to 28.9x and 1.1 to 735.7x, respectively, on the overall number of executions per second when targeting Google's fuzzer test suite with 120 cores. In addition, the primitives improve AFL's throughput up to 7.7x with 30 cores, which is a more common setting in data centers. Our fuzzer-agnostic primitives can be easily applied to any fuzzer with fundamental performance improvement and directly benefit large-scale fuzzing and cloud-based fuzzing services.",
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"title": "Accounting for the Correspondence in Commented Data",
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"paperAbstract": "We propose a logical framework, based on Datalog, to study the foundations of querying JSON data. The main feature of our approach, which we call J-Logic, is the emphasis on paths. Paths are sequences of keys and are used to access the tree structure of nested JSON objects. J-Logic also features \"packing\" as a means to generate a new key from a path or subpath. J-Logic with recursion is computationally complete, but many queries can be expressed without recursion, such as deep equality. We give a necessary condition for queries to be expressible without recursion. Most of our results focus on the deterministic nature of JSON objects as partial functions from keys to values. Predicates defined by J-Logic programs may not properly describe objects, however. Nevertheless we show that every object-to-object transformation in J-Logic can be defined using only objects in intermediate results. Moreover we show that it is decidable whether a positive, nonrecursive J-Logic program always returns an object when given objects as inputs. Regarding packing, we show that packing is unnecessary if the output does not require new keys. Finally, we show the decidability of query containment for positive, nonrecursive J-Logic programs.",
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"title": "J-Logic: Logical Foundations for JSON Querying",
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"year": 2017
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"paperAbstract": "Developing a small but useful set of inputs for tests is challenging. We show that a domain-specific language backed by a constraint solver can help the programmer with this process. The solver can generate a set of test inputs and guarantee that each input is <em>different</em> from other inputs in a way that is useful for testing. \nThis paper presents Iorek: a tool that empowers the programmer with the ability to express to any SMT solver what it means for inputs to be different. The core of Iorek is a rich language for constraining the set of inputs, which includes a novel bounded enumeration mechanism that makes it easy to define and encode a flexible notion of difference over a recursive structure. We demonstrate the flexibility of this mechanism for generating strings. \nWe use Iorek to test real services and find that it is effective at finding bugs. We also build Iorek into a random testing tool and show that it increases coverage.",
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"name": "Sihuan Li"
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"name": "Hongbo Li"
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"name": "Kaiming Ouyang"
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"name": "Yuanlai Liu"
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"paperAbstract": "While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage.",
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"paperAbstract": "Machine learning classifiers are basic research tools used in numerous types of network analysis and modeling. To reduce the need for domain expertise and costs of running local ML classifiers, network researchers can instead rely on centralized Machine Learning as a Service (MLaaS) platforms.\n In this paper, we evaluate the effectiveness of MLaaS systems ranging from fully-automated, turnkey systems to fully-customizable systems, and find that with more user control comes greater risk. Good decisions produce even higher performance, and poor decisions result in harsher performance penalties. We also find that server side optimizations help fully-automated systems outperform default settings on competitors, but still lag far behind well-tuned MLaaS systems which compare favorably to standalone ML libraries. Finally, we find classifier choice is the dominating factor in determining model performance, and that users can approximate the performance of an optimal classifier choice by experimenting with a small subset of random classifiers. While network researchers should approach MLaaS systems with caution, they can achieve results comparable to standalone classifiers if they have sufficient insight into key decisions like classifiers and feature selection.",
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"https://conferences.sigcomm.org/imc/2017/papers/imc17-final51.pdf",
"http://www.cs.ucsb.edu/~bolunwang/docs/mlaas-imc17.pdf"
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"title": "Complexity vs. performance: empirical analysis of machine learning as a service",
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"year": 2017
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"name": "Lukas Stadler"
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"paperAbstract": "Computer systems are increasingly featuring powerful parallel devices with the advent of many-core CPUs and GPUs. This offers the opportunity to solve computationally-intensive problems at a fraction of the time traditional CPUs need. However, exploiting heterogeneous hardware requires the use of low-level programming language approaches such as OpenCL, which is incredibly challenging, even for advanced programmers.\n On the application side, interpreted dynamic languages are increasingly becoming popular in many domains due to their simplicity, expressiveness and flexibility. However, this creates a wide gap between the high-level abstractions offered to programmers and the low-level hardware-specific interface. Currently, programmers must rely on high performance libraries or they are forced to write parts of their application in a low-level language like OpenCL. Ideally, nonexpert programmers should be able to exploit heterogeneous hardware directly from their interpreted dynamic languages.\n In this paper, we present a technique to transparently and automatically offload computations from interpreted dynamic languages to heterogeneous devices. Using just-in-time compilation, we automatically generate OpenCL code at runtime which is specialized to the actual observed data types using profiling information. We demonstrate our technique using R, which is a popular interpreted dynamic language predominately used in big data analytic. Our experimental results show the execution on a GPU yields speedups of over 150x compared to the sequential FastR implementation and the obtained performance is competitive with manually written GPU code. We also show that when taking into account start-up time, large speedups are achievable, even when the applications run for as little as a few seconds.",
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"title": "Just-In-Time GPU Compilation for Interpreted Languages with Partial Evaluation",
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"year": 2017
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"doi": "10.1145/3064176.3064189",
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"paperAbstract": "Remote Direct Memory Access (RDMA) has been widely deployed in modern data centers. However, existing usages of RDMA lead to a dilemma between performance and redesign cost. They either directly replace socket-based send/receive primitives with the corresponding RDMA counterpart (server-reply), which only achieves moderate performance improvement; or push performance further by using one-sided RDMA operations to totally bypass the server (server-bypass), at the cost of redesigning the software.\n In this paper, we introduce two interesting observations about RDMA. First, RDMA has asymmetric performance characteristics, which can be used to improve server-reply's performance. Second, the performance of server-bypass is not as good as expected in many cases, because more rounds of RDMA may be needed if the server is totally bypassed. We therefore introduce a new RDMA paradigm called Remote Fetching Paradigm (RFP). Although RFP requires users to set several parameters to achieve the best performance, it supports the legacy RPC interfaces and hence avoids the need of redesigning application-specific data structures. Moreover, with proper parameters, it can achieve even higher IOPS than that of the previous paradigms.\n We have designed and implemented an in-memory key-value store based on RFP to evaluate its effectiveness. Experimental results show that RFP improves performance by 1.6×~4× compared with both server-reply and server-bypass paradigms.",
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"title": "RFP: When RPC is Faster than Server-Bypass with RDMA",
"venue": "EuroSys",
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},
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"name": "Xinyu Wang"
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"name": "Isil Dillig"
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"paperAbstract": "In application domains that store data in a tabular format, a common task is to fill the values of some cells using values stored in other cells. For instance, such data completion tasks arise in the context of <em>missing value imputation</em> in data science and <em>derived data</em> computation in spreadsheets and relational databases. Unfortunately, end-users and data scientists typically struggle with many data completion tasks that require non-trivial programming expertise. This paper presents a synthesis technique for automating data completion tasks using <em>programming-by-example (PBE)</em> and a very lightweight sketching approach. Given a <em>formula sketch</em> (e.g., <pre>AVG</pre>(<pre>?</pre><sub>1</sub>, <pre>?</pre><sub>2</sub>)) and a few input-output examples for each hole, our technique synthesizes a program to automate the desired data completion task. Towards this goal, we propose a domain-specific language (DSL) that combines spatial and relational reasoning over tabular data and a novel synthesis algorithm that can generate DSL programs that are consistent with the input-output examples. The key technical novelty of our approach is a new version space learning algorithm that is based on <em>finite tree automata</em> (FTA). The use of FTAs in the learning algorithm leads to a more compact representation that allows more sharing between programs that are consistent with the examples. We have implemented the proposed approach in a tool called DACE and evaluate it on 84 benchmarks taken from online help forums. We also illustrate the advantages of our approach by comparing our technique against two existing synthesizers, namely Prose and Sketch.",
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"title": "Synthesis of data completion scripts using finite tree automata",
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"authors": [
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"name": "James Larisch"
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"name": "Dave Levin"
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"doi": "10.1109/SP.2017.17",
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"paperAbstract": "Currently, no major browser fully checks for TLS/SSL certificate revocations. This is largely due to the fact that the deployed mechanisms for disseminating revocations (CRLs, OCSP, OCSP Stapling, CRLSet, and OneCRL) are each either incomplete, insecure, inefficient, slow to update, not private, or some combination thereof. In this paper, we present CRLite, an efficient and easily-deployable system for proactively pushing all TLS certificate revocations to browsers. CRLite servers aggregate revocation information for all known, valid TLS certificates on the web, and store them in a space-efficient filter cascade data structure. Browsers periodically download and use this data to check for revocations of observed certificates in real-time. CRLite does not require any additional trust beyond the existing PKI, and it allows clients to adopt a fail-closed security posture even in the face of network errors or attacks that make revocation information temporarily unavailable. We present a prototype of name that processes TLS certificates gathered by Rapid7, the University of Michigan, and Google's Certificate Transparency on the server-side, with a Firefox extension on the client-side. Comparing CRLite to an idealized browser that performs correct CRL/OCSP checking, we show that CRLite reduces latency and eliminates privacy concerns. Moreover, CRLite has low bandwidth costs: it can represent all certificates with an initial download of 10 MB (less than 1 byte per revocation) followed by daily updates of 580 KB on average. Taken together, our results demonstrate that complete TLS/SSL revocation checking is within reach for all clients.",
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"paperAbstract": "Starting with Dining Cryptographers networks (DC-net), several peer-to-peer (P2P) anonymous communication protocols have been proposed. Despite their strong anonymity guarantees none of those has been employed in practice so far: Most fail to simultaneously handle the crucial problems of slot collisions and malicious peers, while the remaining ones handle those with a significant increased latency (communication rounds) linear in the number of participating peers in the best case, and quadratic in the worst case. We conceptualize these P2P anonymous communication protocols as P2P mixing, and present a novel P2P mixing protocol, DiceMix, that only requires constant (i.e., four) communication rounds in the best case, and 4 + 2f rounds in the worst case of f malicious peers. As every individual malicious peer can prevent a protocol run from success by omitting his messages, we find DiceMix with its worst-case linear-round complexity to be an optimal P2P mixing solution. On the application side, we find DiceMix to be an ideal privacy-enhancing primitive for crypto-currencies such as Bitcoin. The public verifiability of their pseudonymous transactions through publicly available ledgers (or blockchains) makes these systems highly vulnerable to a variety of linkability and deanonymization attacks. DiceMix can allow pseudonymous users to make their transactions unlinkable to each other in a manner fully compatible with the existing systems. We demonstrate the efficiency of DiceMix with a proof-of-concept implementation. In our evaluation, DiceMix requires less than 8 seconds to mix 50 messages (160 bits, i.e., Bitcoin addresses), while the best protocol in the literate requires almost 3 minutes in a very similar setting. As a representative example, we use DiceMix to define a protocol for creating unlinkable Bitcoin transactions. Finally, we discover a generic attack on P2P mixing protocols that exploits the implicit unfairness of a protocol with a dishonest majority to break anonymity. Our attack uses the attacker\u2019s realworld ability to omit some communication from a honest peer to deanonymize her input message. We also discuss how this attack is resolved in our application to crypto-currencies by employing uncorrelated input messages across different protocol runs.",
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"paperAbstract": "Email is still among the most popular online activities. People spend a significant amount of time sending, reading and responding to email in order to communicate with others, manage tasks and archive personal information. Most previous research on email is based on either relatively small data samples from user surveys and interviews, or on consumer email accounts such as those from Yahoo! Mail or Gmail. Much less has been published on how people interact with enterprise email even though it contains less automatically generated commercial email and involves more organizational behavior than is evident in personal accounts. In this paper, we extend previous work on predicting email reply behavior by looking at enterprise settings and considering more than dyadic communications. We characterize the influence of various factors such as email content and metadata, historical interaction features and temporal features on email reply behavior. We also develop models to predict whether a recipient will reply to an email and how long it will take to do so. Experiments with the publicly-available Avocado email collection show that our methods outperform all baselines with large gains. We also analyze the importance of different features on reply behavior predictions. Our findings provide new insights about how people interact with enterprise email and have implications for the design of the next generation of email clients.",
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"paperAbstract": "Dynamic programming languages are becoming increasingly popular, and this motivates the need for just-in-time (JIT) compilation to close the productivity/performance gap. Unfortunately, developing custom JIT-optimizing virtual machines (VMs) requires significant effort. Recent work has shown the promiseofmeta-JITframeworks, which abstract the language definition from the VM internals. Meta-JITs can enable automatic generation of high-performance JIT-optimizing VMs from high-level language specifications. This paper provides a detailed workload characterization of meta-tracing JITs for two different dynamic programming languages: Python and Racket. We propose a new cross-layer methodology, and then we use this methodology to characterize a diverse selection of benchmarks at the application, framework, interpreter, JIT-intermediate-representation, and microarchitecture level. Our work is able to provide initial answers to important questions about meta-tracing JITs including the potential performance improvement over optimized interpreters, the source of various overheads, and the continued performance gap between JIT-compiled code and statically compiled languages.",
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"doi": "10.1145/3133933",
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"paperAbstract": "Data replication is used in distributed systems to maintain up-to-date copies of shared data across multiple computers in a network. However, despite decades of research, algorithms for achieving consistency in replicated systems are still poorly understood. Indeed, many published algorithms have later been shown to be incorrect, even some that were accompanied by supposed mechanised proofs of correctness. In this work, we focus on the correctness of Conflict-free Replicated Data Types (CRDTs), a class of algorithm that provides strong eventual consistency guarantees for replicated data. We develop a modular and reusable framework in the Isabelle/HOL interactive proof assistant for verifying the correctness of CRDT algorithms. We avoid correctness issues that have dogged previous mechanised proofs in this area by including a network model in our formalisation, and proving that our theorems hold in all possible network behaviours. Our axiomatic network model is a standard abstraction that accurately reflects the behaviour of real-world computer networks. Moreover, we identify an abstract convergence theorem, a property of order relations, which provides a formal definition of strong eventual consistency. We then obtain the first machine-checked correctness theorems for three concrete CRDTs: the Replicated Growable Array, the Observed-Remove Set, and an Increment-Decrement Counter. We find that our framework is highly reusable, developing proofs of correctness for the latter two CRDTs in a few hours and with relatively little CRDT-specific code.",
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"http://dominic-mulligan.co.uk/wp-content/uploads/2017/10/oopsla.pdf",
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"https://arxiv.org/pdf/1707.01747v2.pdf",
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"http://doi.acm.org/10.1145/3133933"
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"title": "Verifying strong eventual consistency in distributed systems",
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"paperAbstract": "Black-box accumulation (BBA) has recently been introduced as a building-block for a variety of user-centric protocols such as loyalty, refund, and incentive systems. Loosely speaking, this building block may be viewed as a cryptographic \"piggy bank\" that allows a user to collect points (aka incentives, coins, etc.) in an anonymous and unlinkable way. A piggy bank may be \"robbed\" at some point by a user, letting her spend the collected points, thereby only revealing the total amount inside the piggy bank and its unique serial number.\n In this paper we present BBA+, a definitional framework extending the BBA model in multiple ways: (1) We support offline systems in the sense that there does not need to be a permanent connection to a serial number database to check whether a presented piggy bank has already been robbed. (2) We enforce the collection of \"negative points\" which users may not voluntarily collect, as this is, for example, needed in pre-payment or reputation systems. (3) The security property formalized for \\bbap schemes is stronger and more natural than for BBA: Essentially, we demand that the amount claimed to be inside a piggy bank must be exactly the amount legitimately collected with this piggy bank. As piggy bank transactions need to be unlinkable at the same time, defining this property is highly non-trivial. (4) We also define a stronger form of privacy, namely forward and backward privacy. Besides the framework, we show how to construct a BBA+ system from cryptographic building blocks and present the promising results of a smartphone-based prototypical implementation. They show that our current instantiation may already be useable in practice, allowing to run transactions within a second---while we have not exhausted the potential for optimizations.",
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"http://doi.acm.org/10.1145/3133956.3134071",
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"title": "BBA+: Improving the Security and Applicability of Privacy-Preserving Point Collection",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"name": "Ryan G. Scott"
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"name": "Omar S. Navarro Leija"
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{
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"paperAbstract": "Achieving determinism on real software systems remains difficult. Even a batch-processing job, whose task is to map input bits to output bits, risks nondeterminism from thread scheduling, system calls, CPU instructions, and leakage of environmental information such as date or CPU model. In this work, we present a system for achieving low-overhead deterministic execution of batch-processing programs that read and write the file system—turning them into pure functions on files. \n We allow multi-process executions where a permissions system prevents races on the file system. Process separation enables different processes to enforce permissions and enforce determinism using distinct mechanisms. Our prototype, DetFlow, allows a statically-typed coordinator process to use shared-memory parallelism, as well as invoking process-trees of sandboxed legacy binaries. DetFlow currently implements the coordinator as a Haskell program with a restricted I/O type for its main function: a new monad we call DetIO. Legacy binaries launched by the coordinator run concurrently, but internally each process schedules threads sequentially, allowing dynamic determinism-enforcement with predictably low overhead. \n We evaluate DetFlow by applying it to bioinformatics data pipelines and software build systems. DetFlow enables determinizing these data-processing workflows by porting a small amount of code to become a statically-typed coordinator. This hybrid approach of static and dynamic determinism enforcement permits freedom where possible but restrictions where necessary.",
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"title": "Monadic composition for deterministic, parallel batch processing",
"venue": "PACMPL",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Xiaoen Ju"
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{
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"name": "Hani Jamjoom"
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{
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"name": "Kang G. Shin"
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],
"doi": "10.1145/3084446",
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"paperAbstract": "Despite their widespread adoption, large-scale graph processing systems do not fully decouple computation and communication, often yielding suboptimal performance. Locally-sufficient computation-computation that relies only on the graph state local to a computing host-can mitigate the effects of this coupling. In this paper, we present Compute-Sync-Merge (CSM), a new programming abstraction that achieves efficient locally-sufficient computation. CSM enforces local sufficiency at the programming abstraction level and enables the activation of vertex-centric computation on all vertex replicas, thus supporting vertex-cut partitioning. We demonstrate the simplicity of expressing several fundamental graph algorithms in CSM. Hieroglyph-our implementation of a graph processing system with CSM support-outperforms state of the art by up to 53x, with a median speedup of 3.5x and an average speedup of 6x across a wide range of datasets.",
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"http://doi.acm.org/10.1145/3078505.3078589"
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"title": "Hieroglyph: Locally-Sufficient Graph Processing via Compute-Sync-Merge",
"venue": "SIGMETRICS",
"year": 2017
},
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"name": "Martin B\u00e4ttig"
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{
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"name": "Thomas R. Gross"
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"doi": "10.1145/3018743.3018747",
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"title": "Synchronized-by-Default Concurrency for Shared-Memory Systems",
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"name": "Zhiqiang Lin"
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"doi": "10.1145/3133956.3134089",
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"paperAbstract": "When accessing online private resources (e.g., user profiles, photos, shopping carts) from a client (e.g., a desktop web-browser or a mobile app), the service providers must implement proper access control, which typically involves both authentication and authorization. However, not all of the service providers follow the best practice, resulting in various access control vulnerabilities. To understand such a threat in a large scale, and identify the vulnerable access control implementations in online services, this paper introduces AuthScope, a tool that is able to automatically execute a mobile app and pinpoint the vulnerable access control implementations, particularly the vulnerable authorizations, in the corresponding online service. The key idea is to use differential traffic analysis to recognize the protocol fields and then automatically substitute the fields and observe the server response. One of the key challenges for a large scale study lies in how to obtain the post-authentication request-and-response messages for a given app. We have thus developed a targeted dynamic activity explorer to perform an in-context analysis and drive the app execution to automatically log in the service. We have tested AuthScope with 4,838 popular mobile apps from Google Play, and identified 597 0-day vulnerable authorizations that map to 306 apps.",
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"title": "AUTHSCOPE: Towards Automatic Discovery of Vulnerable Authorizations in Online Services",
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"year": 2017
},
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"name": "Yuan He"
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{
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{
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"name": "Xiaojiang Chen"
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"doi": "10.1145/3117811.3117828",
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"paperAbstract": "With parallel decoding for backscatter communication, tags are allowed to transmit concurrently and more efficiently. Existing parallel decoding mechanisms, however, assume that signals of the tags are highly stable, and hence may not perform optimally in the naturally dynamic backscatter systems. This paper introduces FlipTracer, a practical system that achieves highly reliable parallel decoding even in hostile channel conditions. FlipTracer is designed with a key insight: although the collided signal is time-varying and irregular, transitions between signals' combined states follow highly stable probabilities, which offers important clues for identifying the collided signals, and provides us with an opportunity to decode the collided signals without relying on stable signals. Motivated by this observation, we propose a graphical model, called one-flip-graph (OFG), to capture the transition pattern of collided signals, and design a reliable approach to construct the OFG in a manner robust to the diversity in backscatter systems. Then FlipTracer can resolve the collided signals by tracking the OFG. We have implemented FlipTracer and evaluated its performance with extensive experiments across a wide variety of scenarios. Our experimental results have shown that FlipTracer achieves a maximum aggregated throughput that approaches 2 Mbps, which is 6x higher than the state-of-the-art.",
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"title": "FlipTracer: Practical Parallel Decoding for Backscatter Communication",
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"year": 2017
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"doi": "10.1145/3079856.3080232",
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"paperAbstract": "A practically feasible low-overhead hardware design that provides strong defenses against memory bus side channel remains elusive. This paper observes that smart memory, memory with compute capability and a packetized interface, can dramatically simplify this problem. InvisiMem expands the trust base to include the logic layer in the smart memory to implement cryptographic primitives, which aid in addressing several memory bus side channel vulnerabilities efficiently. This allows the secure host processor to send encrypted addresses over the untrusted memory bus, and thereby eliminates the need for expensive address obfuscation techniques based on Oblivious RAM (ORAM). In addition, smart memory enables efficient solutions for ensuring freshness without using expensive Merkle trees, and mitigates memory bus timing channel using constant heart-beat packets. We demonstrate that InvisiMem designs have one to two orders of magnitude of lower overheads for performance, space, energy, and memory bandwidth, compared to prior solutions.",
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"title": "InvisiMem: Smart memory defenses for memory bus side channel",
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"paperAbstract": "Normal tuple-generating dependencies (NTGDs) are TGDs enriched with default negation, a.k.a. negation as failure. Query answering under NTGDs, where negation is interpreted according to the stable model semantics, is an intriguing new problem that gave rise to flourishing research activity in the database and KR communities. So far, all the existing works that investigate this problem, except for one recent paper that adopts an operational semantics based on the chase, follow the so-called logic programming (LP) approach. According to the LP approach, the existentially quantified variables are first eliminated via Skolemization, which leads to a normal logic program, and then the standard stable model semantics for normal logic programs is applied. However, as we discuss in the paper, Skolemization is not appropriate in the presence of default negation since it fails to capture the intended meaning of NTGDs, while the operational semantics mentioned above fails to overcome the limitations of the LP approach. This reveals the need to adopt an alternative approach to stable model semantics that is directly applicable to NTGDs with existentially quantified variables. We propose such an approach based on a recent characterization of stable models in terms of second-order logic, which indeed overcomes the limitations of the LP approach. We then perform an in-depth complexity analysis of query answering under prominent classes of NTGDs based on the main decidability paradigms for TGDs, namely weak-acyclicity, guardedness and stickiness. Interestingly, weakly-acyclic NTGDs give rise to robust and highly expressive query languages that allow us to solve in a declarative way problems in the second level of the polynomial hierarchy.",
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"title": "Stable Model Semantics for Tuple-Generating Dependencies Revisited",
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"year": 2017
},
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"paperAbstract": "Intel SGX promises powerful security: an arbitrary number of user-mode enclaves protected against physical attacks and privileged software adversaries. However, to achieve this, Intel extended the x86 architecture with an isolation mechanism approaching the complexity of an OS microkernel, implemented by an inscrutable mix of silicon and microcode. While hardware-based security can offer performance and features that are difficult or impossible to achieve in pure software, hardware-only solutions are difficult to update, either to patch security flaws or introduce new features.\n Komodo illustrates an alternative approach to attested, on-demand, user-mode, concurrent isolated execution. We decouple the core hardware mechanisms such as memory encryption, address-space isolation and attestation from the management thereof, which Komodo delegates to a privileged software monitor that in turn implements enclaves. The monitor's correctness is ensured by a machine-checkable proof of both functional correctness and high-level security properties of enclave integrity and confidentiality. We show that the approach is practical and performant with a concrete implementation of a prototype in verified assembly code on ARM TrustZone. Our ultimate goal is to achieve security equivalent to or better than SGX while enabling deployment of new enclave features independently of CPU upgrades.\n The Komodo specification, prototype implementation, and proofs are available at https://github.com/Microsoft/Komodo.",
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"title": "Komodo: Using verification to disentangle secure-enclave hardware from software",
"venue": "SOSP",
"year": 2017
},
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"title": "Partitioning Trillion-Edge Graphs in Minutes",
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"name": "Kesheng Wu"
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"name": "Surendra Byna"
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"paperAbstract": "User-Defined Functions (UDF) allow application programmers to specify analysis operations on data, while leaving the data management tasks to the system. This general approach enables numerous custom analysis functions and is at the heart of the modern Big Data systems. Even though the UDF mechanism can theoretically support arbitrary operations, a wide variety of common operations -- such as computing the moving average of a time series, the vorticity of a fluid flow, etc., -- are hard to express and slow to execute. Since these operations are traditionally performed on multi-dimensional arrays, we propose to extend the expressiveness of structural locality for supporting UDF operations on arrays. We further propose an in situ UDF mechanism, called ArrayUDF, to implement the structural locality. ArrayUDF allows users to define computations on adjacent array cells without the use of join operations and executes the UDF directly on arrays stored in data files without requiring to load their content into a data management system. Additionally, we present a thorough theoretical analysis of the data access cost to exploit the structural locality, which enables ArrayUDF to automatically select the best array partitioning strategy for a given UDF operation. In a series of performance evaluations on large scientific datasets, we have observed that -- using the generic UDF interface -- ArrayUDF consistently outperforms Spark, SciDB, and RasDaMan.",
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"year": 2017
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"paperAbstract": "We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2×. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46× on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.",
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"http://doi.ieeecomputersociety.org/10.1109/CLUSTER.2017.28"
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"title": "Parallel and Efficient Sensitivity Analysis of Microscopy Image Segmentation Workflows in Hybrid Systems",
"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
"year": 2017
},
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"authors": [
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"name": "He Sun"
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{
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"name": "Luca Zanetti"
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"doi": "10.1145/3087556.3087569",
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"paperAbstract": "Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks. Over the past decades, researchers have proposed a number of algorithmic design methods for graph clustering. However, most of these methods are based on complicated spectral techniques or convex optimisation, and cannot be applied directly for clustering many networks that occur in practice, whose information is often collected on different sites. Designing a simple and distributed clustering algorithm is of great interest, and has wide applications for processing big datasets. In this paper we present a simple and distributed algorithm for graph clustering: for a wide class of graphs that are characterised by a strong cluster-structure, our algorithm finishes in a poly-logarithmic number of rounds, and recovers a partition of the graph close to an optimal partition. The main component of our algorithm is an application of the random matching model of load balancing, which is a fundamental protocol in distributed computing and has been extensively studied in the past 20 years. Hence, our result highlights an intrinsic and interesting connection between graph clustering and load balancing.\n At a technical level, we present a purely algebraic result characterising the early behaviours of load balancing processes for graphs exhibiting a cluster-structure. We believe that this result can be further applied to analyse other gossip processes, such as rumour spreading and averaging processes.",
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"http://arxiv.org/abs/1607.04984",
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"title": "Distributed Graph Clustering by Load Balancing",
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"year": 2017
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"paperAbstract": "We study the classic set cover problem in the streaming model: the sets that comprise the instance are revealed one by one in a stream and the goal is to solve the problem by making one or few passes over the stream while maintaining a sublinear space <i>o</i>(<i>mn</i>) in the input size; here <i>m</i> denotes the number of the sets and <i>n</i> is the universe size. Notice that in this model, we are mainly concerned with the space requirement of the algorithms and hence do not restrict their computation time.\n Our main result is a resolution of the space-approximation tradeoff for the streaming set cover problem: we show that any α-approximation algorithm for the set cover problem requires Ω(<i>mn</i>1<sup>/α</sup>) space, even if it is allowed polylog(<i>n</i>) passes over the stream, and even if the sets are arriving in a random order in the stream. This space-approximation tradeoff matches the best known bounds achieved by the recent algorithm of Har-Peled et.al. (PODS 2016) that requires only <i>O</i>(α) passes over the stream in an adversarial order, hence settling the space complexity of approximating the set cover problem in data streams in a quite robust manner. Additionally, our approach yields tight lower bounds for the space complexity of (1- ε)-approximating the streaming maximum coverage problem studied in several recent works.",
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"https://arxiv.org/pdf/1703.01847v1.pdf",
"http://www.seas.upenn.edu/~sassadi/stuff/papers/multipass-streaming-setcover.pdf",
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"https://arxiv.org/pdf/1703.01847.pdf",
"http://doi.acm.org/10.1145/3034786.3056116",
"http://arxiv.org/abs/1703.01847"
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"title": "Tight Space-Approximation Tradeoff for the Multi-Pass Streaming Set Cover Problem",
"venue": "PODS",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Juan G\u00f3mez-Luna"
},
{
"ids": [
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],
"name": "Izzat El Hajj"
},
{
"ids": [
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"name": "Li-Wen Chang"
},
{
"ids": [
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"name": "Victor Garcia-Flores"
},
{
"ids": [
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"name": "Simon Garcia De Gonzalo"
},
{
"ids": [
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],
"name": "Thomas B. Jablin"
},
{
"ids": [
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],
"name": "Antonio J. Pe\u00f1a"
},
{
"ids": [
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"name": "Wen-mei W. Hwu"
}
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"doi": "10.1109/ISPASS.2017.7975269",
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"paperAbstract": "Heterogeneous system architectures are evolving towards tighter integration among devices, with emerging features such as shared virtual memory, memory coherence, and systemwide atomics. Languages, device architectures, system specifications, and applications are rapidly adapting to the challenges and opportunities of tightly integrated heterogeneous platforms. Programming languages such as OpenCL 2.0, CUDA 8.0, and C++ AMP allow programmers to exploit these architectures for productive collaboration between CPU and GPU threads. To evaluate these new architectures and programming languages, and to empower researchers to experiment with new ideas, a suite of benchmarks targeting these architectures with close CPU-GPU collaboration is needed. In this paper, we classify applications that target heterogeneous architectures into generic collaboration patterns including data partitioning, fine-grain task partitioning, and coarse-grain task partitioning. We present Chai, a new suite of 14 benchmarks that cover these patterns and exercise different features of heterogeneous architectures with varying intensity. Each benchmark in Chai has seven different implementations in different programming models such as OpenCL, C++ AMP, and CUDA, and with and without the use of the latest heterogeneous architecture features. We characterize the behavior of each benchmark with respect to varying input sizes and collaboration combinations, and evaluate the impact of using the emerging features of heterogeneous architectures on application performance.",
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"http://elhajj2.web.engr.illinois.edu/docs/paper-chai-ispass17.pdf",
"https://doi.org/10.1109/ISPASS.2017.7975269"
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"title": "Chai: Collaborative heterogeneous applications for integrated-architectures",
"venue": "2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)",
"year": 2017
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"paperAbstract": "Training neural networks has become a big bottleneck. For example, training ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs.\n We use both self-host Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From the algorithm aspect, we focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters.\n We redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster than existing counter-part methods (Async SGD, Async MSGD, and Hogwild SGD) in all comparisons. Sync EASGD achieves 5.3X speedup over original EASGD on the same platform. We achieve 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.",
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"https://people.eecs.berkeley.edu/~youyang/publications/sc2017.pdf",
"http://doi.acm.org/10.1145/3126908.3126912",
"https://people.eecs.berkeley.edu/~youyang/publications/imagenet_minutes.pdf",
"https://arxiv.org/pdf/1708.02983v1.pdf",
"http://gauss.cs.ucsb.edu/~aydin/sc2017_deep_learning.pdf",
"http://arxiv.org/abs/1708.02983"
],
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"sources": [
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"title": "Scaling deep learning on GPU and knights landing clusters",
"venue": "SC",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Yi Cao"
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"name": "Javad Nejati"
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"name": "Muhammad Wajahat"
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"name": "Aruna Balasubramanian"
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{
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"name": "Anshul Gandhi"
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"doi": "10.1145/3084443",
"doiUrl": "https://doi.org/10.1145/3084443",
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"paperAbstract": "Modeling the energy consumption of applications on mobile devices is an important topic that has received much attention in recent years. However, there has been very little research on modeling the energy consumption of the mobile Web. This is primarily due to the short-lived yet complex page load process that makes it infeasible to rely on coarse-grained resource monitoring for accurate power estimation.\n We present RECON, a modeling approach that accurately estimates the energy consumption of any Web page load and deconstructs it into the energy contributions of individual page load activities. Our key intuition is to leverage low-level application semantics in addition to coarse-grained resource utilizations for modeling the page load energy consumption. By exploiting fine-grained information about the individual activities that make up the page load, RECON enables fast and accurate energy estimations without requiring complex models. Experiments across 80 Web pages and under four different optimizations show that RECON can estimate the energy consumption for a Web page load with an average error of less than 7%. Importantly, RECON helps to analyze and explain the energy effects of an optimization on the individual components of Web page loads.",
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"http://doi.acm.org/10.1145/3084443",
"http://doi.acm.org/10.1145/3078505.3078587",
"http://netsys.cs.stonybrook.edu/sites/netsys.cs.stonybrook.edu/files/sigm17_paper16_0.pdf"
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"title": "Deconstructing the Energy Consumption of the Mobile Page Load",
"venue": "SIGMETRICS",
"year": 2017
},
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"name": "Dongyu Meng"
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"paperAbstract": "Deep learning has shown impressive performance on hard perceptual problems. However, researchers found deep learning systems to be vulnerable to small, specially crafted perturbations that are imperceptible to humans. Such perturbations cause deep learning systems to mis-classify adversarial examples, with potentially disastrous consequences where safety or security is crucial. Prior defenses against adversarial examples either targeted specific attacks or were shown to be ineffective.\n We propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet neither modifies the protected classifier nor requires knowledge of the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. The detector networks learn to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since they assume no specific process for generating adversarial examples, they generalize well. The reformer network moves adversarial examples towards the manifold of normal examples, which is effective for correctly classifying adversarial examples with small perturbation. We discuss the intrinsic difficulties in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we use diversity to strengthen MagNet. We show empirically that MagNet is effective against the most advanced state-of-the-art attacks in blackbox and graybox scenarios without sacrificing false positive rate on normal examples.",
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"https://arxiv.org/pdf/1705.09064v1.pdf",
"http://arxiv.org/abs/1705.09064",
"http://doi.acm.org/10.1145/3133956.3134057",
"http://web.cs.ucdavis.edu/~hchen/paper/meng2017.pdf",
"https://arxiv.org/pdf/1705.09064v2.pdf"
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"title": "MagNet: A Two-Pronged Defense against Adversarial Examples",
"venue": "CCS",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Katherine Q. Ye"
},
{
"ids": [
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],
"name": "Matthew Green"
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{
"ids": [
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"name": "Naphat Sanguansin"
},
{
"ids": [
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"name": "Lennart Beringer"
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{
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"name": "Adam Petcher"
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{
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"name": "Andrew W. Appel"
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"paperAbstract": "We have formalized the functional specification of HMAC-DRBG (NIST 800-90A), and we have proved its cryptographic security-that its output is pseudorandom--using a hybrid game-based proof. We have also proved that the mbedTLS implementation (C program) correctly implements this functional specification. That proof composes with an existing C compiler correctness proof to guarantee, end-to-end, that the machine language program gives strong pseudorandomness. All proofs (hybrid games, C program verification, compiler, and their composition) are machine-checked in the Coq proof assistant. Our proofs are modular: the hybrid game proof holds on any implementation of HMAC-DRBG that satisfies our functional specification. Therefore, our functional specification can serve as a high-assurance reference.",
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"http://doi.acm.org/10.1145/3133956.3133974",
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"https://arxiv.org/pdf/1708.08542v1.pdf",
"http://www.cs.princeton.edu/~appel/papers/verified-hmac-drbg.pdf",
"https://www.cs.cmu.edu/~kqy/resources/Verified-HMAC-DRBG.pdf",
"http://arxiv.org/abs/1708.08542"
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"title": "Verified Correctness and Security of mbedTLS HMAC-DRBG",
"venue": "CCS",
"year": 2017
},
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{
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"name": "Friedrich Steimann"
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"name": "Marcus Frenkel"
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{
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"name": "Markus V\u00f6lter"
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"doi": "10.1145/3136014.3136034",
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"paperAbstract": "While contemporary projectional editors make sure that the edited programs conform to the programming languageâ\u0080\u0099s metamodel, they do not enforce that they are also well-formed, that is, that they obey the well-formedness rules defined for the language. We show how, based on a constraint-based capture of well-formedness, projectional editors can be empowered to enforce well-formedness in much the same way they enforce conformance with the metamodel. The resulting <em>robust edits</em> may be more complex than ordinary, well-formedness breaking edits, and hence may require more user involvement; yet, maintaining well-formedness at all times ensures that necessary corrections of a program are linked to the edit that necessitated them, and that the projectional editorâ\u0080\u0099s services are never compromised by inconsistent programs. Robust projectional editing is not a straitjacket, however: If a programmer prefers to work without it, its constraint-based capture of well-formedness will still catch all introduced errors â\u0080\u0094 unlike many other editor services, well-formedness checking and robust editing are based on the same implementation, and are hence guaranteed to behave consistently.",
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"sources": [
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"title": "Robust projectional editing",
"venue": "SLE",
"year": 2017
},
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"name": "Kubilay Atasu"
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{
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"name": "Thomas P. Parnell"
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{
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"name": "Celestine D\u00fcnner"
},
{
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"name": "Michail Vlachos"
},
{
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"name": "Haralampos Pozidis"
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"paperAbstract": "Energy consumption is rapidly becoming a limiting factor in scientific computing. As a result, hardware manufacturers increasingly prioritise energy efficiency in their processor designs. Performance engineers are also beginning to explore software optimisation and hardware/software co-design as a means to reduce energy consumption. Energy efficiency metrics developed by the hardware community are often re-purposed to guide these software optimisation efforts. In this paper we argue that established metrics, and in particular those in the Energy Delay Product (Et) family, are unsuitable for energy-aware software optimisation. A good metric should provide meaningful values for a single experiment, allow fair comparison between experiments, and drive optimisation in a sensible direction. We show that Et metrics are unable to fulfil these basic requirements and present suitable alternatives for guiding energy-aware software optimisation. We finish with a practical demonstration of the utility of our proposed metrics.",
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"paperAbstract": "Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: (1) perfect precision and high recall of matching pairs, (2) a proof of end-to-end privacy, and (3) communication and computational costs that scale subquadratically in the number of input records. We show that all of the existing solutions for PRL? including secure 2-party computation (S2PC), and their variants that use non-private or differentially private (DP) blocking to ensure subquadratic cost -- violate at least one of the three desiderata. In particular, S2PC techniques guarantee end-to-end privacy but have either low recall or quadratic cost. In contrast, no end-to-end privacy guarantee has been formalized for solutions that achieve subquadratic cost. This is true even for solutions that compose DP and S2PC: DP does not permit the release of any exact information about the databases, while S2PC algorithms for PRL allow the release of matching records.\n In light of this deficiency, we propose a novel privacy model, called <i>output constrained differential privacy</i>, that shares the strong privacy protection of DP, but allows for the truthful release of the output of a certain function applied to the data. We apply this to PRL, and show that protocols satisfying this privacy model permit the disclosure of the true matching records, but their execution is insensitive to the presence or absence of a single non-matching record. We find that prior work that combine DP and S2PC techniques even fail to satisfy this end-to-end privacy model. Hence, we develop novel protocols that provably achieve this end-to-end privacy guarantee, together with the other two desiderata of PRL. Our empirical evaluation also shows that our protocols obtain high recall, scale near linearly in the size of the input databases and the output set of matching pairs, and have communication and computational costs that are at least 2 orders of magnitude smaller than S2PC baselines.",
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"https://arxiv.org/pdf/1702.00535v3.pdf",
"https://arxiv.org/pdf/1702.00535v4.pdf",
"https://arxiv.org/pdf/1702.00535v2.pdf"
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"title": "Composing Differential Privacy and Secure Computation: A Case Study on Scaling Private Record Linkage",
"venue": "CCS",
"year": 2017
},
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"name": "Chao Li"
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"doi": "10.1109/CLOUD.2017.13",
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"paperAbstract": "In the age of Big Data, advances in distributed technologies and cloud storage services provide highly efficient and cost-effective solutions to large scale data storage and management. Supporting self-emerging data using clouds is a challenging problem. While straight-forward centralized approaches provide a basic solution to the problem, unfortunately they are limited to a single point of trust. Supporting attack-resilient timed release of encrypted data stored in clouds requires new mechanisms for self emergence of data encryption keys that enables encrypted data to become accessible at a future point in time. Prior to the release time, the encryption key remains undiscovered and unavailable in a secure distributed system, making the private data unavailable. In this paper, we propose Emerge, a self-emerging timed data release protocol for securely hiding data encryption keys of private encrypted data in a large-scale Distributed Hash Table (DHT) network that makes the data available and accessible only at the defined release time. We develop a suite of erasure-coding-based routing path construction schemes for securely storing and routing encryption keys in DHT networks that protect an adversary from inferring the encryption key prior to the release time (release-ahead attack) or from destroying the key altogether (drop attack). Through extensive experimental evaluation, we demonstrate that the proposed schemes are resilient to both release-ahead attack and drop attack as well as to attacks that arise due to traditional churn issues in DHT networks.",
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"title": "Emerge: Self-Emerging Data Release Using Cloud Data Storage",
"venue": "2017 IEEE 10th International Conference on Cloud Computing (CLOUD)",
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"paperAbstract": "Emerging 5G mobile networks are envisioned to become multi-service environments, enabling the dynamic deployment of services with a diverse set of performance requirements, accommodating the needs of mobile network operators, verticals and over-the-top (OTT) service providers. Virtualizing the mobile network in a flexible way is of paramount importance for a cost-effective realization of this vision. While virtualization has been extensively studied in the case of the mobile core, virtualizing the radio access network (RAN) is still at its infancy. In this paper, we present Orion, a novel RAN slicing system that enables the dynamic on-the-fly virtualization of base stations, the flexible customization of slices to meet their respective service needs and which can be used in an end-to-end network slicing setting. Orion guarantees the functional and performance isolation of slices, while allowing for the efficient use of RAN resources among them. We present a concrete prototype implementation of Orion for LTE, with experimental results, considering alternative RAN slicing approaches, indicating its efficiency and highlighting its isolation capabilities. We also present an extension to Orion for accommodating the needs of OTT providers.",
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"title": "Orion: RAN Slicing for a Flexible and Cost-Effective Multi-Service Mobile Network Architecture",
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"year": 2017
},
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"authors": [
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"name": "Damon McCoy"
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"doi": "10.1145/3131365.3131385",
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"paperAbstract": "Doxing is online abuse where a malicious party harms another by releasing identifying or sensitive information. Motivations for doxing include personal, competitive, and political reasons, and web users of all ages, genders and internet experience have been targeted. Existing research on doxing is primarily qualitative. This work improves our understanding of doxing by being the first to take a quantitative approach. We do so by designing and deploying a tool which can detect dox files and measure the frequency, content, targets, and effects of doxing on popular dox-posting sites.\n This work analyzes over 1.7 million text files posted to paste-bin.com, 4chan.org and 8ch.net, sites frequently used to share doxes online, over a combined period of approximately thirteen weeks. Notable findings in this work include that approximately 0.3% of shared files are doxes, that online social networking accounts mentioned in these dox files are more likely to close than typical accounts, that justice and revenge are the most often cited motivations for doxing, and that dox files target males more frequently than females.\n We also find that recent anti-abuse efforts by social networks have reduced how frequently these doxing victims closed or restricted their accounts after being attacked. We also propose mitigation steps, such a service that can inform people when their accounts have been shared in a dox file, or law enforcement notification tools to inform authorities when individuals are at heightened risk of abuse.",
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"title": "Fifteen minutes of unwanted fame: detecting and characterizing doxing",
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"year": 2017
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"name": "Sidharth Kumar"
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"name": "John Edwards"
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"paperAbstract": "Hierarchical data representations have been shown to be effective tools for coping with large-scale scientific data. Writing hierarchical data on supercomputers, however, is challenging as it often involves all-to-one communication during aggregation of low-resolution data which tends to span the entire network domain, resulting in several bottlenecks. We introduce the concept of indexing templates, which succinctly describe data organization and can be used to alter movement of data in beneficial ways. We present two techniques, domain partitioning and localized aggregation, that leverage indexing templates to alleviate congestion and synchronization overheads during data aggregation. We report experimental results that show significant I/O speedup using our proposed schemes on two of today's fastest supercomputers, Mira and Shaheen II, using the Uintah and S3D simulation frameworks.",
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"title": "Reducing Network Congestion and Synchronization Overhead During Aggregation of Hierarchical Data",
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"paperAbstract": "Low-power microcontrollers lack some of the hardware features and memory resources that enable multiprogrammable systems. Accordingly, microcontroller-based operating systems have not provided important features like fault isolation, dynamic memory allocation, and flexible concurrency. However, an emerging class of embedded applications are software platforms, rather than single purpose devices, and need these multiprogramming features. Tock, a new operating system for low-power platforms, takes advantage of limited hardware-protection mechanisms as well as the type-safety features of the Rust programming language to provide a multiprogramming environment for microcontrollers. Tock isolates software faults, provides memory protection, and efficiently manages memory for dynamic application workloads written in any language. It achieves this while retaining the dependability requirements of long-running applications.",
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"https://sing.stanford.edu/site/publications/levy17-tock.pdf",
"http://doi.acm.org/10.1145/3132747.3132786",
"http://web.eecs.umich.edu/~prabal/pubs/papers/levy17tock.pdf"
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"title": "Multiprogramming a 64kB Computer Safely and Efficiently",
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"year": 2017
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"paperAbstract": "Modern web browsers have accrued an incredibly broad set of features since being invented for hypermedia dissemination in 1990. Many of these features benefit users by enabling new types of web applications. However, some features also bring risk to users' privacy and security, whether through implementation error, unexpected composition, or unintended use. Currently there is no general methodology for weighing these costs and benefits. Restricting access to only the features which are necessary for delivering desired functionality on a given website would allow users to enforce the principle of lease privilege on use of the myriad APIs present in the modern web browser.\n However, security benefits gained by increasing restrictions must be balanced against the risk of breaking existing websites. This work addresses this problem with a methodology for weighing the costs and benefits of giving websites default access to each browser feature. We model the benefit as the number of websites that require the feature for some user-visible benefit, and the cost as the number of CVEs, lines of code, and academic attacks related to the functionality. We then apply this methodology to 74 Web API standards implemented in modern browsers. We find that allowing websites default access to large parts of the Web API poses significant security and privacy risks, with little corresponding benefit.\n We also introduce a configurable browser extension that allows users to selectively restrict access to low-benefit, high-risk features on a per site basis. We evaluated our extension with two hardened browser configurations, and found that blocking 15 of the 74 standards avoids 52.0% of code paths related to previous CVEs, and 50.0% of implementation code identified by our metric, without affecting the functionality of 94.7% of measured websites.",
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"http://doi.acm.org/10.1145/3133956.3133966"
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"title": "Most Websites Don't Need to Vibrate: A Cost-Benefit Approach to Improving Browser Security",
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"year": 2017
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"paperAbstract": "Using multiple datacenters allows for higher availability, load balancing and reduced latency to customers of cloud services. To distribute multiple copies of data, cloud providers depend on inter-datacenter WANs that ought to be used efficiently considering their limited capacity and the ever-increasing data demands. In this paper, we focus on applications that transfer objects from one datacenter to several datacenters over dedicated inter-datacenter networks. We present DCCast, a centralized Point to Multi-Point (P2MP) algorithm that uses forwarding trees to efficiently deliver an object from a source datacenter to required destination datacenters. With low computational overhead, DCCast selects forwarding trees that minimize bandwidth usage and balance load across all links. With simulation experiments on Google\u2019s GScale network, we show that DCCast can reduce total bandwidth usage and tail Transfer Completion Times (TCT) by up to 50% compared to delivering the same objects via independent point-to-point (P2P) transfers.",
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"http://arxiv.org/abs/1707.02096",
"https://www.usenix.org/conference/hotcloud17/program/presentation/noormohammadpour",
"https://www.usenix.org/system/files/conference/hotcloud17/hotcloud17-paper-noormohammadpour.pdf"
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"title": "DCCast: Efficient Point to Multipoint Transfers Across Datacenters",
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"year": 2017
},
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"name": "Bogdan Ghit"
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"name": "Dick H. J. Epema"
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"doi": "10.1145/3078597.3078600",
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"paperAbstract": "Providing fault-tolerance is of major importance for data analytics frameworks such as Hadoop and Spark, which are typically deployed in large clusters that are known to experience high failures rates. Unexpected events such as compute node failures are in particular an important challenge for in-memory data analytics frameworks, as the widely adopted approach to deal with them is to recompute work already done. Recomputing lost work, however, requires allocation of extra resource to re-execute tasks, thus increasing the job runtimes. To address this problem, we design a checkpointing system called Panda that is tailored to the intrinsic characteristics of data analytics frameworks. In particular, Panda employs fine-grained checkpointing at the level of task outputs and dynamically identifies tasks that are worthwhile to be checkpointed rather than be recomputed. As has been abundantly shown, tasks of data analytics jobs may have very variable runtimes and output sizes. These properties form the basis of three checkpointing policies which we incorporate into Panda.\n We first empirically evaluate Panda on a multicluster system with single data analytics applications under space-correlated failures, and find that Panda is close to the performance of a fail-free execution in unmodified Spark for a large range of concurrent failures. Then we perform simulations of complete workloads, mimicking the size and operation of a Google cluster, and show that Panda provides significant improvements in the average job runtime for wide ranges of the failure rate and system load.",
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"title": "Better Safe than Sorry: Grappling with Failures of In-Memory Data Analytics Frameworks",
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"year": 2017
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"paperAbstract": "Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. In this paper, we present new and efficient protocols for privacy preserving machine learning for linear regression, logistic regression and neural network training using the stochastic gradient descent method. Our protocols fall in the two-server model where data owners distribute their private data among two non-colluding servers who train various models on the joint data using secure two-party computation (2PC). We develop new techniques to support secure arithmetic operations on shared decimal numbers, and propose MPC-friendly alternatives to non-linear functions such as sigmoid and softmax that are superior to prior work. We implement our system in C++. Our experiments validate that our protocols are several orders of magnitude faster than the state of the art implementations for privacy preserving linear and logistic regressions, and scale to millions of data samples with thousands of features. We also implement the first privacy preserving system for training neural networks.",
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"paperAbstract": "Next-generation genome sequencing technology has reached a point at which it is becoming cost-effective to sequence all patients. Biobanks and researchers are faced with an oncoming deluge of genomic data, whose processing requires new and scalable bioinformatics architectures and systems. Processing raw genetic sequence data is computationally expensive and datasets are large. Current software systems can require many hours to process a single genome and generally run only on a single computer. Common file formats are monolithic and roworiented, a barrier to distributed computation. To address these challenges, we built Persona, a cluster-scale, high-throughput bioinformatics framework. Persona currently supports paired-read alignment, sorting, and duplicate marking using well-known algorithms and techniques. Persona can significantly reduce end-to-end processing times for bioinformatics computations. A new Aggregate Genomic Data (AGD) format unifies sample data and analysis results, while enabling efficient distributed computation and I/O. In a case study on sequence alignment, Persona sustains 1.353 gigabases aligned per second with 101 base pair reads on a 32-node cluster and can align a full genome in \u223c16.7 seconds using the SNAP algorithm. Our results demonstrate that: (1) alignment computation with Persona scales linearly across servers with no measurable completion-time imbalance and negligible framework overheads; (2) on a single server, sorting with Persona and AGD is up to 2.3\u00d7 faster than commonly used tools, while duplicate marking is 3\u00d7 faster; (3) with AGD, a 7 node COTS network storage system can service up to 60 alignment compute nodes; (4) server cost dominates for a balanced system running Persona, while long-term data storage dwarfs the cost of computation. \u2217EPFL \u2020U. Politehnica of Bucharest (work done during EPFL internship) \u2021Carnegie Mellon University (work done during EPFL internship) \u00a7Stanford University",
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"title": "A Work-Efficient Parallel Sparse Matrix-Sparse Vector Multiplication Algorithm",
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"paperAbstract": "Nearly all modern software has security flaws---either known or unknown by the users. However, metrics for evaluating software security (or lack thereof) are noisy at best. Common evaluation methods include counting the past vulnerabilities of the program, or comparing the size of the Trusted Computing Base (TCB), measured in lines of code (LoC) or binary size. Other than deleting large swaths of code from project, it is difficult to assess whether a code change decreased the likelihood of a future security vulnerability. Developers need a practical, constructive way of evaluating security.\n This position paper argues that we actually have all the tools needed to design a better, empirical method of security evaluation. We discuss related work that estimates the severity and vulnerability of certain attack vectors based on code properties that can be determined via static analysis. This paper proposes a grand, unified model that can predict the risk and severity of vulnerabilities in a program. Our prediction model uses machine learning to correlate these code features of open-source applications with the history of vulnerabilities reported in the CVE (Common Vulnerabilities and Exposures) database. Based on this model, one can incorporate an analysis into the standard development cycle that predicts whether the code is becoming more or less prone to vulnerabilities.",
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"title": "A Clairvoyant Approach to Evaluating Software (In)Security",
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"title": "Drone Relays for Battery-Free Networks",
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"name": "Pantea Zardoshti"
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"title": "Practical Experience with Transactional Lock Elision",
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"paperAbstract": "Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly, systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions:1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms.2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality.3) An analysis of attacks against protected search for different base queries.4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.",
"pdfUrls": [
"https://arxiv.org/pdf/1703.02014v1.pdf",
"https://doi.org/10.1109/SP.2017.10",
"https://arxiv.org/pdf/1703.02014v2.pdf",
"http://arxiv.org/abs/1703.02014"
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"sources": [
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"title": "SoK: Cryptographically Protected Database Search",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
"0646a88dfd7e7ce7233041eaad62076ccc55624c": {
"authors": [
{
"ids": [
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"name": "Andrea Bittau"
},
{
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"name": "\u00dalfar Erlingsson"
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{
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"name": "Petros Maniatis"
},
{
"ids": [
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"name": "Ilya Mironov"
},
{
"ids": [
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"name": "Ananth Raghunathan"
},
{
"ids": [
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"name": "David Lie"
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{
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"name": "Mitch Rudominer"
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{
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"name": "Ushasree Kode"
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{
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"name": "Julien Tinn\u00e9s"
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{
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"name": "Bernhard Seefeld"
}
],
"doi": "10.1145/3132747.3132769",
"doiUrl": "https://doi.org/10.1145/3132747.3132769",
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"Algorithm",
"Blinding (cryptography)",
"Cryptography",
"Differential privacy",
"Enthusiast System Architecture",
"Exception handling",
"Experience",
"Experiment",
"Pipeline (computing)",
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"Randomness",
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"paperAbstract": "The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring.\n With ESA, the privacy of monitored users' data is guaranteed by its processing in a three-step pipeline. First, the data is encoded to control scope, granularity, and randomness. Second, the encoded data is collected in batches subject to a randomized threshold, and blindly shuffled, to break linkability and to ensure that individual data items get \"lost in the crowd\" of the batch. Third, the anonymous, shuffled data is analyzed by a specific analysis engine that further prevents statistical inference attacks on analysis results.\n ESA extends existing best-practice methods for sensitive-data analytics, by using cryptography and statistical techniques to make explicit how data is elided and reduced in precision, how only common-enough, anonymous data is analyzed, and how this is done for only specific, permitted purposes. As a result, ESA remains compatible with the established workflows of traditional database analysis.\n Strong privacy guarantees, including differential privacy, can be established at each processing step to defend against malice or compromise at one or more of those steps. Prochlo develops new techniques to harden those steps, including the Stash Shuffle, a novel scalable and efficient oblivious-shuffling algorithm based on Intel's SGX, and new applications of cryptographic secret sharing and blinding. We describe ESA and Prochlo, as well as experiments that validate their ability to balance utility and privacy.",
"pdfUrls": [
"http://arxiv.org/abs/1710.00901",
"http://www.eecg.toronto.edu/~lie/papers/prochlo_sosp2017.pdf",
"https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46411.pdf",
"https://arxiv.org/pdf/1710.00901v1.pdf",
"http://doi.acm.org/10.1145/3132747.3132769"
],
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"sources": [
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"title": "Prochlo: Strong Privacy for Analytics in the Crowd",
"venue": "SOSP",
"year": 2017
},
"06479f0b9b71e8c0744ec7291ab0867ab9ec5059": {
"authors": [
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"ids": [
"2522763"
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"name": "Jorge Albericio"
},
{
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"name": "Alberto Delmas"
},
{
"ids": [
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"name": "Patrick Judd"
},
{
"ids": [
"15384887"
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"name": "Sayeh Sharify"
},
{
"ids": [
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],
"name": "Gerard O'Leary"
},
{
"ids": [
"2655550"
],
"name": "Roman Genov"
},
{
"ids": [
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"name": "Andreas Moshovos"
}
],
"doi": "10.1145/3123939.3123982",
"doiUrl": "https://doi.org/10.1145/3123939.3123982",
"entities": [
"16-bit",
"8-bit",
"Artificial neural network",
"Bitap algorithm",
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"Convolutional neural network",
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"paperAbstract": "Deep Neural Networks expose a high degree of parallelism, making them amenable to highly data parallel architectures. However, data-parallel architectures often accept inefficiency in individual computations for the sake of overall efficiency. We show that on average, activation values of convolutional layers during inference in modern Deep Convolutional Neural Networks (CNNs) contain 92% zero bits. Processing these zero bits entails ineffectual computations that could be skipped. We propose <i>Pragmatic</i> (<i>PRA</i>), a massively data-parallel architecture that eliminates most of the ineffectual computations on-the-fly, improving performance and energy efficiency compared to state-of-the-art high-performance accelerators [5]. The idea behind <i>PRA</i> is deceptively simple: use serial-parallel shift-and-add multiplication while skipping the zero bits of the serial input. However, a straightforward implementation based on shift-and-add multiplication yields unacceptable area, power and memory access overheads compared to a conventional bit-parallel design. <i>PRA</i> incorporates a set of design decisions to yield a practical, area and energy efficient design.\n Measurements demonstrate that for convolutional layers, <i>PRA</i> is 4.31X faster than <i>DaDianNao</i> [5] (<i>DaDN</i>) using a 16-bit fixed-point representation. While <i>PRA</i> requires 1.68X more area than <i>DaDN</i>, the performance gains yield a 1.70X increase in energy efficiency in a 65nm technology. With 8-bit quantized activations, <i>PRA</i> is 2.25X faster and 1.31X more energy efficient than an 8-bit version of <i>DaDN.</i>",
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"http://doi.acm.org/10.1145/3123939.3123982",
"https://openreview.net/pdf?id=ryeF7mVFl",
"https://arxiv.org/pdf/1610.06920v1.pdf",
"https://arxiv.org/pdf/1610.06920.pdf",
"http://arxiv.org/abs/1610.06920",
"https://openreview.net/pdf?id=By14kuqxx"
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"sources": [
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"title": "Bit-pragmatic deep neural network computing",
"venue": "MICRO",
"year": 2017
},
"0665e1a0ca3fb7dec851eeaf830ed277fab26572": {
"authors": [
{
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"name": "Yan Zheng"
},
{
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"name": "Jeff M. Phillips"
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],
"doi": "10.1145/3097983.3098000",
"doiUrl": "https://doi.org/10.1145/3097983.3098000",
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"paperAbstract": "Kernel regression is an essential and ubiquitous tool for non-parametric data analysis, particularly popular among time series and spatial data. However, the central operation which is performed many times, evaluating a kernel on the data set, takes linear time. This is impractical for modern large data sets.\n In this paper we describe coresets for kernel regression: compressed data sets which can be used as proxy for the original data and have provably bounded worst case error. The size of the coresets are independent of the raw number of data points; rather they only depend on the error guarantee, and in some cases the size of domain and amount of smoothing. We evaluate our methods on very large time series and spatial data, and demonstrate that they incur negligible error, can be constructed extremely efficiently, and allow for great computational gains.",
"pdfUrls": [
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"http://doi.acm.org/10.1145/3097983.3098000",
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"title": "Coresets for Kernel Regression",
"venue": "KDD",
"year": 2017
},
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],
"doi": "10.1145/3079856.3080235",
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"paperAbstract": "This work presents a minimally-intrusive, high-performance, post-silicon validation framework for validating memory consistency in multi-core systems. Our framework generates constrained-random tests that are instrumented with observability-enhancing code for memory consistency verification. For each test, we generate a set of compact signatures reflecting the memory-ordering patterns observed over many executions of the test, with each of the signatures corresponding to a unique memory-ordering pattern. We then leverage an efficient and novel analysis to quickly determine if the observed execution patterns represented by each unique signature abide by the memory consistency model. Our analysis derives its efficiency by exploiting the structural similarities among the patterns observed.\n We evaluated our framework, MTraceCheck, on two platforms: an x86-based desktop and an ARM-based SoC platform, both running multi-threaded test programs in a bare-metal environment. We show that MTraceCheck reduces the perturbation introduced by the memory-ordering monitoring activity by 93% on average, compared to a baseline register flushing approach that saves the register's state after each load operation. We also reduce the computation requirements of our consistency checking analysis by 81% on average, compared to a conventional topological sorting solution. We finally demonstrate the effectiveness of MTraceCheck on buggy designs, by evaluating multiple case studies where it successfully exposes subtle bugs in a full-system simulation environment.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3079856.3080235",
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"title": "MTraceCheck: Validating non-deterministic behavior of memory consistency models in post-silicon validation",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
"066e1cd75a4f37a3c58089e24ccf43eb5adf1f19": {
"authors": [
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"ids": [
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"name": "Hung T. Nguyen"
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{
"ids": [
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"name": "Tri P. Nguyen"
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{
"ids": [
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],
"name": "Tam N. Vu"
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{
"ids": [
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],
"name": "Thang N. Dinh"
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],
"doi": "10.1145/3084457",
"doiUrl": "https://doi.org/10.1145/3084457",
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"Estimation theory",
"Experiment",
"Ground truth",
"Influence line",
"Information",
"Sampling (signal processing)",
"Shoe size"
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"paperAbstract": "Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed <i>outward influence</i> (OI), defined as the (expected) number of nodes that a subset of nodes <i>S</i> will activate, <i>excluding the nodes in S</i>. Thus, OI equals, the de facto standard measure, <i>influence spread</i> of <i>S</i> minus |<i>S</i>|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods.\n Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence <i>at scale and with rigorous theoretical guarantees</i>. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log<sup>4 <i>n</i></sup>) times faster in theory and up to <i>several orders of magnitude faster</i> in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the \"ground truth\" for influence spread.",
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"http://doi.acm.org/10.1145/3084457",
"https://arxiv.org/pdf/1704.04794v1.pdf",
"http://arxiv.org/abs/1704.04794",
"http://doi.acm.org/10.1145/3078505.3078526"
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"title": "Outward Influence and Cascade Size Estimation in Billion-scale Networks",
"venue": "SIGMETRICS",
"year": 2017
},
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"authors": [
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"name": "Luke Valenta"
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"name": "David Adrian"
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"name": "Antonio Sanso"
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{
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"name": "Shaanan Cohney"
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{
"ids": [
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"name": "Joshua Fried"
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{
"ids": [
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"name": "Marcella Hastings"
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{
"ids": [
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"name": "J. Alex Halderman"
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"name": "Nadia Heninger"
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"doi": "",
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"Diffie\u2013Hellman key exchange",
"Directory System Agent",
"HTTPS",
"Internet Key Exchange",
"Key escrow",
"Key exchange",
"Library",
"Load balancing (computing)",
"Open-source software",
"OpenSSL",
"Opportunistic TLS",
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"paperAbstract": "Several recent standards, including NIST SP 80056A and RFC 5114, advocate the use of \u201cDSA\u201d parameters for Diffie-Hellman key exchange. While it is possible to use such parameters securely, additional validation checks are necessary to prevent well-known and potentially devastating attacks. In this paper, we observe that many Diffie-Hellman implementations do not properly validate key exchange inputs. Combined with other protocol properties and implementation choices, this can radically decrease security. We measure the prevalence of these parameter choices in the wild for HTTPS, POP3S, SMTP with STARTTLS, SSH, IKEv1, and IKEv2, finding millions of hosts using DSA and other non-\u201csafe\u201d primes for Diffie-Hellman key exchange, many of them in combination with potentially vulnerable behaviors. We examine over 20 open-source cryptographic libraries and applications and observe that until January 2016, not a single one validated subgroup orders by default. We found feasible full or partial key recovery vulnerabilities in OpenSSL, the Exim mail server, the Unbound DNS client, and Amazon\u2019s load balancer, as well as susceptibility to weaker attacks in many other applications.",
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"https://www.ndss-symposium.org/ndss2017/ndss-2017-programme/measuring-small-subgroup-attacks-against-diffie-hellman/",
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"http://www.cis.upenn.edu/~lukev/files/subgroup-slides.pdf",
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"http://eprint.iacr.org/2016/995.pdf",
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"title": "Measuring small subgroup attacks against Diffie-Hellman",
"venue": "NDSS",
"year": 2016
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"authors": [
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"name": "Peng Wang"
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{
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"name": "Di Wang"
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"paperAbstract": "We present TiML (Timed ML), an ML-like functional language with time-complexity annotations in types. It uses indexed types to express sizes of data structures and upper bounds on running time of functions; and refinement kinds to constrain these indices, expressing data-structure invariants and pre/post-conditions. Indexed types are flexible enough that TiML avoids a built-in notion of â\u0080\u009csize,â\u0080\u009d and the programmer can choose to index user-defined datatypes in any way that helps her analysis. TiMLâ\u0080\u0099s distinguishing characteristic is supporting highly automated time-bound verification applicable to data structures with nontrivial invariants. The programmer provides type annotations, and the typechecker generates verification conditions that are discharged by an SMT solver. Type and index inference are supported to lower annotation burden, and, furthermore, big-O complexity can be inferred from recurrences generated during typechecking by a recurrence solver based on heuristic pattern matching (e.g. using the Master Theorem to handle divide-and-conquer-like recurrences). We have evaluated TiMLâ\u0080\u0099s usability by implementing a broad suite of case-study modules, demonstrating that TiML, though lacking full automation and theoretical completeness, is versatile enough to verify worst-case and/or amortized complexities for algorithms and data structures like classic list operations, merge sort, Dijkstraâ\u0080\u0099s shortest-path algorithm, red-black trees, Braun trees, functional queues, and dynamic tables with bounds like <i>m</i> <i>n</i> log<i>n</i>. The learning curve and annotation burden are reasonable, as we argue with empirical results on our case studies. We formalized TiMLâ\u0080\u0099s type-soundness proof in Coq.",
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"title": "TiML: a functional language for practical complexity analysis with invariants",
"venue": "PACMPL",
"year": 2017
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"name": "Fangming Liu"
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{
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"name": "Tao Wang"
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{
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"name": "John C. S. Lui"
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"paperAbstract": "Current IaaS clouds provide performance guarantee on CPU and memory but no quantitative network performance for VM instances. Our measurements from three production IaaS clouds show that for the VMs with same CPU and memory, or similar pricing, the difference in bandwidth performance can be as much as 16\u00d7, which reveals a severe price-performance anomaly due to a lack of pricing for bandwidth guarantee. Considering the low network utilization in cloud-scale datacenters, we address this by presenting SoftBW, a system that enables pricing bandwidth with over commitment on bandwidth guarantee. SoftBW leverages usage-based charging to guarantee price-performance consistency among tenants, and implements a fulfillment based scheduling to provide bandwidth/fairness guarantee under bandwidth over commitment. Both testbed experiments and large-scale simulation results validate SoftBW\u2019s ability of providing efficient bandwidth guarantee, and show that by using bandwidth over commitment, SoftBW increases 3.9\u00d7 network utilization while incurring less than 5% guarantee failure.",
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"title": "BARRACUDA: binary-level analysis of runtime RAces in CUDA programs",
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"title": "Near-Optimal Access Partitioning for Memory Hierarchies with Multiple Heterogeneous Bandwidth Sources",
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"title": "Multiple Pattern Matching for Network Security Applications: Acceleration through Vectorization",
"venue": "2017 46th International Conference on Parallel Processing (ICPP)",
"year": 2017
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"paperAbstract": "Energy management is a key issue for mobile devices. On current Android devices, power management relies heavily on OS modules known as governors. These modules are created for various hardware components, including the CPU, to support DVFS. They implement algorithms that attempt to balance performance and power consumption. In this paper we make the observation that the existing governors are (1) general-purpose by nature (2) focused on power reduction and (3) are not energy-optimal for many applications. We thus establish the need for an application-specific approach that could overcome these drawbacks and provide higher energy efficiency for suitable applications. We also show that existing methods manage power and performance in an independent and isolated fashion and that co-ordinated control of multiple components can save more energy. In addition, we note that on mobile devices, energy savings cannot be achieved at the expense of performance. Consequently, we propose a solution that minimizes energy consumption of specific applications while maintaining a user-specified performance target. Our solution consists of two stages: (1) offline profiling and (2) online controlling. Utilizing the offline profiling data of the target application, our control theory based online controller dynamically selects the optimal system configuration (in this paper, combination of CPU frequency and memory bandwidth) for the application, while it is running. Our energy management solution is tested on a Nexus 6 smartphone with 6 real-world applications. We achieve 4 - 31% better energy than default governors with a worst case performance loss of",
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"paperAbstract": "DRAM is the primary technology used for main memory in modern systems. Unfortunately, as DRAM scales down to smaller technology nodes, it faces key challenges in both data integrity and latency, which strongly affects overall system reliability and performance. To develop reliable and high-performance DRAM-based main memory in future systems, it is critical to characterize, understand, and analyze various aspects (e.g., reliability, latency) of existing DRAM chips. To enable this, there is a strong need for a publicly-available DRAM testing infrastructure that can flexibly and efficiently test DRAM chips in a manner accessible to both software and hardware developers. This paper develops the first such infrastructure, SoftMC (Soft Memory Controller), an FPGA-based testing platform that can control and test memory modules designed for the commonly-used DDR (Double Data Rate) interface. SoftMC has two key properties: (i) it provides flexibility to thoroughly control memory behavior or to implement a wide range of mechanisms using DDR commands, and (ii) it is easy to use as it provides a simple and intuitive high-level programming interface for users, completely hiding the low-level details of the FPGA. We demonstrate the capability, flexibility, and programming ease of SoftMC with two example use cases. First, we implement a test that characterizes the retention time of DRAM cells. Experimental results we obtain using SoftMC are consistent with the findings of prior studies on retention time in modern DRAM, which serves as a validation of our infrastructure. Second, we validate two recently-proposed mechanisms, which rely on accessing recently-refreshed or recently-accessed DRAM cells faster than other DRAM cells. Using our infrastructure, we show that the expected latency reduction effect of these mechanisms is not observable in existing DRAM chips, which demonstrates the usefulness of SoftMC in testing new ideas on existing memory modules. We discuss several other use cases of SoftMC, including the ability to characterize emerging non-volatile memory modules that obey the DDR standard. We hope that our open-source release of SoftMC fills a gap in the space of publicly-available experimental memory testing infrastructures and inspires new studies, ideas, and methodologies in memory system design.",
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"http://www.ece.cmu.edu/~safari/pubs/softMC_hpca17-lightning-talk.pdf",
"http://www.ece.cmu.edu/~safari/pubs/softMC_hpca17-talk.pdf",
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"title": "SoftMC: A Flexible and Practical Open-Source Infrastructure for Enabling Experimental DRAM Studies",
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"paperAbstract": "Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-parameter tuning and more generally for the efficient global optimization of expensive black-box functions. Systems implementing BO has successfully solved difficult problems in automatic design choices and machine learning hyper-parameters tunings. Many recent advances in the methodologies and theories underlying Bayesian optimization have extended the framework to new applications and provided greater insights into the behavior of these algorithms. Still, these established techniques always require a user-defined space to perform optimization. This pre-defined space specifies the ranges of hyper-parameter values. In many situations, however, it can be difficult to prescribe such spaces, as a prior knowledge is often unavailable. Setting these regions arbitrarily can lead to inefficient optimization - if a space is too large, we can miss the optimum with a limited budget, on the other hand, if a space is too small, it may not contain the optimum point that we want to get. The unknown search space problem is intractable to solve in practice. Therefore, in this paper, we narrow down to consider specifically the setting of "weakly specified" search space for Bayesian optimization. By weakly specified space, we mean that the pre-defined space is placed at a sufficiently good region so that the optimization can expand and reach to the optimum. However, this pre-defined space need not include the global optimum. We tackle this problem by proposing the filtering expansion strategy for Bayesian optimization. Our approach starts from the initial region and gradually expands the search space. Wedevelop an efficient algorithm for this strategy and derive its regret bound. These theoretical results are complemented by an extensive set of experiments on benchmark functions and tworeal-world applications which demonstrate the benefits of our proposed approach.",
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"doi": "10.1145/3117811.3117822",
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"paperAbstract": "In this paper, we design a pluggable data management solution for modern mobile platforms (e.g., Android). Our goal is to allow data management mechanisms and policies to be implemented independently of core app logic. Our design allows a user to install data management solutions as apps, install multiple such solutions on a single device, and choose a suitable solution each for one or more apps. It allows app developers to focus their effort on app logic and helps the developers of data management solutions to achieve wider deployability. It also gives increased control of data management to end users and allows them to use different solutions for different apps. We present a prototype implementation of our design called BlueMountain, and implement several data management solutions for file and database management to demonstrate the utility and ease of using our design. We perform detailed microbenchmarks as well as end-to-end measurements for files and databases to demonstrate the performance overhead incurred by our implementation.",
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"title": "BlueMountain: An Architecture for Customized Data Management on Mobile Systems",
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"paperAbstract": "JIT spraying allows an attacker to subvert a JustIn-Time compiler, introducing instruction sequences useful to the attacker into executable regions of the victim program\u2019s address space as a side effect of compiling seemingly innocuous code in a safe language like JavaScript. We present new JIT spraying attacks against Google\u2019s V8 and Mozilla\u2019s SpiderMonkey JavaScript engines on ARM. The V8 attack is the first JIT spraying attack not to rely on instruction decoding ambiguity, and the SpiderMonkey attack uses the first ARM payload that executes unintended instructions derived from intended instruction bytes without resynchronizing to the intended instruction stream. We review the JIT spraying defenses proposed in the literature and their currently-deployed implementations and conclude that the current state of JIT spraying mitigation, which prioritizes low performance overhead, leaves many exploitable attacker options unchecked. We perform an empirical evaluation of mitigations with low but non-zero overhead in a unified framework and find that full, robust defense implementations of diversification defenses can effectively mitigate JIT spraying attacks in the literature as well as our new attacks with a combined average overhead of 4.56% on x86-64 and 4.88% on ARM32.",
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"title": "A Call to ARMs: Understanding the Costs and Benefits of JIT Spraying Mitigations",
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"paperAbstract": "Public cloud software marketplaces already offer users a wealth of choice in operating systems, database management systems, financial software, and virtual networking, all deployable and configurable at the click of a button. Unfortunately, this level of customization has not extended to emerging hypervisor-level services, partly because traditional virtual machines (VMs) are fully controlled by only one hypervisor at a time. Currently, a VM in a cloud platform cannot concurrently use hypervisorlevel services from multiple third-parties in a compartmentalized manner. We propose the notion of a multihypervisor VM, which is an unmodified guest that can simultaneously use services from multiple coresident, but isolated, hypervisors. We present a new virtualization architecture, called Span virtualization, that leverages nesting to allow multiple hypervisors to concurrently control a guest\u2019s memory, virtual CPU, and I/O resources. Our prototype of Span virtualization on the KVM/QEMU platform enables a guest to use services such as introspection, network monitoring, guest mirroring, and hypervisor refresh, with performance comparable to traditional nested VMs.",
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"title": "Contention in Structured Concurrency: Provably Efficient Dynamic Non-Zero Indicators for Nested Parallelism",
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"paperAbstract": "Maximum coverage and minimum set cover problems---here collectively called coverage problems---have been studied extensively in streaming models. However, previous research not only achieves suboptimal approximation factors and space complexities but also study a restricted set-arrival model which makes an explicit or implicit assumption on oracle access to the sets, ignoring the complexity of reading and storing the whole set at once. In this paper, we address the above shortcomings and present algorithms with improved approximation factor and improved space complexity, and prove that our results are almost tight. Moreover, unlike most of the previous work, our results hold in a more general edge-arrival model.\n More specifically, consider an instance with <i>n</i> sets, together covering <i>m</i> elements. Information arrives in the form of \"edges\" from sets to elements (denoting membership) in arbitrary order. <ol><li>We present (almost) optimal approximation algorithms for maximum coverage and minimum set cover problems in the streaming model with an (almost) optimal space complexity of Õ(n); i.e., the space is <i>independent of the size of the sets or the size of the ground set of elements</i>. These results not only improve the best known algorithms for the set-arrival model, but also are the first such algorithms for the more powerful <i>edge-arrival</i> model.</li> <li>In order to achieve the above results, we introduce a new general sketching technique for coverage functions: One can apply this sketching scheme to convert an α-approximation algorithm for a coverage problem to a (1-ε)α-approximation algorithm for the same problem in streaming model.</li> <li>We show the significance of our sketching technique by ruling out the possibility of solving coverage problems via accessing (as a black box) a (1 ± ε)-approximate oracle (e.g., a sketch function) that estimates the coverage function on any subfamily of the sets. Finally, we show that our streaming algorithms achieve an almost optimal space complexity.</li></ol>",
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"title": "Almost Optimal Streaming Algorithms for Coverage Problems",
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"paperAbstract": "Flexplane enables users to program data plane algorithms and conduct experiments that run real application traffic over them at hardware line rates. Flexplane explores an intermediate point in the design space between past work on software routers and emerging work on programmable hardware chipsets. Like software routers, Flexplane enables users to express resource management schemes in a high-level language (C++), but unlike software routers, Flexplane runs at close to hardware line rates. To achieve these two goals, a centralized emulator faithfully emulates, in real-time on a multi-core machine, the desired data plane algorithms with very succinct representations of the original packets. Real packets traverse the network when notified by the emulator, sharing the same fate and relative delays as their emulated counterparts. Flexplane accurately predicts the behavior of several network schemes such as RED and DCTCP, sustains aggregate throughput of up to 760 Gbits/s on a 10-core machine (\u21e1 20\u21e5 faster than software routers), and enables experiments with real-world operating systems and applications (e.g., Spark) running on diverse network schemes at line rate, including those such as HULL and pFabric that are not available in hardware today.",
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"paperAbstract": "Deep learning models can take weeks to train on a single GPU-equipped machine, necessitating scaling out DL training to a GPU-cluster. However, current distributed DL implementations can scale poorly due to substantial parameter synchronization over the network, because the high throughput of GPUs allows more data batches to be processed per unit time than CPUs, leading to more frequent network synchronization. We present Poseidon, an efficient communication architecture for distributed DL on GPUs. Poseidon exploits the layered model structures in DL programs to overlap communication and computation, reducing bursty network communication. Moreover, Poseidon uses a hybrid communication scheme that optimizes the number of bytes required to synchronize each layer, according to layer properties and the number of machines. We show that Poseidon is applicable to different DL frameworks by plugging Poseidon into Caffe and TensorFlow. We show that Poseidon enables Caffe and TensorFlow to achieve 15.5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification. Moreover, Poseidon-enabled TensorFlow achieves 31.5x speed-up with 32 single-GPU machines on Inception-V3, a 50% improvement over the open-source TensorFlow (20x speed-up).",
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"paperAbstract": "As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) can intercept and manipulate a large fraction of Bitcoin traffic.This paper presents the first taxonomy of routing attacks and their impact on Bitcoin, considering both small-scale attacks, targeting individual nodes, and large-scale attacks, targeting the network as a whole. While challenging, we show that two key properties make routing attacks practical: (i) the efficiency of routing manipulation; and (ii) the significant centralization of Bitcoin in terms of mining and routing. Specifically, we find that any network attacker can hijack few (<100) BGP prefixes to isolate ∼50% of the mining power—even when considering that mining pools are heavily multi-homed. We also show that on-path network attackers can considerably slow down block propagation by interfering with few key Bitcoin messages.We demonstrate the feasibility of each attack against the deployed Bitcoin software. We also quantify their effectiveness on the current Bitcoin topology using data collected from a Bitcoin supernode combined with BGP routing data. The potential damage to Bitcoin is worrying. By isolating parts of the network or delaying block propagation, attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of exploits such as double spending. To prevent such effects in practice, we provide both short and long-term countermeasures, some of which can be deployed immediately.",
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"title": "Hijacking Bitcoin: Routing Attacks on Cryptocurrencies",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
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"paperAbstract": "Many online application services are now provided by cloud-deployed VM clusters. Although economical, VMs in the cloud are prone to interference due to contention for physical resources among colocated users. Worse, this interference is dynamic and unpredictable. Current provider-centric solutions are application-oblivious and are thus not always aware of the user's SLO requirements or application bottlenecks. Further, such solutions rely on VM scheduling and migration, approaches that are not agile enough to mitigate volatile interference.This paper presents DIAL, an interference-aware load balancer that can be employed by cloud users without requiring any assistance from the provider. DIAL addresses timevarying interference by dynamically shifting load away from compromised VMs without violating the application's tail latency SLOs. The key idea behind DIAL is to infer the demand for contended resources on the physical hosts, which is otherwise hidden from users. Estimates of the colocated load are then used to drive the load distribution for the application VMs. Our experimental results on OpenStack and AWS clouds show that DIAL can reduce application tail latencies by as much as 70% and 48% compared to interference-oblivious and existing interference-aware load balancers, respectively.",
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"title": "DIAL: Reducing Tail Latencies for Cloud Applications via Dynamic Interference-aware Load Balancing",
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"doi": "10.1145/3133956.3134007",
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"paperAbstract": "HTTPS error warnings are supposed to alert browser users to network attacks. Unfortunately, a wide range of non-attack circumstances trigger hundreds of millions of spurious browser warnings per month. Spurious warnings frustrate users, hinder the widespread adoption of HTTPS, and undermine trust in browser warnings. We investigate the root causes of HTTPS error warnings in the field, with the goal of resolving benign errors.\n We study a sample of over 300 million errors that Google Chrome users encountered in the course of normal browsing. After manually reviewing more than 2,000 error reports, we developed automated rules to classify the top causes of HTTPS error warnings. We are able to automatically diagnose the root causes of two-thirds of error reports. To our surprise, we find that more than half of errors are caused by client-side or network issues instead of server misconfigurations. Based on these findings, we implemented more actionable warnings and other browser changes to address client-side error causes. We further propose solutions for other classes of root causes.",
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"name": "Xiaofo Yu"
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"name": "Sean Zou"
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"name": "Lijin Guo"
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"name": "Changfeng Liu"
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"name": "Chaoran Wang"
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"name": "Karrie Karahalios"
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"paperAbstract": "Data exploration and analysis, especially for non-programmers, remains a tedious and frustrating process of trial-and-error\u2014data scientists spend many hours poring through visualizations in the hope of finding those that match desired patterns. We demonstrate zenvisage, an interactive data exploration system tailored towards \u201cfastforwarding\u201d to desired trends, patterns, or insights, without much effort from the user. zenvisage\u2019s interface supports simple dragand-drop and sketch-based interactions as specification mechanisms for the exploration need, as well as an intuitive data exploration language called ZQL for more complex needs. zenvisage is being developed in collaboration with ad analysts, battery scientists, and genomic data analysts, and will be demonstrated on similar datasets.",
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"title": "Fast-Forwarding to Desired Visualizations with Zenvisage",
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"paperAbstract": "Several research groups have noted that hardware transactional memory (HTM), even in the case of aborts, can have the side effect of warming up the branch predictor and caches, thereby accelerating subsequent execution. We propose to employ this side effect deliberately, in cases where execution must wait for action in another thread. In doing so, we allow "warm-up" transactions to observe inconsistent state. We must therefore ensure that they never accidentally commit. To that end, we propose that the hardware allow the program to specify, at the start of a transaction, that it should in all cases abort, even if it (accidentally) executes a commit instruction. We discuss several scenarios in which always-abort HTM (AAHTM) can be useful, and present lock and barrier implementations that employ it. We demonstrate the value of these implementations on several real-world applications, obtaining performance improvements of up to 2.5x with almost no programmer effort.",
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"paperAbstract": "About eight decades ago, Zipf postulated that the word frequency distribution of languages is a <i>power law</i>, i.e., it is a straight line on a log-log plot. Over the years, this phenomenon has been documented and studied extensively. For many corpora, however, the empirical distribution barely resembles a power law: when plotted on a log-log scale, the distribution is concave and appears to be composed of two differently sloped straight lines joined by a smooth curve. A simple generative model is proposed to capture this phenomenon. The word frequency distributions produced by this model are shown to match the observations both analytically and empirically.",
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"paperAbstract": "During exploratory statistical analysis, data scientists repeatedly compute statistics on data sets to infer knowledge. Moreover, statistics form the building blocks of core machine learning classification and filtering algorithms. Modern data systems, software libraries, and domain-specific tools provide support to compute statistics but lack a cohesive framework for storing, organizing, and reusing them. This creates a significant problem for exploratory statistical analysis as data grows: Despite existing overlap in exploratory workloads (which are repetitive in nature), statistics are always computed from scratch. This leads to repeated data movement and recomputation, hindering interactive data exploration.\n We address this challenge in Data Canopy, where descriptive and dependence statistics are synthesized from a library of basic aggregates. These basic aggregates are stored within an in-memory data structure, and and are reused for overlapping data parts and for various statistical measures. What this means for exploratory statistical analysis is that repeated requests to compute different statistics do not trigger a full pass over the data. We discuss in detail the basic design elements in Data Canopy, which address multiple challenges: (1) How to decompose statistics into basic aggregates for maximal reuse? (2) How to represent, store, maintain, and access these basic aggregates? (3) Under different scenarios, which basic aggregates to maintain? (4) How to tune Data Canopy in a hardware conscious way for maximum performance and how to maintain good performance as data grows and memory pressure increases?\n We demonstrate experimentally that Data Canopy results in an average speed-up of at least 10x after just 100 exploratory queries when compared with state-of-the-art systems used for exploratory statistical analysis.",
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"title": "Data Canopy: Accelerating Exploratory Statistical Analysis",
"venue": "SIGMOD Conference",
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"authors": [
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"name": "Jose Picado"
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"name": "Arash Termehchy"
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"name": "Alan Fern"
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"paperAbstract": "Learning novel relations from relational databases is an important problem with many applications. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same database may be represented under different schemas for various reasons, such as data quality, efficiency and usability. The output of current relational learning algorithms tends to vary quite substantially over the choice of schema. This variation complicates their off-the-shelf application. We introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We show that current algorithms are not schema independent. We propose Castor, a relational learning algorithm that achieves schema independence by leveraging data dependencies.",
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"title": "Schema Independent Relational Learning",
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"name": "Yixin Sun"
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{
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"name": "Anne Edmundson"
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"name": "Nick Feamster"
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"name": "Mung Chiang"
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"paperAbstract": "This paper investigates the problem of deriving a white box performance model of Hardware Transactional Memory (HTM) systems. The proposed model targets TSX, a popular implementation of HTM integrated in Intel processors starting with the Haswell family in 2013.An inherent difficulty with building white-box models of commercially available HTM systems is that their internals are either vaguely documented or undisclosed by their manufacturers. We tackle this challenge by designing a set of experiments that allow us to shed lights on the internal mechanisms used in TSX to manage conflicts among transactions and to track their readsets and writesets.We exploit the information inferred from this experimental study to build an analytical model of TSX focused on capturing the impact on performance of two key mechanisms: the concurrency control scheme and the management of transactional meta-data in the processor's caches. We validate the proposed model by means of an extensive experimental study encompassing a broad range of workloads executed on a real system.",
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"paperAbstract": "How to choose a strong but still easily memorable password? An often recommended advice is to memorize a random sentence (the mnemonic) and to concatenate the words\u2019 initials: a so-called mnemonic password. The paper in hand analyzes the effectiveness of this advice\u2014in terms of the obtained password strength\u2014and sheds light on various related aspects. While it is infeasible to obtain a sufficiently large sample of human-chosen mnemonics, the password strength depends only on the distribution of certain character probabilities. We provide several pieces of evidence that these character probabilities are approximately the same for human-chosen mnemonics and sentences from a web crawl and exploit this connection for our analyses. The presented analyses are independent of cracking software, avoid privacy concerns, and allow full control over the details of how passwords are generated from sentences. In particular, the paper introduces the following original research contributions: (1) construction of one of the largest corpora of human-chosen mnemonics, (2) construction of two web sentence corpora from the 27.3 TB ClueWeb12 web crawl, (3) demonstration of the suitability of web sentences as substitutes for mnemonics in password strength analyses, (4) improved estimation of password probabilities by position-dependent language models, and (5) analysis of the obtained password strength using web sentence samples of different sentence complexity and using 18 generation rules for mnemonic password construction. Our findings include both expected and less expected results, among others: mnemonic passwords from lowercase letters only provide comparable strength to mnemonic passwords that exploit the 7-bit visible ASCII character set, less complex mnemonics reduce password strength in offline scenarios by less than expected, and longer mnemonic passwords provide more security in an offline but not necessarily in an online scenario. When compared to passwords generated by uniform sampling from a dictionary, distributions of mnemonic passwords can reach the same strength against offline attacks with less characters.",
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"paperAbstract": "Rapid convergence to a desired allocation of network resources to endpoint traffic is a difficult problem. The reason is that congestion control decisions are distributed across the endpoints, which vary their offered load in response to changes in application demand and network feedback on a packet-by-packet basis. We propose a different approach for datacenter networks, flowlet control, in which congestion control decisions are made at the granularity of a flowlet, not a packet. With flowlet control, allocations have to change only when flowlets arrive or leave. We have implemented this idea in a system called Flowtune using a centralized allocator that receives flowlet start and end notifications from endpoints. The allocator computes optimal rates using a new, fast method for network utility maximization, and updates endpoint congestion-control parameters. Experiments show that Flowtune outperforms DCTCP, pFabric, sfqCoDel, and XCP on tail packet delays in various settings, converging to optimal rates within a few packets rather than over several RTTs. Benchmarks on an EC2 deployment show a fairer rate allocation than Linux\u2019s Cubic. A data aggregation benchmark shows 1.61\u00d7 lower p95 coflow completion time.",
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"http://www.usenix.org./system/files/conference/nsdi17/nsdi17-perry.pdf",
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"title": "Flowtune: Flowlet Control for Datacenter Networks",
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"paperAbstract": "Traditionally, Internet Access Providers (APs) only charge end-users for Internet access services; however, to recoup infrastructure costs and increase revenues, some APs have recently adopted two-sided pricing schemes under which both end-users and content providers are charged. Meanwhile, with the rapid growth of traffic, network congestion could seriously degrade user experiences and influence providers' utility. To optimize profit and social welfare, APs and regulators need to design appropriate pricing strategies and regulatory policies that take the effects of network congestion into consideration. In this paper, we model two-sided networks under which users' traffic demands are influenced by exogenous pricing and endogenous congestion parameters and derive the system congestion under an equilibrium. We characterize the structures and sensitivities of profit- and welfare-optimal two-sided pricing schemes and reveal that 1) the elasticity of system throughput plays a crucial role in determining the structures of optimal pricing, 2) the changes of optimal pricing under varying AP's capacity and users' congestion sensitivity are largely driven by the type of data traffic, e.g., text or video, and 3) APs and regulators will be incentivized to shift from one-sided to two-sided pricing when APs' capacities and user demand for video traffic grow. Our results can help APs design optimal two-sided pricing and guide regulators to legislate desirable policies.",
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"title": "On Optimal Two-Sided Pricing of Congested Networks",
"venue": "SIGMETRICS",
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"paperAbstract": "Data-driven applications rely on the correctness of their data to function properly and effectively. Errors in data can be incredibly costly and disruptive, leading to loss of revenue, incorrect conclusions, and misguided policy decisions. While data cleaning tools can purge datasets of many errors before the data is used, applications and users interacting with the data can introduce new errors. Subsequent valid updates can obscure these errors and propagate them through the dataset causing more discrepancies. Even when some of these discrepancies are discovered, they are often corrected superficially, on a case-by-case basis, further obscuring the true underlying cause, and making detection of the remaining errors harder.\n In this paper, we propose QFix, a framework that derives explanations and repairs for discrepancies in relational data, by analyzing the effect of queries that operated on the data and identifying potential mistakes in those queries. QFix is flexible, handling scenarios where only a subset of the true discrepancies is known, and robust to different types of update workloads. We make four important contributions: (a) we formalize the problem of diagnosing the causes of data errors based on the queries that operated on and introduced errors to a dataset; (b) we develop exact methods for deriving diagnoses and fixes for identified errors using state-of-the-art tools; (c) we present several optimization techniques that improve our basic approach without compromising accuracy, and (d) we leverage a tradeoff between accuracy and performance to scale diagnosis to large datasets and query logs, while achieving near-optimal results. We demonstrate the effectiveness of QFix through extensive evaluation over benchmark and synthetic data.",
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"paperAbstract": "Cloud computing has become indispensable in today's computer landscape. The flexibility it offers for customers as well as for providers has become a crucial factor for large parts of the computer industry. Virtualization is the key technology that allows for sharing of hardware resources among different customers. The controlling software component, called hypervisor, provides a virtualized view of the computer resources and ensures separation of different guest virtual machines. However, this important cornerstone of cloud computing is not necessarily trustworthy or bug-free. To mitigate this threat AMD introduced Secure Encrypted Virtualization, short SEV, which transparently encrypts a virtual machines memory.\n In this paper we analyse to what extend the proposed features can resist a malicious hypervisor and discuss the tradeoffs imposed by additional protection mechanisms. To do so, we developed a model of SEV's security capabilities based on the available documentation as actual silicon implementations are not yet on the market.\n We found that the first proposed version of SEV is not up to the task owing to three design shortcomings. First the virtual machine control block is not encrypted and handled directly by the hypervisor, allowing it to bypass VM memory encryption by executing conveniently chosen gadgets. Secondly, the general purpose registers are not encrypted upon vmexit, leaking potentially sensitive data. Finally, the control over the nested pagetables allows a malicious hypervisor to closely monitor the execution state of a VM and attack it with memory replay attacks.",
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"name": "Xiang Chen"
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"name": "Bowei Chen"
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"doi": "10.1145/3077136.3080802",
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"paperAbstract": "Displaying banner advertisements (in short, ads) on webpages has usually been discussed as an Internet economics topic where a publisher uses auction models to sell an online user's page view to advertisers and the one with the highest bid can have her ad displayed to the user. This is also called <i>real-time bidding</i> (RTB) and the ad displaying process ensures that the publisher's benefit is maximized or there is an equilibrium in ad auctions. However, the benefits of the other two stakeholders - the advertiser and the user - have been rarely discussed. In this paper, we propose a two-stage computational framework that selects a banner ad based on the optimized trade-offs among all stakeholders. The first stage is still auction based and the second stage re-ranks ads by considering the benefits of all stakeholders. Our metric variables are: the publisher's revenue, the advertiser's utility, the ad memorability, the ad click-through rate (CTR), the contextual relevance, and the visual saliency. To the best of our knowledge, this is the first work that optimizes trade-offs among all stakeholders in RTB by incorporating multimedia metrics. An algorithm is also proposed to determine the optimal weights of the metric variables. We use both ad auction datasets and multimedia datasets to validate the proposed framework. Our experimental results show that the publisher can significantly improve the other stakeholders' benefits by slightly reducing her revenue in the short-term. In the long run, advertisers and users will be more engaged, the increased demand of advertising and the increased supply of page views can then boost the publisher's revenue.",
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"title": "Optimizing Trade-offs Among Stakeholders in Real-Time Bidding by Incorporating Multimedia Metrics",
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"year": 2017
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"paperAbstract": "Random forests are among the most successful methods used in data mining because of their extraordinary accuracy and effectiveness. However, their use is primarily limited to multidimensional data because they sample features from the original data set. In this paper, we propose a method for extending random forests to work with any arbitrary set of data objects, as long as similarities can be computed among the data objects. Furthermore, since it is understood that similarity computation between all <i>O</i>(<i>n</i><sup>2</sup>) pairs of <i>n</i> objects might be expensive, our method computes only a very small fraction of the <i>O</i>(<i>n</i><sup>2</sup>) pairwise similarities between objects to construct the forests. Our results show that the proposed similarity forest approach is very efficient and accurate on a wide variety of data sets. Therefore, this paper significantly extends the applicability of random forest methods to arbitrary data domains. Furthermore, the approach even outperforms traditional random forests on multidimensional data. We show that similarity forests are robust to the noisy similarity values that are ubiquitous in real-world applications. In many practical settings, the similarity values between objects are incompletely specified because of the difficulty in collecting such values. Similarity forests can be used in such cases with straightforward modifications.",
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"title": "Similarity Forests",
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"paperAbstract": "Computational applications are subject to various kinds of numerical errors, ranging from deterministic round-off errors to soft errors caused by non-deterministic bit flips, which do not lead to application failure but corrupt application results. Non-deterministic bit flips are typically mitigated in hardware using various error correcting codes (ECC). But in practice, due to performance and cost concerns, these techniques do not guarantee error-free execution. On large-scale computing platforms, soft errors occur with non-negligible probability in RAM and on the CPU, and it has become clear that applications must tolerate them. For some applications, this tolerance is intrinsic as result quality can remain acceptable even in the presence of soft errors (e.g., data analysis applications, multimedia applications). Tolerance can also be built into the application, resolving data corruptions in software during application execution. By contrast, today's optical networks hold on to a rigid error-free standard, which imposes limits on network performance scalability. In this work we propose high-bandwidth, low-latency approximate networks with the following three features:(1) Optical links that exploit multi-level quadrature amplitude modulation (QAM) for achieving high bandwidth, (2) Avoidance of forward error correction (FEC), which makes optical link error-prone but affords lower latency, and(3) The use of symbol mapping coding between bit sequence and QAM to ensure data integrity that is sufficient for practical soft-error-tolerant applications. Discrete-event simulation results for application benchmarks show that approx networks achieve speedups up to 2.94 when compared to conventional networks.",
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"http://doi.ieeecomputersociety.org/10.1109/HPCA.2017.38"
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"title": "High-Bandwidth Low-Latency Approximate Interconnection Networks",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
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"authors": [
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"name": "Marios Pomonis"
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{
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"name": "Theofilos Petsios"
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{
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"name": "Angelos D. Keromytis"
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{
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"name": "Michalis Polychronakis"
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{
"ids": [
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"name": "Vasileios P. Kemerlis"
}
],
"doi": "10.1145/3064176.3064216",
"doiUrl": "https://doi.org/10.1145/3064176.3064216",
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"paperAbstract": "The abundance of memory corruption and disclosure vulnerabilities in kernel code necessitates the deployment of hardening techniques to prevent privilege escalation attacks. As more strict memory isolation mechanisms between the kernel and user space, like Intel's SMEP, become commonplace, attackers increasingly rely on code reuse techniques to exploit kernel vulnerabilities. Contrary to similar attacks in more restrictive settings, such as web browsers, in kernel exploitation, non-privileged local adversaries have great flexibility in abusing memory disclosure vulnerabilities to dynamically discover, or infer, the location of certain code snippets and construct code-reuse payloads. Recent studies have shown that the coupling of code diversification with the enforcement of a \"read XOR execute\" (R^X) memory safety policy is an effective defense against the exploitation of userland software, but so far this approach has not been applied for the protection of the kernel itself.\n In this paper, we fill this gap by presenting kR^X: a kernel hardening scheme based on execute-only memory and code diversification. We study a previously unexplored point in the design space, where a hypervisor or a super-privileged component is not required. Implemented mostly as a set of GCC plugins, kR^X is readily applicable to the x86-64 Linux kernel and can benefit from hardware support (e.g., MPX on modern Intel CPUs) to optimize performance. In full protection mode, kR^X incurs a low runtime overhead of 4.04%, which drops to 2.32% when MPX is available.",
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"http://doi.acm.org/10.1145/3064176.3064216",
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"https://cs.brown.edu/~vpk/papers/krx.eurosys17.pdf",
"http://www.cs.columbia.edu/~theofilos/files/slides/krx.pdf",
"http://www3.cs.stonybrook.edu/~mikepo/papers/krx.eurosys17.pdf",
"http://cs.brown.edu/~vpk/papers/krx.eurosys17.pdf"
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"title": "kR^X: Comprehensive Kernel Protection against Just-In-Time Code Reuse",
"venue": "EuroSys",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Zi Yan"
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{
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"name": "J\u00e1n Vesel\u00fd"
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"name": "Guilherme Cox"
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{
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"name": "Abhishek Bhattacharjee"
}
],
"doi": "10.1145/3079856.3080211",
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"paperAbstract": "To improve system performance, operating systems (OSes) often undertake activities that require modification of virtual-to-physical address translations. For example, the OS may migrate data between physical pages to manage heterogeneous memory devices. We refer to such activities as page remappings. Unfortunately, page remappings are expensive. We show that a big part of this cost arises from address translation coherence, particularly on systems employing virtualization. In response, we propose hardware translation invalidation and coherence or HATRIC, a readily implementable hardware mechanism to piggyback translation coherence atop existing cache coherence protocols. We perform detailed studies using KVM-based virtualization, showing that HATRIC achieves up to 30% performance and 10% energy benefits, for per-CPU area overheads of 0.2%. We also quantify HATRIC's benefits on systems running Xen and find up to 33% performance improvements.",
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"https://arxiv.org/pdf/1701.07517v2.pdf",
"http://paul.rutgers.edu/~jv356/pub/ziyan-isca17.pdf",
"http://arxiv.org/abs/1701.07517",
"https://arxiv.org/pdf/1701.07517.pdf",
"https://arxiv.org/pdf/1701.07517v1.pdf"
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"title": "Hardware translation coherence for virtualized systems",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"paperAbstract": "In this paper, we present CrowdDQS, a system that uses the most recent set of crowdsourced voting evidence to dynamically issue questions to workers on Amazon Mechanical Turk (AMT). CrowdDQS posts all questions to AMT in a single batch, but delays the decision of the exact question to issue a worker until the last moment, concentrating votes on uncertain questions to maximize accuracy. Unlike previous works, CrowdDQS also (1) optionally can decide when it is more beneficial to issue gold standard questions with known answers than to solicit new votes (both can help us estimate worker accuracy, but gold standard questions provide a less noisy estimate of worker accuracy at the expense of not obtaining new votes), (2) estimates worker accuracies in real-time even with limited evidence (with or without gold standard questions), and (3) infers the distribution of worker skill levels to actively block poor workers. We deploy our system live on AMT to over 1000 crowdworkers, and find that CrowdDQS can accurately answer questions using up to <b>6x fewer votes</b> than standard approaches. We also find there are many non-obvious practical challenges involved in deploying such a system seamlessly to crowdworkers, and discuss techniques to overcome these challenges.",
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"paperAbstract": "Although the security of Cyber-Physical Systems (CPS) has been recently receiving significant attention from the research community, undoubtedly, there still exists a substantial lack of a comprehensive and a holistic understanding of attackers\u2019 malicious strategies, aims and intentions. To this end, this paper uniquely exploits passive monitoring and analysis of a newly deployed network telescope IP address space in a first attempt ever to build broad notions of real CPS maliciousness. Specifically, we approach this problem by inferring, investigating, characterizing and reporting large-scale probing activities that specifically target more than 20 diverse, heavily employed CPS protocols. To permit such analysis, we initially devise and evaluate a novel probabilistic model that aims at filtering noise that is embedded in network telescope traffic. Subsequently, we generate amalgamated statistics, inferences and insights characterizing such inferred scanning activities in terms of their probe types, the distribution of their sources and their packets\u2019 headers, among numerous others, in addition to examining and visualizing the co-occurrence patterns of such events. Further, we propose and empirically evaluate an innovative hybrid approach rooted in time-series analysis and context triggered piecewise hashing to infer, characterize and cluster orchestrated and well-coordinated probing activities targeting CPS protocols, which are generated from Internet-scale unsolicited sources. Our analysis and evaluations, which draw upon extensive network telescope data observed over a recent one month period, demonstrate a staggering 33 thousand probes towards ample of CPS protocols, the lack of interest in UDP-based CPS services, and the prevalence of probes towards the ICCP and Modbus protocols. Additionally, we infer a considerable 74% of CPS probes that were persistent throughout the entire analyzed period targeting prominent protocols such as DNP3 and BACnet. Further, we uncover close to 9 thousand large-scale, stealthy, previously undocumented orchestrated probing events targeting a number of such CPS protocols. We validate the various outcomes through cross-validations against publicly available threat repositories. We concur that the devised approaches, techniques, and methods provide a solid first step towards better comprehending real CPS unsolicited objectives and intents.",
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"paperAbstract": "In JavaScript programs, asynchrony arises in situations such as web-based user-interfaces, communicating with servers through HTTP requests, and non-blocking I/O. Event-based programming is the most popular approach for managing asynchrony, but suffers from problems such as lost events and event races, and results in code that is hard to understand and debug. Recently, ECMAScript 6 has added support for promises, an alternative mechanism for managing asynchrony that enables programmers to chain asynchronous computations while supporting proper error handling. However, promises are complex and error-prone in their own right, so programmers would benefit from techniques that can reason about the correctness of promise-based code. \nSince the ECMAScript 6 specification is informal and intended for implementers of JavaScript engines, it does not provide a suitable basis for formal reasoning. This paper presents Î\u00bb<sub><i>p</i></sub>, a core calculus that captures the essence of ECMAScript 6 promises. Based on Î\u00bb<sub><i>p</i></sub>, we introduce the promise graph, a program representation that can assist programmers with debugging of promise-based code. We then report on a case study in which we investigate how the promise graph can be helpful for debugging errors related to promises in code fragments posted to the StackOverflow website.",
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"paperAbstract": "Anecdotes, news reports, and policy briefings collectively suggest that Internet censorship practices are pervasive. The scale and diversity of Internet censorship practices makes it difficult to precisely monitor where, when, and how censorship occurs, as well as what is censored. The potential risks in performing the measurements make this problem even more challenging. As a result, many accounts of censorship begin—and end—with anecdotes or short-term studies from only a handful of vantage points. We seek to instead continuously monitor information about Internet reachability, to capture the onset or termination of censorship across regions and ISPs. To achieve this goal, we introduce Augur, a method and accompanying system that utilizes TCP/IP side channels to measure reachability between two Internet locations without directly controlling a measurement vantage point at either location. Using these side channels, coupled with techniques to ensure safety by not implicating individual users, we develop scalable, statistically robust methods to infer network-layer filtering, and implement a corresponding system capable of performing continuous monitoring of global censorship. We validate our measurements of Internet-wide disruption in nearly 180 countries over 17 days against sites known to be frequently blocked, we also identify the countries where connectivity disruption is most prevalent.",
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"http://www.icir.org/vern/papers/oakland_2017_augur.pdf",
"https://www.cs.princeton.edu/~rensafi/papers/Augur-Oakland17.pdf",
"https://people.eecs.berkeley.edu/~frankli/papers/augur_oakland_2017.pdf",
"https://doi.org/10.1109/SP.2017.55"
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"paperAbstract": "Domain names have been exploited for illicit online activities for decades. In the past, miscreants mostly registered new domains for their attacks. However, the domains registered for malicious purposes can be deterred by existing reputation and blacklisting systems. In response to the arms race, miscreants have recently adopted a new strategy, called <i>domain shadowing</i>, to build their attack infrastructures. Specifically, instead of registering new domains, miscreants are beginning to compromise legitimate ones and spawn malicious subdomains under them. This has rendered almost all existing countermeasures ineffective and fragile because subdomains inherit the trust of their apex domains, and attackers can virtually spawn an infinite number of shadowed domains.\n In this paper, we conduct the first study to understand and detect this emerging threat. Bootstrapped with a set of manually confirmed shadowed domains, we identify a set of novel features that uniquely characterize domain shadowing by analyzing the deviation from their apex domains and the correlation among different apex domains. Building upon these features, we train a classifier and apply it to detect shadowed domains on the daily feeds of VirusTotal, a large open security scanning service. Our study highlights domain shadowing as an increasingly rampant threat. Moreover, while previously confirmed domain shadowing campaigns are exclusively involved in exploit kits, we reveal that they are also widely exploited for phishing attacks. Finally, we observe that instead of algorithmically generating subdomain names, several domain shadowing cases exploit the wildcard DNS records.",
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"http://doi.acm.org/10.1145/3133956.3134049"
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"title": "Don't Let One Rotten Apple Spoil the Whole Barrel: Towards Automated Detection of Shadowed Domains",
"venue": "CCS",
"year": 2017
},
"0a85b3afc89958583642b7fd39b37e745a053190": {
"authors": [
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"ids": [
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"name": "Cheng Tan"
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{
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"name": "Lingfan Yu"
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{
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"name": "Joshua B. Leners"
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{
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"name": "Michael Walfish"
}
],
"doi": "10.1145/3132747.3132760",
"doiUrl": "https://doi.org/10.1145/3132747.3132760",
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"Client\u2013server model",
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"paperAbstract": "You put a program on a concurrent server, but you don't trust the server; later, you get a trace of the actual requests that the server received from its clients and the responses that it delivered. You separately get logs from the server; these are untrusted. How can you use the logs to efficiently verify that the responses were derived from running the program on the requests? This is the Efficient Server Audit Problem, which abstracts real-world scenarios, including running a web application on an untrusted provider. We give a solution based on several new techniques, including simultaneous replay and efficient verification of concurrent executions. We implement the solution for PHP web applications. For several applications, our verifier achieves 5.6-10.9x speedup versus simply re-executing, with <10% overhead for the server.",
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"http://doi.acm.org/10.1145/3132747.3132760",
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"title": "The Efficient Server Audit Problem, Deduplicated Re-execution, and the Web",
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},
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"name": "Carsten Lutz"
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"name": "Fabio Papacchini"
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"paperAbstract": "We study the complexity of ontology-mediated querying when ontologies are formulated in the guarded fragment of first-order logic (GF). Our general aim is to classify the data complexity on the level of ontologies where query evaluation w.r.t. an ontology O is considered to be in PTime if all (unions of conjunctive) queries can be evaluated in PTime w.r.t. O and coNP-hard if at least one query is coNP-hard w.r.t. O. We identify several large and relevant fragments of GF that enjoy a dichotomy between PTime and coNP, some of them additionally admitting a form of counting. In fact, almost all ontologies in the BioPortal repository fall into these fragments or can easily be rewritten to do so. We then establish a variation of Ladner's Theorem on the existence of NP-intermediate problems and use this result to show that for other fragments, there is provably no such dichotomy. Again for other fragments (such as full GF), establishing a dichotomy implies the Feder-Vardi conjecture on the complexity of constraint satisfaction problems. We also link these results to Datalog-rewritability and study the decidability of whether a given ontology enjoys PTime query evaluation, presenting both positive and negative results.",
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"title": "Dichotomies in Ontology-Mediated Querying with the Guarded Fragment",
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"year": 2017
},
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"name": "J\u00f6rg Thalheim"
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{
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"name": "Antonio Rodrigues"
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{
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"name": "Istemi Ekin Akkus"
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{
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"name": "Pramod Bhatotia"
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{
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"name": "Ruichuan Chen"
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"name": "Bimal Viswanath"
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"name": "Lei Jiao"
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"doi": "10.1145/3135974.3135977",
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"paperAbstract": "Major cloud computing operators provide powerful monitoring tools to understand the current (and prior) state of the distributed systems deployed in their infrastructure. While such tools provide a detailed monitoring mechanism at scale, they also pose a significant challenge for the application developers/operators to transform the <i>huge space of monitored metrics</i> into <i>useful insights.</i> These insights are essential to build effective management tools for improving the efficiency, resiliency, and dependability of distributed systems.\n This paper reports on our experience with building and deploying S<scp>ieve</scp>---a platform to derive actionable insights from monitored metrics in distributed systems. S<scp>ieve</scp> builds on two core components: a metrics reduction framework, and a metrics dependency extractor. More specifically, S<scp>ieve</scp> first reduces the dimensionality of metrics by automatically filtering out unimportant metrics by observing their signal over time. Afterwards, S<scp>ieve</scp> infers metrics dependencies between distributed components of the system using a predictive-causality model by testing for Granger Causality.\n We implemented S<scp>ieve</scp> as a generic platform and deployed it for two microservices-based distributed systems: OpenStack and Share-Latex. Our experience shows that (1) S<scp>ieve</scp> can reduce the number of metrics by at least an order of magnitude (10 -- 100×), while preserving the statistical equivalence to the total number of monitored metrics; (2) S<scp>ieve</scp> can dramatically improve existing monitoring infrastructures by reducing the associated overheads over the entire system stack (CPU---80%, storage---90%, and network---50%); (3) Lastly, S<scp>ieve</scp> can be effective to support a wide-range of workflows in distributed systems---we showcase two such workflows: Orchestration of autoscaling, and Root Cause Analysis (RCA).",
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"http://doi.acm.org/10.1145/3135974.3135977",
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"title": "Scaling betweenness centrality using communication-efficient sparse matrix multiplication",
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"doi": "10.1145/3126908.3126947",
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"paperAbstract": "Near-term quantum computers will soon reach sizes that are challenging to directly simulate, even when employing the most powerful supercomputers. Yet, the ability to simulate these early devices using classical computers is crucial for calibration, validation, and benchmarking. In order to make use of the full potential of systems featuring multi- and many-core processors, we use automatic code generation and optimization of compute kernels, which also enables performance portability. We apply a scheduling algorithm to quantum supremacy circuits in order to reduce the required communication and simulate a 45-qubit circuit on the Cori II super-computer using 8, 192 nodes and 0.5 petabytes of memory. To our knowledge, this constitutes the largest quantum circuit simulation to this date. Our highly-tuned kernels in combination with the reduced communication requirements allow an improvement in time-to-solution over state-of-the-art simulations by more than an order of magnitude at every scale.",
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"http://doi.acm.org/10.1145/3126908.3126947"
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"title": "0.5 Petabyte Simulation of a 45-qubit Quantum Circuit",
"venue": "SC",
"year": 2017
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"paperAbstract": "A memory-hard function (MHF) ƒ<i>n</i> with parameter <i>n</i> can be computed in sequential time and space <i>n</i>. Simultaneously, a high <i>amortized parallel</i> area-time complexity (aAT) is incurred per evaluation. In practice, MHFs are used to limit the rate at which an adversary (using a custom computational device) can evaluate a security sensitive function that still occasionally needs to be evaluated by honest users (using an off-the-shelf general purpose device). The most prevalent examples of such sensitive functions are Key Derivation Functions (KDFs) and password hashing algorithms where rate limits help mitigate off-line dictionary attacks. As the honest users' inputs to these functions are often (low-entropy) passwords special attention is given to a class of side-channel resistant MHFs called iMHFs.\n Essentially all iMHFs can be viewed as some mode of operation (making <i>n</i> calls to some round function) given by a directed acyclic graph (DAG) with very low indegree. Recently, a combinatorial property of a DAG has been identified (called \"depth-robustness\") which results in good provable security for an iMHF based on that DAG. Depth-robust DAGs have also proven useful in other cryptographic applications. Unfortunately, up till now, all known very depth-robust DAGs are impractically complicated and little is known about their exact (i.e. non-asymptotic) depth-robustness both in theory and in practice.\n In this work we build and analyze (both formally and empirically) several exceedingly simple and efficient to navigate practical DAGs for use in iMHFs and other applications. For each DAG we: <ul><li>Prove that their depth-robustness is asymptotically maximal.</li> <li>Prove bounds of at least 3 orders of magnitude better on their exact depth-robustness compared to known bounds for other practical iMHF.</li> <li>Implement and empirically evaluate their depth-robustness and aAT against a variety of state-of-the art (and several new) depth-reduction and low aAT attacks. We find that, against all attacks, the new DAGs perform significantly better in practice than Argon2i, the most widely deployed iMHF in practice.</li></ul>\n Along the way we also improve the best known empirical attacks on the aAT of Argon2i by implementing and testing several heuristic versions of a (hitherto purely theoretical) depth-reduction attack. Finally, we demonstrate practicality of our constructions by modifying the Argon2i code base to use one of the new high aAT DAGs. Experimental benchmarks on a standard off-the-shelf CPU show that the new modifications do not adversely affect the impressive throughput of Argon2i (despite seemingly enjoying significantly higher aAT).",
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"https://www.cs.purdue.edu/homes/bharsha/papers/practicalgraphs.pdf",
"http://eprint.iacr.org/2017/443",
"http://doi.acm.org/10.1145/3133956.3134031"
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"title": "Practical Graphs for Optimal Side-Channel Resistant Memory-Hard Functions",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Tiffany Bao"
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"name": "Ruoyu Wang"
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{
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"name": "Yan Shoshitaishvili"
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{
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"name": "David Brumley"
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],
"doi": "10.1109/SP.2017.67",
"doiUrl": "https://doi.org/10.1109/SP.2017.67",
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"paperAbstract": "Developing a remote exploit is not easy. It requires a comprehensive understanding of a vulnerability and delicate techniques to bypass defense mechanisms. As a result, attackers may prefer to reuse an existing exploit and make necessary changes over developing a new exploit from scratch. One such adaptation is the replacement of the original shellcode (i.e., the attacker-injected code that is executed as the final step of the exploit) in the original exploit with a replacement shellcode, resulting in a modified exploit that carries out the actions desired by the attacker as opposed to the original exploit author. We call this a shellcode transplant. Current automated shellcode placement methods are insufficient because they over-constrain the replacement shellcode, and so cannot be used to achieve shellcode transplant. For example, these systems consider the shellcode as an integrated memory chunk and require that the execution path of the modified exploit must be same as the original one. To resolve these issues, we present ShellSwap, a system that uses symbolic tracing, with a combination of shellcode layout remediation and path kneading to achieve shellcode transplant. We evaluated the ShellSwap system on a combination of 20 exploits and 5 pieces of shellcode that are independently developed and different from the original exploit. Among the 100 test cases, our system successfully generated 88% of the exploits.",
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"https://users.ece.cmu.edu/~youzhib/paper/bao2017.pdf",
"https://users.cs.fiu.edu/~carbunar/teaching/cen5079/cen5079.2017/presentations/shellcode.pdf",
"https://doi.org/10.1109/SP.2017.67"
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"title": "Your Exploit is Mine: Automatic Shellcode Transplant for Remote Exploits",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"paperAbstract": "We present an algorithm that enumerates all the minimal triangulations of a graph in incremental polynomial time. Consequently, we get an algorithm for enumerating all the proper tree decompositions, in incremental polynomial time, where ``proper'' means that the tree decomposition cannot be improved by removing or splitting a bag. The algorithm can incorporate any method for (ordinary, single result) triangulation or tree decomposition, and can serve as an anytime algorithm to improve such a method. We describe an extensive experimental study of an implementation on real data from different fields. Our experiments show that the algorithm improves upon central quality measures over the underlying tree decompositions, and is able to produce a large number of high-quality decompositions.",
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"paperAbstract": "The methodology of community detection can be divided into two principles: imposing a network model on a given graph, or optimizing a designed objective function. The former provides guarantees on theoretical detectability but falls short when the graph is inconsistent with the underlying model. The latter is model-free but fails to provide quality assurance for the detected communities. In this paper, we propose a novel unified framework to combine the advantages of these two principles. The presented method, SGC-GEN, not only considers the detection error caused by the corresponding model mismatch to a given graph, but also yields a theoretical guarantee on community detectability by analyzing Spectral Graph Clustering (SGC) under GENerative community models (GCMs). SGC-GEN incorporates the predictability on correct community detection with a measure of community fitness to GCMs. It resembles the formulation of supervised learning problems by enabling various community detection loss functions and model mismatch metrics. We further establish a theoretical condition for correct community detection using the normalized graph Laplacian matrix under a GCM, which provides a novel data-driven loss function for SGC-GEN. In addition, we present an effective algorithm to implement SGC-GEN, and show that the computational complexity of SGC-GEN is comparable to the baseline methods. Our experiments on 18 real-world datasets demonstrate that SGC-GEN possesses superior and robust performance compared to 6 baseline methods under 7 representative clustering metrics.",
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"title": "Revisiting Spectral Graph Clustering with Generative Community Models",
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"year": 2017
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"paperAbstract": "Modern data center solid state drives (SSDs) integrate multiple general-purpose embedded cores to manage flash translation layer, garbage collection, wear-leveling, and etc., to improve the performance and the reliability of SSDs. As the performance of these cores steadily improves there are opportunities to repurpose these cores to perform application driven computations on stored data, with the aim of reducing the communication between the host processor and the SSD. Reducing host-SSD bandwidth demand cuts down the I/O time which is a bottleneck for many applications operating on large data sets. However, the embedded core performance is still significantly lower than the host processor, as generally wimpy embedded cores are used within SSD for cost effective reasons. So there is a trade-off between the computation overhead associated with near SSD processing and the reduction in communication overhead to the host system.\n In this work, we design a set of application programming interfaces (APIs) that can be used by the host application to offload a data intensive task to the SSD processor. We describe how these APIs can be implemented by simple modifications to the existing Non-Volatile Memory Express (NVMe) command interface between the host and the SSD processor. We then quantify the computation versus communication tradeoffs for near storage computing using applications from two important domains, namely data analytics and data integration. Using a fully functional SSD evaluation platform we perform design space exploration of our proposed approach by varying the bandwidth and computation capabilities of the SSD processor. We evaluate static and dynamic approaches for dividing the work between the host and SSD processor, and show that our design may improve the performance by up to 20% when compared to processing at the host processor only, and 6X when compared to processing at the SSD processor only.",
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"title": "Summarizer: trading communication with computing near storage",
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"year": 2017
},
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"authors": [
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"name": "Andrey Rodchenko"
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"name": "Christos Kotselidis"
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"name": "Andy Nisbet"
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"name": "Antoniu Pop"
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"name": "Mikel Luj\u00e1n"
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"paperAbstract": "Managed applications, written in programming languages such as Java, C# and others, represent a significant share of workloads in the mobile, desktop, and server domains. Microarchitectural timing simulation of such workloads is useful for characterization and performance analysis, of both hardware and software, as well as for research and development of novel hardware extensions. This paper introduces MaxSim, a simulation platform based on the Maxine VM, the ZSim simulator, and the McPAT modeling framework. MaxSim is able to simulate fast and accurately managed workloads running on top of Maxine VM and its capabilities are showcased with novel simulation techniques for: 1) low-intrusive microarchitectural profiling via pointer tagging on the x86-64 platforms, 2) modeling of hardware extensions related, but not limited to, tagged pointers, and 3) modeling of complex software changes via address-space morphing. Low-intrusive microarchitectural profiling is achieved by utilizing tagged pointers to collect type- and allocation-site-related hardware events. Furthermore, MaxSim allows, through a novel technique called address space morphing, the easy modeling of complex object layout transformations. Finally, through the codesigned capabilities of MaxSim, novel hardware extensions can be implemented and evaluated. We showcase MaxSim's capabilities by simulating the whole set of the DaCapo-9.12-bach benchmarks in less than a day while performing an up-to-date microarchitectural power and performance characterization. Furthermore, we demonstrate a hardware/software co-designed optimization that performs dynamic load elimination for array length retrieval achieving up to 14% L1 data cache loads reduction and up to 4% dynamic energy reduction. MaxSim is available at https://github.com/arodchen/MaxSim released as free software.",
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"title": "MaxSim: A simulation platform for managed applications",
"venue": "2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)",
"year": 2017
},
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"name": "David Hallac"
},
{
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"name": "Youngsuk Park"
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{
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"name": "Stephen P. Boyd"
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"paperAbstract": "The Internet's inter-domain routing infrastructure, provided today by BGP, is extremely rigid and does not facilitate the introduction of new inter-domain routing protocols. This rigidity has made it incredibly difficult to widely deploy critical fixes to BGP. It has also depressed ASes' ability to sell value-added services or replace BGP entirely with a more sophisticated protocol. Even if operators undertook the significant effort needed to fix or replace BGP, it is likely the next protocol will be just as difficult to change or evolve. To help, this paper identifies two features needed in the routing infrastructure (i.e., within any inter-domain routing protocol) to facilitate evolution to new protocols. To understand their utility, it presents D-BGP, a version of BGP that incorporates them.",
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"paperAbstract": "Convolutional neural networks (CNNs) are revolutionizing machine learning, but they present significant computational challenges. Recently, many FPGA-based accelerators have been proposed to improve the performance and efficiency of CNNs. Current approaches construct a single processor that computes the CNN layers one at a time; the processor is optimized to maximize the throughput at which the collection of layers is computed. However, this approach leads to inefficient designs because the same processor structure is used to compute CNN layers of radically varying dimensions.\n We present a new CNN accelerator paradigm and an accompanying automated design methodology that partitions the available FPGA resources into multiple processors, each of which is tailored for a different subset of the CNN convolutional layers. Using the same FPGA resources as a single large processor, multiple smaller specialized processors increase computational efficiency and lead to a higher overall throughput. Our design methodology achieves 3.8x higher throughput than the state-of-the-art approach on evaluating the popular AlexNet CNN on a Xilinx Virtex-7 FPGA. For the more recent SqueezeNet and GoogLeNet, the speedups are 2.2x and 2.0x.",
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"paperAbstract": "Differential testing uses similar programs as cross-referencing oracles to find semantic bugs that do not exhibit explicit erroneous behaviors like crashes or assertion failures. Unfortunately, existing differential testing tools are domain-specific and inefficient, requiring large numbers of test inputs to find a single bug. In this paper, we address these issues by designing and implementing NEZHA, an efficient input-format-agnostic differential testing framework. The key insight behind NEZHA's design is that current tools generate inputs by simply borrowing techniques designed for finding crash or memory corruption bugs in individual programs (e.g., maximizing code coverage). By contrast, NEZHA exploits the behavioral asymmetries between multiple test programs to focus on inputs that are more likely to trigger semantic bugs. We introduce the notion of δ-diversity, which summarizes the observed asymmetries between the behaviors of multiple test applications. Based on δ-diversity, we design two efficient domain-independent input generation mechanisms for differential testing, one gray-box and one black-box. We demonstrate that both of these input generation schemes are significantly more efficient than existing tools at finding semantic bugs in real-world, complex software. NEZHA's average rate of finding differences is 52 times and 27 times higher than that of Frankencerts and Mucerts, two popular domain-specific differential testing tools that check SSL/TLS certificate validation implementations, respectively. Moreover, performing differential testing with NEZHA results in 6 times more semantic bugs per tested input, compared to adapting state-of-the-art general-purpose fuzzers like American Fuzzy Lop (AFL) to differential testing by running them on individual test programs for input generation. NEZHA discovered 778 unique, previously unknown discrepancies across a wide variety of applications (ELF and XZ parsers, PDF viewers and SSL/TLS libraries), many of which constitute previously unknown critical security vulnerabilities. In particular, we found two critical evasion attacks against ClamAV, allowing arbitrary malicious ELF/XZ files to evade detection. The discrepancies NEZHA found in the X.509 certificate validation implementations of the tested SSL/TLS libraries range from mishandling certain types of KeyUsage extensions, to incorrect acceptance of specially crafted expired certificates, enabling man-in-the-middle attacks. All of our reported vulnerabilities have been confirmed and fixed within a week from the date of reporting.",
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"http://www.cs.columbia.edu/~theofilos/files/slides/nezha.pdf",
"http://0x0atang.github.io/files/nezha17_slides.pdf",
"https://doi.org/10.1109/SP.2017.27",
"http://www.nsl.cs.columbia.edu/papers/2017/nezha.oakland17.pdf"
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"title": "NEZHA: Efficient Domain-Independent Differential Testing",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"authors": [
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"doi": "10.1145/3143361.3143383",
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"paperAbstract": "Internet communication today typically involves intermediary middleboxes like caches, compression proxies, or virus scanners. Unfortunately, as encryption becomes more widespread, these middleboxes become blind and we lose their security, functionality, and performance benefits. Despite initial efforts in both industry and academia, we remain unsure how to integrate middleboxes into secure sessions---it is not even clear how to define \"secure\" in this multi-entity context.\n In this paper, we first describe a design space for secure multi-entity communication protocols, highlighting tradeoffs between mutually incompatible properties. We then target real-world requirements unmet by existing protocols, like outsourcing middleboxes to untrusted infrastructure and supporting legacy clients. We propose a security definition and present Middlebox TLS (mbTLS), a protocol that provides it (in part by using Intel SGX to protect middleboxes from untrusted hardware). We show that mbTLS is deployable today and introduces little overhead, and we describe our experience building a simple mbTLS HTTP proxy.",
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"https://www.microsoft.com/en-us/research/uploads/prod/2018/01/securecomm_conext17.pdf",
"http://doi.acm.org/10.1145/3143361.3143383"
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"title": "And Then There Were More: Secure Communication for More Than Two Parties",
"venue": "CoNEXT",
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},
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"name": "Falko Bause"
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"name": "Johannes May"
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],
"doi": "10.1145/3030207.3030215",
"doiUrl": "https://doi.org/10.1145/3030207.3030215",
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"paperAbstract": "Quantitative aspects of modern IT systems are often specified by service level agreements (SLAs) which relate the maximal load of a system with guaranteed bounds for response times and delays. These quantities are specified for single services which are combined in a service oriented architecture (SOA) to composed services offered to potential users or other service providers. To derive SLAs for composed services and to plan the required capacity to guarantee SLAs, appropriate methods and tools have to be used that compute results based on information given in SLAs. In this paper it is argued that most available approaches are not sufficient to analyze systems based on SLA information. A new method and a tool are presented that support the efficient calculation of bounds for delays in composed systems based on bounds for the load and the delay of the individual components which are specified in the SLAs of the components. Furthermore, the presented tool can be used to generate bounds for the required processing capacity which a provider has to provide in order to guarantee the quality of service defined in the SLAs.\n The presented approach is in some sense a counterpart to mean value analysis for queueing networks but rather than mean values, worst case bounds for different quantities like response times or departure processes are computed. Analysis is based on min/+ algebra but the mathematical approach is hidden from the user by a graphical interface allowing a simple graphical specification and result representation for networks of composed services.",
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"title": "A Tool Supporting the Analytical Evaluation of Service Level Agreements",
"venue": "ICPE",
"year": 2017
},
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"authors": [
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"name": "Ajeya Naithani"
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"name": "Stijn Eyerman"
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"doi": "10.1109/HPCA.2017.12",
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"paperAbstract": "Reliability to soft errors is an increasingly important issue as technology continues to shrink. In this paper, we show that applications exhibit different reliability characteristics on big, high-performance cores versus small, power-efficient cores, and that there is significant opportunity to improve system reliability through reliability-aware scheduling on heterogeneous multicore processors. We monitor the reliability characteristics of all running applications, and dynamically schedule applications to the different core types in a heterogeneous multicore to maximize system reliability. Reliability-aware scheduling improves reliability by 25.4% on average (and up to 60.2%) compared to performance-optimized scheduling on a heterogeneous multicore processor with two big cores and two small cores, while de-grading performance by 6.3% only. We also introduce a novel system-level reliability metric for multiprogram workloads on (heterogeneous) multicores. We further show that our reliability-aware scheduler is robust across core count, number of big and small cores, and their frequency settings. The hardware cost in support of our reliability-aware scheduler is limited to 296 bytes per core.",
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"http://doi.ieeecomputersociety.org/10.1109/HPCA.2017.12"
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"title": "Reliability-Aware Scheduling on Heterogeneous Multicore Processors",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
"0b643dc29fa5aca25d78d831f95009872f83cb26": {
"authors": [
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"ids": [
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"name": "Drew Zagieboylo"
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"ids": [
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"name": "Kazi A. Zaman"
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],
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"paperAbstract": "Video game crash events are characterized primarily by large media payloads and by highly bursty traffic patterns, with hundreds of thousands or millions of reports being issued in only a few minutes. These events are invaluable in quickly responding to game breaking issues that directly impact user experience. Even the slightest delay in capturing, processing and reporting these events can lead to user abandonment and significant financial cost.\n A traditional standalone RESTful service, backed by a vertically scaled SQL database is neither a reliable nor cost-effective solution to this problem. An architecture that decouples capture and persistence and uses a horizontally scalable NoSQL database is not only easier to provision, but also uses fewer cpu and memory resources to provide the same end to end latency and throughput.\n By replacing our RESTful implementation with one that takes advantage both of the aforementioned design and multi-tenant provisioning, we have reduced our dedicated cpu footprint by 63% and memory footprint by 59%. Additionally, we have decreased our data loss during spikes to essentially 0, maintained sub-second persistence latency and improved query latency in the average case by 54% with only a 3% sacrifice for worst case queries.",
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"sources": [
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"title": "Cost-Efficient and Reliable Reporting of Highly Bursty Video Game Crash Data",
"venue": "ICPE",
"year": 2017
},
"0b6ab61a4872ee95ea90ff8bd82d5b70d39d1072": {
"authors": [
{
"ids": [
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"name": "Zhihao Jia"
},
{
"ids": [
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"name": "Sean Treichler"
},
{
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"name": "Galen M. Shipman"
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{
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"name": "Michael Bauer"
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{
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"name": "Noah Watkins"
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{
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"name": "Carlos Maltzahn"
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{
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"name": "Patrick S. McCormick"
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{
"ids": [
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"name": "Alexander Aiken"
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],
"doi": "10.1109/HiPC.2017.00043",
"doiUrl": "https://doi.org/10.1109/HiPC.2017.00043",
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"paperAbstract": "Rust is a new system programming language that offers a practical and safe alternative to C. Rust is unique in that it enforces safety without runtime overhead, most importantly, without the overhead of garbage collection. While zero-cost safety is remarkable on its own, we argue that the superpowers of Rust go beyond safety. In particular, Rust's linear type system enables capabilities that cannot be implemented efficiently in traditional languages, both safe and unsafe, and that dramatically improve security and reliability of system software. We show three examples of such capabilities: zero-copy software fault isolation, efficient static information flow analysis, and automatic checkpointing. While these capabilities have been in the spotlight of systems research for a long time, their practical use is hindered by high cost and complexity. We argue that with the adoption of Rust these mechanisms will become commoditized.",
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"title": "System Programming in Rust: Beyond Safety",
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"paperAbstract": "We report on our discovery of an algorithmic flaw in the construction of primes for RSA key generation in a widely-used library of a major manufacturer of cryptographic hardware. The primes generated by the library suffer from a significant loss of entropy. We propose a practical factorization method for various key lengths including 1024 and 2048 bits. Our method requires no additional information except for the value of the public modulus and does not depend on a weak or a faulty random number generator. We devised an extension of Coppersmith's factorization attack utilizing an alternative form of the primes in question. The library in question is found in NIST FIPS 140-2 and CC~EAL~5+ certified devices used for a wide range of real-world applications, including identity cards, passports, Trusted Platform Modules, PGP and tokens for authentication or software signing. As the relevant library code was introduced in 2012 at the latest (and probably earlier), the impacted devices are now widespread. Tens of thousands of such keys were directly identified, many with significant impacts, especially for electronic identity documents, software signing, Trusted Computing and PGP. We estimate the number of affected devices to be in the order of at least tens of millions.\n The worst cases for the factorization of 1024 and 2048-bit keys are less than 3 CPU-months and 100 CPU-years on single core of common recent CPUs, respectively, while the expected time is half of that of the worst case. The attack can be parallelized on multiple CPUs. Worse still, all susceptible keys contain a strong fingerprint that is verifiable in microseconds on an ordinary laptop -- meaning that all vulnerable keys can be quickly identified, even in very large datasets.",
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"title": "The Return of Coppersmith's Attack: Practical Factorization of Widely Used RSA Moduli",
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"year": 2017
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"paperAbstract": "Frameworks and libraries provide application programming interfaces (APIs) that serve as building blocks in modern software development. As APIs present the opportunity of increased productivity, it also calls for correct use to avoid buggy code. The usage-based specification mining technique has shown great promise in solving this problem through a data-driven approach. These techniques leverage the use of the API in large corpora to understand the recurring usages of the APIs and infer behavioral specifications (preconditions and postconditions) from such usages. A challenge for such technique is thus inference in the presence of insufficient usages, in terms of both frequency and richness. We refer to this as a \"sparse usage problem.\" This paper presents the first technique to solve the sparse usage problem in usage-based precondition mining. Our key insight is to leverage implicit beliefs to overcome sparse usage. An implicit belief (IB) is the knowledge implicitly derived from the fact about the code. An IB about a program is known implicitly to a programmer via the language's constructs and semantics, and thus not explicitly written or specified in the code. The technical underpinnings of our new precondition mining approach include a technique to analyze the data and control flow in the program leading to API calls to infer preconditions that are implicitly present in the code corpus, a catalog of 35 code elements in total that can be used to derive implicit beliefs from a program, and empirical evaluation of all of these ideas. We have analyzed over 350 millions lines of code and 7 libraries that suffer from the sparse usage problem. Our approach realizes 6 implicit beliefs and we have observed that adding single-level context sensitivity can further improve the result of usage based precondition mining. The result shows that we achieve overall 60% in precision and 69% in recall and the accuracy is relatively improved by 32% in precision and 78% in recall compared to base usage-based mining approach for these libraries.",
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"title": "Exploiting implicit beliefs to resolve sparse usage problem in usage-based specification mining",
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},
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"paperAbstract": "Previous attacks that link the sender and receiver of traffic in the Tor network (\u201ccorrelation attacks\u201d) have generally relied on analyzing traffic from TCP connections. The TCP connections of a typical client application, however, are often accompanied by DNS requests and responses. This additional traffic presents more opportunities for correlation attacks. This paper quantifies how DNS traffic can make Tor users more vulnerable to correlation attacks. We investigate how incorporating DNS traffic can make existing correlation attacks more powerful and how DNS lookups can leak information to third parties about anonymous communication. We (i) develop a method to identify the DNS resolvers of Tor exit relays; (ii) develop a new set of correlation attacks (DefecTor attacks) that incorporate DNS traffic to improve precision; (iii) analyze the Internet-scale effects of these new attacks on Tor users; and (iv) develop improved methods to evaluate correlation attacks. First, we find that there exist adversaries who can mount DefecTor attacks: for example, Google\u2019s DNS resolver observes almost 40% of all DNS requests exiting the Tor network. We also find that DNS requests often traverse ASes that the corresponding TCP connections do not transit, enabling additional ASes to gain information about Tor users\u2019 traffic. We then show that an adversary who can mount a DefecTor attack can often determine the website that a Tor user is visiting with perfect precision, particularly for less popular websites where the set of DNS names associated with that website may be unique to the site. We also use the Tor Path Simulator (TorPS) in combination with traceroute data from vantage points co-located with Tor exit relays to estimate the power of AS-level adversaries who might mount DefecTor attacks in practice.",
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"http://arxiv.org/abs/1609.08187",
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"title": "The Effect of DNS on Tor's Anonymity",
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"paperAbstract": "Recent hardware switch architectures make it feasible to perform flexible packet processing inside the network. This allows operators to configure switches to parse and process custom packet headers using flexible match+action tables in order to exercise control over how packets are processed and routed. However, flexible switches have limited state, support limited types of operations, and limit per-packet computation in order to be able to operate at line rate. Our work addresses these limitations by providing a set of general building blocks that mask these limitations using approximation techniques and thereby enabling the implementation of realistic network protocols. In particular, we use these building blocks to tackle the network resource allocation problem within datacenters and realize approximate variants of congestion control and load balancing protocols, such as XCP, RCP, and CONGA, that require explicit support from the network. Our evaluations show that these approximations are accurate and that they do not exceed the hardware resource limits associated with these flexible switches. We demonstrate their feasibility by implementing RCP with the production Cavium CNX880xx switch. This implementation provides significantly faster and lowervariance flow completion times compared with TCP.",
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"paperAbstract": "We implemented (a simplified version of) the branching-program obfuscator due to Gentry et al. (GGH15), which is itself a variation of the first obfuscation candidate by Garg et al. (GGHRSW13). To keep within the realm of feasibility, we had to give up on some aspects of the construction, specifically the \"multiplicative bundling\" factors that protect against mixed-input attacks. Hence our implementation can only support read-once branching programs.\n To be able to handle anything more than just toy problems, we developed a host of algorithmic and code-level optimizations. These include new variants of discrete Gaussian sampler and lattice trapdoor sampler, efficient matrix-manipulation routines, and many tradeoffs. We expect that these optimizations will find other uses in lattice-based cryptography beyond just obfuscation.\n Our implementation is the first obfuscation attempt using the GGH15 graded encoding scheme, offering performance advantages over other graded encoding methods when obfuscating finite-state machines with many states. In out most demanding setting, we were able to obfuscate programs with input length of 20 nibbles (80 bits) and over 100 states, which seems out of reach for prior implementations. Although further optimizations are surely possible, we do not expect any implementation of current schemes to be able to handle much larger parameters.",
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"title": "Implementing BP-Obfuscation Using Graph-Induced Encoding",
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"paperAbstract": "Applications running in modern multithreaded environments are sometimes overthreaded. The excess threads do not improve performance, and in fact may act to degrade performance via scalability collapse, which can manifest even when there are fewer ready threads than available cores. Often, such software also has highly contended locks. We leverage the existence of such locks by modifying the lock admission policy so as to intentionally limit the number of distinct threads circulating over the lock in a given period. Specifically, if there are more threads circulating than are necessary to keep the lock saturated (continuously held), our approach will selectively cull and passivate some of those excess threads. We borrow the concept of swapping from the field of memory management and impose concurrency restriction (CR) if a lock suffers from contention. The resultant admission order is unfair over the short term but we explicitly provide long-term fairness by periodically shifting threads between the set of passivated threads and those actively circulating. Our approach is palliative, but is often effective at avoiding or reducing scalability collapse, and in the worst case does no harm. Specifically, throughput is either unaffected or improved, and unfairness is bounded, relative to common test-and-set locks which allow unbounded bypass and starvation1. By reducing competition for shared resources, such as pipelines, processors and caches, concurrency restriction may also reduce overall resource consumption and improve the overall load carrying capacity of a system.",
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"http://arxiv.org/pdf/1511.06035v2.pdf",
"https://arxiv.org/pdf/1511.06035v4.pdf",
"https://arxiv.org/pdf/1511.06035v1.pdf",
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"https://arxiv.org/pdf/1511.06035v3.pdf",
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"http://arxiv.org/pdf/1511.06035v4.pdf",
"https://arxiv.org/pdf/1511.06035v6.pdf",
"http://arxiv.org/pdf/1511.06035v3.pdf",
"http://arxiv.org/pdf/1511.06035v1.pdf",
"http://doi.acm.org/10.1145/3064176.3064203",
"https://arxiv.org/pdf/1511.06035v2.pdf"
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"title": "Malthusian Locks",
"venue": "EuroSys",
"year": 2017
},
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"authors": [
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"name": "Janis Kalofolias"
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{
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"name": "Jilles Vreeken"
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"doi": "10.1109/ICDM.2017.29",
"doiUrl": "https://doi.org/10.1109/ICDM.2017.29",
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"paperAbstract": "Subgroup discovery is a local pattern mining technique to find interpretable descriptions of sub-populations that stand out on a given target variable. That is, these sub-populations are exceptional with regard to the global distribution. In this paper we argue that in many applications, such as scientific discovery, subgroups are only useful if they are additionally representative of the global distribution with regard to a control variable. That is, when the distribution of this control variable is the same, or almost the same, as over the whole data. We formalise this objective function and give an efficient algorithm to compute its tight optimistic estimator for the case of a numeric target and a binary control variable. This enables us to use the branch-and-bound framework to efficiently discover the top-k subgroups that are both exceptional as well as representative. Experimental evaluation on a wide range of datasets shows that with this algorithm we discover meaningful representative patterns and are up to orders of magnitude faster in terms of node evaluations as well as time.",
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"http://arxiv.org/abs/1709.07941",
"https://arxiv.org/pdf/1709.07941v1.pdf",
"http://doi.ieeecomputersociety.org/10.1109/ICDM.2017.29",
"http://pubman.mpdl.mpg.de/pubman/item/escidoc:2488423:1/component/escidoc:2488422/arXiv:1709.07941.pdf"
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"title": "Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
"year": 2017
},
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"name": "Yulong Ao"
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{
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"name": "Chao Yang"
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{
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"name": "Xinliang Wang"
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"paperAbstract": "Stencil computation arises from a broad set of scientific and engineering applications and often plays a critical role in the performance of extreme-scale simulations. Due to the memory bound nature, it is a challenging task to opti- mize stencil computation kernels on modern supercomputers with relatively high computing throughput whilst relatively low data-moving capability. This work serves as a demon- stration on the details of the algorithms, implementations and optimizations of a real-world stencil computation in 3D nonhydrostatic atmospheric modeling on the newly announced Sunway TaihuLight supercomputer. At the algorithm level, we present a computation-communication overlapping technique to reduce the inter-process communication overhead, a locality- aware blocking method to fully exploit on-chip parallelism with enhanced data locality, and a collaborative data accessing scheme for sharing data among different threads. In addition, a variety of effective hardware specific implementation and optimization strategies on both the process- and thread-level, from the fine-grained data management to the data layout transformation, are developed to further improve the per- formance. Our experiments demonstrate that a single-process many-core speedup of as high as 170x can be achieved by using the proposed algorithm and optimization strategies. The code scales well to millions of cores in terms of strong scalability. And for the weak-scaling tests, the code can scale in a nearly ideal way to the full system scale of more than 10 million cores, sustaining 25.96 PFLOPS in double precision, which is 20% of the peak performance.",
"pdfUrls": [
"https://doi.org/10.1109/IPDPS.2017.9"
],
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"title": "26 PFLOPS Stencil Computations for Atmospheric Modeling on Sunway TaihuLight",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"authors": [
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],
"name": "Konstantin Avrachenkov"
},
{
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"1776100"
],
"name": "Jasper Goseling"
},
{
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],
"name": "Berksan Serbetci"
}
],
"doi": "10.1145/3084465",
"doiUrl": "https://doi.org/10.1145/3084465",
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"Cache (computing)",
"Iteration",
"Maxima and minima",
"Nash equilibrium",
"Numerical analysis",
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"Simulated annealing",
"Simulation",
"Synthetic data"
],
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"journalPages": "27:1-27:25",
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"paperAbstract": "We consider caching in cellular networks in which each base station is equipped with a cache that can store a limited number of files. The popularity of the files is known and the goal is to place files in the caches such that the probability that a user at an arbitrary location in the plane will find the file that she requires in one of the covering caches is maximized.\n We develop distributed asynchronous algorithms for deciding which contents to store in which cache. Such cooperative algorithms require communication only between caches with overlapping coverage areas and can operate in asynchronous manner. The development of the algorithms is principally based on an observation that the problem can be viewed as a potential game. Our basic algorithm is derived from the best response dynamics. We demonstrate that the complexity of each best response step is independent of the number of files, linear in the cache capacity and linear in the maximum number of base stations that cover a certain area. Then, we show that the overall algorithm complexity for a discrete cache placement is polynomial in both network size and catalog size. In practical examples, the algorithm converges in just a few iterations. Also, in most cases of interest, the basic algorithm finds the best Nash equilibrium corresponding to the global optimum. We provide two extensions of our basic algorithm based on stochastic and deterministic simulated annealing which find the global optimum.\n Finally, we demonstrate the hit probability evolution on real and synthetic networks numerically and show that our distributed caching algorithm performs significantly better than storing the most popular content, probabilistic content placement policy and Multi-LRU caching policies.",
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"http://doi.acm.org/10.1145/3084465",
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"http://arxiv.org/abs/1704.04465",
"https://arxiv.org/pdf/1704.04465v3.pdf",
"https://arxiv.org/pdf/1704.04465v1.pdf",
"https://arxiv.org/pdf/1704.04465v4.pdf",
"http://doi.acm.org/10.1145/3078505.3078534"
],
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"title": "A Low-Complexity Approach to Distributed Cooperative Caching with Geographic Constraints",
"venue": "SIGMETRICS",
"year": 2017
},
"0c6f81e60514edbc6a936a5f8593838f14658653": {
"authors": [
{
"ids": [
"1870110"
],
"name": "Arun Subramaniyan"
},
{
"ids": [
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],
"name": "Reetuparna Das"
}
],
"doi": "10.1145/3079856.3080207",
"doiUrl": "https://doi.org/10.1145/3079856.3080207",
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"Finite-state machine",
"Graphics processing unit",
"Intrusion detection system",
"Limiter",
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"Motif",
"Multi-core processor",
"Non-deterministic Turing machine",
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"Parallel computing",
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"Von Neumann architecture",
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"journalName": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
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"paperAbstract": "Finite State Machines (FSM) are widely used computation models for many application domains. These embarrassingly sequential applications with irregular memory access patterns perform poorly on conventional von-Neumann architectures. The Micron Automata Processor (AP) is an in-situ memory-based computational architecture that accelerates non-deterministic finite automata (NFA) processing in hardware. However, each FSM on the AP is processed sequentially, limiting potential speedups.\n In this paper, we explore the FSM parallelization problem in the context of the AP. Extending classical parallelization techniques to NFAs executing on AP is non-trivial because of high state-transition tracking overheads and exponential computation complexity. We present the associated challenges and propose solutions that leverage both the unique properties of the NFAs (connected components, input symbol ranges, convergence, common parent states) and unique features in the AP (support for simultaneous transitions, low-overhead flow switching, state vector cache) to realize parallel NFA execution on the AP.\n We evaluate our techniques against several important benchmarks including NFAs used for network intrusion detection, malware detection, text processing, protein motif searching, DNA sequencing, and data analytics. Our proposed parallelization scheme demonstrates significant speedup (25.5x on average) compared to sequential execution on AP. Prior work has already shown that sequential execution on AP is at least an order of magnitude better than GPUs, multi-core processors and Xeon Phi accelerator.",
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"http://doi.acm.org/10.1145/3079856.3080207"
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"title": "Parallel automata processor",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
"0c862eb2eb56d0a28cd6f4ac7684ca624b7a6169": {
"authors": [
{
"ids": [
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],
"name": "Djillali Boukhelef"
},
{
"ids": [
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],
"name": "Kamel Boukhalfa"
},
{
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],
"name": "Jalil Boukhobza"
},
{
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],
"name": "Hamza Ouarnoughi"
},
{
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],
"name": "Laurent Lemarchand"
}
],
"doi": "",
"doiUrl": "",
"entities": [
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],
"id": "0c862eb2eb56d0a28cd6f4ac7684ca624b7a6169",
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"journalName": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
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"paperAbstract": "Solid State Drives (SSD) are integrated together with Hard Disk Drives (HDD) in Hybrid Storage Systems (HSS) for Cloud environment. When it comes to storing data, some placement strategies are used to find the best location (SSD or HDD). These strategies should minimize the cost of data placement while satisfying Service Level Objectives (SLO). This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a Genetic based approach (G-COPS) and an ad-hoc Heuristic approach (H-COPS) based on incremental optimization. While G-COPS proved to be closer to the optimal solution in case of small instances, H-COPS showed a better scalability as it approached the exact solution even for large instances (by 10% in average). In addition, H-COPS showed small execution times (few seconds) even for large instances which makes it a good candidate to be used in runtime. Both H-COPS and G-COPS performed better than state-of-the-art solutions as they satisfied SLOs while reducing the overall cost by more than 40% for problems of small and large instances.",
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"title": "COPS: Cost Based Object Placement Strategies on Hybrid Storage System for DBaaS Cloud",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
"year": 2017
},
"0c9603eb7214879b8a999af8b043627172ea3619": {
"authors": [
{
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],
"name": "Animesh Chhotaray"
},
{
"ids": [
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],
"name": "Adib Nahiyan"
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{
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"name": "Thomas Shrimpton"
},
{
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"name": "Domenic Forte"
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{
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],
"name": "Mark Mohammad Tehranipoor"
}
],
"doi": "10.1145/3133956.3134040",
"doiUrl": "https://doi.org/10.1145/3133956.3134040",
"entities": [
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"Block cipher",
"Cipher",
"Ciphertext",
"Cryptocurrency",
"Cryptography",
"Denial-of-service attack",
"Encryption",
"Key (cryptography)",
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"Syntax error",
"System on a chip",
"Trojan horse (computing)"
],
"id": "0c9603eb7214879b8a999af8b043627172ea3619",
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"paperAbstract": "We provide an analysis of IEEE standard P1735, which describes methods for encrypting electronic-design intellectual property (IP), as well as the management of access rights for such IP. We find a surprising number of cryptographic mistakes in the standard. In the most egregious cases, these mistakes enable attack vectors that allow us to recover the entire underlying plaintext IP. Some of these attack vectors are well-known, e.g. padding-oracle attacks. Others are new, and are made possible by the need to support the typical uses of the underlying IP; in particular, the need for commercial system-on-chip (SoC) tools to synthesize multiple pieces of IP into a fully specified chip design and to provide syntax errors. We exploit these mistakes in a variety of ways, leveraging a commercial SoC tool as a black-box oracle.\n In addition to being able to recover entire plaintext IP, we show how to produce standard-compliant ciphertexts of IP that have been modified to include targeted hardware Trojans. For example, IP that correctly implements the AES block cipher on all but one (arbitrary) plaintext that induces the block cipher to return the secret key. We outline a number of other attacks that the standard allows, including on the cryptographic mechanism for IP licensing. Unfortunately, we show that obvious \"quick fixes\" to the standard (and the tools that support it) do not stop all of our attacks. This suggests that the standard requires a significant overhaul, and that IP-authors using P1735 encryption should consider themselves at risk.",
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"title": "Standardizing Bad Cryptographic Practice - A Teardown of the IEEE Standard for Protecting Electronic-design Intellectual Property",
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"paperAbstract": "Fast, byte-addressable NVM promises near cache latency and near memory bus throughput for file system operations. However, unanticipated cache line eviction may lead to disordered metadata update and thus existing NVM file systems (NVMFS) use synchronous cache flushes to ensure consistency, which extends critical path latency. In this paper, we revisit soft updates, an intriguing idea that eliminates most synchronous metadata updates through delayed writes and dependency tracking, in the context of NVMFS. We show that on one hand byteaddressability of NVM significantly simplifies dependency tracking and enforcement by allowing better directory organization and closely matching the per-pointer dependency tracking of soft updates. On the other hand, per-cache-line failure atomicity of NVM cannot ensure the correctness of soft updates, which relies on block write atomicity; page cache, which is necessary for dual views in soft updates, becomes inefficient due to double writes and duplicated metadata. To guarantee the correctness and consistency without synchronous cache flushes and page cache, we propose pointer-based dual views, which shares most data structures but uses different pointers in different views, to allow delayed persistency and eliminate file system checking after a crash. In this way, our system, namely SoupFS1, significantly shortens the critical path latency by delaying almost all synchronous cache flushes. We have implemented SoupFS as a POSIX-compliant file system for Linux and evaluated it against state-of-the-art NVMFS like PMFS and NOVA. Performance results show that SoupFS can have notably lower latency and modestly higher throughput compared to existing NVMFS.",
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"paperAbstract": "Deadlock avoidance mechanisms for lossless lowdistance networks typically increase the order of virtual channel (VC) index with each hop. This restricts the number of buffer resources depending on the routing mechanism and limits performance due to an inefficient use. Dynamic buffer organizations increase implementation complexity and only provide small gains in this context because a significant amount of buffering needs to be allocated statically to avoid congestion. We introduce FlexVC, a simple buffer management mechanism which permits a more flexible use of VCs. It combines statically partitioned buffers, opportunistic routing and a relaxed distancebased deadlock avoidance policy. FlexVC mitigates Head-of-Line blocking and reduces up to 50% the memory requirements. Simulation results in a Dragonfly network show congestion reduction and up to 37.8% throughput improvement, outperforming more complex dynamic approaches. FlexVC merges different flows of traffic in the same buffers, which in some cases makes more difficult to identify the traffic pattern in order to support nonminimal adaptive routing. An alternative denoted FlexVCminCred improves congestion sensing for adaptive routing by tracking separately packets routed minimally and nonminimally, rising throughput up to 20.4% with 25% savings in buffer area.",
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"paperAbstract": "Bias-resistant public randomness is a critical component in many (distributed) protocols. Generating public randomness is hard, however, because active adversaries may behave dishonestly to bias public random choices toward their advantage. Existing solutions do not scale to hundreds or thousands of participants, as is needed in many decentralized systems. We propose two large-scale distributed protocols, RandHound and RandHerd, which provide publicly-verifiable, unpredictable, and unbiasable randomness against Byzantine adversaries. RandHound relies on an untrusted client to divide a set of randomness servers into groups for scalability, and it depends on the pigeonhole principle to ensure output integrity, even for non-random, adversarial group choices. RandHerd implements an efficient, decentralized randomness beacon. RandHerd is structurally similar to a BFT protocol, but uses RandHound in a one-time setup to arrange participants into verifiably unbiased random secret-sharing groups, which then repeatedly produce random output at predefined intervals. Our prototype demonstrates that RandHound and RandHerd achieve good performance across hundreds of participants while retaining a low failure probability by properly selecting protocol parameters, such as a group size and secret-sharing threshold. For example, when sharding 512 nodes into groups of 32, our experiments show that RandHound can produce fresh random output after 240 seconds. RandHerd, after a setup phase of 260 seconds, is able to generate fresh random output in intervals of approximately 6 seconds. For this configuration, both protocols operate at a failure probability of at most 0.08% against a Byzantine adversary.",
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"title": "Scalable Bias-Resistant Distributed Randomness",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
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"paperAbstract": "An unambiguous and easy-to-understand memory consistency model is crucial for ensuring correct synchronization and guiding future design of heterogeneous systems. In a widely adopted approach, the memory model guarantees sequential consistency (SC) as long as programmers obey certain rules. The popular data-race-free-0 (DRF0) model exemplifies this SC-centric approach by requiring programmers to avoid data races. Recent industry models, however, have extended such SC-centric models to incorporate relaxed atomics. These extensions can improve performance, but are difficult to specify formally and use correctly. This work addresses the impact of relaxed atomics on consistency models for heterogeneous systems in two ways. First, we introduce a new model, Data-Race-Free-Relaxed (DRFrlx), that extends DRF0 to provide SC-centric semantics for the common use cases of relaxed atomics. Second, we evaluate the performance of relaxed atomics in CPU-GPU systems for these use cases. We find mixed results -- for most cases, relaxed atomics provide only a small benefit in execution time, but for some cases, they help significantly (e.g., up to 51% for DRFrlx over DRF0).",
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"paperAbstract": "Differential privacy has emerged as a preferred standard for ensuring privacy in analysis tasks on sensitive datasets. Recent algorithms have allowed for significantly lower error by adapting to properties of the input data. These so-called data-dependent algorithms have different error rates for different inputs. There is now a complex and growing landscape of algorithms without a clear winner that can offer low error over all datasets. As a result, the best possible error rates are not attainable in practice, because the data curator cannot know which algorithm to select prior to actually running the algorithm.\n We address this challenge by proposing a novel meta-algorithm designed to relieve the data curator of the burden of algorithm selection. It works by learning (from non-sensitive data) the association between dataset properties and the best-performing algorithm. The meta-algorithm is deployed by first testing the input for low-sensitivity properties and then using the results to select a good algorithm. The result is an end-to-end differentially private system: Pythia, which we show offers improvements over using any single algorithm alone. We empirically demonstrate the benefit of Pythia for the tasks of releasing histograms, answering 1- and 2-dimensional range queries, as well as for constructing private Naive Bayes classifiers.",
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"title": "Pythia: Data Dependent Differentially Private Algorithm Selection",
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"paperAbstract": "We report our experience building and evaluating pmemcached, a version of memcached ported to byteaddressable persistent memory. Persistent memory is expected to not only improve overall performance of applications\u2019 persistence tier, but also vastly reduce the \u201cwarm up\u201d time needed for applications after a restart. We decided to test this hypothesis on memcached, a popular key-value store. We took the extreme view of persisting memcached\u2019s entire state, resulting in a virtually instantaneous warm up phase. Since memcached is already optimized for DRAM, we expected our port to be a straightforward engineering effort. However, the effort turned out to be surprisingly complex during which we encountered several non-trivial problems that challenged the boundaries of memcached\u2019s architecture. We detail these experiences and corresponding lessons learned.",
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"paperAbstract": "Energy efficiency is considered a challenging problem in modern multicore systems. Partitioning the cores into multiple voltage and frequency islands (VFI) provides a compromise between simple global Dynamic Voltage Frequency Scaling (DVFS) and fine-grain per-core, per-task DVFS. This paper formulates the optimization problem of scheduling tasks statically on multiple VFIs as a Mixed Integer Linear Programming (MILP) such that for a given energy budget, the program execution time (makespan) is minimized. Our proposed solution consists of two steps. In the first step, we use an Integer Linear Programming (ILP)-based algorithm, from our previous work, to assign per-core fine-grain dynamic Voltage/Frequency (V/F) levels to each task in a task set (program) to minimize the makespan for a given energy budget. In the second step, which is the focus of this paper, we use the MILP framework to schedule this task set, with the given V/F levels provided in step one, on the islands of a VFI-enabled multicore system to again minimize the makespan subject to (1) the energy budget and (2) the task set's precedence (dependency) constraints. Together with the solutions obtained by MILP, a round-robin algorithm is used to compare these two methodologies to ILP that provides the best solution. Our experimental results show that across all the benchmarks considered, the MILP-based and round-robin makespan solutions are on average 1.2 and 2.28 times slower than the ILP-based makespan solutions, respectively.",
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"title": "Optimal Energy-Aware Scheduling in VFI-enabled Multicore Systems",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"name": "Hung Dang"
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"name": "Yue Huang"
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{
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"name": "Ee-Chien Chang"
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"doi": "10.1145/3133956.3133978",
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"paperAbstract": "Learning-based systems have been shown to be vulnerable to evasion through adversarial data manipulation. These attacks have been studied under assumptions that the adversary has certain knowledge of either the target model internals, its training dataset or at least classification scores it assigns to input samples. In this paper, we investigate a much more constrained and realistic attack scenario wherein the target classifier is minimally exposed to the adversary, revealing only its final classification decision (e.g., reject or accept an input sample). Moreover, the adversary can only manipulate malicious samples using a blackbox morpher. That is, the adversary has to evade the targeted classifier by morphing malicious samples \"in the dark\". We present a scoring mechanism that can assign a real-value score which reflects evasion progress to each sample based on the limited information available. Leveraging on such scoring mechanism, we propose an evasion method -- EvadeHC? and evaluate it against two PDF malware detectors, namely PDFRate and Hidost. The experimental evaluation demonstrates that the proposed evasion attacks are effective, attaining 100% evasion rate on the evaluation dataset. Interestingly, EvadeHC outperforms the known classifier evasion techniques that operate based on classification scores output by the classifiers. Although our evaluations are conducted on PDF malware classifiers, the proposed approaches are domain agnostic and are of wider application to other learning-based systems.",
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"http://doi.acm.org/10.1145/3133956.3133978",
"https://arxiv.org/pdf/1705.07535v1.pdf",
"https://acmccs.github.io/papers/p119-dangA.pdf"
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"title": "Evading Classifiers by Morphing in the Dark",
"venue": "CCS",
"year": 2017
},
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"name": "Ning Guo"
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"doi": "10.1145/3123939.3124550",
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"paperAbstract": "Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x' that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from 95% to 0.5%.In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with 100% probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples.",
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"title": "Towards Evaluating the Robustness of Neural Networks",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
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"paperAbstract": "We present Minesweeper, a tool to verify that a network satisfies a wide range of intended properties such as reachability or isolation among nodes, waypointing, black holes, bounded path length, load-balancing, functional equivalence of two routers, and fault-tolerance. Minesweeper translates network configuration files into a logical formula that captures the stable states to which the network forwarding will converge as a result of interactions between routing protocols such as OSPF, BGP and static routes. It then combines the formula with constraints that describe the intended property. If the combined formula is satisfiable, there exists a stable state of the network in which the property does not hold. Otherwise, no stable state (if any) violates the property. We used Minesweeper to check four properties of 152 real networks from a large cloud provider. We found 120 violations, some of which are potentially serious security vulnerabilities. We also evaluated Minesweeper on synthetic benchmarks, and found that it can verify rich properties for networks with hundreds of routers in under five minutes. This performance is due to a suite of model-slicing and hoisting optimizations that we developed, which reduce runtime by over 460x for large networks.",
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"title": "A General Approach to Network Configuration Verification",
"venue": "SIGCOMM",
"year": 2017
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"paperAbstract": "MapReduce is a framework for processing large data sets much used in the context of cloud computing. MapReduce implementations like Hadoop can tolerate crashes and file corruptions, but not arbitrary faults. Unfortunately, there is evidence that arbitrary faults do occur and can affect the correctness of MapReduce job executions. Furthermore, many outages of major cloud offerings have been reported, raising concerns about the dependence on a single cloud. In this paper we propose a novel execution system that allows to scale out MapReduce computations to a cloud-of-clouds and tolerate arbitrary faults, malicious faults, and cloud outages. Our system, Chrysaor, is based on a fine-grained replication scheme that tolerates faults at the task level. Our solution has three important properties: it tolerates the above-mentioned classes of faults at reasonable cost, it requires minimal modifications to the users' applications, and it does not involve changes to the Hadoop source code. We performed an extensive evaluation of our system in Amazon EC2, showing that our fine-grained solution is efficient in terms of computation by recovering only faulty tasks. This is achieved without incurring a significant penalty for the baseline case (i.e., without faults) in most workloads.",
"pdfUrls": [
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"title": "Chrysaor: Fine-Grained, Fault-Tolerant Cloud-of-Clouds MapReduce",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
"year": 2017
},
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"authors": [
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"name": "Anselm Busse"
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"name": "Reinhardt Karnapke"
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{
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],
"doi": "10.1145/3078468.3078475",
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"paperAbstract": "Tracing and profiling low-level kernel functions (e.g. as found in the process scheduler) is a challenging task, though, necessary in both research and production in order to acquire detailed insights and achieve peak performance. Several kernel functions are known to be not traceable because of architectural limitations, whereas tracking other functions causes side effects and skews profiling results.\n In this paper, we present a novel, simulation-based approach to analyze the behavior and performance of kernel functions. Kernel code is executed on a simulated hardware platform avoiding the bias caused by collecting the tracing data within the system under observation. From the flat call trace generated by the simulator, we reconstruct the entire call graph and enrich it with detailed profiling statistics. Specifying regions of interest enables developers to systematically explore the system behavior and identify performance bottlenecks. As case study, we analyze the process scheduler of the Linux kernel. We are interested in quantifying the synchronization overhead caused by a growing number of CPU cores in a custom, semi-partitioned scheduler design. Conventional tracing methods were not able to obtain measurements with the required accuracy and granularity.",
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"title": "Simulation-based tracing and profiling for system software development",
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"year": 2017
},
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"authors": [
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"paperAbstract": "Stochastic gradient descent (SGD) is one of the most popular numerical algorithms used in machine learning and other domains. Since this is likely to continue for the foreseeable future, it is important to study techniques that can make it run fast on parallel hardware. In this paper, we provide the first analysis of a technique called Buck-wild! that uses both asynchronous execution and low-precision computation. We introduce the DMGC model, the first conceptualization of the parameter space that exists when implementing low-precision SGD, and show that it provides a way to both classify these algorithms and model their performance. We leverage this insight to propose and analyze techniques to improve the speed of low-precision SGD. First, we propose software optimizations that can increase throughput on existing CPUs by up to 11X. Second, we propose architectural changes, including a new cache technique we call an obstinate cache, that increase throughput beyond the limits of current-generation hardware. We also implement and analyze low-precision SGD on the FPGA, which is a promising alternative to the CPU for future SGD systems.",
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"http://stanford.edu/~cdesa/papers/isca2017_buckwild.pdf",
"http://doi.acm.org/10.1145/3079856.3080248",
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"title": "Understanding and optimizing asynchronous low-precision stochastic gradient descent",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"name": "Janos Szurdi"
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"name": "Nicolas Christin"
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],
"doi": "10.1145/3131365.3131399",
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"paperAbstract": "While website domain typosquatting is highly annoying for legitimate domain operators, research has found that it relatively rarely presents a great risk to individual users. However, any application (e.g., email, ftp,...) relying on the domain name system for name resolution is equally vulnerable to domain typosquatting, and consequences may be more dire than with website typosquatting.\n This paper presents the first in-depth measurement study of email typosquatting. Working in concert with our IRB, we registered 76 typosquatting domain names to study a wide variety of user mistakes, while minimizing the amount of personal information exposed to us. In the span of over seven months, we received millions of emails at our registered domains. While most of these emails are spam, we infer, from our measurements, that every year, three of our domains should receive approximately 3,585 \"legitimate\" emails meant for somebody else. Worse, we find, by examining a small sample of all emails, that these emails may contain sensitive information (e.g., visa documents or medical records).\n We then project from our measurements that 1,211 typosquatting domains registered by unknown entities receive in the vicinity of 800,000 emails a year. Furthermore, we find that millions of registered typosquatting domains have MX records pointing to only a handful of mail servers. However, a second experiment in which we send \"honey emails\" to typosquatting domains only shows very limited evidence of attempts at credential theft (despite some emails being read), meaning that the threat, for now, appears to remain theoretical.",
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"http://doi.acm.org/10.1145/3131365.3131399",
"https://conferences.sigcomm.org/imc/2017/slides/imc17-presentation-toshare.pdf",
"http://www.andrew.cmu.edu/user/nicolasc/publications/Szurdi-IMC17.pdf",
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"title": "Email typosquatting",
"venue": "IMC",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Sandeep S. Kulkarni"
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{
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"name": "Nitin H. Vaidya"
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],
"doi": "10.1145/3087801.3087818",
"doiUrl": "https://doi.org/10.1145/3087801.3087818",
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"paperAbstract": "Practical algorithms for determining causality by assigning timestamps to events have focused on online algorithms, where a permanent timestamp is assigned to an event as soon as it is created. We address the problem of reducing size of the timestamp by utilizing the underlying topology (which is o\u0089en not fully connected since not all processes talk to each other) and deferring the assignment of a timestamp to an event for a suitably chosen period of time a\u0089er the event occurs. Speci\u0080cally, we focus on inline timestamps, which are a generalization of o\u0084ine timestamps that are assigned a\u0089er the computation terminates. We show that for a graph with vertex coverVC, it is possible to assign inline timestamps which contains only 2|VC|+2 elements. In particular, for a system withn processes and K events per process, the size of a timestamp for any event is at most log2 n+ (2|VC|+1) log2(K +1)) bits. By contrast, if online timestamps are desired, then even for a star network, vector timestamp of length n (for the case of integer elements) or n\u2212 1 (for the case of real-valued elements) is required. Moreover, in addition to being e\u0081cient, the inline timestamps developed can be used to solve typical problems such as predicate detection, replay, recovery that are solved with vector clocks.",
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"http://doi.acm.org/10.1145/3087801.3087818"
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"title": "Effectiveness of Delaying Timestamp Computation",
"venue": "PODC",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Yukihiro Tagami"
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],
"doi": "10.1145/3097983.3097987",
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"paperAbstract": "Extreme multi-label classification methods have been widely used in Web-scale classification tasks such as Web page tagging and product recommendation. In this paper, we present a novel graph embedding method called \"AnnexML\". At the training step, AnnexML constructs a <i>k</i>-nearest neighbor graph of label vectors and attempts to reproduce the graph structure in the embedding space. The prediction is efficiently performed by using an approximate nearest neighbor search method that efficiently explores the learned <i>k</i>-nearest neighbor graph in the embedding space. We conducted evaluations on several large-scale real-world data sets and compared our method with recent state-of-the-art methods. Experimental results show that our AnnexML can significantly improve prediction accuracy, especially on data sets that have larger a label space. In addition, AnnexML improves the trade-off between prediction time and accuracy. At the same level of accuracy, the prediction time of AnnexML was up to 58 times faster than that of SLEEC, which is a state-of-the-art embedding-based method.",
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"http://doi.acm.org/10.1145/3097983.3097987"
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"sources": [
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"title": "AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification",
"venue": "KDD",
"year": 2017
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"paperAbstract": "Defending against malware involves analysing large amounts of suspicious samples. To deal with such quantities we rely heavily on automatic approaches to determine whether a sample is malicious or not. Unfortunately, complete and precise automatic analysis of malware is far from an easy task. This is because malware is often designed to contain several techniques and countermeasures specifically to hinder analysis. One of these techniques is for the malware to propagate through the operating system so as to execute in the context of benign processes. The malware does this by writing memory to a given process and then proceeds to have this memory execute. In some cases these propagations are trivial to capture because they rely on well-known techniques. However, in the cases where malware deploys novel code injection techniques, rely on code-reuse attacks and potentially deploy dynamically generated code, the problem of capturing a complete and precise view of the malware execution is non-trivial.\n In this paper we present a unified approach to tracing malware propagations inside the host in the context of code injections and code-reuse attacks. We also present, to the knowledge of the authors, the first approach to identifying dynamically generated code based on information-flow analysis. We implement our techniques in a system called Tartarus and match Tartarus with both synthetic applications and real-world malware. We compare Tartarus to previous works and show that our techniques substantially improve the precision for collecting malware execution traces, and that our approach can capture intrinsic characteristics of novel code injection techniques.",
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"title": "Neural Ranking Models with Weak Supervision",
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"paperAbstract": "Many popular modern processors include an important hardware security feature in the form of a DRTM (Dynamic Root of Trust for Measurement) that helps bootstrap trust and resists software attacks. However, despite substantial body of prior research on trust establishment, security of DRTM was treated without involvement of the human user, who represents a vital missing link. The basic challenge is: how can a human user determine whether an expected DRTM is currently active on her device?\n In this paper, we define the notion of \"presence attestation\", which is based on mandatory, though minimal, user participation. We present three concrete presence attestation schemes: sight-based, location-based and scene-based. They vary in terms of security and usability features, and are suitable for different application contexts. After analyzing their security, we assess their usability and performance based on prototype implementations.",
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"paperAbstract": "The Named Data Networking (NDN) architecture retrieves content by names rather than connecting to specific hosts. It provides benefits such as highly efficient and resilient content distribution, which fit well to data-intensive distributed computing. This paper presents and discusses our experience in modifying Apache Hadoop, a popular MapReduce framework, to operate on an NDN network. Through this first-of-its-kind implementation process, we demonstrate the feasibility of running an existing, large, and complex piece of distributed software commonly seen in data centers over NDN. We show advantages such as simplified network code and reduced network traffic which are beneficial in a data center environment. There are also challenges faced by NDN, that are being addressed by the community, which can be magnified under data center traffic. Through detailed evaluation, we show a reduction of 16% for overall data transmission between Hadoop nodes while writing data with default replication settings. Preliminary results also show promise for in-network caching of repeated reads in distributed applications. We also show that overall performance is currently slower under NDN, and we identify challenges and opportunities for further NDN improvements.",
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"paperAbstract": "Security and privacy in cloud computing are critical components for various organizations that depend on the cloud in their daily operations. Customers' data and the organizations' proprietary information have been subject to various attacks in the past. In this paper, we develop a set of Moving Target Defense (MTD) strategies that randomize the location of the Virtual Machines (VMs) to harden the cloud against a class of Multi-Armed Bandit (MAB) policy-based attacks. These attack policies capture the behavior of adversaries that seek to explore the allocation of VMs in the cloud and exploit the ones that provide the highest rewards (e.g., access to critical datasets, ability to observe credit card transactions, etc). We assess through simulation experiments the performance of our MTD strategies, showing that they can make MAB policy-based attacks no more effective than random attack policies. Additionally, we show the effects of critical parameters – such as discount factors, the time between randomizing the locations of the VMs and variance in the rewards obtained – on the performance of our defenses. We validate our results through simulations and a real OpenStack system implementation in our lab to assess migration times and down times under different system loads.",
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"paperAbstract": "Fast IPv4 scanning has enabled researchers to answer a wealth of new security and measurement questions. However, while increased network speeds and computational power have enabled comprehensive scans of the IPv4 address space, a brute-force approach does not scale to IPv6. Systems are limited to scanning a small fraction of the IPv6 address space and require an algorithmic approach to determine a small set of candidate addresses to probe. In this paper, we first explore the considerations that guide designing such algorithms. We introduce a new approach that identifies dense address space regions from a set of known \"seed\" addresses and generates a set of candidates to scan. We compare our algorithm 6Gen against Entropy/IP---the current state of the art---finding that we can recover between 1--8 times as many addresses for the five candidate datasets considered in the prior work. However, during our analysis, we uncover widespread IP aliasing in IPv6 networks. We discuss its effect on target generation and explore preliminary approaches for detecting aliased regions.",
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"paperAbstract": "Password reuse has been long understood as a problem: credentials stolen from one site may be leveraged to gain access to another site for which they share a password. Indeed, it is broadly understood that attackers exploit this fact and routinely leverage credentials extracted from a site they have breached to access high-value accounts at other sites (e.g., email accounts). However, as a consequence of such acts, this same phenomena of password reuse attacks can be harnessed to indirectly infer site compromises---even those that would otherwise be unknown. In this paper we describe such a measurement technique, in which unique honey accounts are registered with individual third-party websites, and thus access to an email account provides indirect evidence of credentials theft at the corresponding website. We describe a prototype system, called Tripwire, that implements this technique using an automated Web account registration system combined with email account access data from a major email provider. In a pilot study monitoring more than 2,300 sites over a year, we have detected 19 site compromises, including what appears to be a plaintext password compromise at an Alexa top-500 site with more than 45 million active users.",
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"paperAbstract": "The C/C++11 memory model defines the semantics of concurrent memory accesses in C/C++, and in particular supports racy \"atomic\" accesses at a range of different consistency levels, from very weak consistency (\"relaxed\") to strong, sequential consistency (\"SC\"). Unfortunately, as we observe in this paper, the semantics of SC atomic accesses in C/C++11, as well as in all proposed strengthenings of the semantics, is flawed, in that (contrary to previously published results) both suggested compilation schemes to the Power architecture are unsound. We propose a model, called RC11 (for Repaired C11), with a better semantics for SC accesses that restores the soundness of the compilation schemes to Power, maintains the DRF-SC guarantee, and provides stronger, more useful, guarantees to SC fences. In addition, we formally prove, for the first time, the correctness of the proposed stronger compilation schemes to Power that preserve load-to-store ordering and avoid \"out-of-thin-air\" reads.",
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"http://sf.snu.ac.kr/gil.hur/publications/scfix.pdf",
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"title": "Repairing sequential consistency in C/C++11",
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"paperAbstract": "Networks are integral parts of modern safety-critical systems and certification demands the provision of guarantees for data transmissions. Deterministic Network Calculus (DNC) can compute a worst-case bound on a data flow's end-to-end delay. Accuracy of DNC results has been improved steadily, resulting in two DNC branches: the classical algebraic analysis and the more recent optimization-based analysis. The optimization-based branch provides a theoretical solution for tight bounds. Its computational cost grows, however, (possibly super-)exponentially with the network size. Consequently, a heuristic optimization formulation trading accuracy against computational costs was proposed. In this article, we challenge optimization-based DNC with a new algebraic DNC algorithm. We show that: <ol><li>no current optimization formulation scales well with the network size and</li> <li>algebraic DNC can be considerably improved in both aspects, accuracy and computational cost.</li></ol>\n To that end, we contribute a novel DNC algorithm that transfers the optimization's search for best attainable delay bounds to algebraic DNC. It achieves a high degree of accuracy and our novel efficiency improvements reduce the cost of the analysis dramatically. In extensive numerical experiments, we observe that our delay bounds deviate from the optimization-based ones by only 1.142% on average while computation times simultaneously decrease by several orders of magnitude.",
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"title": "Quality and Cost of Deterministic Network Calculus: Design and Evaluation of an Accurate and Fast Analysis",
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"paperAbstract": "Bitmap compression has been studied extensively in the database area and many efficient compression schemes were proposed, e.g., BBC, WAH, EWAH, and Roaring. Inverted list compression is also a well-studied topic in the information retrieval community and many inverted list compression algorithms were developed as well, e.g., VB, PforDelta, GroupVB, Simple8b, and SIMDPforDelta. We observe that they essentially solve the same problem, i.e., how to store a collection of sorted integers with as few as possible bits and support query processing as fast as possible. Due to historical reasons, bitmap compression and inverted list compression were developed as two separated lines of research in the database area and information retrieval area. Thus, a natural question is: <i>Which one is better between bitmap compression and inverted list compression?</i>\n To answer the question, we present the first comprehensive experimental study to compare a series of <b>9</b> bitmap compression methods and <b>12</b> inverted list compression methods. We compare these <b>21</b> algorithms on synthetic datasets with different distributions (uniform, zipf, and markov) as well as <b>8</b> real-life datasets in terms of the space overhead, decompression time, intersection time, and union time. Based on the results, we provide many lessons and guidelines that can be used for practitioners to decide which technique to adopt in future systems and also for researchers to develop new algorithms.",
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"title": "An Experimental Study of Bitmap Compression vs. Inverted List Compression",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
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"name": "Thanh-Chung Dao"
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"paperAbstract": "This paper reports our experiments to compare various deployment strategies of memcached-like in-memory storage for Hadoop on supercomputers, where each node often does not have a local disk but shares a slow central disk. For the experiments, we developed our own memcached-like file system, named SEMem, for Hadoop. Since SEMem was designed for supercomputers, it uses MPI for communication. SEMem is configurable to adopt various deployment strategies and our experiments revealed that a good deployment strategy was allocating some nodes that work only for in-memory storage but do not directly perform map-reduce computation.",
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"title": "SEMem: Deployment of MPI-Based In-Memory Storage for Hadoop on Supercomputers",
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"year": 2017
},
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"paperAbstract": "One of the most common statistics computed over data elements is the number of distinct keys. A thread of research pioneered by Flajolet and Martin three decades ago culminated in the design of optimal approximate counting sketches, which have size that is double logarithmic in the number of distinct keys and provide estimates with a small relative error. Moreover, the sketches are composable, and thus suitable for streamed, parallel, or distributed computation.\n We consider here all statistics of the frequency distribution of keys, where a contribution of a key to the aggregate is concave and grows (sub)linearly with its frequency. These fundamental aggregations are very common in text, graphs, and logs analysis and include logarithms, low frequency moments, and cap statistics.\n We design composable sketches of double-logarithmic size for all concave sublinear statistics. Our design combines theoretical optimality and practical simplicity. In a nutshell, we specify tailored mapping functions of data elements to output elements so that our target statistics on the data elements is approximated by the (max-) distinct statistics of the output elements, which can be approximated using off-the-shelf sketches. Our key insight is relating these target statistics to the <i>complement Laplace</i> transform of the input frequencies.",
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"title": "HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics",
"venue": "KDD",
"year": 2017
},
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"paperAbstract": "We formulate and study an optimization problem that arises in the energy management of data centers and, more generally, multiprocessor environments. Data centers host a large number of heterogeneous servers. Each server has an active state and several standby/sleep states with individual power consumption rates. The demand for computing capacity varies over time. Idle servers may be transitioned to low-power modes so as to rightsize the pool of active servers. The goal is to find a state transition schedule for the servers that minimizes the total energy consumed. On a small scale the same problem arises in multi-core architectures with heterogeneous processors on a chip. One has to determine active and idle periods for the cores so as to guarantee a certain service and minimize the consumed energy.\n For this power/capacity management problem, we develop two main results. We use the terminology of the data center setting. First, we investigate the scenario that each server has two states, i.e. an active state and a sleep state. We show that an optimal solution, minimizing energy consumption, can be computed in polynomial time by a combinatorial algorithm. The algorithm resorts to a single-commodity min-cost flow computation. Second, we study the general scenario that each server has an active state and multiple standby/sleep states. We devise a \\tau-approximation algorithm that relies on a two-commodity min-cost flow computation. Here \\tau is the number of different server types. A data center has a large collection of machines but only a relatively small number of different server architectures. Moreover, in the optimization one can assign servers with comparable energy consumption to the same class. Technically, both of our algorithms involve non-trivial flow modification procedures. In particular, given a fractional two-commodity flow, our algorithm executes advanced rounding and flow packing routines.",
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"title": "On Energy Conservation in Data Centers",
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"title": "Dissecting Tor Bridges: A Security Evaluation of their Private and Public Infrastructures",
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"paperAbstract": "Every day, social media users send millions of microblogs on every imaginable topics. If we could predict which topics a user will join in the future, it would be easy to determine what topics will become popular and what kinds of users a topic may attract. It also can be of great interest for many applications. In this study, we investigate the problem of predicting whether a user will join a topic based on his posting history. We introduce a novel deep convolutional neural network with external neural memory and attention mechanism to perform this problem. User's posting history and topics were modeled with an external neural memory architecture. The convolutional neural network based matching methods were used to construct the relations between users and topics. Final decisions were made based on these matching results. To train and evaluate the proposed method, we collected a large-scale dataset from Twitter. The experimental results demonstrated that the proposed method could perform significantly better than other methods. Comparing to the state-of-the-art deep neural networks, our approach achieves a relative improvement of 18.2\\% in F1-score and 28.9\\% in MAP@10.",
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"title": "Predicting Which Topics You Will Join in the Future on Social Media",
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"year": 2017
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"name": "Quan Chen"
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"name": "Moeiz Riaz"
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"name": "Zhongzhi Luan"
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"doi": "10.1145/3079856.3080224",
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"paperAbstract": "Modern user facing applications consist of multiple processing stages with a number of service instances in each stage. The latency profile of these multi-stage applications is intrinsically variable, making it challenging to provide satisfactory responsiveness. Given a limited power budget, improving the end-to-end latency requires intelligently boosting the bottleneck service across stages using multiple boosting techniques. However, prior work fail to acknowledge the multi-stage nature of user-facing applications and perform poorly in improving responsiveness on power constrained CMP, as they are unable to accurately identify bottleneck service and apply the boosting techniques adaptively.\n In this paper, we present PowerChief, a runtime framework that 1) provides joint design of service and query to monitor the latency statistics across service stages and accurately identifies the bottleneck service during runtime; 2) adaptively chooses the boosting technique to accelerate the bottleneck service with improved responsiveness; 3) dynamically reallocates the constrained power budget across service stages to accommodate the chosen boosting technique. Evaluated with real world multi-stage applications, PowerChief improves the average latency by 20.3x and 32.4x (99% tail latency by 13.3x and 19.4x) for Sirius and Natural Language Processing applications respectively compared to stage-agnostic power allocation. In addition, for the given QoS target, PowerChief reduces the power consumption of Sirius and Web Search applications by 23% and 33% respectively over prior work.",
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"title": "PowerChief: Intelligent power allocation for multi-stage applications to improve responsiveness on power constrained CMP",
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"year": 2017
},
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},
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"paperAbstract": "We describe a SIMD technique for drawing values from multiple discrete distributions, such as sampling from the random variables of a mixture model, that avoids computing a complete table of partial sums of the relative probabilities. A table of alternate (\"butterfly-patterned\") form is faster to compute, making better use of coalesced memory accesses; from this table, complete partial sums are computed on the fly during a binary search. Measurements using CUDA 7.5 on an NVIDIA Titan Black GPU show that this technique makes an entire machine-learning application that uses a Latent Dirichlet Allocation topic model with 1024 topics about about 13% faster (when using single-precision floating-point data) or about 35% faster (when using double-precision floating-point data) than doing a straightforward matrix transposition after using coalesced accesses.",
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"title": "Using Butterfly-Patterned Partial Sums to Draw from Discrete Distributions",
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"name": "David Lion"
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{
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"name": "Ding Yuan"
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"paperAbstract": "Complex and unforeseen failures in distributed systems must be diagnosed and replicated in a development environment so that developers can understand the underlying problem and verify the resolution. System logs often form the only source of diagnostic information, and developers reconstruct a failure using manual guesswork. This is an unpredictable and time-consuming process which can lead to costly service outages while a failure is repaired.\n This paper describes Pensieve, a tool capable of reconstructing near-minimal failure reproduction steps from log files and system bytecode, without human involvement. Unlike existing solutions that use symbolic execution to search for the entire path leading to the failure, Pensieve is based on the Partial Trace Observation, which states that programmers do not simulate the entire execution to understand the failure, but follow a combination of control and data dependencies to reconstruct a simplified trace that only contains events that are likely to be relevant to the failure. Pensieve follows a set of carefully designed rules to infer a chain of causally dependent events leading to the failure symptom while aggressively skipping unrelated code paths to avoid the path-explosion overheads of symbolic execution models.",
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"paperAbstract": "Auto-scaling, a key property of cloud computing, allows application owners to acquire and release resourceson demand. However, the shared environment, along with theexponentially large configuration space of available parameters, makes configuration of auto-scaling policies a challenging task. Inparticular, it is difficult to quantify, a priori, the impact of a policyon Quality of Service (QoS) provision. To address this problem, we propose a novel approach based on performance modellingand formal verification to produce performance guaranteeson particular rule-based auto-scaling policies. We demonstratethe usefulness and efficiency of our model through a detailedvalidation process on the Amazon EC2 cloud, using two typesof load patterns. Our experimental results show that it can bevery effective in helping a cloud application owner configure anauto-scaling policy in order to minimise the QoS violations.",
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"title": "Performance Modelling and Verification of Cloud-Based Auto-Scaling Policies",
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"name": "Kurt Derr"
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"doi": "10.1145/3117811.3117845",
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"paperAbstract": "The current mechanisms for locating spectrum offenders are time consuming, human-intensive, and expensive. In this paper, we propose a novel approach to locate spectrum offenders using crowdsourcing. In such a participatory sensing system, privacy and bandwidth concerns preclude distributed sensing devices from reporting raw signal samples to a central agency; instead, devices would be limited to measurements of received power. However, this limitation enables a smart attacker to evade localization by simultaneously transmitting from multiple infected devices. Existing localization methods are insufficient or incapable of locating multiple sources when the powers from each source cannot be separated at the receivers. In this paper, we first propose a simple and efficient method that simultaneously locates multiple transmitters using the received power measurements from the selected devices. Second, we build sampling approaches to select sensing devices required for localization. Next, we enhance our sampling to also take into account incentives for participation in crowdsourcing. We experimentally evaluate our localization framework under a variety of settings and find that we are able to localize multiple sources transmitting simultaneously with reasonably high accuracy in a timely manner.",
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"http://doi.acm.org/10.1145/3117811.3117845"
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"title": "Simultaneous Power-Based Localization of Transmitters for Crowdsourced Spectrum Monitoring",
"venue": "MobiCom",
"year": 2017
},
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"name": "Hung Q. Ngo"
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"name": "Dan Suciu"
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"doi": "10.1145/3034786.3056105",
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"paperAbstract": "Recent works on bounding the output size of a conjunctive query with functional dependencies and degree bounds have shown a deep connection between fundamental questions in information theory and database theory. We prove analogous output bounds for <i>disjunctive datalog rules</i>, and answer several open questions regarding the tightness and looseness of these bounds along the way. The bounds are intimately related to Shannon-type information inequalities. We devise the notion of a \"proof sequence\" of a specific class of Shannon-type information inequalities called \"Shannon flow inequalities\". We then show how a proof sequence can be used as symbolic instructions to guide an algorithm called <b>PANDA</b>, which answers disjunctive datalog rules within the size bound predicted. We show that <b>PANDA</b> can be used as a black-box to devise algorithms matching precisely the fractional hypertree width and the submodular width runtimes for aggregate and conjunctive queries <i>with</i> functional dependencies and degree bounds.\n Our results improve upon known results in three ways. First, our bounds and algorithms are for the much more general class of disjunctive datalog rules, of which conjunctive queries are a special case. Second, the runtime of <b>PANDA</b> matches precisely the submodular width bound, while the previous algorithm by Marx has a runtime that is polynomial in this bound. Third, our bounds and algorithms work for queries with input cardinality bounds, functional dependencies, <i>and</i> degree bounds.\n Overall, our results showed a deep connection between three seemingly unrelated lines of research; and, our results on proof sequences for Shannon flow inequalities might be of independent interest.",
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"http://homes.cs.washington.edu/~suciu/pods055-abo-khamis.pdf",
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"title": "What Do Shannon-type Inequalities, Submodular Width, and Disjunctive Datalog Have to Do with One Another?",
"venue": "PODS",
"year": 2017
},
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"name": "Yuxin Sun"
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"paperAbstract": "The local computation of Linial [FOCS\u201987] and Naor and Stockmeyer [STOC\u201993] concerns with the question of whether a locally definable distributed computing problem can be solved locally: more specifically, for a given local CSP whether a CSP solution can be constructed by a distributed algorithm using local information. In this paper, we consider the problem of sampling a uniform CSP solution by distributed algorithms, and ask whether a locally definable joint distribution can be sampled from locally. More broadly, we consider sampling from Gibbs distributions induced by weighted local CSPs, especially the Markov random fields (MRFs), in the LOCAL model. We give two Markov chain based distributed algorithms which we believe to represent two fundamental approaches for sampling from Gibbs distributions via distributed algorithms. The first algorithm generically parallelizes the single-site sequential Markov chain by iteratively updating a random independent set of variables in parallel, and achieves an O(\u2206 log n) time upper bound in the LOCAL model, where \u2206 is the maximum degree, when the Dobrushin\u2019s condition for the Gibbs distribution is satisfied. The second algorithm is a novel parallel Markov chain which proposes to update all variables simultaneously yet still guarantees to converge correctly with no bias. It surprisingly parallelizes an intrinsically sequential process: stabilizing to a joint distribution with massive local dependencies, and may achieve an optimal O(log n) time upper bound independent of the maximum degree \u2206 under a stronger mixing condition. We also show a strong \u03a9(diam) lower bound for sampling: in particular for sampling independent set in graphs with maximum degree \u2206 \u2265 6. Independent sets are trivial to construct locally and the sampling lower bound holds even when every node is aware of the entire graph. This gives a strong separation between sampling and constructing locally checkable labelings. Department of Computer Science and Technology, Nanjing University. Emails: fengwm@smail.nju.edu.cn and sunyuxinxa@126.com State Key Laboratory for Novel Software Technology, Nanjing University. Email: yinyt@nju.edu.cn. Research supported by the National Science Foundation of China under Grant No. 61672275 and No. 61272081.",
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"http://arxiv.org/abs/1702.00142"
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"title": "What Can be Sampled Locally?",
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"title": "Unsupervised Feature Learning with Discriminative Encoder",
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"paperAbstract": "CPU caches introduce variations into the execution time of programs that can be exploited by adversaries to recover private information about users or cryptographic keys. \n Establishing the security of countermeasures against this threat often requires intricate reasoning about the interactions of program code, memory layout, and hardware architecture and has so far only been done for restricted cases. \n In this paper we devise novel techniques that provide support for bit-level and arithmetic reasoning about memory accesses in the presence of dynamic memory allocation. These techniques enable us to perform the first rigorous analysis of widely deployed software countermeasures against cache attacks on modular exponentiation, based on executable code.",
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"paperAbstract": "Checkpoint/restart has been widely used to cope with fail-stop errors. The checkpointing frequency is most often optimized by assuming an exponential failure distribution. However, field studies show that most often failures do not follow a constant failure rate exponential distribution. Therefore, the optimal checkpointing frequency should be computed and tuned considering the different distributions that failures follow. Moreover, due to operating system and input/output jitter and hybrid solutions that combine checkpointing with other techniques, such as data compression, checkpointing time can no longer be assumed constant. Thus, time varying checkpointing time should be accounted for to realistically model the application execution.In this study, we develop a mathematical theory and model to optimize the checkpointing frequency with respect to arbitrary failure distributions while capturing time-dependent non-constant checkpointing time. We show that we can provide closed-form formulas for important failure distributions in most cases. By instantiating our model, we study and analyze 10 important failure distributions to obtain the optimal checkpointing frequency for these distributions. Experimental evaluation shows that our model is highly accurate and deviates from the simulations less than 1% on average.",
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"title": "Toward a General Theory of Optimal Checkpoint Placement",
"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
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"name": "Andrea C. Arpaci-Dusseau"
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"paperAbstract": "We present NICE, a key-value storage system design that leverages new software-defined network capabilities to build cluster-based network-efficient storage system. NICE presents novel techniques to co-design network routing and multicast with storage replication, consistency, and load balancing to achieve higher efficiency, performance, and scalability. We implement the NICEKV prototype. NICEKV follows the NICE approach in designing four essential network-centric storage mechanisms: request routing, replication, consistency, and load balancing. Our evaluation shows that the proposed approach brings significant performance gains compared to the current key-value systems design: up to 7× put/get performance improvement, up to 2× reduction in network load, 3× to 9× load reduction on the storage nodes, and the elimination of scalability bottlenecks present in current designs.",
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"title": "NICE: Network-Integrated Cluster-Efficient Storage",
"venue": "HPDC",
"year": 2017
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"name": "Cesar A. Stuardo"
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"paperAbstract": "We highlight the problem of scalability bugs, a new class of bugs that appear in \"cloud-scale\" distributed systems. Scalability bugs are latent bugs that are cluster-scale dependent, whose symptoms typically surface in large-scale deployments, but not in small or medium-scale deployments. The standard practice to test large distributed systems is to deploy them on a large number of machines (\"real-scale testing\"), which is difficult and expensive. New methods are needed to reduce developers' burdens in finding, reproducing, and debugging scalability bugs. We propose \"scale check,\" an approach that helps developers find and replay scalability bugs at real scales, but do so only on one machine and still achieve a high accuracy (i.e., similar observed behaviors as if the nodes are deployed in real-scale testing).",
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"http://doi.acm.org/10.1145/3102980.3102985"
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"title": "Scalability Bugs: When 100-Node Testing is Not Enough",
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"paperAbstract": "Application scalability is a critical aspect to efficiently use NUMA machines with many cores. To achieve that, various techniques ranging from task placement to data sharding are used in practice. However, from the perspective of an operating system, these techniques often do not work as expected because various subsystems in the OS interact and share data structures among themselves, resulting in scalability bottlenecks. Although current OSes attempt to tackle this problem by introducing a wide range of synchronization primitives such as spinlock and mutex, the widely used synchronization mechanisms are not designed to handle both underand over-subscribed scenarios in a scalable fashion. In particular, the current blocking synchronization primitives that are designed to address both scenarios are NUMA oblivious, meaning that they suffer from cache-line contention in an undersubscribed situation, and even worse, inherently spur long scheduler intervention, which leads to sub-optimal performance in an over-subscribed situation. In this work, we present several design choices to implement scalable blocking synchronization primitives that can address both underand over-subscribed scenarios. Such design decisions include memory-efficient NUMAaware locks (favorable for deployment) and schedulingaware, scalable parking and wake-up strategies. To validate our design choices, we implement two new blocking synchronization primitives, which are variants of mutex and read-write semaphore in the Linux kernel. Our evaluation shows that these locks can scale real-world applications by 1.2\u20131.6\u00d7 and some of the file system operations up to 4.7\u00d7 in both underand over-subscribed scenarios. Moreover, they use 1.5\u201310\u00d7 less memory than the stateof-the-art NUMA-aware locks on a 120-core machine.",
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"title": "Scalable NUMA-aware Blocking Synchronization Primitives",
"venue": "USENIX Annual Technical Conference",
"year": 2017
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"doi": "10.1145/3123939.3123975",
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"paperAbstract": "Contemporary discrete GPUs support rich memory management features such as virtual memory and demand paging. These features simplify GPU programming by providing a virtual address space abstraction similar to CPUs and eliminating manual memory management, but they introduce high performance overheads during (1) address translation and (2) page faults. A GPU relies on high degrees of <i>thread-level parallelism</i> (TLP) to hide memory latency. Address translation can undermine TLP, as a single miss in the translation lookaside buffer (TLB) invokes an expensive serialized page table walk that often stalls <i>multiple</i> threads. Demand paging can also undermine TLP, as multiple threads often stall while they wait for an expensive data transfer over the system I/O (e.g., PCIe) bus when the GPU demands a page.\n In modern GPUs, we face a trade-off on how the page size used for memory management affects address translation and demand paging. The address translation overhead is lower when we employ a <i>larger</i> page size (e.g., 2MB <i>large pages</i>, compared with conventional 4KB <i>base pages</i>), which increases TLB coverage and thus reduces TLB misses. Conversely, the demand paging overhead is lower when we employ a <i>smaller</i> page size, which decreases the system I/O bus transfer latency. Support for <i>multiple page sizes</i> can help relax the page size trade-off so that address translation and demand paging optimizations work together synergistically. However, existing page <i>coalescing</i> (i.e., merging base pages into a large page) and <i>splintering</i> (i.e., splitting a large page into base pages) policies require costly base page migrations that undermine the benefits multiple page sizes provide. In this paper, we observe that GPGPU applications present an opportunity to support multiple page sizes without costly data migration, as the applications perform most of their memory allocation <i>en masse</i> (i.e., they allocate a large number of base pages at once). We show that this <i>en masse</i> allocation allows us to create intelligent memory allocation policies which ensure that base pages that are contiguous in virtual memory are allocated to contiguous physical memory pages. As a result, coalescing and splintering operations no longer need to migrate base pages.\n We introduce <i>Mosaic</i>, a GPU memory manager that provides <i>application-transparent</i> support for multiple page sizes. <i>Mosaic</i> uses base pages to transfer data over the system I/O bus, and allocates physical memory in a way that (1) preserves base page contiguity and (2) ensures that a large page frame contains pages from only a single memory protection domain. We take advantage of this allocation strategy to design a novel in-place page size selection mechanism that avoids data migration. This mechanism allows the TLB to use large pages, reducing address translation overhead. During data transfer, this mechanism enables the GPU to transfer <i>only</i> the base pages that are needed by the application over the system I/O bus, keeping demand paging overhead low. Our evaluations show that <i>Mosaic</i> reduces address translation overheads while efficiently achieving the benefits of demand paging, compared to a contemporary GPU that uses only a 4KB page size. Relative to a state-of-the-art GPU memory manager, <i>Mosaic</i> improves the performance of homogeneous and heterogeneous multi-application workloads by 55.5% and 29.7% on average, respectively, coming within 6.8% and 15.4% of the performance of an ideal TLB where all TLB requests are hits.",
"pdfUrls": [
"http://pages.cs.wisc.edu/~jayneel/papers/micro17_mosaic.pdf",
"http://doi.acm.org/10.1145/3123939.3123975",
"http://www.pdl.cmu.edu/PDL-FTP/NVM/17micro_mosaic.pdf",
"https://people.inf.ethz.ch/omutlu/pub/mosaic-application-transparent-multiple-page-sizes-for-GPUs_micro17.pdf"
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"title": "Mosaic: a GPU memory manager with application-transparent support for multiple page sizes",
"venue": "MICRO",
"year": 2017
},
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{
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"name": "Vicent Sanz Marco"
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"name": "Ben Taylor"
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"name": "Barry Porter"
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{
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"name": "Zheng Wang"
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],
"doi": "10.1145/3135974.3135984",
"doiUrl": "https://doi.org/10.1145/3135974.3135984",
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"paperAbstract": "Data analytic applications built upon big data processing frameworks such as Apache Spark are an important class of applications. Many of these applications are not latency-sensitive and thus can run as batch jobs in data centers. By running multiple applications on a computing host, task co-location can significantly improve the server utilization and system throughput. However, effective task co-location is a non-trivial task, as it requires an understanding of the computing resource requirement of the co-running applications, in order to determine what tasks, and how many of them, can be co-located. State-of-the-art co-location schemes either require the user to supply the resource demands which are often far beyond what is needed; or use a one-size-fits-all function to estimate the requirement, which, unfortunately, is unlikely to capture the diverse behaviors of applications.\n In this paper, we present a mixture-of-experts approach to model the memory behavior of Spark applications. We achieve this by learning, off-line, a range of specialized memory models on a range of typical applications; we then determine at runtime which of the memory models, or experts, best describes the memory behavior of the target application. We show that by accurately estimating the resource level that is needed, a co-location scheme can effectively determine how many applications can be co-located on the same host to improve the system throughput, by taking into consideration the memory and CPU requirements of co-running application tasks. Our technique is applied to a set of representative data analytic applications built upon the Apache Spark framework. We evaluated our approach for system throughput and average normalized turnaround time on a multi-core cluster. Our approach achieves over 83.9% of the performance delivered using an ideal memory predictor. We obtain, on average, 8.69x improvement on system throughput and a 49% reduction on turnaround time over executing application tasks in isolation, which translates to a 1.28x and 1.68x improvement over a state-of-the-art co-location scheme for system throughput and turnaround time respectively.",
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"http://arxiv.org/abs/1710.00610"
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"title": "Improving spark application throughput via memory aware task co-location: a mixture of experts approach",
"venue": "Middleware",
"year": 2017
},
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"name": "You Zhou"
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"name": "Shigang Chen"
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"doi": "10.1109/CLOUD.2017.29",
"doiUrl": "https://doi.org/10.1109/CLOUD.2017.29",
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"paperAbstract": "Software-defined datacenters combine centralized resource management, software-defined networking, and virtualized infrastructure to meet diverse requirements of cloud computing. To fully realizing their capability in traffic engineering and flow-based bandwidth management, it is critical for the switches to measure network traffic for both individual flows between virtual machines and aggregate flows between clusters of physical or virtual machines. This paper proposes a novel hierarchical traffic measurement scheme for software-defined datacenter networks. It measures both aggregate flows and individual flows that are organized in a hierarchy with an arbitrary number of levels. The measurement is performed based on a new concept of hierarchical virtual counter arrays, which record each packet only once by updating a single counter, yet the sizes of all flows that the packet belongs to will be properly updated. We demonstrate that the new measurement scheme not only supports hierarchical traffic measurement with accuracy, but does so with memory efficiency, using a fewer number of counters than the number of flows.",
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"paperAbstract": "The power and procurement cost of bandwidth in system-wide networks has forced a steady drop in the byte/flop ratio. This trend of computation becoming faster relative to the network is expected to hold. In this paper, we explore how cost-oriented task placement enables reducing the cost of system-wide networks by enabling high performance even on tapered topologies where more bandwidth is provisioned at lower levels. We describe APHiD, an efficient hierarchical placement algorithm that uses new techniques to improve the quality of heuristic solutions and reduces the demand on high-level, expensive bandwidth in hierarchical topologies. We apply APHiD to a tapered fat-tree, demonstrating that APHiD maintains application scalability even for severely tapered network configurations. Using simulation, we show that for tapered networks APHiD improves performance by more than 50% over random placement and even 15% in some cases over costlier, state-of-the-art placement algorithms.",
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"title": "APHiD: Hierarchical Task Placement to Enable a Tapered Fat Tree Topology for Lower Power and Cost in HPC Networks",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
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"name": "Dumitrel Loghin"
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"doi": "10.1109/ICPP.2017.43",
"doiUrl": "https://doi.org/10.1109/ICPP.2017.43",
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"paperAbstract": "Clouds offer great flexibility for scaling applications due to the wide spectrum of resources with different cost-performance, inherent resource elasticity and pay-peruse charging. However, determining cost-time-efficient cloud configurations to execute a given application in the large resource configuration space remains a key challenge. The growing importance of elastic applications for which the accuracy is a function of resource consumption introduces new opportunities to exploit resource elasticity on clouds. In this paper, we introduce CELIA, a measurement-driven analytical modeling approach to determine cost-time-optimal cloud resource configurations to execute a given elastic application with a time deadline and a cost budget. We evaluate CELIA with three representative elastic applications on more than ten million configurations consisting of Amazon EC2 resource types with different cost-performance. Using CELIA, we show that multiple cost-time Pareto-optimal configurations exist among feasible cloud configurations that execute an elastic application within a time deadline and cost budget. These Pareto-optimal configurations exhibit up to 30% cost savings for an elastic application representing n-body simulation. We investigate the impact of fixed-time scaling on the cost of executing elastic applications on cloud. We show that cost gradient with respect to resource demand is smaller when cloud resources with better cost-performance are used. Furthermore, we show that the relative increase in cost is always smaller compared to the relative reduction of execution time deadline. For example, tightening the execution time deadline by two-thirds incurs only 40% increase in cost for the n-body simulation application.",
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"title": "CELIA: Cost-Time Performance of Elastic Applications on Cloud",
"venue": "2017 46th International Conference on Parallel Processing (ICPP)",
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"name": "Nan Zhang"
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"paperAbstract": "Finding the maxima of a database based on a user preference, especially when the ranking function is a linear combination of the attributes, has been the subject of recent research. A critical observation is that the <i>em convex hull</i> is the subset of tuples that can be used to find the maxima of any linear function. However, in real world applications the convex hull can be a significant portion of the database, and thus its performance is greatly reduced. Thus, computing a subset limited to $r$ tuples that minimizes the <i>regret ratio</i> (a measure of the user's dissatisfaction with the result from the limited set versus the one from the entire database) is of interest.\n In this paper, we make several fundamental theoretical as well as practical advances in developing such a compact set. In the case of two dimensional databases, we develop an optimal linearithmic time algorithm by leveraging the ordering of skyline tuples. In the case of higher dimensions, the problem is known to be NPcomplete. As one of our main results of this paper, we develop an approximation algorithm that runs in linearithmic time and guarantees a regret ratio, within any arbitrarily small user-controllable distance from the optimal regret ratio. The comprehensive set of experiments on both synthetic and publicly available real datasets confirm the efficiency, quality of output, and scalability of our proposed algorithms.",
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"title": "Efficient Computation of Regret-ratio Minimizing Set: A Compact Maxima Representative",
"venue": "SIGMOD Conference",
"year": 2017
},
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"paperAbstract": "Data have been generated and collected at an accelerating pace. Hadoop has made analyzing large scale data much simpler to developers/analysts using commodity hardware. Interestingly, it has been shown that most Hadoop jobs have small input size and do not run for long time. For example, higher level query languages, such as Hive and Pig, would handle a complex query by breaking it into smaller adhoc ones. Although Hadoop is designed for handling complex queries with large data sets, we found that it is highly inefficient to operate at small scale data, despite a new Uber mode was introduced specifically to handle jobs with small input size. In this paper, we propose an optimized Hadoop extension called MRapid, which significantly speeds up the execution of short jobs. It is completely backward compatible to Hadoop, and imposes negligible overhead. Our experiments on Microsoft Azure public cloud show that MRapid can improve performance by up to 88% compared to the original Hadoop.",
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"title": "MRapid: An Efficient Short Job Optimizer on Hadoop",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
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"name": "Sophia Drossopoulou"
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"title": "Orca: GC and type system co-design for actor languages",
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"year": 2017
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"paperAbstract": "The blockchain technology has emerged as an attractive solution to address performance and security issues in distributed systems. Blockchain's public and distributed peer-to-peer ledger capability benefits cloud computing services which require functions such as, assured data provenance, auditing, management of digital assets, and distributed consensus. Blockchain's underlying consensus mechanism allows to build a tamper-proof environment, where transactions on any digital assets are verified by set of authentic participants or miners. With use of strong cryptographic methods, blocks of transactions are chained together to enable immutability on the records. However, achieving consensus demands computational power from the miners in exchange of handsome reward. Therefore, greedy miners always try to exploit the system by augmenting their mining power. In this paper, we first discuss blockchain's capability in providing assured data provenance in cloud and present vulnerabilities in blockchain cloud. We model the block withholding (BWH) attack in a blockchain cloud considering distinct pool reward mechanisms. BWH attack provides rogue miner ample resources in the blockchain cloud for disrupting honest miners' mining efforts, which was verified through simulations.",
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"title": "Security Implications of Blockchain Cloud with Analysis of Block Withholding Attack",
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"paperAbstract": "Due to the centralized control, network-wide monitoring and flow-level scheduling of Software-Defined-Networking (SDN), it can be utilized to achieve Quality of Service (QoS) for cloud applications and services, such as voice over IP, video conference and online games, etc. However, most existing approaches stay at the QoS framework design and test level, while few works focus on studying the basic QoS techniques supported by SDN. In this paper, we enable SDN with QoS guaranteed abilities, which could provide end-to-end QoS routing for each cloud user service. First of all, we implement an application identification technique on SDN controller to determine required QoS levels for each application type. Then, we implement a queue scheduling technique on SDN switch. It queues the application flows into different queues and schedules the flows out of the queues with different priorities. At last, we evaluate the effectiveness of the proposed SDN-based QoS technique through an experimental analysis. Results show that when the output interface has sufficiently available bandwidth, the delay can be reduced by 28% on average. In addition, for the application flow with the highest priority, our methods can reduce 99.99% delay and increase 90.17% throughput on average when the output interface utilization approaches to the maximum bandwidth limitation.",
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"paperAbstract": "ide-area data transfers in high-performance computing infrastructures are increasingly being carried over dynamically provisioned dedicated network connections that provide high capacities with no competing traffic. We present extensive TCP throughput measurements and time traces over a suite of physical and emulated 10 Gbps connections with 0-366 ms round-trip times (RTTs). Contrary to the general expectation, they show significant statistical and temporal variations, in addition to the overall dependencies on the congestion control mechanism, buffer size, and the number of parallel streams. We analyze several throughput profiles that have highly desirable concave regions wherein the throughput decreases slowly with RTTs, in stark contrast to the convex profiles predicted by various TCP analytical models. We present a generic throughput model that abstracts the ramp-up and sustainment phases of TCP flows, which provides insights into qualitative trends observed in measurements across TCP variants: (i) slow-start followed by well-sustained throughput leads to concave regions; (ii) large buffers and multiple parallel streams expand the concave regions in addition to improving the throughput; and (iii) stable throughput dynamics, indicated by a smoother Poincare map and smaller Lyapunov exponents, lead to wider concave regions. These measurements and analytical results together enable us to select a TCP variant and its parameters for a given connection to achieve high throughput with statistical guarantees.",
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"paperAbstract": "Rapid development of bike-sharing systems has brought people enormous convenience during the past decade. On the other hand, high transport flexibility comes with dynamic distribution of shared bikes, leading to an unbalanced bike usage and growing maintenance cost. In this paper, we consider to rebalance bicycle utilization by means of directing users to different stations. For the first time, we devise a trip advisor that recommends bike check-in and check-out stations with joint consideration of service quality and bicycle utilization. From historical data, we firstly identify that biased bike usage is rooted from circumscribed bicycle circulation among few active stations. Therefore, with defined station activeness, we optimize the bike circulation by leading users to shift bikes between highly active stations and inactive ones. We extensively evaluate the performance of our design through real-world datasets. Evaluation results show that the percentage of frequent used bikes decreases by 33.6% on usage number and 28.6% on usage time.",
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"http://doi.ieeecomputersociety.org/10.1109/ICDM.2017.26"
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"title": "Data-Driven Utilization-Aware Trip Advisor for Bike-Sharing Systems",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
"year": 2017
},
"12db88fcf7cfee093c64e4e7737458e694a38181": {
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"name": "Guy Shapiro"
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"name": "Sagi Grimberg"
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"name": "Nadav Amit"
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"paperAbstract": "Direct network I/O allows network controllers (NICs) to expose multiple instances of themselves, to be used by untrusted software without a trusted intermediary. Direct I/O thus frees researchers from legacy software, fueling studies that innovate in multitenant setups. Such studies, however, overwhelmingly ignore one serious problem: direct memory accesses (DMAs) of NICs disallow page faults, forcing systems to either pin entire address spaces to physical memory and thereby hinder memory utilization, or resort to APIs that pin/unpin memory buffers before/after they are DMAed, which complicates the programming model and hampers performance.\n We solve this problem by designing and implementing page fault support for InfiniBand and Ethernet NICs. A main challenge we tackle---unique to NICs---is handling receive DMAs that trigger page faults, leaving the NIC without memory to store the incoming data. We demonstrate that our solution provides all the benefits associated with \"regular\" virtual memory, notably (1) a simpler programming model that rids users from the need to pin, and (2) the ability to employ all the canonical memory optimizations, such as memory overcommitment and demand-paging based on actual use. We show that, as a result, benchmark performance improves by up to 1.9x.",
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"http://doi.acm.org/10.1145/3037697.3037710",
"http://www.cs.technion.ac.il/~dan/papers/npf-asplos-2017.pdf"
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"title": "Page Fault Support for Network Controllers",
"venue": "ASPLOS",
"year": 2017
},
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"name": "Nicholas Chang-Fong"
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"name": "Aleksander Essex"
}
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"paperAbstract": "Software implementations of discrete logarithm based cryptosystems over finite fields typically make the assumption that any domain parameters they encounter define cyclic groups for which the discrete logarithm problem is assumed to be hard. In this paper we explore this trust assumption and examine situations where it may not be justified. In particular we focus on groups for which the order is unknown and not easily determined, and explore the scenario in which the modulus is trapdoored to make computing discrete logarithms efficient for an entity with knowledge of the trapdoor, while simultaneously leaving its very existence as matter of speculation to everyone else. We conducted an investigation of discrete logarithm domain parameters in use across the Internet and discovered a multitude of instances of groups of unknown order in use in TLS and STARTTLS spanning numerous countries, organizations, and implementations. Although our disclosures resulted in a number of organizations taking down their suspicious parameters, none were able or willing to rule out the possibility that their parameters were trapdoors, and obtaining conclusive evidence in each case could be as hard as factoring an RSA modulus, highlighting a key feature of this attack method\u2014deniability.",
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"http://www.internetsociety.org/sites/default/files/ndss2017-04a_2-dorey_slides.pdf",
"http://www.internetsociety.org/sites/default/files/ndss2017_04A-2_Dorey_paper.pdf"
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"title": "Indiscreet Logs: Diffie-Hellman Backdoors in TLS",
"venue": "NDSS",
"year": 2017
},
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"name": "Lei Yang"
},
{
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"name": "Qiongzheng Lin"
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{
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"name": "Chunhui Duan"
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{
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"name": "Zhenlin An"
}
],
"doi": "10.1145/3117811.3117835",
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"paperAbstract": "Deployment of billions of Commercial Off-The-Shelf (COTS) RFID tags has drawn much of the attention of the research community because of the performance gaps of current systems. In particular, hash-enabled protocol (HEP) is one of the most thoroughly studied topics in the past decade. HEPs are designed for a wide spectrum of notable applications (e.g., missing detection) without need to collect all tags. HEPs assume that each tag contains a hash function, such that a tag can select a random but predicable time slot to reply with a one-bit presence signal that shows its existence. However, the hash function has never been implemented in COTS tags in reality, which makes HEPs a 10-year untouchable mirage. This work designs and implements a group of analog on-tag hash primitives (called Tash) for COTS Gen2-compatible RFID systems, which moves prior HEPs forward from theory to practice. In particular, we design three types of hash primitives, namely, tash function, tash table function and tash operator. All of these hash primitives are implemented through selective reading, which is a fundamental and mandatory functionality specified in Gen2 protocol, without any hardware modification and fabrication. We further apply our hash primitives in two typical HEP applications (i.e., cardinality estimation and missing detection) to show the feasibility and effectiveness of Tash. Results from our prototype, which is composed of one ImpinJ reader and 3,000 Alien tags, demonstrate that the new design lowers 60% of the communication overhead in the air. The tash operator can additionally introduce an overhead drop of 29.7%.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3117811.3117835",
"https://arxiv.org/pdf/1707.08883v1.pdf",
"http://arxiv.org/abs/1707.08883"
],
"pmid": "",
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"s2Url": "https://semanticscholar.org/paper/12f10f72e6aa43b37aa1289773f453e4af93516d",
"sources": [
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],
"title": "Analog On-Tag Hashing: Towards Selective Reading as Hash Primitives in Gen2 RFID Systems",
"venue": "MobiCom",
"year": 2017
},
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"name": "Giulio Ermanno Pibiri"
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{
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"name": "Rossano Venturini"
}
],
"doi": "10.1145/3077136.3080798",
"doiUrl": "https://doi.org/10.1145/3077136.3080798",
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"paperAbstract": "The efficient indexing of large and sparse <i>N</i>-gram datasets is crucial in several applications in Information Retrieval, Natural Language Processing and Machine Learning. Because of the stringent efficiency requirements, dealing with billions of <i>N</i>-grams poses the challenge of introducing a compressed representation that preserves the query processing speed.\n In this paper we study the problem of reducing the space required by the representation of such datasets, maintaining the capability of looking up for a given <i>N</i>-gram within micro seconds. For this purpose we describe compressed, exact and lossless data structures that achieve, at the same time, high space reductions and no time degradation with respect to state-of-the-art software packages. In particular, we present a trie data structure in which each word following a context of fixed length <i>k</i>, i.e., its preceding <i>k</i> words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we are able to lower the space of representation to compression levels that were never achieved before. Despite the significant savings in space, we show that our technique introduces a negligible penalty at query time.",
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"http://pages.di.unipi.it/pibiri/papers/IIR17.pdf",
"http://doi.acm.org/10.1145/3077136.3080798",
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"title": "Efficient Data Structures for Massive N-Gram Datasets",
"venue": "SIGIR",
"year": 2017
},
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"authors": [
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"name": "Yi Ding"
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"name": "Chenghao Liu"
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{
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"name": "Peilin Zhao"
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{
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"name": "Steven C. H. Hoi"
}
],
"doi": "10.1109/ICDM.2017.18",
"doiUrl": "https://doi.org/10.1109/ICDM.2017.18",
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"paperAbstract": "Learning to optimize AUC performance for classifying label imbalanced data in online scenarios has been extensively studied in recent years. Most of the existing work has attempted to address the problem directly in the original feature space, which may not suitable for non-linearly separable datasets. To solve this issue, some kernel-based learning methods are proposed for non-linearly separable datasets. However, such kernel approaches have been shown to be inefficient and failed to scale well on large scale datasets in practice. Taking this cue, in this work, we explore the use of scalable kernel-based learning techniques as surrogates to existing approaches: random Fourier features and Nyström method, for tackling the problem and bring insights to the differences between the two methods based on their online performance. In contrast to the conventional kernel-based learning methods which suffer from high computational complexity of the kernel matrix, our proposed approaches elevate this issue with linear features that approximate the kernel function/matrix. Specifically, two different surrogate kernel-based learning models are presented for addressing the online AUC maximization task: (i) the Fourier Online AUC Maximization (FOAM) algorithm that samples the basis functions from a data-independent distribution to approximate the kernel functions; and (ii) the Nyström Online AUC Maximization (NOAM) algorithm that samples a subset of instances from the training data to approximate the kernel matrix by a low rank matrix. Another novelty of the present work is the proposed mini-batch Online Gradient Descent method for model updating to control the noise and reduce the variance of gradients. We provide theoretical analyses for the two proposed algorithms. Empirical studies on commonly used large scale datasets show that the proposed algorithms outperformed existing state-of-the-art methods in terms of both AUC performance and computational efficiency.",
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"title": "Large Scale Kernel Methods for Online AUC Maximization",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
"year": 2017
},
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"name": "Antonino Tumeo"
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"name": "Jian Yin"
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{
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"paperAbstract": "Emerging applications for data analytics and knowledge discovery typically have irregular or unpredictable communication patterns that do not scale well on parallel systems designed for traditional bulk-synchronous HPC applications. New network architectures that focus on minimizing (short) message latencies, rather than maximizing (large) transfer bandwidths, are emerging as possible alternatives to better support those applications with irregular communication patterns. We explore a system based upon one such novel network architecture, the Data Vortex interconnection network, and examine how this system performs by running benchmark code written for the Data Vortex network, as well as a reference MPI-over- Infiniband implementation, on the same cluster. Simple communication primitives (ping-pong and barrier synchronization), a few common communication kernels (distributed 1D Fast Fourier Transform, breadth-first search, Giga-Updates Per Second) and three prototype applications (a proxy application for simulating neutron transport-”SNAP”, a finite difference simulation for computing incompressible fluid flow, and an implementation of the heat equation) were all implemented for both network models. The results were compared and analyzed to determine what characteristics make an application a good candidate for porting to a Data Vortex system, and to what extent applications could potentially benefit from this new architecture.",
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"paperAbstract": "In this paper we study how to estimate the back-off rates in an idealized CSMA network consisting of n links to achieve a given throughput vector using free energy approximations. More specifically, we introduce the class of region-based free energy approximations with clique belief and present a closed form expression for the back-off rates based on the zero gradient points of the free energy approximation (in terms of the conflict graph, target throughput vector and counting numbers).Next we introduce the size k_max clique free energy approximation as a special case and derive an explicit expression for the counting numbers, as well as a recursion tocompute the back-off rates. We subsequently show that the size k_max clique approximation coincides with a Kikuchi free energy approximation and prove that it is exact on chordal conflict graphs when k_max = n. As a by-product these results provide us with an explicit expression of a fixed point of the inverse generalized belief propagation algorithm for CSMA networks.Using numerical experiments we compare the accuracy of the novel approximation method with existing methods.",
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"paperAbstract": "Increasingly complex memory systems and onchip interconnects are developed to mitigate the data movement bottlenecks in manycore processors. One example of such a complex system is the Xeon Phi KNL CPU with three different types of memory, fifteen memory configuration options, and a complex on-chip mesh network connecting up to 72 cores. Users require a detailed understanding of the performance characteristics of the different options to utilize the system efficiently. Unfortunately, peak performance is rarely achievable and achievable performance is hardly documented. We address this with capability models of the memory subsystem, derived by systematic measurements, to guide users to navigate the complex optimization space. As a case study, we provide an extensive model of all memory configuration options for Xeon Phi KNL. We demonstrate how our capability model can be used to automatically derive new close-to-optimal algorithms for various communication functions yielding improvements 5x and 24x over Intel’s tuned OpenMP and MPI implementations, respectively. Furthermore, we demonstrate how to use the models to assess how efficiently a bitonic sort application utilizes the memory resources. Interestingly, our capability models predict and explain that the high bandwidthMCDRAM does not improve the bitonic sort performance over DRAM.",
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"paperAbstract": "Designing a cost-effective network for data centers that can deliver sufficient bandwidth and provide high availability has drawn tremendous attentions recently. In this paper, we propose a novel server-centric network structure called RCube, which is energy efficient and can deploy a redundancy scheme to improve the availability of data centers. Moreover, RCube shares many good properties with BCube, a well known server-centric network structure, yet its network size can be adjusted more conveniently. We also present a routing algorithm to find paths in RCube and an algorithm to build multiple parallel paths between any pair of source and destination servers. In addition, we theoretically analyze the power efficiency of the network and availability of RCube under server failure. Our comprehensive simulations demonstrate that RCube provides higher availability and flexibility to make trade-off among many factors, such as power consumption and aggregate throughput, than BCube, while delivering similar performance to BCube in many critical metrics, such as average path length, path distribution and graceful degradation, which makes RCube a very promising empirical structure for an enterprise data center network product.",
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"paperAbstract": "Mathematical constructs are necessary for computation on the underlying algebraic structures of cryptosystems. They are often written in assembly language and optimized manually for efficiency. We develop a certified technique to verify low-level mathematical constructs in X25519, the default elliptic curve Diffie-Hellman key exchange protocol used in OpenSSH. Our technique translates an algebraic specification of mathematical constructs into an algebraic problem. The algebraic problem in turn is solved by the computer algebra system Singular. The proof assistant Coq certifies the translation and solution to algebraic problems. Specifications about output ranges and potential program overflows are translated to SMT problems and verified by SMT solvers. We report our case studies on verifying arithmetic computation over a large finite field and the Montgomery Ladderstep, a crucial loop in X25519.",
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"name": "Muthuramakrishnan Venkitasubramaniam"
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"paperAbstract": "We design and implement a simple zero-knowledge argument protocol for NP whose communication complexity is proportional to the square-root of the verification circuit size. The protocol can be based on any collision-resistant hash function. Alternatively, it can be made non-interactive in the random oracle model, yielding concretely efficient zk-SNARKs that do not require a trusted setup or public-key cryptography.\n Our protocol is attractive not only for very large verification circuits but also for moderately large circuits that arise in applications. For instance, for verifying a SHA-256 preimage in zero-knowledge with 2<sup>-40</sup> soundness error, the communication complexity is roughly 44KB (or less than 34KB under a plausible conjecture), the prover running time is 140 ms, and the verifier running time is 62 ms. This proof is roughly 4 times shorter than a similar proof of ZKB++ (Chase et al., CCS 2017), an optimized variant of ZKBoo (Giacomelli et al., USENIX 2016).\n The communication complexity of our protocol is independent of the circuit structure and depends only on the number of gates. For 2<sup>-40</sup> soundness error, the communication becomes smaller than the circuit size for circuits containing roughly 3 million gates or more. Our efficiency advantages become even bigger in an amortized setting, where several instances need to be proven simultaneously.\n Our zero-knowledge protocol is obtained by applying an optimized version of the general transformation of Ishai et al. (STOC 2007) to a variant of the protocol for secure multiparty computation of Damgard and Ishai (Crypto 2006). It can be viewed as a simple zero-knowledge interactive PCP based on \"interleaved\" Reed-Solomon codes.",
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"title": "Ligero: Lightweight Sublinear Arguments Without a Trusted Setup",
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"year": 2017
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"paperAbstract": "Despite soaring investments in IT infrastructure, the state of operational network security continues to be abysmal. We argue that this is because existing enterprise security approaches fundamentally lack precision in one or more dimensions: (1) isolation to ensure that the enforcement mechanism does not induce interference across different principals; (2) context to customize policies for different devices; and (3) agility to rapidly change the security posture in response to events. To address these shortcomings, we present PSI, a new enterprise network security architecture that addresses these pain points. PSI enables fine-grained and dynamic security postures for different network devices. These are implemented in isolated enclaves and thus provides precise instrumentation on these above dimensions by construction. To this end, PSI leverages recent advances in software-defined networking (SDN) and network functions virtualization (NFV). We design expressive policy abstractions and scalable orchestration mechanisms to implement the security postures. We implement PSI using an industry-grade SDN controller (OpenDaylight) and integrate several commonly used enforcement tools (e.g., Snort, Bro, Squid). We show that PSI is scalable and is an enabler for new detection and prevention capabilities that would be difficult to realize with existing solutions.",
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"title": "PSI: Precise Security Instrumentation for Enterprise Networks",
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"paperAbstract": "Comparable Encryption proposed by Furukawa (ESORICS 2013, CANS 2014) is a variant of order-preserving encryption (OPE) and order-revealing encryption (ORE); we cannot compare a ciphertext of <i>v</i> and another ciphertext of <i>v'</i>, but we can compare a <i>ciphertext</i> of <i>v</i> and a <i>token</i> of <i>b</i> and compare a token of $b$ and another token of <i>b'</i>. Comparable encryption allows us to implement range and point queries while keeping the order of <i>v</i>'s as secret as possible.\n Recently, Karras, Malhotra, Bhatt, Nikitin, Antyukhov, and Idreos independently re-define comparable encryption and propose two schemes, a basic one and an \"ambiguous\" one, based on linear algebra~(SIGMOD 2016). The basic scheme is just comparable encryption. To hide the order revealed by tokens, they also proposed an ambiguous scheme where each ciphertext has two interpretations <i>v</i> and <i>v</i><sub>dummy</sub>. In the context of an indexed database, this means that every encryption has two places in the database corresponding to the two interpretations, masking the correct placement in the database unless the dummy value is detectable. They assessed that their basic scheme (and ambiguous scheme upon the basic scheme) is secure against known-plaintext attacks; the adversary will require <i>O</i>(ℓ) plaintext-ciphertext pairs to recover secret key, where ℓ is a security parameter.\n This paper cryptanalyzes their comparable encryption schemes by using simple linear algebra. We show that a few tokens and a few plaintext-ciphertext pairs instead of <i>O</i>(ℓ) pairs allow us to mount several attacks efficiently. Our attacks are summarized as follows: <ul><li>Attacks against the basic scheme: -A ciphertext-only attack using two tokens orders the ciphertexts correctly. -A known-plaintext attack using two tokens and two plaintexts reveals exact value of <i>v</i>.</li> <li>Attacks against the ambiguous scheme: -A ciphertext-only attack using two tokens orders the ciphertexts with a constant probability. -A known-plaintext attack using three tokens and three plaintexts reveals exact value of <i>v</i>.</li></ul>",
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"http://doi.acm.org/10.1145/3035918.3035948"
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"title": "Cryptanalysis of Comparable Encryption in SIGMOD'16",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
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"name": "Ruben Mayer"
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"name": "Ahmad Slo"
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{
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"name": "Muhammad Adnan Tariq"
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{
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"name": "Kurt Rothermel"
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{
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"name": "Manuel Gr\u00e4ber"
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{
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"name": "Umakishore Ramachandran"
}
],
"doi": "10.1145/3135974.3135983",
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"paperAbstract": "Distributed Complex Event Processing (DCEP) is a paradigm to infer the occurrence of complex situations in the surrounding world from basic events like sensor readings. In doing so, DCEP operators detect event patterns on their incoming event streams. To yield high operator throughput, data parallelization frameworks divide the incoming event streams of an operator into overlapping windows that are processed in parallel by a number of operator instances. In doing so, the basic assumption is that the different windows can be processed independently from each other. However, consumption policies enforce that events can only be part of one pattern instance; then, they are consumed, i.e., removed from further pattern detection. That implies that the constituent events of a pattern instance detected in one window are excluded from all other windows as well, which breaks the data parallelism between different windows. In this paper, we tackle this problem by means of speculation: Based on the likelihood of an event's consumption in a window, subsequent windows may speculatively suppress that event. We propose the SPECTRE framework for speculative processing of multiple dependent windows in parallel. Our evaluations show an up to linear scalability of SPECTRE with the number of CPU cores.",
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"title": "SPECTRE: supporting consumption policies in window-based parallel complex event processing",
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"paperAbstract": "Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional autoencoders tend to learn possibly trivial representations of text documents due to their confoundin properties such as high-dimensionality, sparsity and power-law word distributions. In this paper, we propose a novel k-competitive autoencoder, called KATE, for text documents. Due to the competition between the neurons in the hidden layer, each neuron becomes specialized in recognizing specific data patterns, and overall the model can learn meaningful representations of textual data. A comprehensive set of experiments show that KATE can learn better representations than traditional autoencoders including denoising, contractive, variational, and k-sparse autoencoders. Our model also outperforms deep generative models, probabilistic topic models, and even word representation models (e.g., Word2Vec) in terms of several downstream tasks such as document classification, regression, and retrieval.",
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"https://arxiv.org/pdf/1705.02033v1.pdf"
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"title": "KATE: K-Competitive Autoencoder for Text",
"venue": "KDD",
"year": 2017
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"title": "Compute Caches",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
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"title": "CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices",
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"name": "Liana L. Fong"
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"paperAbstract": "Stochastic gradient descent (SGD) is widely used by many machine learning algorithms. It is efficient for big data ap- plications due to its low algorithmic complexity. SGD is inherently serial and its parallelization is not trivial. How to parallelize SGD on many-core architectures (e.g. GPUs) for high efficiency is a big challenge. In this paper, we present cuMF_SGD, a parallelized SGD solution for matrix factorization on GPUs. We first design high-performance GPU computation kernels that accelerate individual SGD updates by exploiting model parallelism. We then design efficient schemes that parallelize SGD updates by exploiting data parallelism. Finally, we scale cuMF SGD to large data sets that cannot fit into one GPU's memory. Evaluations on three public data sets show that cuMF_SGD outperforms existing solutions, including a 64- node CPU system, by a large margin using only one GPU card.",
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"title": "CuMF_SGD: Parallelized Stochastic Gradient Descent for Matrix Factorization on GPUs",
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"year": 2017
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"paperAbstract": "Production compilers commonly perform dozens of transformations on an intermediate representation. Running those transformations in separate passes harms performance. One approach to recover performance is to combine transformations by hand in order to reduce number of passes. Such an approach harms modularity, and thus makes it hard to maintain and evolve a compiler over the long term, and makes reasoning about performance harder. This paper describes a methodology that allows a compiler writer to define multiple transformations separately, but fuse them into a single traversal of the intermediate representation when the compiler runs. This approach has been implemented in a compiler for the Scala language. Our performance evaluation indicates that this approach reduces the running time of tree transformations by 35% and shows that this is due to improved cache friendliness. At the same time, the approach improves total memory consumption by reducing the object tenuring rate by 50%. This approach enables compiler writers to write transformations that are both modular and fast at the same time.",
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"paperAbstract": "Noninterference is a popular semantic security condition because it offers strong end-to-end guarantees, it is inherently compositional, and it can be enforced using a simple security type system. Unfortunately, it is too restrictive for real systems. Mechanisms for downgrading information are needed to capture real-world security requirements, but downgrading eliminates the strong compositional security guarantees of noninterference.\n We introduce nonmalleable information flow, a new formal security condition that generalizes noninterference to permit controlled downgrading of both confidentiality and integrity. While previous work on robust declassification prevents adversaries from exploiting the downgrading of confidentiality, our key insight is transparent endorsement, a mechanism for downgrading integrity while defending against adversarial exploitation. Robust declassification appeared to break the duality of confidentiality and integrity by making confidentiality depend on integrity, but transparent endorsement makes integrity depend on confidentiality, restoring this duality. We show how to extend a security-typed programming language with transparent endorsement and prove that this static type system enforces nonmalleable information flow, a new security property that subsumes robust declassification and transparent endorsement. Finally, we describe an implementation of this type system in the context of Flame, a flow-limited authorization plugin for the Glasgow Haskell Compiler.",
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"http://www.cs.cornell.edu/andru/papers/nmifc/nmifc.pdf"
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"title": "Nonmalleable Information Flow Control",
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"year": 2017
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"doi": "10.1145/3077136.3080826",
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"paperAbstract": "By incorporating healthiness into the food recommendation / ranking process we have the potential to improve the eating habits of a growing number of people who use the Internet as a source of food inspiration. In this paper, using insights gained from various data sources, we explore the feasibility of substituting meals that would typically be recommended to users with similar, healthier dishes. First, by analysing a recipe collection sourced from Allrecipes.com, we quantify the potential for finding replacement recipes, which are comparable but have different nutritional characteristics and are nevertheless highly rated by users. Building on this, we present two controlled user studies (n=107, n=111) investigating how people perceive and select recipes. We show participants are unable to reliably identify which recipe contains most fat due to their answers being biased by lack of information, misleading cues and limited nutritional knowledge on their part. By applying machine learning techniques to predict the preferred recipes, good performance can be achieved using low-level image features and recipe meta-data as predictors. Despite not being able to consciously determine which of two recipes contains most fat, on average, participants select the recipe with the most fat as their preference. The importance of image features reveals that recipe choices are often visually driven. A final user study (n=138) investigates to what extent the predictive models can be used to select recipe replacements such that users can be ``nudged'' towards choosing healthier recipes. Our findings have important implications for online food systems.",
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"paperAbstract": "BulletProof Hosting (BPH) services provide criminal actors with technical infrastructure that is resilient to complaints of illicit activities, which serves as a basic building block for streamlining numerous types of attacks. Anecdotal reports have highlighted an emerging trend of these BPH services reselling infrastructure from lower end service providers (hosting ISPs, cloud hosting, and CDNs) instead of from monolithic BPH providers. This has rendered many of the prior methods of detecting BPH less effective, since instead of the infrastructure being highly concentrated within a few malicious Autonomous Systems (ASes) it is now agile and dispersed across a larger set of providers that have a mixture of benign and malicious clients. In this paper, we present the first systematic study on this new trend of BPH services. By collecting and analyzing a large amount of data (25 snapshots of the entire Whois IPv4 address space, 1.5 TB of passive DNS data, and longitudinal data from several blacklist feeds), we are able to identify a set of new features that uniquely characterizes BPH on sub-allocations and that are costly to evade. Based upon these features, we train a classifier for detecting malicious sub-allocated network blocks, achieving a 98% recall and 1.5% false discovery rates according to our evaluation. Using a conservatively trained version of our classifier, we scan the whole IPv4 address space and detect 39K malicious network blocks. This allows us to perform a large-scale study of the BPH service ecosystem, which sheds light on this underground business strategy, including patterns of network blocks being recycled and malicious clients being migrated to different network blocks, in an effort to evade IP address based blacklisting. Our study highlights the trend of agile BPH services and points to potential methods of detecting and mitigating this emerging threat.",
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"title": "Under the Shadow of Sunshine: Understanding and Detecting Bulletproof Hosting on Legitimate Service Provider Networks",
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"paperAbstract": "Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.",
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"title": "The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
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"paperAbstract": "As modern 64-bit x86 processors no longer support the segmentation capabilities of their 32-bit predecessors, most research projects assume that strong in-process memory isolation is no longer an affordable option. Instead of strong, deterministic isolation, new defense systems therefore rely on the probabilistic pseudo-isolation provided by randomization to \"hide\" sensitive (or safe) regions. However, recent attacks have shown that such protection is insufficient; attackers can leak these safe regions in a variety of ways.\n In this paper, we revisit isolation for x86-64 and argue that hardware features enabling efficient deterministic isolation do exist. We first present a comprehensive study on commodity hardware features that can be repurposed to isolate safe regions in the same address space (e.g., Intel MPX and MPK). We then introduce MemSentry, a framework to harden modern defense systems with commodity hardware features instead of information hiding. Our results show that some hardware features are more effective than others in hardening such defenses in each scenario and that features originally conceived for other purposes (e.g., Intel MPX for bounds checking) are surprisingly efficient at isolating safe regions compared to their software equivalent (i.e., SFI).",
"pdfUrls": [
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"http://doi.acm.org/10.1145/3064176.3064217",
"https://www.cs.ucy.ac.cy/~eliasathan/papers/eurosys17.pdf"
],
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"title": "No Need to Hide: Protecting Safe Regions on Commodity Hardware",
"venue": "EuroSys",
"year": 2017
},
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"authors": [
{
"ids": [
"1678662"
],
"name": "Yang Li"
},
{
"ids": [
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"name": "Saugata Ghose"
},
{
"ids": [
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],
"name": "Jongmoo Choi"
},
{
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"name": "Jin Sun"
},
{
"ids": [
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],
"name": "Hui Wang"
},
{
"ids": [
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],
"name": "Onur Mutlu"
}
],
"doi": "10.1109/CLUSTER.2017.130",
"doiUrl": "https://doi.org/10.1109/CLUSTER.2017.130",
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"Computer data storage",
"Data-intensive computing",
"Dynamic random-access memory",
"Locality of reference",
"Memory hierarchy",
"Memory management",
"Memory-level parallelism",
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"paperAbstract": "While the memory footprints of cloud and HPC applications continue to increase, fundamental issues with DRAM scaling are likely to prevent traditional main memory systems, composed of monolithic DRAM, from greatly growing in capacity. Hybrid memory systems can mitigate the scaling limitations of monolithic DRAM by pairing together multiple memory technologies (e.g., different types of DRAM, or DRAM and non-volatile memory) at the same level of the memory hierarchy. The goal of a hybrid main memory is to combine the different advantages of the multiple memory types in a cost-effective manner while avoiding the disadvantages of each technology. Memory pages are placed in and migrated between the different memories within a hybrid memory system, based on the properties of each page. It is important to make intelligent page management (i.e., placement and migration) decisions, as they can significantly affect system performance.In this paper, we propose utility-based hybrid memory management (UH-MEM), a new page management mechanism for various hybrid memories, that systematically estimates the utility (i.e., the system performance benefit) of migrating a page between different memory types, and uses this information to guide data placement. UH-MEM operates in two steps. First, it estimates how much a single application would benefit from migrating one of its pages to a different type of memory, by comprehensively considering access frequency, row buffer locality, and memory-level parallelism. Second, it translates the estimated benefit of a single application to an estimate of the overall system performance benefit from such a migration.We evaluate the effectiveness of UH-MEM with various types of hybrid memories, and show that it significantly improves system performance on each of these hybrid memories. For a memory system with DRAM and non-volatile memory, UH-MEM improves performance by 14% on average (and up to 26%) compared to the best of three evaluated state-of-the-art mechanisms across a large number of data-intensive workloads.",
"pdfUrls": [
"http://www.andrew.cmu.edu/user/yangli1/UHMEM.pdf",
"http://www.pdl.cmu.edu/PDL-FTP/NVM/17cluster_uhmem.pdf",
"http://doi.ieeecomputersociety.org/10.1109/CLUSTER.2017.130"
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"title": "Utility-Based Hybrid Memory Management",
"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
"year": 2017
},
"154c5aa62805ae6556df409570b88f397a0cee6a": {
"authors": [
{
"ids": [
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"name": "Patrick Reisert"
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{
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"name": "Alexandru Calotoiu"
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{
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"name": "Sergei Shudler"
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{
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"name": "Felix Wolf"
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"doi": "10.1007/978-3-319-64203-1_8",
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"sources": [
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"title": "Following the Blind Seer - Creating Better Performance Models Using Less Information",
"venue": "Euro-Par",
"year": 2017
},
"1552f34f65ff71157275a23af374063a79e9a5d1": {
"authors": [
{
"ids": [
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"name": "Sunwoo Lee"
},
{
"ids": [
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"name": "Dipendra Jha"
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{
"ids": [
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"name": "Ankit Agrawal"
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{
"ids": [
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"name": "Alok N. Choudhary"
},
{
"ids": [
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],
"name": "Wei-keng Liao"
}
],
"doi": "10.1109/HiPC.2017.00030",
"doiUrl": "https://doi.org/10.1109/HiPC.2017.00030",
"entities": [
"Backpropagation",
"Channel (communications)",
"Computation",
"Convolutional neural network",
"Data dependency",
"Distributed memory",
"ImageNet",
"Inter-process communication",
"Parallel computing",
"Scalability"
],
"id": "1552f34f65ff71157275a23af374063a79e9a5d1",
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"journalName": "2017 IEEE 24th International Conference on High Performance Computing (HiPC)",
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"paperAbstract": "Training Convolutional Neural Network (CNN) is a computationally intensive task whose parallelization has become critical in order to complete the training in an acceptable time. However, there are two obstacles to developing a scalable parallel CNN in a distributed-memory computing environment. One is the high degree of data dependency exhibited in the model parameters across every two adjacent minibatches and the other is the large amount of data to be transferred across the communication channel. In this paper, we present a parallelization strategy that maximizes the overlap of inter-process communication with the computation. The overlapping is achieved by using a thread per compute node to initiate communication after the gradients are available. The output data of backpropagation stage is generated at each model layer, and the communication for the data can run concurrently with the computation of other layers. To study the effectiveness of the overlapping and its impact on the scalability, we evaluated various model architectures and hyperparameter settings. When training VGG-A model using ImageNet data sets, we achieve speedups of 62.97\u00d7 and 77.97\u00d7 on 128 compute nodes using mini-batch sizes of 256 and 512, respectively.",
"pdfUrls": [
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"title": "Parallel Deep Convolutional Neural Network Training by Exploiting the Overlapping of Computation and Communication",
"venue": "2017 IEEE 24th International Conference on High Performance Computing (HiPC)",
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"paperAbstract": "Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs.\n We design, implement, and evaluate DeepXplore, the first whitebox framework for systematically testing real-world DL systems. First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques.\n DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets including ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model's accuracy by up to 3%.",
"pdfUrls": [
"https://arxiv.org/pdf/1705.06640v4.pdf",
"http://www.cs.columbia.edu/~junfeng/papers/deepxplore-sosp17.pdf",
"https://arxiv.org/pdf/1705.06640v1.pdf",
"http://doi.acm.org/10.1145/3132747.3132785",
"http://www.srl.inf.ethz.ch/riai2017/deepxplore.pdf",
"https://arxiv.org/pdf/1705.06640v2.pdf",
"https://arxiv.org/pdf/1705.06640v3.pdf",
"http://arxiv.org/abs/1705.06640",
"http://www.cs.columbia.edu/~suman/docs/deepxplore.pdf"
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"title": "DeepXplore: Automated Whitebox Testing of Deep Learning Systems",
"venue": "SOSP",
"year": 2017
},
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"name": "Swapnil Haria"
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"name": "Mark D. Hill"
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"name": "Haris Volos"
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"name": "Kimberly Keeton"
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"paperAbstract": "Emerging non-volatile memory (NVM) technologies promise durability with read and write latencies comparable to volatile memory (DRAM). We define Persistent Memory (PM) as NVM accessed with byte addressability at low latency via normal memory instructions. Persistent-memory applications ensure the consistency of persistent data by inserting ordering points between writes to PM allowing the construction of higher-level transaction mechanisms. An epoch is a set of writes to PM between ordering points.\n To put systems research in PM on a firmer footing, we developed and analyzed a PM benchmark suite called WHISPER (Wisconsin-HP Labs Suite for Persistence) that comprises ten PM applications we gathered to cover all current interfaces to PM. A quantitative analysis reveals several insights: (a) only 4% of writes in PM-aware applications are to PM and the rest are to volatile memory, (b) software transactions are often implemented with 5 to 50 ordering points (c) 75% of epochs update exactly one 64B cache line, (d) 80% of epochs from the same thread depend on previous epochs from the same thread, while few epochs depend on epochs from other threads.\n Based on our analysis, we propose the Hands-off Persistence System (HOPS) to track updates to PM in hardware. Current hardware design requires applications to force data to PM as each epoch ends. HOPS provides high-level ISA primitives for applications to express durability and ordering constraints separately and enforces them automatically, while achieving 24.3% better performance over current approaches to persistence.",
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"http://pages.cs.wisc.edu/~sankey/ASPLOS_main.pdf",
"http://research.cs.wisc.edu/multifacet/papers/asplos17_whisper.pdf",
"http://doi.acm.org/10.1145/3037697.3037730"
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"title": "An Analysis of Persistent Memory Use with WHISPER",
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"year": 2017
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"paperAbstract": "Writing reliable concurrent software remains a huge challenge for today's programmers. Programmers rarely reason about their code by explicitly considering different possible inter-leavings of its execution. We consider the problem of detecting data races from individual executions in a sound manner. The classical approach to solving this problem has been to use Lamport's happens-before (HB) relation. Until now HB remains the only approach that runs in linear time. Previous efforts in improving over HB such as causally-precedes (CP) and maximal causal models fall short due to the fact that they are not implementable efficiently and hence have to compromise on their race detecting ability by limiting their techniques to bounded sized fragments of the execution. We present a new relation weak-causally-precedes (WCP) that is provably better than CP in terms of being able to detect more races, while still remaining sound. Moreover, it admits a linear time algorithm which works on the entire execution without having to fragment it.",
"pdfUrls": [
"http://arxiv.org/abs/1704.02432",
"http://doi.acm.org/10.1145/3062341.3062374",
"https://arxiv.org/pdf/1704.02432v3.pdf",
"https://arxiv.org/pdf/1704.02432v2.pdf",
"https://arxiv.org/pdf/1704.02432v1.pdf",
"https://pdfs.semanticscholar.org/159d/2d0eb9553b405fbbfb01deabc8f7f4ce51aa.pdf"
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"title": "Dynamic race prediction in linear time",
"venue": "PLDI",
"year": 2017
},
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"authors": [
{
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"name": "Bob Goodwin"
},
{
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"name": "Michael Hopcroft"
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{
"ids": [
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"name": "Dan Luu"
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{
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"name": "Alex Clemmer"
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{
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"name": "Mihaela Curmei"
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{
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"name": "Sameh Elnikety"
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{
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"name": "Yuxiong He"
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],
"doi": "10.1145/3077136.3080789",
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"paperAbstract": "Since the mid-90s there has been a widely-held belief that signature files are inferior to inverted files for text indexing. In recent years the Bing search engine has developed and deployed an index based on bit-sliced signatures. This index, known as BitFunnel, replaced an existing production system based on an inverted index. The driving factor behind the shift away from the inverted index was operational cost savings. This paper describes algorithmic innovations and changes in the cloud computing landscape that led us to reconsider and eventually field a technology that was once considered unusable. The BitFunnel algorithm directly addresses four fundamental limitations in bit-sliced block signatures. At the same time, our mapping of the algorithm onto a cluster offers opportunities to avoid other costs associated with signatures. We show these innovations yield a significant efficiency gain versus classic bit-sliced signatures and then compare BitFunnel with Partitioned Elias-Fano Indexes, MG4J, and Lucene.",
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"http://doi.acm.org/10.1145/3077136.3080789"
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"sources": [
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"title": "BitFunnel: Revisiting Signatures for Search",
"venue": "SIGIR",
"year": 2017
},
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"authors": [
{
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"name": "Milind Chabbi"
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{
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"name": "Abdelhalim Amer"
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{
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"name": "Shasha Wen"
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{
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"name": "Xu Liu"
}
],
"doi": "10.1145/3018743.3018768",
"doiUrl": "https://doi.org/10.1145/3018743.3018768",
"entities": [
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"Music Construction Set",
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"paperAbstract": "The popularity of Non-Uniform Memory Access (NUMA) architectures has led to numerous locality-preserving hierarchical lock designs, such as HCLH, HMCS, and cohort locks. Locality-preserving locks trade fairness for higher throughput. Hence, some instances of acquisitions can incur long latencies, which may be intolerable for certain applications. Few locks admit a waiting thread to abandon its protocol on a timeout. State-of-the-art abortable locks are not fully locality aware, introduce high overheads, and unsuitable for frequent aborts. Enhancing locality-aware locks with lightweight timeout capability is critical for their adoption. In this paper, we design and evaluate the HMCS-T lock, a Hierarchical MCS (HMCS) lock variant that admits a timeout. HMCS-T maintains the locality benefits of HMCS while ensuring aborts to be lightweight. HMCS-T offers the progress guarantee missing in most abortable queuing locks. Our evaluations show that HMCS-T offers the timeout feature at a moderate overhead over its HMCS analog. HMCS-T, used in an MPI runtime lock, mitigated the poor scalability of an MPI+OpenMP BFS code and resulted in 4.3x superior scaling.",
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"paperAbstract": "Hashing has been a widely-adopted technique for nearest neighbor search in large-scale image retrieval tasks. Recent research has shown that leveraging supervised information can lead to high quality hashing. However, the cost of annotating data is often an obstacle when applying supervised hashing to a new domain. Moreover, the results can suffer from the robustness problem as the data at training and test stage may come from different distributions. This paper studies the exploration of generating synthetic data through semi-supervised generative adversarial networks (GANs), which leverages largely unlabeled and limited labeled training data to produce highly compelling data with intrinsic invariance and global coherence, for better understanding statistical structures of natural data. We demonstrate that the above two limitations can be well mitigated by applying the synthetic data for hashing. Specifically, a novel deep semantic hashing with GANs (DSH-GANs) is presented, which mainly consists of four components: a deep convolution neural networks (CNN) for learning image representations, an adversary stream to distinguish synthetic images from real ones, a hash stream for encoding image representations to hash codes and a classification stream. The whole architecture is trained end-to-end by jointly optimizing three losses, i.e., adversarial loss to correct label of synthetic or real for each sample, triplet ranking loss to preserve the relative similarity ordering in the input real-synthetic triplets and classification loss to classify each sample accurately. Extensive experiments conducted on both CIFAR-10 and NUS-WIDE image benchmarks validate the capability of exploiting synthetic images for hashing. Our framework also achieves superior results when compared to state-of-the-art deep hash models.",
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"paperAbstract": "A Suffix tree is a fundamental and versatile string data structure that is frequently used in important application areas such as text processing, information retrieval, and computational biology. Sequentially, the construction of suffix trees takes linear time, and optimal parallel algorithms exist only for the PRAM model. Recent works mostly target low core-count shared-memory implementations but achieve suboptimal complexity, and prior distributed-memory parallel algorithms have quadratic worst-case complexity. Suffix trees can be constructed from suffix and longest common prefix (LCP) arrays by solving the All-Nearest-Smaller-Values(ANSV) problem. In this paper, we formulate a more generalized version of the ANSV problem, and present a distributed-memory parallel algorithm for solving it in O(n/p +p) time. Our algorithm minimizes the overall and per-node communication volume. Building on this, we present a parallel algorithm for constructing a distributed representation of suffix trees, yielding both superior theoretical complexity and better practical performance compared to previous distributed-memory algorithms. We demonstrate the construction of the suffix tree for the human genome given its suffix and LCP arrays in under 2 seconds on 1024 Intel Xeon cores.",
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"paperAbstract": "High performance computing (HPC) applications, such as metagenomics and other big data systems, need to store and analyze huge volumes of semi-structured data. Such applications often rely on NoSQL-based datastores, and optimizing these databases is a challenging endeavor, with over 50 configuration parameters in Cassandra alone. As the application executes, database workloads can change rapidly from read-heavy to write-heavy ones, and a system tuned with a read-optimized configuration becomes suboptimal when the workload becomes write-heavy.\n In this paper, we present a method and a system for optimizing NoSQL configurations for Cassandra and ScyllaDB when running HPC and metagenomics workloads. First, we identify the significance of configuration parameters using ANOVA. Next, we apply neural networks using the most significant parameters and their workload-dependent mapping to predict database throughput, as a surrogate model. Then, we optimize the configuration using genetic algorithms on the surrogate to maximize the workload-dependent performance. Using the proposed methodology in our system (R<scp>afiki</scp>), we can predict the throughput for unseen workloads and configuration values with an error of 7.5% for Cassandra and 6.9-7.8% for ScyllaDB. Searching the configuration spaces using the trained surrogate models, we achieve performance improvements of 41% for Cassandra and 9% for ScyllaDB over the default configuration with respect to a read-heavy workload, and also significant improvement for mixed workloads. In terms of searching speed, R<scp>afiki</scp>, using only 1/10000-th of the searching time of exhaustive search, reaches within 15% and 9.5% of the theoretically best achievable performances for Cassandra and ScyllaDB, respectively---supporting optimizations for highly dynamic workloads.",
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"title": "Rafiki: a middleware for parameter tuning of NoSQL datastores for dynamic metagenomics workloads",
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"year": 2017
},
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"paperAbstract": "Interactive service providers have strict requirements on high-percentile (<i>tail</i>) latency to meet user expectations. If providers meet tail latency targets with less energy, they increase profits, because energy is a significant operating expense. Unfortunately, optimizing tail latency and energy are typically conflicting goals. Our work resolves this conflict by exploiting servers with per-core Dynamic Voltage and Frequency Scaling (DVFS) and Asymmetric Multicore Processors (AMPs). We introduce the <i>Adaptive Slow-to-Fast</i> scheduling framework, which matches the heterogeneity of the workload --- a mix of short and long requests --- to the heterogeneity of the hardware --- cores running at different speeds. The scheduler prioritizes long requests to faster cores by exploiting the insight that long requests reveal themselves. We use control theory to design threshold-based scheduling policies that use individual request progress, load, competition, and latency targets to optimize performance and energy. We configure our framework to optimize Energy Efficiency for a given Tail Latency (EETL) for both DVFS and AMP. In this framework, each request self-schedules, starting on a slow core and then migrating itself to faster cores. At high load, when a desired AMP core speed <i>s</i> is not available for a request but a faster core is, the longest request on an <i>s</i> core type migrates early to make room for the other request. Compared to per-core DVFS systems, EETL for AMPs delivers the same tail latency, reduces energy by 18% to 50%, and improves capacity (throughput) by 32% to 82%. We demonstrate that our framework effectively exploits dynamic DVFS and static AMP heterogeneity to reduce provisioning and operational costs for interactive services.",
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"title": "Exploiting heterogeneity for tail latency and energy efficiency",
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"year": 2017
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"paperAbstract": "Both the operational and academic security communities have used dynamic analysis sandboxes to execute malware samples for roughly a decade. Network information derived from dynamic analysis is frequently used for threat detection, network policy, and incident response. Despite these common and important use cases, the efficacy of the network detection signal derived from such analysis has yet to be studied in depth. This paper seeks to address this gap by analyzing the network communications of 26.8 million samples that were collected over a period of five years. Using several malware and network datasets, our large scale study makes three core contributions. (1) We show that dynamic analysis traces should be carefully curated and provide a rigorous methodology that analysts can use to remove potential noise from such traces. (2) We show that Internet miscreants are increasingly using potentially unwanted programs (PUPs) that rely on a surprisingly stable DNS and IP infrastructure. This indicates that the security community is in need of better protections against such threats, and network policies may provide a solid foundation for such protections. (3) Finally, we see that, for the vast majority of malware samples, network traffic provides the earliest indicator of infection—several weeks and often months before the malware sample is discovered. Therefore, network defenders should rely on automated malware analysis to extract indicators of compromise and not to build early detection systems.",
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"title": "A Lustrum of Malware Network Communication: Evolution and Insights",
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"title": "Leeway: Addressing Variability in Dead-Block Prediction for Last-Level Caches",
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"paperAbstract": "Network operators often face the problem of remote outages in transit networks leading to significant (sometimes on the order of minutes) downtimes. The issue is that BGP, the Internet routing protocol, often converges slowly upon such outages, as large bursts of messages have to be processed and propagated router by router.\n In this paper, we present SWIFT, a fast-reroute framework which enables routers to restore connectivity in few seconds upon remote outages. SWIFT is based on two novel techniques. First, SWIFT deals with slow outage notification by predicting the overall extent of a remote failure out of few control-plane (BGP) messages. The key insight is that significant inference speed can be gained at the price of some accuracy. Second, SWIFT introduces a new data-plane encoding scheme, which enables quick and flexible update of the affected forwarding entries. SWIFT is deployable on existing devices, without modifying BGP.\n We present a complete implementation of SWIFT and demonstrate that it is both fast and accurate. In our experiments with real BGP traces, SWIFT predicts the extent of a remote outage in few seconds with an accuracy of ~90% and can restore connectivity for 99% of the affected destinations.",
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"http://www.caida.org/publications/papers/2017/swift/swift.pdf",
"http://www0.cs.ucl.ac.uk/staff/S.Vissicchio/papers/Swift_sigcomm17.pdf"
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"title": "SWIFT: Predictive Fast Reroute",
"venue": "SIGCOMM",
"year": 2017
},
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"authors": [
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"name": "Wenjie Lu"
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"name": "Jun Sakuma"
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"paperAbstract": "In recent years, there has been a growing trend towards outsourcing of computational tasks with the development of cloud services. The Gentry\u2019s pioneering work of fully homomorphic encryption (FHE) and successive works have opened a new vista for secure and practical cloud computing. In this paper, we consider performing statistical analysis on encrypted data. To improve the efficiency of the computations, we take advantage of the batched computation based on the Chinese-Remainder-Theorem. We propose two building blocks that work with FHE: a novel batch greater-than primitive, and matrix primitive for encrypted matrices. With these building blocks, we construct secure procedures and protocols for different types of statistics including the histogram (count), contingency table (with cell suppression) for categorical data; k-percentile for ordinal data; and principal component analysis and linear regression for numerical data. To demonstrate the effectiveness of our methods, we ran experiments in five real datasets. For instance, we can compute a contingency table with more than 50 cells from 4000 of data in just 5 minutes, and we can train a linear regression model with more than 40k of data and dimension as high as 6 within 15 minutes. We show that the FHE is not as slow as commonly believed and it becomes feasible to perform a broad range of statistical analysis on thousands of encrypted data.",
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"http://eprint.iacr.org/2016/1163"
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"title": "Leader Election in Asymmetric Labeled Unidirectional Rings",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
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"paperAbstract": "The Ab Initio Molecular Dynamics (AIMD) method allows scientists to treat the dynamics of molecular and condensed phase systems while retaining a first-principles-based description of their interactions. This extremely important method has tremendous computational requirements, because the electronic Schrodinger equation, approximated using Kohn-Sham Density Functional Theory (DFT), is solved at every time step. With the advent of manycore architectures, application developers have a significant amount of processing power within each compute node that can only be exploited through massive parallelism. A compute intensive application such as AIMD forms a good candidate to leverage this processing power. In this paper, we focus on adding thread level parallelism to the plane wave DFT methodology implemented in NWChem. Through a careful optimization of tall-skinny matrix products, which are at the heart of the Lagrange Multiplier and non-local pseudopotential kernels, as well as 3D FFTs, our OpenMP implementation delivers excellent strong scaling on the latest Intel Knights Landing (KNL) processor. We assess the efficiency of our Lagrange multipliers kernels by building a Roofline model of the platform, and verify that our implementation is close to the roofline for various problem sizes. Finally, we present strong scaling results on the complete AIMD simulation for a 64 water molecules test case, that scales up to all 68 cores of the Knights Landing processor.",
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"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"paperAbstract": "Demand is mounting in the industry for scalable GPU-based deep learning systems. Unfortunately, existing training applications built atop popular deep learning frameworks, including Caffe, Theano, and Torch, etc, are incapable of conducting distributed GPU training over large-scale clusters.To remedy such a situation, this paper presents Nexus, a platform that allows existing deep learning frameworks to easily scale out to multiple machines without sacrificing model accuracy. Nexus leverages recently proposed distributed parameter management architecture to orchestrate distributed training by a large number of learners spread across the cluster. Through characterizing the run-time behavior of existing single-node based applications, Nexus is equipped with a suite of optimization schemes, including hierarchical and hybrid parameter aggregation, enhanced network and computation layer, and quality-guided communication adjustment, etc, to strengthen the communication channels and resource utilization. Empirical evaluations with a diverse set of deep learning applications demonstrate that Nexus is easy to integrate and can deliver efficient distributed training services to major deep learning frameworks. In addition, Nexus's optimization schemes are highly effective to shorten the training time with targeted accuracy bounds.",
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"paperAbstract": "The four-index integral transform is a fundamental and computationally demanding calculation used in many computational chemistry suites such as NWChem. It transforms a four-dimensional tensor from one basis to another. This transformation is most efficiently implemented as a sequence of four tensor contractions that each contract a four- dimensional tensor with a two-dimensional transformation matrix. Differing degrees of permutation symmetry in the intermediate and final tensors in the sequence of contractions cause intermediate tensors to be much larger than the final tensor and limit the number of electronic states in the modeled systems.\n Loop fusion, in conjunction with tiling, can be very effective in reducing the total space requirement, as well as data movement. However, the large number of possible choices for loop fusion and tiling, and data/computation distribution across a parallel system, make it challenging to develop an optimized parallel implementation for the four-index integral transform. We develop a novel approach to address this problem, using lower bounds modeling of data movement complexity. We establish relationships between available aggregate physical memory in a parallel computer system and ineffective fusion configurations, enabling their pruning and consequent identification of effective choices and a characterization of optimality criteria. This work has resulted in the development of a significantly improved implementation of the four-index transform that enables higher performance and the ability to model larger electronic systems than the current implementation in the NWChem quantum chemistry software suite.",
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"paperAbstract": "Big Data applications suffer from unpredictable and unacceptably high pause times due to bad memory management (Garbage Collection, GC) decisions. This is a problem for all applications but it is even more important for applications with low pause time requirements such as credit-card fraud detection or targeted website advertisement systems, which can easily fail to comply with Service Level Agreements due to long GC cycles (during which the application is stopped). This problem has been previously identified and is related to Big Data applications keeping in memory (for a long period of time, from the GC's perspective) massive amounts of data objects.\n Memory management approaches have been proposed to reduce the GC pause time by allocating objects with similar lifetimes close to each other. However, they either do not provide a general solution for all types of Big Data applications (thus only solving the problem for a specific set of applications), and/or require programmer effort and knowledge to change/annotate the application code.\n This paper proposes POLM2, a profiler that automatically: i) estimates application allocation profiles based on execution records, and ii) instruments application bytecode to help the GC taking advantage of the profiling information. Thus, no programmer effort is required to change the source code to allocate objects according to their lifetimes. POLM2 is implemented for the OpenJDK HotSpot Java Virtual Machine 8 and uses NG2C, a recently proposed GC which supports multi-generational pretenuring. Results show that POLM2 is able to: i) achieve pauses as low as NG2C (which requires manual source code modification), and ii) significantly reduce application pauses by up to 80% when compared to G1 (default collector in OpenJDK). POLM2 does not negatively impact neither application throughput nor memory utilization.",
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"title": "POLM2: automatic profiling for object lifetime-aware memory management for hotspot big data applications",
"venue": "Middleware",
"year": 2017
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"name": "Kaiyuan Zhang"
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{
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"name": "Xi Wang"
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"name": "Karin Strauss"
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"name": "Luis Ceze"
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"paperAbstract": "Modern image storage services, especially those associated with social media services, host massive collections of images. These images are often replicated at many different resolutions to support different devices and contexts, incurring substantial capacity overheads. One approach to alleviate these overheads is to resize them at request time. However, this approach can be inefficient, as reading full-size source images for resizing uses more bandwidth than reading pre-resized images. We propose repurposing the progressive JPEG standard and customizing the organization of image data to reduce the bandwidth overheads of dynamic resizing. We show that at a PSNR of 32 dB, dynamic resizing with progressive JPEG provides 2.5\u00d7 read data savings over baseline JPEG, and that progressive JPEG with customized encode parameters can further improve these savings (up to 5.8\u00d7 over the baseline). Finally, we characterize the decode overheads of progressive JPEG to assess the feasibility of directly decoding progressive JPEG images on energy-limited devices. Our approach does not require modifications to current JPEG software stacks.",
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"title": "Customizing Progressive JPEG for Efficient Image Storage",
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"year": 2017
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"paperAbstract": "Recent years have seen a rapid growth of interest in exploiting monitoring data collected from enterprise applications for automated management and performance analysis. In spite of this trend, even simple performance inference problems involving queueing theoretic formulas often incur computational bottlenecks, for example upon computing likelihoods in models of batch systems. Motivated by this issue, we revisit the solution of multiclass closed queueing networks, which are popular models used to describe batch and distributed applications with parallelism constraints. We first prove that the normalizing constant of the equilibrium state probabilities of a closed model can be reformulated exactly as a multidimensional integral over the unit simplex. This gives as a by-product novel explicit expressions for the multiclass normalizing constant. We then derive a method based on cubature rules to efficiently evaluate the proposed integral form in small and medium-sized models. For large models, we propose novel asymptotic expansions and Monte Carlo sampling methods to efficiently and accurately approximate normalizing constants and likelihoods. We illustrate the resulting accuracy gains in problems involving optimization-based inference.",
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"paperAbstract": "Redundant computations appear during the execution of real programs. Multiple factors contribute to these unnecessary computations, such as repetitive inputs and patterns, calling functions with the same parameters or bad programming habits. Compilers minimize non useful code with static analysis. However, redundant execution might be dynamic and there are no current approaches to reduce these inefficiencies. Additionally, many algorithms can be computed with different levels of accuracy. Approximate computing exploits this fact to reduce execution time at the cost of slightly less accurate results. In this case, expert developers determine the desired tradeoff between performance and accuracy for each application. In this paper, we present Approximate Task Memoization (ATM), a novel approach in the runtime system that transparently exploits both dynamic redundancy and approximation at the task granularity of a parallel application. Memoization of previous task executions allows predicting the results of future tasks without having to execute them and without losing accuracy. To further increase performance improvements, the runtime system can memoize similar tasks, which leads to task approximate computing. By defining how to measure task similarity and correctness, we present an adaptive algorithm in the runtime system that automatically decides if task approximation is beneficial or not. When evaluated on a real 8-core processor with applications from different domains (financial analysis, stencil-computation, machine-learning and linear-algebra), ATM achieves a 1.4x average speedup when only applying memoization techniques. When adding task approximation, ATM achieves a 2.5x average speedup with an average 0.7% accuracy loss (maximum of 3.2%).",
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"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
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"paperAbstract": "In a RFID enabled system, tagged products is ordinarily required to be transferred from an owner to another, which makes the ownership of corresponding tags change for several times. To overcome problem of temporary ownership transfer, a lightweight anonymous group ownership transfer protocol is proposed in this paper. It involves ownership transfer and ownership recovery for group tags, which are all based upon mutual authentication. Compared with most of previous protocols using hash functions and cryptographic suites, our scheme only utilizes simple operations, which are completely compliant with EPC Class-1 Generation-2 standard. Through our scheme, the window problem in multi-owner environment is first solved. Security analysis shows that our protocol can provide security and privacy preservation. It is also demonstrated that it resists various attacks. Performance comparisons indicate that our scheme can meet necessary security requirements and achieve better performance.",
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"paperAbstract": "Cloud providers have begun to offer their surplus capacity in the form of low-cost transient servers, which can be revoked unilaterally at any time. While the low cost of transient servers makes them attractive for a wide range of applications, such as data processing and scientific computing, failures due to server revocation can severely degrade application performance. Since different transient server types offer different cost and availability tradeoffs, we present the notion of server portfolios that is based on financial portfolio modeling. Server portfolios enable construction of an \"optimal\" mix of severs to meet an application's sensitivity to cost and revocation risk. We implement model-driven portfolios in a system called ExoSphere, and show how diverse applications can use portfolios and application-specific policies to gracefully handle transient servers. We show that ExoSphere enables widely-used parallel applications such as Spark, MPI, and BOINC to be made transiency-aware with modest effort. Our experiments show that allowing the applications to use suitable transiency-aware policies, ExoSphere is able to achieve 80% cost savings when compared to on-demand servers and greatly reduces revocation risk compared to existing approaches.",
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"title": "Portfolio-driven Resource Management for Transient Cloud Servers",
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"paperAbstract": "Many fields of study in compilers give rise to the concept of a join point—a place where different execution paths come together. Join points are often treated as functions or continuations, but we believe it is time to study them in their own right. We show that adding join points to a direct-style functional intermediate language is a simple but powerful change that allows new optimizations to be performed, including a significant improvement to list fusion. Finally, we report on recent work on adding join points to the intermediate language of the Glasgow Haskell Compiler.",
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"paperAbstract": "CPU-FPGA heterogeneous platforms offer a promising solution for high-performance and energy-efficient computing systems by providing specialized accelerators with post-silicon reconfigurability. To unleash the power of FPGA, however, the programmability gap has to be filled so that applications specified in high-level programming languages can be efficiently mapped and scheduled on FPGA. The above problem is even more challenging for irregular applications, in which the execution dependency can only be determined at run time. Thus over-serialized accelerators are generated from existing works that rely on compile time analysis to schedule the computation.\n In this work, we propose a comprehensive software-hardware co-design framework, which captures parallelism in irregular applications and aggressively schedules pipelined execution on reconfigurable platform. Based on an inherently parallel abstraction packaging parallelism for runtime schedule, our framework significantly differs from existing works that tend to schedule executions at compile time. An irregular application is formulated as a set of tasks with their dependencies specified as rules describing the conditions under which a subset of tasks can be executed concurrently. Then datapaths on FPGA will be generated by transforming applications in the formulation into task pipelines orchestrated by evaluating rules at runtime, which could exploit fine-grained pipeline parallelism as handcrafted accelerators do.\n An evaluation shows that this framework is able to produce datapath with its quality close to handcrafted designs. Experiments show that generated accelerators are dramatically more efficient than those created by current high-level synthesis tools. Meanwhile, accelerators generated for a set of irregular applications attain 0.5x~1.9x performance compared to equivalent software implementations we selected on a server-grade 10-core processor, with the memory subsystem remaining as the bottleneck.",
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"http://doi.acm.org/10.1145/3079856.3080228"
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"title": "Aggressive pipelining of irregular applications on reconfigurable hardware",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"authors": [
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"name": "Xiaoqing Luo"
},
{
"ids": [
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"name": "Frank Mueller"
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{
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"name": "Philip H. Carns"
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{
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"name": "Jonathan Jenkins"
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{
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"name": "Robert Latham"
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{
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"name": "Robert B. Ross"
},
{
"ids": [
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],
"name": "Shane Snyder"
}
],
"doi": "10.1109/IPDPS.2017.45",
"doiUrl": "https://doi.org/10.1109/IPDPS.2017.45",
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"paperAbstract": "Today’s rapid development of supercomputers has caused I/O performance to become a major performance bottleneck for many scientific applications. Trace analysis tools have thus become vital for diagnosing root causes of I/O problems. This work contributes an I/O tracing framework with (a) techniques to gather a set of lossless, elastic I/O trace files for small number of nodes, (b) a mathematical model to analyze trace data and extrapolate it to larger number of nodes, and (c) a replay engine for the extrapolated trace file to verify its accuracy. The traces can in principle be extrapolated even beyond the scale of presentday systems and provide a test if applications scale in terms of I/O. We conducted our experiments on three platforms: a commodity Linux cluster, an IBM BG/Q system, and a discrete event simulation of an IBM BG/P system. We investigate a combination of synthetic benchmarks on all platforms as well as a production scientific application on the BG/Q system. The extrapolated I/O trace replays closely resemble the I/O behavior of equivalent applications in all cases.",
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"title": "ScalaIOExtrap: Elastic I/O Tracing and Extrapolation",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"name": "Bo Wu"
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"name": "Xu Liu"
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"name": "Xiaobo Zhou"
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"name": "Changjun Jiang"
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"doi": "10.1145/3037697.3037742",
"doiUrl": "https://doi.org/10.1145/3037697.3037742",
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"paperAbstract": "GPUs are widely adopted in HPC and cloud computing platforms to accelerate general-purpose workloads. However, modern GPUs do not support flexible preemption, leading to performance and priority inversion problems in multi-tasking environments.\n In this paper, we propose and develop FLEP, the first software system that enables flexible kernel preemption and kernel scheduling on commodity GPUs. The FLEP compilation engine transforms the GPU program into preemptable forms, which can be interrupted during execution and yield all or part of the streaming multi-processors (SMs) in the GPU. The FLEP runtime engine intercepts all kernel invocations and determines which kernels and how those kernels should be preempted and scheduled. Experimental results on two-kernel co-runs demonstrate up to 24.2X speedup for high-priority kernels and up to 27X improvement on normalized average turnaround time for kernels with the same priority. FLEP reduces the preemption latency by up to 41% compared to yielding the whole GPU when the waiting kernels only need several SMs. With all the benefits, FLEP only introduces 2.5% runtime overhead, which is substantially lower than the kernel slicing approach.",
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"title": "FLEP: Enabling Flexible and Efficient Preemption on GPUs",
"venue": "ASPLOS",
"year": 2017
},
"18c3380550d93ae6b4101d9b06fe2b37b803dce3": {
"authors": [
{
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"name": "Yao Zhang"
},
{
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"name": "Arvind Ramanathan"
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{
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"name": "Anil Vullikanti"
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{
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"name": "Laura L. Pullum"
},
{
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"name": "B. Aditya Prakash"
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"doi": "10.1109/ICDM.2017.71",
"doiUrl": "https://doi.org/10.1109/ICDM.2017.71",
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"paperAbstract": "Given a contact network and coarse-grained diagnostic information like electronic Healthcare Reimbursement Claims (eHRC) data, can we develop efficient intervention policies to control an epidemic? Immunization is an important problem in multiple areas especially epidemiology and public health. However, most existing studies focus on developing pre-emptive strategies assuming prior epidemiological models. In practice, disease spread is usually complicated, hence assuming an underlying model may deviate from true spreading patterns, leading to possibly inaccurate interventions. Additionally, the abundance of health care surveillance data (like eHRC) makes it possible to study data-driven strategies without too many restrictive assumptions. Hence, such an approach can help public-health experts take more practical decisions. In this paper, we take into account propagation log and contact networks for controlling propagation. We formulate the novel and challenging Data-Driven Immunization problem without assuming classical epidemiological models. To solve it, we first propose an efficient sampling approach to align surveillance data with contact networks, then develop an efficient algorithm with the provably approximate guarantee for immunization. Finally, we show the effectiveness and scalability of our methods via extensive experiments on multiple datasets, and conduct case studies on nation-wide real medical surveillance data.",
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"title": "Data-Driven Immunization",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
"year": 2017
},
"18c5049806ab27e4a2a7ba6c388309843d6f90ab": {
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{
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"name": "Yanick Fratantonio"
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{
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"name": "Chenxiong Qian"
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{
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"name": "Simon P. Chung"
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{
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"name": "Wenke Lee"
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],
"doi": "10.1109/SP.2017.39",
"doiUrl": "https://doi.org/10.1109/SP.2017.39",
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"journalName": "2017 IEEE Symposium on Security and Privacy (SP)",
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"paperAbstract": "The effectiveness of the Android permission system fundamentally hinges on the user's correct understanding of the capabilities of the permissions being granted. In this paper, we show that both the end-users and the security community have significantly underestimated the dangerous capabilities granted by the SYSTEM_ALERT_WINDOW and the BIND_ACCESSIBILITY_SERVICE permissions: while it is known that these are security-sensitive permissions and they have been abused individually (e.g., in UI redressing attacks, accessibility attacks), previous attacks based on these permissions rely on vanishing side-channels to time the appearance of overlay UI, cannot respond properly to user input, or make the attacks literally visible. This work, instead, uncovers several design shortcomings of the Android platform and shows how an app with these two permissions can completely control the UI feedback loop and create devastating attacks. In particular, we demonstrate how such an app can launch a variety of stealthy, powerful attacks, ranging from stealing user's login credentials and security PIN, to the silent installation of a God-mode app with all permissions enabled, leaving the victim completely unsuspecting. To make things even worse, we note that when installing an app targeting a recent Android SDK, the list of its required permissions is not shown to the user and that these attacks can be carried out without needing to lure the user to knowingly enable any permission. In fact, the SYSTEM_ALERT_WINDOW permission is automatically granted for apps installed from the Play Store and our experiment shows that it is practical to lure users to unknowingly grant the BIND_ACCESSIBILITY_SERVICE permission by abusing capabilities from the SYSTEM_ALERT_WINDOW permission. We evaluated the practicality of these attacks by performing a user study: none of the 20 human subjects that took part of the experiment even suspected they had been attacked. We also found that it is straightforward to get a proof-of-concept app requiring both permissions accepted on the official store. We responsibly disclosed our findings to Google. Unfortunately, since these problems are related to design issues, these vulnerabilities are still unaddressed. We conclude the paper by proposing a novel defense mechanism, implemented as an extension to the current Android API, which would protect Android users and developers from the threats we uncovered.",
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"http://iisp.gatech.edu/sites/default/files/images/cloak_and_dagger_-_from_two_permissions_to_complete_control.pdf",
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"title": "Cloak and Dagger: From Two Permissions to Complete Control of the UI Feedback Loop",
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"year": 2017
},
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"authors": [
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"name": "Erik Derr"
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{
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"name": "Sven Bugiel"
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"name": "Sascha Fahl"
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{
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"name": "Yasemin Acar"
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{
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"name": "Michael Backes"
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],
"doi": "10.1145/3133956.3134059",
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"paperAbstract": "Third-party libraries in Android apps have repeatedly been shown to be hazards to the users' privacy and an amplification of their host apps' attack surface. A particularly aggravating factor to this situation is that the libraries' version included in apps are very often outdated.\n This paper makes the first contribution towards solving the problem of library outdatedness on Android. First, we conduct a survey with 203 app developers from Google Play to retrieve first-hand information about their usage of libraries and requirements for more effective library updates. With a subsequent study of library providers' semantic versioning practices, we uncover that those providers are likely a contributing factor to the app developers' abstinence from library updates in order to avoid ostensible re-integration efforts and version incompatibilities. Further, we conduct a large-scale library updatability analysis of 1,264,118 apps to show that, based on the library API usage, 85.6% of the libraries could be upgraded by at least one version without modifying the app code, 48.2% even to the latest version. Particularly alarming are our findings that 97.8% out of 16,837 actively used library versions with a known security vulnerability could be easily fixed through a drop-in replacement of the vulnerable library with the fixed version. Based on these results, we conclude with a thorough discussion of solutions and actionable items for different actors in the app ecosystem to effectively remedy this situation.",
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"name": "Murat Kantarcioglu"
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"name": "Zhiqiang Lin"
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{
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"name": "Latifur Khan"
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],
"doi": "10.1145/3133956.3134095",
"doiUrl": "https://doi.org/10.1145/3133956.3134095",
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"paperAbstract": "Recently, using secure processors for trusted computing in cloud has attracted a lot of attention. Over the past few years, efficient and secure data analytic tools (e.g., map-reduce framework, machine learning models, and SQL querying) that can be executed over encrypted data using the trusted hardware have been developed. However, these prior efforts do not provide a simple, secure and high level language based framework that is suitable for enabling generic data analytics for non-security experts who do not have concepts such as \"oblivious execution\". In this paper, we thus provide such a framework that allows data scientists to perform the data analytic tasks with secure processors using a Python/Matlab-like high level language. Our framework automatically compiles programs written in our language to optimal execution code by managing issues such as optimal data block sizes for I/O, vectorized computations to simplify much of the data processing, and optimal ordering of operations for certain tasks. Furthermore, many language constructs such as if-statements are removed so that a non-expert user is less likely to create a piece of code that may reveal sensitive information while allowing oblivious data processing (i.e., hiding access patterns). Using these design choices, we provide guarantees for efficient and secure data analytics. We show that our framework can be used to run the existing big data benchmark queries over encrypted data using the Intel SGX efficiently. Our empirical results indicate that our proposed framework is orders of magnitude faster than the general oblivious execution alternatives.",
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"http://www.utdallas.edu/~zhiqiang.lin/file/CCS17b-slides.pdf",
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"title": "SGX-BigMatrix: A Practical Encrypted Data Analytic Framework With Trusted Processors",
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"name": "Heehoon Kim"
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"name": "Hyoungwook Nam"
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"name": "Wookeun Jung"
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"doi": "10.1109/ISPASS.2017.7975270",
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"paperAbstract": "Thanks to modern deep learning frameworks that exploit GPUs, convolutional neural networks (CNNs) have been greatly successful in visual recognition tasks. In this paper, we analyze the GPU performance characteristics of five popular deep learning frameworks: Caffe, CNTK, TensorFlow, Theano, and Torch in the perspective of a representative CNN model, AlexNet. Based on the characteristics obtained, we suggest possible optimization methods to increase the efficiency of CNN models built by the frameworks. We also show the GPU performance characteristics of different convolution algorithms each of which uses one of GEMM, direct convolution, FFT, and the Winograd method. We also suggest criteria to choose convolution algorithms for GPUs and methods to build efficient CNN models on GPUs. Since scaling DNNs in a multi-GPU context becomes increasingly important, we also analyze the scalability of the CNN models built by the deep learning frameworks in the multi-GPU context and their overhead. The result indicates that we can increase the speed of training the AlexNet model up to 2X by just changing options provided by the frameworks.",
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"title": "Performance analysis of CNN frameworks for GPUs",
"venue": "2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)",
"year": 2017
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"paperAbstract": "How do we create content that will become viral in a whole network after we share it with friends or followers' Significant research activity has been dedicated to the problem of strategically selecting a <i>seed set</i> of initial adopters so as to maximize a meme's spread in a network. This line of work assumes that the success of such a campaign depends solely on the choice of a <i>tunable</i> seed set of adopters, while the way users perceive the propagated meme is <i>fixed</i>. Yet, in many real-world settings, the opposite holds: a meme's propagation depends on users' perceptions of its <i>tunable</i> characteristics, while the set of initiators is <i>fixed</i>.\n In this paper, we address the natural problem that arises in such circumstances: Suggest content, expressed as a limited set of <i>attributes</i>, for a creative promotion campaign that starts out from a given seed set of initiators, so as to maximize its expected spread over a social network. To our knowledge, no previous work addresses this problem. We find that the problem is NP-hard and inapproximable. As a tight approximation guarantee is not admissible, we design an efficient heuristic, Explore-Update, as well as a conventional Greedy solution. Our experimental evaluation demonstrates that Explore-Update selects near-optimal attribute sets with real data, achieves 30% higher spread than baselines, and runs an order of magnitude faster than the Greedy solution.",
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"title": "Content Recommendation for Viral Social Influence",
"venue": "SIGIR",
"year": 2017
},
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"name": "Kevin T. Pedretti"
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{
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"paperAbstract": "Certification of keys and attributes is in practice typically realized by a hierarchy of issuers. Revealing the full chain of issuers for certificate verification, however, can be a privacy issue since it can leak sensitive information about the issuer's organizational structure or about the certificate owner. Delegatable anonymous credentials solve this problem and allow one to hide the full delegation (issuance) chain, providing privacy during both delegation and presentation of certificates. However, the existing delegatable credentials schemes are not efficient enough for practical use.\n In this paper, we present the first hierarchical (or delegatable) anonymous credential system that is practical. To this end, we provide a surprisingly simple ideal functionality for delegatable credentials and present a generic construction that we prove secure in the UC model. We then give a concrete instantiation using a recent pairing-based signature scheme by Groth and describe a number of optimizations and efficiency improvements that can be made when implementing our concrete scheme. The latter might be of independent interest for other pairing-based schemes as well. Finally, we report on an implementation of our scheme in the context of transaction authentication for blockchain, and provide concrete performance figures.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3133956.3134025"
],
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"title": "Practical UC-Secure Delegatable Credentials with Attributes and Their Application to Blockchain",
"venue": "CCS",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Matthew J. Luckie"
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"name": "Robert Beverly"
}
],
"doi": "10.1145/3098822.3098858",
"doiUrl": "https://doi.org/10.1145/3098822.3098858",
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"Internet backbone",
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"paperAbstract": "We propose and evaluate a new metric for understanding the dependence of the AS-level Internet on individual routers. Whereas prior work uses large volumes of reachability probes to infer outages, we design an efficient active probing technique that directly and unambiguously reveals router restarts. We use our technique to survey 149,560 routers across the Internet for 2.5 years. 59,175 of the surveyed routers (40%) experience at least one reboot, and we quantify the resulting impact of each router outage on global IPv4 and IPv6 BGP reachability.\n Our technique complements existing data and control plane outage analysis methods by providing a causal link from BGP reachability failures to the responsible router(s) and multi-homing configurations. While we found the Internet core to be largely robust, we identified specific routers that were single points of failure for the prefixes they advertised. In total, 2,385 routers -- 4.0% of the routers that restarted over the course of 2.5 years of probing -- were single points of failure for 3,396 IPv6 prefixes announced by 1,708 ASes. We inferred 59% of these routers were the customer-edge border router. 2,374 (70%) of the withdrawn prefixes were not covered by a less specific prefix, so 1,726 routers (2.9%) of those that restarted were single points of failure for at least one network. However, a covering route did not imply reachability during a router outage, as no previously-responsive address in a withdrawn more specific prefix responded during a one-week sample. We validate our reboot and single point of failure inference techniques with four networks, finding no false positive or false negative reboots, but find some false negatives in our single point of failure inferences.",
"pdfUrls": [
"http://www.caida.org/publications/papers/2017/impact_router_outages_as/impact_router_outages_as.pdf",
"http://doi.acm.org/10.1145/3098822.3098858",
"http://www.caida.org/~mjl/pubs/spf.pdf"
],
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"title": "The Impact of Router Outages on the AS-level Internet",
"venue": "SIGCOMM",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Shaileshh Bojja Venkatakrishnan"
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"name": "Giulia C. Fanti"
},
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"name": "Pramod Viswanath"
}
],
"doi": "10.1145/3084459",
"doiUrl": "https://doi.org/10.1145/3084459",
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"Communications protocol",
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"De-anonymization",
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"paperAbstract": "Bitcoin and other cryptocurrencies have surged in popularity over the last decade. Although Bitcoin does not claim to provide anonymity for its users, it enjoys a public perception of being a privacy preserving financial system. In reality, cryptocurrencies publish users' entire transaction histories in plaintext, albeit under a pseudonym; this is required for transaction validation. Therefore, if a user's pseudonym can be linked to their human identity, the privacy fallout can be significant. Recently, researchers have demonstrated deanonymization attacks that exploit weaknesses in the Bitcoin network's peer-to-peer (P2P) networking protocols. In particular, the P2P network currently forwards content in a structured way that allows observers to deanonymize users. In this work, we redesign the P2P network from first principles with the goal of providing strong, provable anonymity guarantees. We propose a simple networking policy called Dandelion which provides quasi-optimal, network-wide anonymity, with minimal cost to the network's utility. We also discuss practical implementation challenges and propose heuristic solutions.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3084459",
"http://diyhpl.us/~bryan/papers2/bitcoin/Dandelion:%20Redesigning%20the%20bitcoin%20network%20for%20anonymity%20-%202017.pdf",
"https://arxiv.org/pdf/1701.04439v1.pdf",
"http://doi.acm.org/10.1145/3078505.3078528",
"http://arxiv.org/abs/1701.04439"
],
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"s2Url": "https://semanticscholar.org/paper/19cf8aa19cc6ce4b13689275243c3e6d0da08cd0",
"sources": [
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"title": "Dandelion: Redesigning the Bitcoin Network for Anonymity",
"venue": "SIGMETRICS",
"year": 2017
},
"19d4a2340a887d61dfbf446a693af660a5432dcd": {
"authors": [
{
"ids": [
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],
"name": "Konstantinos Xirogiannopoulos"
},
{
"ids": [
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],
"name": "Amol Deshpande"
}
],
"doi": "10.1145/3035918.3035949",
"doiUrl": "https://doi.org/10.1145/3035918.3035949",
"entities": [
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"Data deduplication",
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"Interconnection",
"List of algorithms",
"Memory footprint",
"Relational database",
"Synthetic data"
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"paperAbstract": "Analyzing interconnection structures among underlying entities or objects in a dataset through the use of graph analytics can provide tremendous value in many application domains. However, graphs are not the primary representation choice for storing most data today, and in order to have access to these analyses, users are forced to <i>manually extract</i> data from their data stores, <i>construct</i> the requisite graphs, and then <i>load</i> them into some graph engine in order to execute their graph analysis task. Moreover, in many cases (especially when the graphs are dense), these graphs can be <i>significantly larger</i> than the initial input stored in the database, making it infeasible to construct or analyze such graphs in memory. In this paper we address both of these challenges by building a system that enables users to <i>declaratively</i> specify graph extraction tasks over a relational database schema and then execute graph algorithms on the extracted graphs. We propose a declarative domain specific language for this purpose, and pair it up with a novel <i>condensed</i>, in-memory representation that significantly reduces the memory footprint of these graphs, permitting analysis of larger-than-memory graphs. We present a general algorithm for creating such a condensed representation for a large class of graph extraction queries against arbitrary schemas. We observe that the condensed representation suffers from a duplication issue, that results in inaccuracies for most graph algorithms. We then present a suite of in-memory representations that handle this <i>duplication</i> in different ways and allow <i>trading off</i> the memory required and the computational cost for executing different graph algorithms. We also introduce several novel <i>deduplication</i> algorithms for removing this duplication in the graph, which are of independent interest for graph compression, and provide a comprehensive experimental evaluation over several real-world and synthetic datasets illustrating these trade-offs.",
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"https://arxiv.org/pdf/1701.07388.pdf",
"https://arxiv.org/pdf/1701.07388v2.pdf",
"https://webcourse.cs.technion.ac.il/236826/Winter2017-2018/ho/WCFiles/Extracting%20and%20Analyzing%20Hidden%20Graphs%20from%20Relational%20Databases%20(2).pdf?7277=",
"http://cs.umd.edu/~kostasx/files/SIGMOD_Poster_final.pdf",
"https://arxiv.org/pdf/1701.07388v1.pdf",
"http://www.cs.umd.edu/sites/default/files/scholarly_papers/Xirogiannopoulos.pdf",
"http://arxiv.org/abs/1701.07388",
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"title": "Extracting and Analyzing Hidden Graphs from Relational Databases",
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"paperAbstract": "With the rapid development of location-based social networks, Point-of-Interest (POI) recommendation has played an important role in helping people discover attractive locations. However, existing POI recommendation methods assume a flat structure of POIs, which are better described in a hierarchical structure in reality. Furthermore, we discover that both users' content and spatial preferences exhibit hierarchical structures. To this end, in this paper, we propose a hierarchical geographical matrix factorization model (HGMF) to utilize the hierarchical structures of both users and POIs for POI recommendation. Specifically, we first describe the POI influence degrees over regions with two-dimensional normal distribution, and learn the influence areas of different layers of POIs as the input of HGMF. Then, we perform matrix factorization on user content preference matrix, user spatial preference matrix, and POIs characteristic matrix jointly with the modeling of implicit hierarchical structures. Moreover, a two-step optimization method is proposed to learn the implicit hierarchical structure and find the solution of HGMF efficiently. Finally, we evaluate HGMF on two large-scale real-world location-based social networks datasets. Our experimental results demonstrate that it outperforms the state-of-the-art methods in terms of precision and recall.",
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"title": "Exploiting Hierarchical Structures for POI Recommendation",
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"doi": "10.1109/CLUSTER.2017.25",
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"paperAbstract": "HPC systems have shifted to burst buffer storage and high radix interconnect topologies in order to meet the challenges of large-scale, data-intensive scientific computing. Both of these technologies have been studied in detail independently, but the interaction between them is not well understood. I/O traffic and communication traffic from concurrently scheduled applications may interfere with each other in unexpected ways, and this behavior may vary considerably depending on resource allocation, scheduling, and routing policies.In this work, we analyze I/O and network traffic interference on burst-buffer-equipped dragonfly-based systems using the high-resolution packet-level simulations provided by the CODES storage and interconnect simulation framework. The analysis is performed using realistic I/O workload sizes, a variety of resource allocation and network routing strategies employed in production environments, and a dragonfly network configuration modeled after current vendor options. We analyze the impact of interference on both I/O and communication traffic.We observe that although average network packet latency is stable across a wide variety of configurations, the maximum network packet latency in the presence of concurrent I/O traffic is highly sensitive to subtle policy changes. Our simulations reveal a worst-case single packet latency of 4,700 times the average latency for sub-optimal configurations. While a topology-aware mapping of compute nodes to burst buffer storage nodes can minimize the variation in maximum packet latency, it can slow down the I/O traffic by creating contention on the burst buffer nodes. Overall, balancing I/O and network performance requires careful selection of routing, data placement, and job placement policies.",
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"title": "Quantifying I/O and Communication Traffic Interference on Dragonfly Networks Equipped with Burst Buffers",
"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
"year": 2017
},
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"authors": [
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"name": "La\u00e9rcio Lima Pilla"
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"doi": "10.1109/HPCA.2017.41",
"doiUrl": "https://doi.org/10.1109/HPCA.2017.41",
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"journalName": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
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"paperAbstract": "In this paper, we evaluate the error criticality of radiation-induced errors on modern High-Performance Computing~(HPC) accelerators (Intel Xeon Phi and NVIDIA K40) through a dedicated set of metrics. We show that, as long as imprecise computing is concerned, the simple mismatch detection is not sufficient to evaluate and compare the radiation sensitivity of HPC devices and algorithms. Our analysis quantifies and qualifies radiation effects on applications' output correlating the number of corrupted elements with their spatial locality. Also, we provide the mean relative error (dataset-wise) to evaluate radiation-induced error magnitude. We apply the selected metrics to experimental results obtained in various radiation test campaigns for a total of more than 400 hours of beam time per device. The amount of data we gathered allows us to evaluate the error criticality of a representative set of algorithms from HPC suites. Additionally, based on the characteristics of the tested algorithms, we draw generic reliability conclusions for broader classes of codes. We show that arithmetic operations are less critical for the K40, while Xeon Phi is more reliable when executing particles interactions solved through Finite Difference Methods. Finally, iterative stencil operations seem the most reliable on both architectures.",
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"title": "Radiation-Induced Error Criticality in Modern HPC Parallel Accelerators",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
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"name": "Arun Thekumparampil"
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"name": "Dongyoon Lee"
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"doi": "10.1145/3064176.3064188",
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"paperAbstract": "The importance of the Event-Driven Architecture (EDA) has never been greater. Web servers and the IoT alike have begun to adopt the EDA, and the popular server-side EDA framework, Node.js, boasts the world's largest package ecosystem. While multi-threaded programming has been well studied in the literature, concurrency bug characteristics and useful development tools remain largely unexplored for server-side EDA-based applications.\n We present the first (to the best of our knowledge) concurrency bug characteristic study of real world open-source event-driven applications, based in Node.js. Like multithreaded programs, event-driven programs are prone to concurrency bugs like atomicity violations and order violations. Our study shows the forms that atomicity violations and ordering violations take in the EDA context, and points out the limitations of existing concurrency error detection tools developed for client-side EDA applications.\n Based on our bug study, we propose Node.fz, a novel testing aid for server-side event-driven programs. Node.fz is a schedule fuzzing test tool for event-driven programs, embodied for server-side Node.js programs. Node.fz randomly perturbs the execution of a Node.js program, allowing Node.js developers to explore a variety of possible schedules. Thanks to its low overhead, Node.fz enables a developer to explore a broader \"schedule space\" with the same test time budget, ensuring that applications will be stable in a wide variety of deployment conditions. We show that Node.fz can expose known bugs much more frequently than vanilla Node.js, and that it can uncover new bugs.",
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"http://doi.acm.org/10.1145/3064176.3064188",
"http://people.cs.vt.edu/~dongyoon/papers/EUROSYS-17-NodeFz.pdf"
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"title": "Node.fz: Fuzzing the Server-Side Event-Driven Architecture",
"venue": "EuroSys",
"year": 2017
},
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"name": "Eyal Ronen"
},
{
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"name": "Adi Shamir"
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{
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"name": "Achi-Or Weingarten"
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"name": "Colin O'Flynn"
}
],
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"doiUrl": "https://doi.org/10.1109/MSP.2018.1331033",
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"paperAbstract": "In this article, we describe a new type of attack on IoT devices, which exploits their ad hoc networking capabilities via the Zigbee wireless protocol, and thus cannot be monitored or stopped by standard Internet-based protective mechanisms. We developed and verified the attack using the Philips Hue smart lamps as a platform, by exploiting a major bug in the implementation of the Zigbee Light Link protocol, and a weakness in the firmware update process. By plugging in a single infected lamp anywhere in the city, an attacker can create a chain reaction in which a worm can jump from any lamp to all its physical neighbors, and thus stealthily infect the whole city if the density of smart lamps in it is high enough. This makes it possible to turn all the city’s smart lights on or off, to brick them, or to use them to disrupt nearby Wi-Fi transmissions.",
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"http://eprint.iacr.org/2016/1047.pdf"
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"title": "IoT Goes Nuclear: Creating a Zigbee Chain Reaction",
"venue": "IEEE Security & Privacy",
"year": 2016
},
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"authors": [
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"name": "Ashlee Edwards"
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"name": "Diane Kelly"
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],
"doi": "10.1145/3077136.3080818",
"doiUrl": "https://doi.org/10.1145/3077136.3080818",
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"paperAbstract": "One of the primary ways researchers have characterized engagement during information search is by increases in search behaviors, such as queries and clicks. However, studies have shown that frustration is also characterized by increases in these same behaviors. This research examines the differences in the search behaviors and physiologies of people who are engaged or frustrated during search. A 2x2 within-subject laboratory experiment was conducted with 40 participants. Engagement was induced by manipulating task interest and frustration was induced by manipulating the quality of the search results. Participants' interactions and physiological responses were recorded, and after they searched, they evaluated their levels of engagement, frustration and stress. Participants reported significantly greater levels of engagement when completing tasks that interested them and significantly less engagement during searches with poor results quality. For all search behaviors measured, only two significant differences were found according to task interest: participants had more scrolls and longer query intervals when searching for interesting tasks, suggesting greater interaction with content. Significant differences were found for nine behaviors according to results quality, including queries issued, number of SERPs displayed and number of SERP clicks, suggesting these are potentially better indicators of frustration rather than engagement. When presented with poor quality results, participants had significantly higher heart rates than when presented with normal quality results. Finally, participants had lower heart rates and greater skin conductance responses when conducting interesting tasks than when conducting uninteresting tasks. This research provides insight into the differences in search behaviors and physiologies of participants when they are engaged versus frustrated and presents techniques that can be used by those wishing to induce engagement and frustration during laboratory IIR studies.",
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"title": "Engaged or Frustrated?: Disambiguating Emotional State in Search",
"venue": "SIGIR",
"year": 2017
},
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"authors": [
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"name": "Theofilos Petsios"
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"name": "Jason Zhao"
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"name": "Angelos D. Keromytis"
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{
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"name": "Suman Jana"
}
],
"doi": "10.1145/3133956.3134073",
"doiUrl": "https://doi.org/10.1145/3133956.3134073",
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"paperAbstract": "Algorithmic complexity vulnerabilities occur when the worst-case time/space complexity of an application is significantly higher than the respective average case for particular user-controlled inputs. When such conditions are met, an attacker can launch Denial-of-Service attacks against a vulnerable application by providing inputs that trigger the worst-case behavior. Such attacks have been known to have serious effects on production systems, take down entire websites, or lead to bypasses of Web Application Firewalls.\n Unfortunately, existing detection mechanisms for algorithmic complexity vulnerabilities are domain-specific and often require significant manual effort. In this paper, we design, implement, and evaluate SlowFuzz, a domain-independent framework for automatically finding algorithmic complexity vulnerabilities. SlowFuzz automatically finds inputs that trigger worst-case algorithmic behavior in the tested binary. SlowFuzz uses resource-usage-guided evolutionary search techniques to automatically find inputs that maximize computational resource utilization for a given application.\n We demonstrate that SlowFuzz successfully generates inputs that match the theoretical worst-case performance for several well-known algorithms. SlowFuzz was also able to generate a large number of inputs that trigger different algorithmic complexity vulnerabilities in real-world applications, including various zip parsers used in antivirus software, regular expression libraries used in Web Application Firewalls, as well as hash table implementations used in Web applications. In particular, SlowFuzz generated inputs that achieve 300-times slowdown in the decompression routine of the bzip utility, discovered regular expressions that exhibit matching times exponential in the input size, and also managed to automatically produce inputs that trigger a high number of collisions in PHP's default hashtable implementation.",
"pdfUrls": [
"http://www1.cs.columbia.edu/~angelos/Papers/2017/ccs2017.pdf",
"http://arxiv.org/abs/1708.08437",
"https://arxiv.org/pdf/1708.08437v1.pdf",
"http://doi.acm.org/10.1145/3133956.3134073",
"http://www.cs.columbia.edu/~theofilos/files/slides/slowfuzz_ccs.pdf"
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"title": "SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities",
"venue": "CCS",
"year": 2017
},
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"authors": [
{
"ids": [
"1696818"
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"name": "Venkatesan T. Chakaravarthy"
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{
"ids": [
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"name": "Jee W. Choi"
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{
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"name": "Douglas J. Joseph"
},
{
"ids": [
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],
"name": "Xing Liu"
},
{
"ids": [
"39229329"
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"name": "Prakash Murali"
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{
"ids": [
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"name": "Yogish Sabharwal"
},
{
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"name": "Dheeraj Sreedhar"
}
],
"doi": "10.1109/IPDPS.2017.86",
"doiUrl": "https://doi.org/10.1109/IPDPS.2017.86",
"entities": [
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"journalName": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"journalPages": "1038-1047",
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"paperAbstract": "The Tucker decomposition expresses a given tensor as the product of a small core tensor and a set of factor matrices. Our objective is to develop an efficient distributed implementation for the case of dense tensors. The implementation is based on the HOOI (Higher Order Orthogonal Iterator) procedure, wherein the tensor-times-matrix product forms the core routine. Prior work have proposed heuristics for reducing the computational load and communication volume incurred by the routine. We study the two metrics in a formal and systematic manner, and design strategies that are optimal under the two fundamental metrics. Our experimental evaluation on a large benchmark of tensors shows that the optimal strategies provide significant reduction in load and volume compared to prior heuristics, and provide up to 7x speed-up in the overall running time.",
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"title": "On Optimizing Distributed Tucker Decomposition for Dense Tensors",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
"1ac7667a185cdcbda194e9852c27dcc169fbbabf": {
"authors": [
{
"ids": [
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"name": "Edward Bortnikov"
},
{
"ids": [
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"name": "Eshcar Hillel"
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{
"ids": [
"1777373"
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"name": "Idit Keidar"
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{
"ids": [
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"name": "Ivan Kelly"
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{
"ids": [
"39388165"
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"name": "Matthieu Morel"
},
{
"ids": [
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"name": "Sameer Paranjpye"
},
{
"ids": [
"2196178"
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"name": "Francisco Perez-Sorrosal"
},
{
"ids": [
"40076877"
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"name": "Ohad Shacham"
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],
"doi": "",
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"paperAbstract": "We present Omid \u2013 a transaction processing service that powers web-scale production systems at Yahoo. Omid provides ACID transaction semantics on top of traditional key-value storage; its implementation over Apache HBase is open sourced as part of Apache Incubator. Omid can serve hundreds of thousands of transactions per second on standard mid-range hardware, while incurring minimal impact on the speed of data access in the underlying key-value store. Additionally, as expected from always-on production services, Omid is highly available.",
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"paperAbstract": "Modern data analytical tasks often witness very wide tables, from a few hundred columns to a few thousand. While it is commonly agreed that column stores are an appropriate data format for wide tables and analytical workloads, the physical <i>order of columns</i> has not been investigated. Column ordering plays a critical role in I/O performance, because in wide tables accessing the columns in a single horizontal partition may involve multiple disk seeks. An optimal column ordering will incur minimal cumulative disk seek costs for the set of queries applied to the data. In this paper, we aim to find such an optimal column layout to maximize I/O performance. Specifically, we study two problems for column stores on HDFS: column ordering and column duplication. Column ordering seeks an approximately optimal order of columns; column duplication complements column ordering in that some columns may be duplicated multiple times to reduce contention among the queries' diverse requirements on the column order. We consider an actual fine-grained cost model for column accesses and propose algorithms that take a query workload as input and output a column ordering strategy with or without storage redundancy that significantly improves the overall I/O performance. Experimental results over real-life data and production query workloads confirm the effectiveness of the proposed algorithms in diverse settings.",
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"title": "Wide Table Layout Optimization based on Column Ordering and Duplication",
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"year": 2017
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"paperAbstract": "Programmers in dynamic languages wishing to constrain and understand the behavior of their programs may turn to gradually-typed languages, which allow types to be specified optionally and check values at the boundary between dynamic and static code. Unfortunately, the performance cost of these run-time checks can be severe, slowing down execution by at least 10x when checks are present. Modern virtual machines (VMs) for dynamic languages use speculative techniques to improve performance: If a particular value was seen once, it is likely that similar values will be seen in the future. They combine optimization-relevant properties of values into cacheable “shapes”, then use a single shape check to subsume checks for each property. Values with the same memory layout or the same field types have the same shape. This greatly reduces the amount of type checking that needs to be performed at run-time to execute dynamic code. While very valuable to the VM’s optimization, these checks do little to benefit the programmer aside from improving performance. We present in this paper a design for intrinsic object contracts, which makes the obligations of gradually-typed languages’ type checks an intrinsic part of object shapes, and thus can subsume run-time type checks into existing shape checks, eliminating redundant checks entirely. With an implementation on a VM for JavaScript used as a target for SafeTypeScript’s soundness guarantees, we demonstrate slowdown averaging 7% in fully-typed code relative to unchecked code, and no more than 45% in pessimal configurations.",
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"title": "The VM already knew that: leveraging compile-time knowledge to optimize gradual typing",
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"paperAbstract": "Recently, there is an increasing interest in building data-center applications with RDMA because of its low-latency, high-throughput, and low-CPU-utilization benefits. However, RDMA is not readily suitable for datacenter applications. It lacks a flexible, high-level abstraction; its performance does not scale; and it does not provide resource sharing or flexible protection. Because of these issues, it is difficult to build RDMA-based applications and to exploit RDMA's performance benefits.\n To solve these issues, we built LITE, a Local Indirection TiEr for RDMA in the Linux kernel that virtualizes native RDMA into a flexible, high-level, easy-to-use abstraction and allows applications to safely share resources. Despite the widely-held belief that kernel bypassing is essential to RDMA's low-latency performance, we show that using a kernel-level indirection can achieve both flexibility and low-latency, scalable performance at the same time. To demonstrate the benefits of LITE, we developed several popular datacenter applications on LITE, including a graph engine, a MapReduce system, a Distributed Shared Memory system, and a distributed atomic logging system. These systems are easy to build and deliver good performance. For example, our implementation of PowerGraph uses only 20 lines of LITE code, while outperforming PowerGraph by 3.5x to 5.6x.",
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"title": "LITE Kernel RDMA Support for Datacenter Applications",
"venue": "SOSP",
"year": 2017
},
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"name": "Xiangru Huang"
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"name": "Wei Dai"
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"name": "Inderjit S. Dhillon"
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"name": "Eric P. Xing"
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"paperAbstract": "Extreme Classification comprises multi-class or multi-label prediction where there is a large number of classes, and is increasingly relevant to many real-world applications such as text and image tagging. In this setting, standard classification methods, with complexity linear in the number of classes, become intractable, while enforcing structural constraints among classes (such as low-rank or tree-structure) to reduce complexity often sacrifices accuracy for efficiency. The recent <i>PD-Sparse</i> method addresses this via an algorithm that is sub-linear in the number of variables, by exploiting <i>primal-dual</i> sparsity inherent in a specific loss function, namely the max-margin loss. In this work, we extend PD-Sparse to be efficiently parallelized in large-scale distributed settings. By introducing separable loss functions, we can scale out the training, with network communication and space efficiency comparable to those in one-versus-all approaches while maintaining an overall complexity sub-linear in the number of classes. On several large-scale benchmarks our proposed method achieves accuracy competitive to the state-of-the-art while reducing the training time from days to tens of minutes compared with existing parallel or sparse methods on a cluster of 100 cores.",
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"http://www.cs.cmu.edu/~eyan/publication/PPDSparse_Poster_KDD.pdf",
"http://www.cs.utexas.edu/~inderjit/public_papers/ppdsparse_kdd17.pdf",
"https://www.cs.cmu.edu/~eyan/publication/ParallelPDSparse.pdf",
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"title": "PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification",
"venue": "KDD",
"year": 2017
},
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"name": "Xiufeng Xie"
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{
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"name": "Xinyu Zhang"
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"doi": "10.1145/3143361.3143381",
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"paperAbstract": "Panoramic or 360° video streaming has been supported by a wide range of content providers and mobile devices. Yet existing work primarily focused on streaming on-demand 360° videos stored on servers. In this paper, we examine a more challenging problem: Can we stream real-time interactive 360° videos across existing LTE cellular networks, so as to trigger new applications such as ubiquitous 360° video chat and panoramic outdoor experience sharing? To explore the feasibility and challenges underlying this vision, we design POI360, a portable interactive 360° video telephony system that jointly investigates both panoramic video compression and responsive video stream rate control. For the challenge that the legacy spatial compression algorithms for 360° video suffer from severe quality fluctuations as the user changes her region-of-interest (ROI), we design an adaptive compression scheme, which dynamically adjusts the compression strategy to stabilize the video quality within ROI under various user input and network condition. In addition, to meet the responsiveness requirement of panoramic video telephony, we leverage the diagnostic statistics on commodity phones to promptly detect cellular link congestion, hence significantly boosting the rate control responsiveness. Extensive field tests for our real-time POI360 prototype validate its effectiveness in enabling panoramic video telephony over the highly dynamic cellular networks.",
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"http://xyzhang.ucsd.edu/papers/XXie_CoNEXT17_POI360.pdf"
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"title": "POI360: Panoramic Mobile Video Telephony over LTE Cellular Networks",
"venue": "CoNEXT",
"year": 2017
},
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"paperAbstract": "We study distributed machine learning in <i>heterogeneous environments</i> in this work. We first conduct a systematic study of existing systems running distributed stochastic gradient descent; we find that, although these systems work well in homogeneous environments, they can suffer performance degradation, sometimes up to 10x, in heterogeneous environments where stragglers are common because their synchronization protocols cannot fit a heterogeneous setting. Our first contribution is a heterogeneity-aware algorithm that uses a constant learning rate schedule for updates before adding them to the global parameter. This allows us to suppress stragglers' harm on robust convergence. As a further improvement, our second contribution is a more sophisticated learning rate schedule that takes into consideration the delayed information of each update. We theoretically prove the valid convergence of both approaches and implement a prototype system in the production cluster of our industrial partner Tencent Inc. We validate the performance of this prototype using a range of machine-learning workloads. Our prototype is 2-12x faster than other state-of-the-art systems, such as Spark, Petuum, and TensorFlow; and our proposed algorithm takes up to 6x fewer iterations to converge.",
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"paperAbstract": "Performance isolation is an important goal in server-class environments. Partitioning the last-level cache of a chip multiprocessor (CMP) across co-running applications has proven useful in this regard. Two popular approaches are (a) hardware support for way partitioning, or (b) operating system support for set partitioning through page coloring. Unfortunately, neither approach by itself is scalable beyond a handful of cores without incurring in significant performance overheads. We propose SWAP, a scalable and fine-grained cache management technique that seamlessly combines set and way partitioning. By cooperatively managing cache ways and sets, SWAP ("Set and WAy Partitioning") can successfully provide hundreds of fine-grained cache partitions for the manycore era.SWAP requires no additional hardware beyond way partitioning. In fact, SWAP can be readily implemented in existing commercial servers whose processors do support hardware way partitioning. In this paper, we prototype SWAP on a 48-core Cavium ThunderX platform running Linux, and we show average speedups over no cache partitioning that are twice as large as those attained with ThunderX's hardware way partitioning alone.",
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"paperAbstract": "Mobile apps have to satisfy various privacy requirements. App publishers are often obligated to provide a privacy policy and notify users of their apps\u2019 privacy practices. But how can we tell whether an app behaves as its policy promises? In this study we introduce a scalable system to help analyze and predict Android apps\u2019 compliance with privacy requirements. Our system is not only intended for regulators and privacy activists, but also meant to assist app publishers and app store owners in their internal assessments of privacy requirement compliance. Our analysis of 17,991 free apps shows the viability of combining machine learning-based privacy policy analysis with static code analysis of apps. Results suggest that 71% of apps that lack a privacy policy should have one. Also, for 9,050 apps that have a policy, we find many instances of potential inconsistencies between what the app policy seems to state and what the code of the app appears to do. Our results suggest that as many as 41% of these apps could be collecting location information and 17% could be sharing such with third parties without disclosing so in their policies. Overall, it appears that each app exhibits a mean of 1.83 inconsistencies.",
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"paperAbstract": "Text passwords---a frequent vector for account compromise, yet still ubiquitous---have been studied for decades by researchers attempting to determine how to coerce users to create passwords that are hard for attackers to guess but still easy for users to type and memorize. Most studies examine one password or a small number of passwords per user, and studies often rely on passwords created solely for the purpose of the study or on passwords protecting low-value accounts. These limitations severely constrain our understanding of password security in practice, including the extent and nature of password reuse, password behaviors specific to categories of accounts (e.g., financial websites), and the effect of password managers and other privacy tools.\n In this paper we report on an <i>in situ</i> study of 154 participants over an average of 147 days each. Participants' computers were instrumented---with careful attention to privacy---to record detailed information about password characteristics and usage, as well as man other computing behaviors such as use of security and privacy web browser extensions. This data allows a more accurate analysis of password characteristics and behaviors across the full range of participants' web-based accounts. Examples of our findings are that the use of symbols and digits in passwords predicts increased likelihood of reuse, while increased password strength predicts decreased likelihood of reuse; that password reuse is more prevalent than previously believed, especially when partial reuse is taken into account; and that password managers may have no impact on password reuse or strength. We also observe that users can be grouped into a handful of behavioral clusters, representative of various password management strategies. Our findings suggest that once a user needs to manage a larger number of passwords, they cope by partially and exactly reusing passwords across most of their accounts.",
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"http://doi.acm.org/10.1145/3133956.3133973",
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"title": "Let's Go in for a Closer Look: Observing Passwords in Their Natural Habitat",
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"doi": "10.1109/HPCC-SmartCity-DSS.2017.52",
"doiUrl": "https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.52",
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"paperAbstract": "The Shopping Mall is in many countries a focus point for the lives of the people. It is the place where people go and shop, and in a certain sense it has become the modern equivalent of a square. Along the years, this attraction center has followed a definite evolutionary path, becoming wider, bigger, looking to maximize the comfort of the customers: a better place, for better sales. In this paper, we analyze the shopping mall under a different perspective, and check if this evolutionary line is in fact the best possible one, or if instead the mall could be shaped differently. We propose a structural modification of the mall (halving), and analyze its impact on the efficiency (the attractiveness of the shops). Counterintuitively, we find that this structural modification leads to a significant increase in the overall mall performance, a result that impacts the recent evolution of the mall, and that suggests that the mall as we know it might be far from being the best one, and some radical changes could be in order.",
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"title": "The Paradox of the Shopping Mall: Costumers Flows and Market Efficiency",
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"name": "Richard W. Vuduc"
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"name": "Michael Thompson"
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"name": "Jimeng Sun"
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"paperAbstract": "In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes. To handle such data, the state-of-the-art tensor model is the so-called PARAFAC2, which yields interpretable and robust output and can naturally handle sparse data. However, its main limitation up to now has been the lack of efficient algorithms that can handle large-scale datasets.\n In this work, we fill this gap by developing a scalable method to compute the PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method exploits special structure within PARAFAC2, leading to a novel algorithmic reformulation that is both faster (in absolute time) and more memory-efficient than prior work. We evaluate SPARTan on both synthetic and real datasets, showing 22X performance gains over the best previous implementation and also handling larger problem instances for which the baseline fails. Furthermore, we are able to apply SPARTan to the mining of temporally-evolving phenotypes on data taken from real and medically complex pediatric patients. The clinical meaningfulness of the phenotypes identified in this process, as well as their temporal evolution over time for several patients, have been endorsed by clinical experts.",
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"https://www.cc.gatech.edu/~iperros3/pdf/kdd17.pdf",
"http://arxiv.org/abs/1703.04219"
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"title": "SPARTan: Scalable PARAFAC2 for Large & Sparse Data",
"venue": "KDD",
"year": 2017
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"name": "Xiaosheng Li"
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"name": "Jessica Lin"
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"doiUrl": "https://doi.org/10.1109/ICDM.2017.37",
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"paperAbstract": "Time series classification has attracted much attention due to the ubiquity of time series. With the advance of technologies, the volume of available time series data becomes huge and the content is changing rapidly. This requires time series data mining methods to have low computational complexities. In this paper, we propose a parameter-free time series classification method that has a linear time complexity. The approach is evaluated on all the 85 datasets in the well-known UCR time series classification archive. The results show that the new method achieves better overall classification accuracy performance than the widely used benchmark, i.e. 1-nearest neighbor with dynamic time warping, while consuming orders of magnitude less running time. The proposed method is also applied on a large real-world bird sounds dataset to verify its effectiveness.",
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"title": "Linear Time Complexity Time Series Classification with Bag-of-Pattern-Features",
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"year": 2017
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"name": "Tadayoshi Kohno"
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"paperAbstract": "The Internet Archive's Wayback Machine is the largest modern web archive, preserving web content since 1996. We discover and analyze several vulnerabilities in how the Wayback Machine archives data, and then leverage these vulnerabilities to create what are to our knowledge the first attacks against a user's view of the archived web. Our vulnerabilities are enabled by the unique interaction between the Wayback Machine's archives, other websites, and a user's browser, and attackers do not need to compromise the archives in order to compromise users' views of a stored page. We demonstrate the effectiveness of our attacks through proof-of-concept implementations. Then, we conduct a measurement study to quantify the prevalence of vulnerabilities in the archive. Finally, we explore defenses which might be deployed by archives, website publishers, and the users of archives, and present the prototype of a defense for clients of the Wayback Machine, ArchiveWatcher.",
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"title": "Rewriting History: Changing the Archived Web from the Present",
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"authors": [
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"name": "Hyunwook Baek"
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"name": "Cheng Jin"
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"name": "Guofei Jiang"
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{
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"name": "Cristian Lumezanu"
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"name": "Jacobus E. van der Merwe"
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"name": "Ning Xia"
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{
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"name": "Qiang Xu"
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"doi": "10.1145/3127479.3129258",
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"paperAbstract": "Network usage accountability is critical in helping operators and customers of multi-tenant data centers deal with concerns such as capacity planning, resource allocation, hotspot detection, link failure detection, and troubleshooting. However, the cost of measurements and instrumentation to achieve flow-level accountability is non-trivial. We propose Polygravity to determine tenant traffic usage via lightweight measurements in multi-tenant data centers. We adopt a tomogravity model widely used in ISP networks, and adapt it to a multi-tenant data center environment. By integrating datacenter-specific domain knowledge, sampling-based partial estimation and gravity-based internal sinks/sources estimation, Polygravity addresses two key challenges for adapting tomogravity to a data center environment: <i>sparse traffic matrices</i> and <i>internal traffic sinks/sources.</i> We conducted extensive evaluation of our approach using realistic data center workloads. Our results show that Polygravity can determine tenant IP flow usage with less than 1% average relative error for tenants with fine-grained domain knowledge. In addition, for tenants with coarse-grained domain knowledge and with partial host-based sampling, Polygravity reduces the relative error of sampling-based estimation by 1/3.",
"pdfUrls": [
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"title": "Polygravity: traffic usage accountability via coarse-grained measurements in multi-tenant data centers",
"venue": "SoCC",
"year": 2017
},
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"name": "Houman Homayoun"
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"doi": "10.1109/IISWC.2017.8167751",
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"title": "The QUIC Transport Protocol: Design and Internet-Scale Deployment",
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"year": 2017
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"paperAbstract": "Driven by CA compromises and the risk of man-in-the-middle attacks, new security features have been added to TLS, HTTPS, and the web PKI over the past five years. These include Certificate Transparency (CT), for making the CA system auditable; HSTS and HPKP headers, to harden the HTTPS posture of a domain; the DNS-based extensions CAA and TLSA, for control over certificate issuance and pinning; and SCSV, for protocol downgrade protection.\n This paper presents the first large scale investigation of these improvements to the HTTPS ecosystem, explicitly accounting for their combined usage. In addition to collecting passive measurements at the Internet uplinks of large University networks on three continents, we perform the largest domain-based active Internet scan to date, covering 193M domains. Furthermore, we track the long-term deployment history of new TLS security features by leveraging passive observations dating back to 2012.\n We find that while deployment of new security features has picked up in general, only SCSV (49M domains) and CT (7M domains) have gained enough momentum to improve the overall security of HTTPS. Features with higher complexity, such as HPKP, are deployed scarcely and often incorrectly. Our empirical findings are placed in the context of risk, deployment effort, and benefit of these new technologies, and actionable steps for improvement are proposed. We cross-correlate use of features and find some techniques with significant correlation in deployment. We support reproducible research and publicly release data and code.",
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"http://www.icir.org/johanna/papers/imc17httpssecurity.pdf",
"https://conferences.sigcomm.org/imc/2017/slides/imc17_1.pdf",
"http://doi.acm.org/10.1145/3131365.3131401"
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"title": "Mission accomplished?: HTTPS security after diginotar",
"venue": "IMC",
"year": 2017
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"paperAbstract": "Processors and operating systems (OSes) support multiple memory page sizes. Superpages increase Translation Lookaside Buffer (TLB) hits, while small pages provide fine-grained memory protection. Ideally, TLBs should perform well for any distribution of page sizes. In reality, set-associative TLBs -- used frequently for their energy efficiency compared to fully-associative TLBs -- cannot (easily) support multiple page sizes concurrently. Instead, commercial systems typically implement separate set-associative TLBs for different page sizes. This means that when superpages are allocated aggressively, TLB misses may, counter intuitively, increase even if entries for small pages remain unused (and vice-versa). We invent MIX TLBs, energy-frugal set-associative structures that concurrently support all page sizes by exploiting superpage allocation patterns. MIX TLBs boost the performance (often by 10-30%) of big-memory applications on native CPUs, virtualized CPUs, and GPUs. MIX TLBs are simple and require no OS or program changes.",
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"paperAbstract": "Convolutional Neural Networks (CNN) are a class of Ar- tificial Neural Networks (ANN) that are highly efficient at the pattern recognition tasks that underlie difficult AI prob- lems in a variety of domains, such as speech recognition, object recognition, and natural language processing. CNNs are, however, computationally intensive to train. This paper presents the first characterization of the per- formance optimization opportunities for training CNNs on CPUs. Our characterization includes insights based on the structure of the network itself (i.e., intrinsic arithmetic inten- sity of the convolution and its scalability under parallelism) as well as dynamic properties of its execution (i.e., sparsity of the computation).\n Given this characterization, we present an automatic framework called spg-CNN for optimizing CNN training on CPUs. It comprises of a computation scheduler for efficient parallel execution, and two code generators: one that opti- mizes for sparsity, and the other that optimizes for spatial reuse in convolutions.\n We evaluate spg-CNN using convolutions from a variety of real world benchmarks, and show that spg-CNN can train CNNs faster than state-of-the-art approaches by an order of magnitude.",
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"title": "Optimizing CNNs on Multicores for Scalability, Performance and Goodput",
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"name": "Gunes Acar"
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"doi": "10.1145/3133956.3134005",
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"paperAbstract": "Recent studies have shown that Tor onion (hidden) service websites are particularly vulnerable to website fingerprinting attacks due to their limited number and sensitive nature. In this work we present a multi-level feature analysis of onion site fingerprintability, considering three state-of-the-art website fingerprinting methods and 482 Tor onion services, making this the largest analysis of this kind completed on onion services to date.\n Prior studies typically report average performance results for a given website fingerprinting method or countermeasure. We investigate which sites are more or less vulnerable to fingerprinting and which features make them so. We find that there is a high variability in the rate at which sites are classified (and misclassified) by these attacks, implying that average performance figures may not be informative of the risks that website fingerprinting attacks pose to particular sites.\n We analyze the features exploited by the different website fingerprinting methods and discuss what makes onion service sites more or less easily identifiable, both in terms of their traffic traces as well as their webpage design. We study misclassifications to understand how onion services sites can be redesigned to be less vulnerable to website fingerprinting attacks. Our results also inform the design of website fingerprinting countermeasures and their evaluation considering disparate impact across sites.",
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"https://users.cs.fiu.edu/~carbunar/teaching/cis5374/cis5374.2017/slides/onion.pdf",
"https://arxiv.org/pdf/1708.08475v2.pdf",
"http://arxiv.org/abs/1708.08475",
"https://arxiv.org/pdf/1708.08475v1.pdf"
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"title": "How Unique is Your .onion?: An Analysis of the Fingerprintability of Tor Onion Services",
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"year": 2017
},
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"paperAbstract": "Smart phones and tablets are rapidly becoming our main method of accessing information and are frequently used to perform on-the-go search tasks. Mobile devices are commonly used in situations where attention must be divided, such as when walking down a street. Research suggests that this increases cognitive load and, therefore, may have an impact on performance. In this work we conducted a laboratory experiment with both device types in which we simulated everyday, common mobile situations that may cause fragmented attention, impact search performance and affect user perception.\n Our results showed that the fragmented attention induced by the simulated conditions significantly affected both participants' objective and perceived search performance, as well as how hurried they felt and how engaged they were in the tasks. Furthermore, the type of device used also impacted how users felt about the search tasks, how well they performed and the amount of time they spent engaged in the tasks. These novel insights provide useful information to inform the design of future interfaces for mobile search and give us a greater understanding of how context and device size affect search behaviour and user experience.",
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"title": "Searching on the Go: The Effects of Fragmented Attention on Mobile Web Search Tasks",
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],
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"paperAbstract": "Systems for verifiable outsourcing incur costs for a <i>prover</i>, a <i>verifier</i>, and precomputation; outsourcing makes sense when the combination of these costs is cheaper than not outsourcing. Yet, when prior works impose quantitative thresholds to analyze whether outsourcing is justified, they generally ignore prover costs. Verifiable ASICs (VA)---in which the prover is a custom chip---is the other way around: its cost calculations ignore precomputation.\n This paper describes a new VA system, called <i>Giraffe</i>; charges Giraffe for all three costs; and identifies regimes where outsourcing is worthwhile. Giraffe's base is an interactive proof geared to data-parallel computation. Giraffe makes this protocol <i>asymptotically optimal</i> for the prover and improves the verifier's main bottleneck by almost 3x, both of which are of independent interest. Giraffe also develops a <i>design template</i> that produces hardware designs automatically for a wide range of parameters, introduces hardware primitives molded to the protocol's data flows, and incorporates program analyses that expand applicability. Giraffe wins even when outsourcing several tens of sub-computations, scales to 500x larger computations than prior work, and can profitably outsource parts of programs that are not worthwhile to outsource in full.",
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"https://www.pepper-project.org/giraffe-ccs17-talk.pdf",
"https://eprint.iacr.org/2017/242.pdf",
"http://www.cs.nyu.edu/~mwalfish/papers/giraffe-ccs17.pdf",
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"title": "Full Accounting for Verifiable Outsourcing",
"venue": "CCS",
"year": 2017
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"paperAbstract": "We present a scalable approach for establishing similarity between stripped binaries (with no debug information). The main challenge in binary similarity, is to establish similarity even when the code has been compiled using different compilers, with different optimization levels, or targeting different architectures. Overcoming this challenge, while avoiding false positives, is invaluable to the process of reverse engineering and the process of locating vulnerable code. \nWe present a technique that is scalable and precise, as it alleviates the need for heavyweight semantic comparison by performing <em>out-of-context re-optimization</em> of procedure fragments. It works by decomposing binary procedures to comparable fragments and transforming them to a <em>canonical, normalized form</em> using the compiler optimizer, which enables finding equivalent fragments through simple syntactic comparison. We use a statistical framework built by analyzing samples collected â\u0080\u009cin the wildâ\u0080\u009d to generate a global context that quantifies the significance of each pair of fragments, and uses it to lift pairwise fragment equivalence to whole procedure similarity. \nWe have implemented our technique in a tool called <pre>GitZ</pre> and performed an extensive evaluation. We show that <pre>GitZ</pre> is able to perform millions of comparisons efficiently, and find similarity with high accuracy.",
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"title": "Similarity of binaries through re-optimization",
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"paperAbstract": "The high accuracy of deep neural networks (NNs) has led to the development of NN accelerators that improve performance by two orders of magnitude. However, scaling these accelerators for higher performance with increasingly larger NNs exacerbates the cost and energy overheads of their memory systems, including the on-chip SRAM buffers and the off-chip DRAM channels.\n This paper presents the hardware architecture and software scheduling and partitioning techniques for TETRIS, a scalable NN accelerator using 3D memory. First, we show that the high throughput and low energy characteristics of 3D memory allow us to rebalance the NN accelerator design, using more area for processing elements and less area for SRAM buffers. Second, we move portions of the NN computations close to the DRAM banks to decrease bandwidth pressure and increase performance and energy efficiency. Third, we show that despite the use of small SRAM buffers, the presence of 3D memory simplifies dataflow scheduling for NN computations. We present an analytical scheduling scheme that matches the efficiency of schedules derived through exhaustive search. Finally, we develop a hybrid partitioning scheme that parallelizes the NN computations over multiple accelerators. Overall, we show that TETRIS improves mthe performance by 4.1x and reduces the energy by 1.5x over NN accelerators with conventional, low-power DRAM memory systems.",
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"http://csl.stanford.edu/~christos/publications/2017.tetris.asplos.pdf",
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"title": "TETRIS: Scalable and Efficient Neural Network Acceleration with 3D Memory",
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"paperAbstract": "TCP does not work well in modern cellular networks because the current congestion-window-based (cwnd-based) congestion control mechanism intimately couples congestion control and packet dispatch, which provides TCP with only indirect control of the effective data rate. The throughput degradation arising from the cwnd-based mechanism is especially serious when the uplink is congested. We describe PropRate, a new rate-based TCP algorithm that directly regulates the packets in the bottleneckbuffer to achieve a trade-off in terms of delay and throughput along a more efficient frontier than conventional cwnd-based TCP variants. To the best of our knowledge, PropRate is the first TCP algorithm that allows an application to set and achieve a target average latency, if the network conditions allow for it. Also, unlike the cwnd-based TCP mechanism, our new rate-based TCP mechanism is significantly more resilient to saturated uplinks in cellular networks. PropRate does not require modifications at the receiver and is amenable to practical deployment in the base stations and proxies in mobile cellular networks.",
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"paperAbstract": "Most high-performance dynamic language virtual machines duplicate language semantics in the interpreter, compiler, and runtime system. This violates the principle to not repeat yourself. In contrast, we define languages solely by writing an interpreter. The interpreter performs specializations, e.g., augments the interpreted program with type information and profiling information. Compiled code is derived automatically using partial evaluation while incorporating these specializations. This makes partial evaluation practical in the context of dynamic languages: It reduces the size of the compiled code while still compiling all parts of an operation that are relevant for a particular program. When a speculation fails, execution transfers back to the interpreter, the program re-specializes in the interpreter, and later partial evaluation again transforms the new state of the interpreter to compiled code. We evaluate our approach by comparing our implementations of JavaScript, Ruby, and R with best-in-class specialized production implementations. Our general-purpose compilation system is competitive with production systems even when they have been heavily optimized for the one language they support. For our set of benchmarks, our speedup relative to the V8 JavaScript VM is 0.83x, relative to JRuby is 3.8x, and relative to GNU R is 5x.",
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"title": "Practical partial evaluation for high-performance dynamic language runtimes",
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"paperAbstract": "The TLS protocol is intended to enable secure end-to-end communication over insecure networks, including the Internet. Unfortunately, this goal has been thwarted a number of times throughout the protocol's tumultuous lifetime, resulting in the need for a new version of the protocol, namely TLS 1.3. Over the past three years, in an unprecedented joint design effort with the academic community, the TLS Working Group has been working tirelessly to enhance the security of TLS.\n We further this effort by constructing the most comprehensive, faithful, and modular symbolic model of the TLS~1.3 draft 21 release candidate, and use the TAMARIN prover to verify the claimed TLS~1.3 security requirements, as laid out in draft 21 of the specification. In particular, our model covers <i>all</i> handshake modes of TLS 1.3.\n Our analysis reveals an unexpected behaviour, which we expect will inhibit strong authentication guarantees in some implementations of the protocol. In contrast to previous models, we provide a novel way of making the relation between the TLS specification and our model explicit: we provide a fully annotated version of the specification that clarifies what protocol elements we modelled, and precisely how we modelled these elements. We anticipate this model artifact to be of great benefit to the academic community and the TLS Working Group alike.",
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"title": "A Comprehensive Symbolic Analysis of TLS 1.3",
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"paperAbstract": "With the emergence of 3D TLC/QLC NAND flash, the capacity of flash-based SSD is growing rapidly, from hundreds of gigabytes to tens/hundreds of terabytes. Accordingly, the Flash Translation Layer (FTL) within such a large SSD is confronted with serious problems that have not ever appeared before. Traditional FTLs either adopt a coarse-grained mapping mechanism, thus facilitating the mapping table being kept in DRAM completely, or adopt a fine-grained mapping mechanism but only keep frequently-accessed mapping information in DRAM depending on the localities of workloads. We argue that both of the above policies are unsuitable for SSDs supplying ultra-large capacity. Firstly, large SSDs introduce so many more mapping entries than ever before that even the coarse-grained mapping mechanism cannot produce a compact enough mapping table to be kept in DRAM completely. Secondly, large SSDs tend to be deployed in data centers to serve IO requests from massive numbers of users under various application backgrounds, where these IO requests exhibit weaker spatial and temporal localities. As a result, the method that keeps frequently-accessed mapping information in DRAM is also impractical for large scale SSDs. In this paper, we propose a novel KV-FTL approach for large scale SSDs, which mostly maps logical addresses to physical addresses via a simple hash function while handling hash collision and out-of-place data update in the traditional manner, i.e., the mapping table. Our KV-FTL is able to accelerate address translation by avoiding loading the mapping table from flash memory to DRAM, thus improving performance, as well as to reduce the write-traffic introduced by the mapping table, thus extending the lifespan of SSDs. To the best of our knowledge, this is the first time the key-value principle has been applied to FTL design. Experimental results show that our KV-FTL extends SSD lifespan by a factor of up to 18.7% with an average of 13.6%; improves read performance ranging from 18.4% to 50.7% with an average of 39% with optimization, and in the case of extremely intensive requests, improves the overall performance for requests with an average of 47%.",
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"title": "KV-FTL: A Novel Key-Value-Based FTL Scheme for Large Scale SSDs",
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"paperAbstract": "Graph analytics systems have gained significant popularity due to the prevalence of graph data. Many of these systems are designed to run in a shared-nothing architecture whereby a cluster of machines can process a large graph in parallel. In more recent proposals, others have argued that a single-machine system can achieve better performance and/or is more cost-effective. There is however no clear consensus which approach is better. In this paper, we classify existing graph analytics systems into four categories based on the architectural differences, i.e., processing infrastructure (centralized vs distributed), and memory consumption (in-memory vs out-of-core). We select eight open-source systems to cover all categories, and perform a comparative measurement study to compare their performance and cost characteristics across a spectrum of input data, applications, and hardware settings. Our results show that the best performing configuration can depend on the type of applications and input graphs, and there is no dominant winner across all categories. Based on our findings, we summarize the trends in performance and cost, and provide several insights that help to illuminate the performance and resource cost tradeoffs across different graph analytics systems and categories.",
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"title": "Architectural implications on the performance and cost of graph analytics systems",
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"paperAbstract": "Erasure coding (EC) has been widely used in cloud storage systems because it effectively reduces storage redundancy while providing the same level of durability. However, EC introduces significant overhead to small write operations which perform partial write to an entire EC group. This has been a major barrier for EC to be widely adopted in small-write-intensive systems such as virtual disk service. Parity logging (PL) appends parity changes to a journal to accelerate partial writes. However, since previous PL schemes have to perform a time-consuming write-after-read for each partial write, i.e., read the current value of the data and then compute and write the parity delta, their write performance is still much lower than that of replication-based storage. This paper presents PARIX, a speculative partial write scheme for fast parity logging. We transform the original formula of parity calculation, so as to use the data deltas (between the current/original data values), instead of the parity deltas, to calculate the parities during journal replay. For each partial write, this allows PARIX to speculatively log only the current value of the data. The original value is needed only once in a journal when performing the first write to the data. For a series of n partial writes to the same data, PARIX performs pure write (instead of write-after-read) for the last n\u22121 ones while only introducing a small penalty of an extra network RTT (round-trip time) to the first one. Evaluation results show that PARIX remarkably outperforms stateof-the-art PL schemes in partial write performance.",
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"paperAbstract": "Bitcoin has not only attracted many users but also been considered as a technical breakthrough by academia. However, the expanding potential of Bitcoin is largely untapped due to its limited throughput. The Bitcoin community is now facing its biggest crisis in history as the community splits on how to increase the throughput. Among various proposals, Bitcoin Unlimited recently became the most popular candidate, as it allows miners to collectively decide the block size limit according to the real network capacity. However, the security of BU is heatedly debated and no consensus has been reached as the issue is discussed in different miner incentive models. In this paper, we systematically evaluate BU's security with three incentive models via testing the two major arguments of BU supporters: the block validity consensus is not necessary for BU's security; such consensus would emerge in BU out of economic incentives. Our results invalidate both arguments and therefore disprove BU's security claims. Our paper further contributes to the field by addressing the necessity of a prescribed block validity consensus for cryptocurrencies.",
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"title": "Canopus: A Paradigm Shift Towards Elastic Extreme-Scale Data Analytics on HPC Storage",
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"paperAbstract": "Memory consistency models (MCMs) which govern inter-module interactions in a shared memory system, are a significant, yet often under-appreciated, aspect of system design. MCMs are defined at the various layers of the hardware-software stack, requiring thoroughly verified specifications, compilers, and implementations at the interfaces between layers. Current verification techniques evaluate segments of the system stack in isolation, such as proving compiler mappings from a high-level language (HLL) to an ISA or proving validity of a microarchitectural implementation of an ISA.\n This paper makes a case for full-stack MCM verification and provides a toolflow, TriCheck, capable of verifying that the HLL, compiler, ISA, and implementation collectively uphold MCM requirements. The work showcases TriCheck's ability to evaluate a proposed ISA MCM in order to ensure that each layer and each mapping is correct and complete. Specifically, we apply TriCheck to the open source RISC-V ISA [55], seeking to verify accurate, efficient, and legal compilations from C11. We uncover under-specifications and potential inefficiencies in the current RISC-V ISA documentation and identify possible solutions for each. As an example, we find that a RISC-V-compliant microarchitecture allows 144 outcomes forbidden by C11 to be observed out of 1,701 litmus tests examined. Overall, this paper demonstrates the necessity of full-stack verification for detecting MCM-related bugs in the hardware-software stack.",
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"title": "TriCheck: Memory Model Verification at the Trisection of Software, Hardware, and ISA",
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"paperAbstract": "While program hangs on large parallel systems can be detected via the widely used timeout mechanism, it is difficult for the users to set the timeout - too small a timeout leads to high false alarm rates and too large a timeout wastes a vast amount of valuable computing resources. To address the above problems with <i>hang detection</i>, this paper presents <i>ParaStack</i>, an extremely lightweight tool to detect hangs in a timely manner with high accuracy, negligible overhead with great scalability, and without requiring the user to select a timeout value. For a detected hang, it provides direction for further analysis by telling users whether the hang is the result of an error in the computation phase or the communication phase. For a computation-error induced hang, our tool pinpoints the faulty process by excluding hundreds and thousands of other processes. We have adapted <i>ParaStack</i> to work with the <i>Torque</i> and <i>Slurm</i> parallel batch schedulers and validated its functionality and performance on <i>Tianhe-2</i> and <i>Stampede</i> that are respectively the world's current 2<sup>nd</sup> and 12<sup>th</sup> fastest supercomputers. Experimental results demonstrate that <i>ParaStack</i> detects hangs in a timely manner at negligible overhead with over 99% accuracy. No false alarm is observed in correct runs taking 66 hours at scale of 256 processes and 39.7 hours at scale of 1024 processes. <i>ParaStack</i> accurately reports the faulty process for computation-error induced hangs.",
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"paperAbstract": "Since their introduction over a decade ago, time series motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbitrarily dissimilar. In the discrete space, this is similar to extracting the text chain \u201dhit, hot, dot, dog\u201d from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time series chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets. International Conference on Data Mining This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c \u00a9 Mitsubishi Electric Research Laboratories, Inc., 2017 201 Broadway, Cambridge, Massachusetts 02139 Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining Yan Zhu, Makoto Imamura, Daniel Nikovski, Eamonn Keogh University of California, Riverside, yzhu015@ucr.edu, eamonn@cs.ucr.edu Tokai University, imamura@tsc.u-tokai.ac.jp Mitsubishi Electric Research Laboratories, nikovski@merl.com Abstract\u2014 Since their introduction over a decade ago, time series motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbitrarily dissimilar. In the discrete space, this is similar to extracting the text chain \u201chit, hot, dot, dog\u201d from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time series chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets. Since their introduction over a decade ago, time series motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbitrarily dissimilar. In the discrete space, this is similar to extracting the text chain \u201chit, hot, dot, dog\u201d from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time series chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets. Keywords\u2014 Time Series, Motifs, Prognostics, Link Analysis",
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"paperAbstract": "The semiconductor industry is fully globalized and integrated circuits (ICs) are commonly defined, designed and fabricated in different premises across the world. This reduces production costs, but also exposes ICs to supply chain attacks, where insiders introduce malicious circuitry into the final products. Additionally, despite extensive post-fabrication testing, it is not uncommon for ICs with subtle fabrication errors to make it into production systems. While many systems may be able to tolerate a few byzantine components, this is not the case for cryptographic hardware, storing and computing on confidential data. For this reason, many error and backdoor detection techniques have been proposed over the years. So far all attempts have been either quickly circumvented, or come with unrealistically high manufacturing costs and complexity.\n This paper proposes Myst, a practical high-assurance architecture, that uses commercial off-the-shelf (COTS) hardware, and provides strong security guarantees, even in the presence of multiple malicious or faulty components. The key idea is to combine protective-redundancy with modern threshold cryptographic techniques to build a system tolerant to hardware trojans and errors. To evaluate our design, we build a Hardware Security Module that provides the highest level of assurance possible with COTS components. Specifically, we employ more than a hundred COTS secure cryptocoprocessors, verified to FIPS140-2 Level 4 tamper-resistance standards, and use them to realize high-confidentiality random number generation, key derivation, public key decryption and signing. Our experiments show a reasonable computational overhead (less than 1% for both Decryption and Signing) and an exponential increase in backdoor-tolerance as more ICs are added.",
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"https://arxiv.org/pdf/1709.03817v2.pdf",
"https://arxiv.org/pdf/1709.03817v1.pdf",
"http://doi.acm.org/10.1145/3133956.3133961",
"https://acmccs.github.io/papers/p1583-mavroudisA.pdf",
"http://arxiv.org/abs/1709.03817"
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"title": "A Touch of Evil: High-Assurance Cryptographic Hardware from Untrusted Components",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"name": "Sheng Di"
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"name": "Rinku Gupta"
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"name": "Marc Snir"
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"name": "Eric Pershey"
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{
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"name": "Franck Cappello"
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],
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"paperAbstract": "Today's large-scale supercomputers are producing a huge amount of log data. Exploring various potential correlations of fatal events is crucial for understanding their causality and improving the working efficiency for system administrators. To this end, we developed a toolkit, named LogAider, that can reveal three types of potential correlations: across-field, spatial, and temporal. Across-field correlation refers to the statistical correlation across fields within a log or across multiple logs based on probabilistic analysis. For analyzing the spatial correlation of events, we developed a generic, easy-to-use visualizer that can view any events queried by userson a system machine graph. LogAider can also mine spatial correlations by an optimized K-meaning clustering algorithm over a Torus network topology. It is also able to disclose the temporal correlations (or error propagations) over a certain period inside a log or across multiple logs, based on an effective similarity analysis strategy. We assessed LogAider using theone-year reliability-availability-serviceability (RAS) log of Mira system (one of the world's most powerful supercomputers), as well as its job log. We find that LogAider very helpful for revealing the potential correlations of fatal system events and job events, with an accurate mining of across-field correlation with both precision and recall of 99.9-100%, as well as precisedetection of temporal-correlation with a high similarity (up to 95%) to the ground-truth.",
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"title": "LOGAIDER: A Tool for Mining Potential Correlations of HPC Log Events",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
"year": 2017
},
"1eb3b01d6c74157f35e59ea751d5a61bcf76ff23": {
"authors": [
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"name": "Runhui Li"
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{
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"name": "Xiaolu Li"
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{
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"name": "Patrick P. C. Lee"
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{
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"name": "Qun Huang"
}
],
"doi": "",
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"entities": [
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"HTTP pipelining",
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"Open-source software",
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"paperAbstract": "We propose repair pipelining, a technique that speeds up the repair performance in general erasure-coded storage. By pipelining the repair of failed data in small-size units across storage nodes, repair pipelining reduces the repair time to approximately the same as the normal read time to the same amount of data in homogeneous environments. We further extend repair pipelining for heterogeneous environments. We implement a repair pipelining prototype called ECPipe and integrate it as a middleware system into two open-source distributed storage systems HDFS and QFS. Experiments on a local testbed and Amazon EC2 show that repair pipelining significantly improves the performance of both degraded reads and full-node recovery over existing repair techniques.",
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"https://www.usenix.org/system/files/conference/atc17/atc17-li_runhui.pdf",
"https://www.usenix.org/conference/atc17/technical-sessions/presentation/li-runhui",
"http://adslab.cse.cuhk.edu.hk/pubs/atc17.pdf"
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"title": "Repair Pipelining for Erasure-Coded Storage",
"venue": "USENIX Annual Technical Conference",
"year": 2017
},
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"authors": [
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"name": "Samira Manabi Khan"
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{
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"name": "Chris Wilkerson"
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{
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"name": "Zhe Wang"
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{
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"name": "Alaa R. Alameldeen"
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"name": "Donghyuk Lee"
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"name": "Onur Mutlu"
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"doi": "10.1145/3123939.3123945",
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"paperAbstract": "DRAM cells in close proximity can fail depending on the data content in neighboring cells. These failures are called <i>data-dependent failures</i>. Detecting and mitigating these failures <i>online</i>, while the system is running in the field, enables various optimizations that improve reliability, latency, and energy efficiency of the system. For example, a system can improve performance and energy efficiency by using a lower refresh rate for most cells and mitigate the failing cells using higher refresh rates or error correcting codes. All these system optimizations depend on accurately detecting <i>every possible</i> data-dependent failure that could occur with any content in DRAM. Unfortunately, detecting <i>all</i> data-dependent failures requires the knowledge of DRAM internals specific to each DRAM chip. As internal DRAM architecture is not exposed to the system, detecting data-dependent failures at the system-level is a major challenge.\n In this paper, we decouple the detection and mitigation of data-dependent failures from physical DRAM organization such that it is possible to detect failures without <i>knowledge</i> of DRAM internals. To this end, we propose <b>MEMCON</b>, a memory content-based detection and mitigation mechanism for data-dependent failures in DRAM. MEMCON does not detect every <i>possible</i> data-dependent failure. Instead, it detects and mitigates failures that occur only with <i>the current content in memory</i> while the programs are running in the system. Such a mechanism needs to detect failures whenever there is a write access that changes the content of memory. As detection of failure with a runtime testing has a high overhead, MEMCON selectively initiates a test on a write, only when the time between two consecutive writes to that page (i.e., write interval) is long enough to provide significant benefit by lowering the refresh rate during that interval. MEMCON builds upon a simple, practical mechanism that predicts the long write intervals based on our observation that the write intervals in real workloads follow a Pareto distribution: the longer a page remains idle after a write, the longer it is expected to remain idle.\n Our evaluation shows that compared to a system that uses an aggressive refresh rate, MEMCON reduces refresh operations by 65--74%, leading to a 10%/17%/40% (min) to 12%/22%/50% (max) performance improvement for a single-core and 10%/23%/52% (min) to 17%/29%/65% (max) performance improvement for a 4-core system using 8/16/32 Gb DRAM chips.",
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"http://www.pdl.cmu.edu/PDL-FTP/NVM/MEMCON-khan_micro17.pdf"
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"paperAbstract": "Projections and measurements of error rates in near-exascale and exascale systems suggest a dramatic growth, due to extreme scale (10^9 cores), concurrency, software complexity, and deep submicron transistor scaling. Such a growth makes resilience a critical concern, and may increase the incidence of errors that "escape", silently corrupting application state. Such errors can often be revealed by application software tests but with long latencies, and thus are known as latent errors. We explore how to efficiently recover from latent errors, with an approach called application-based focused recovery (ABFR). Specifically we present a case study of stencil computations, a widely useful computational structure, showing how ABFR focuses recovery effort where needed, using intelligent testing and pruning to reduce recovery effort, and enables recovery effort to be overlapped with application computation. We analyze and characterize the ABFR approach on stencils, creating a performance model parameterized by error rate and detection interval (latency). We compare projections from the model to experimental results with the Chombo stencil application, validating the model and showing that ABFR on stencil can achieve a significant reductions in error recovery cost (up to 400x) and recovery latency (up to 4x). Such reductions enable efficient execution at scale with high latent error rates.",
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"title": "Resilience for Stencil Computations with Latent Errors",
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"paperAbstract": "Digitally signed malware can bypass system protection mechanisms that install or launch only programs with valid signatures. It can also evade anti-virus programs, which often forego scanning signed binaries. Known from advanced threats such as Stuxnet and Flame, this type of abuse has not been measured systematically in the broader malware landscape. In particular, the methods, effectiveness window, and security implications of code-signing PKI abuse are not well understood. We propose a threat model that highlights three types of weaknesses in the code-signing PKI. We overcome challenges specific to code-signing measurements by introducing techniques for prioritizing the collection of code signing certificates that are likely abusive. We also introduce an algorithm for distinguishing among different types of threats. These techniques allow us to study threats that breach the trust encoded in the Windows code signing PKI. The threats include stealing the private keys associated with benign certificates and using them to sign malware or by impersonating legitimate companies that do not develop software and, hence, do not own code-signing certificates. Finally, we discuss the actionable implications of our findings and propose concrete steps for improving the security of the code-signing ecosystem.",
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"title": "Certified Malware: Measuring Breaches of Trust in the Windows Code-Signing PKI",
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"doi": "10.1145/3035918.3064029",
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"paperAbstract": "Database management system (DBMS) configuration tuning is an essential aspect of any data-intensive application effort. But this is historically a difficult task because DBMSs have hundreds of configuration \"knobs\" that control everything in the system, such as the amount of memory to use for caches and how often data is written to storage. The problem with these knobs is that they are not standardized (i.e., two DBMSs use a different name for the same knob), not independent (i.e., changing one knob can impact others), and not universal (i.e., what works for one application may be sub-optimal for another). Worse, information about the effects of the knobs typically comes only from (expensive) experience.\n To overcome these challenges, we present an automated approach that leverages past experience and collects new information to tune DBMS configurations: we use a combination of supervised and unsupervised machine learning methods to (1) select the most impactful knobs, (2) map unseen database workloads to previous workloads from which we can transfer experience, and (3) recommend knob settings. We implemented our techniques in a new tool called OtterTune and tested it on two DBMSs. Our evaluation shows that OtterTune recommends configurations that are as good as or better than ones generated by existing tools or a human expert.",
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"http://doi.acm.org/10.1145/3035918.3064029",
"http://www.pdl.cmu.edu/PDL-FTP/Database/parameters.pdf",
"http://www.cs.cmu.edu/afs/.cs.cmu.edu/Web/Posters/CSSpeakingSkills-DVAken17.pdf",
"http://www.cl.cam.ac.uk/~ey204/teaching/ACS/R244_2017_2018/presentation/S7/Ioana_Otto.pdf",
"http://www.pdl.cmu.edu/ftp/News/newsletter17.pdf",
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"title": "Automatic Database Management System Tuning Through Large-scale Machine Learning",
"venue": "SIGMOD Conference",
"year": 2017
},
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"name": "Sajjad Rizvi"
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"name": "Bernard Wong"
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"doi": "10.1145/3143361.3143394",
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"paperAbstract": "Achieving consensus among a set of distributed entities (or participants) is a fundamental problem at the heart of many distributed systems. A critical problem with most consensus protocols is that they do not scale well. As the number of participants trying to achieve consensus increases, increasing network traffic can quickly overwhelm the network from topology-oblivious broadcasts, or a central coordinator for centralized consensus protocols. Thus, either achieving strong consensus is restricted to a handful of participants, or developers must resort to weaker models of consensus.\n We propose Canopus, a highly-parallel consensus protocol that is 'plug-compatible' with ZooKeeper, which exploits modern data center network topology, parallelism, and consensus semantics to achieve scalability with respect to the number of participants and throughput (i.e., the number of key-value reads/writes per second). In our prototype implementation, compared to EPaxos and ZooKeeper, Canopus increases throughput by more than 4x and 16x respectively for read-heavy workloads.",
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"http://doi.acm.org/10.1145/3143361.3143394",
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"title": "Canopus: A Scalable and Massively Parallel Consensus Protocol",
"venue": "CoNEXT",
"year": 2017
},
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"paperAbstract": "We present FRAPpuccino (or FRAP), a provenancebased fault detection mechanism for Platform as a Service (PaaS) users, who run many instances of an application on a large cluster of machines. FRAP models, records, and analyzes the behavior of an application and its impact on the system as a directed acyclic provenance graph. It assumes that most instances behave normally and uses their behavior to construct a model of legitimate behavior. Given a model of legitimate behavior, FRAP uses a dynamic sliding window algorithm to compare a new instance\u2019s execution to that of the model. Any instance that does not conform to the model is identified as an anomaly. We present the FRAP prototype and experimental results showing that it can accurately detect application anomalies.",
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"title": "FRAPpuccino: Fault-detection through Runtime Analysis of Provenance",
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"paperAbstract": "Transport Layer Security (TLS), has become the de-facto standard for secure Internet communication. When used correctly, it provides secure data transfer, but used incorrectly, it can leave users vulnerable to attacks while giving them a false sense of security. Numerous efforts have studied the adoption of TLS (and its predecessor, SSL) and its use in the desktop ecosystem, attacks, and vulnerabilities in both desktop clients and servers. However, there is a dearth of knowledge of how TLS is used in mobile platforms. In this paper we use data collected by Lumen, a mobile measurement platform, to analyze how 7,258 Android apps use TLS in the wild. We analyze and fingerprint handshake messages to characterize the TLS APIs and libraries that apps use, and also evaluate weaknesses. We see that about 84% of apps use default OS APIs for TLS. Many apps use third-party TLS libraries; in some cases they are forced to do so because of restricted Android capabilities. Our analysis shows that both approaches have limitations, and that improving TLS security in mobile is not straightforward. Apps that use their own TLS configurations may have vulnerabilities due to developer inexperience, but apps that use OS defaults are vulnerable to certain attacks if the OS is out of date, even if the apps themselves are up to date. We also study certificate verification, and see low prevalence of security measures such as certificate pinning, even among high-risk apps such as those providing financial services, though we did observe major third-party tracking and advertisement services deploying certificate pinning.",
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"paperAbstract": "Applications like social networking, urban monitoring and market feed processing require stateful stream query: a query consults not only streaming data but also stored data to extract timely information; useful information from streaming data also needs to be continuously and consistently integrated into stored data to serve inflight and future queries. However, prior streaming systems either focus on stream computation, or are not stateful, or cannot provide low latency and high throughput to handle the fast-evolving linked data and increasing concurrency of queries.\n This paper presents Wukong+S, a distributed stream querying engine that provides sub-millisecond stateful query at millions of queries per-second over fast-evolving linked data. Wukong+S uses an integrated design that combines the stream processor and the persistent store with efficient state sharing, which avoids the cross-system cost and sub-optimal query plan in conventional composite designs (e.g., Storm/Heron+Wukong). Wukong+S uses a hybrid store to differentially manage timeless data and timing data accordingly and provides an efficient stream index with locality-aware partitioning to facilitate fast access to streaming data. Wukong+S further provides decentralized vector timestamps with bounded snapshot scalarization to scale with nodes and massive queries at efficient memory usage.\n We have designed Wukong+S conforming to the RDF data model and Continuous SPARQL (C-SPARQL) query interface and have implemented Wukong+S by extending a state-of-the-art static RDF store (namely Wukong). Evaluation on an 8-node RDMA-capable cluster using LSBench and CityBench shows that Wukong+S significantly outperforms existing system designs (e.g., CSPARQL-engine, Storm/Heron+Wukong, and Spark Streaming/Structured Streaming) for both latency and throughput, usually at the scale of orders of magnitude.",
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"title": "Sub-millisecond Stateful Stream Querying over Fast-evolving Linked Data",
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"name": "NhatHai Phan"
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"name": "Han Hu"
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"name": "Dejing Dou"
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"paperAbstract": "In this paper, we focus on developing a novel mechanism to preserve differential privacy in deep neural networks, such that: (1) The privacy budget consumption is totally independent of the number of training steps; (2) It has the ability to adaptively inject noise into features based on the contribution of each to the output; and (3) It could be applied in a variety of different deep neural networks. To achieve this, we figure out a way to perturb affine transformations of neurons, and loss functions used in deep neural networks. In addition, our mechanism intentionally adds "more noise" into features which are "less relevant" to the model output, and vice-versa. Our theoretical analysis further derives the sensitivities and error bounds of our mechanism. Rigorous experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is highly effective and outperforms existing solutions.",
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"title": "Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning",
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"name": "Zongheng Yang"
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"paperAbstract": "We present ZipG, a distributed memory-efficient graph store for serving interactive graph queries. ZipG achieves memory efficiency by storing the input graph data using a compressed representation. What differentiates ZipG from other graph stores is its ability to execute a wide range of graph queries directly on this compressed representation. ZipG can thus execute a larger fraction of queries in main memory, achieving query interactivity. ZipG exposes a minimal API that is functionally rich enough to implement published functionalities from several industrial graph stores. We demonstrate this by implementing and evaluating graph queries from Facebook TAO, LinkBench, Graph Search and several other workloads on top of ZipG. On a single server with 244GB memory, ZipG executes tens of thousands of queries from these workloads for raw graph data over half a TB; this leads to an order of magnitude (sometimes as much as 23×) higher throughput than Neo4j and Titan. We get similar gains in distributed settings compared to Titan.",
"pdfUrls": [
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"title": "ZipG: A Memory-efficient Graph Store for Interactive Queries",
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"year": 2017
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"paperAbstract": "Logic locking has been conceived as a promising proactive defense strategy against intellectual property (IP) piracy, counterfeiting, hardware Trojans, reverse engineering, and overbuilding attacks. Yet, various attacks that use a working chip as an oracle have been launched on logic locking to successfully retrieve its secret key, undermining the defense of all existing locking techniques. In this paper, we propose stripped-functionality logic locking (SFLL), which strips some of the functionality of the design and hides it in the form of a secret key(s), thereby rendering on-chip implementation functionally different from the original one. When loaded onto an on-chip memory, the secret keys restore the original functionality of the design. Through security-aware synthesis that creates a controllable mismatch between the reverse-engineered netlist and original design, SFLL provides a quantifiable and provable resilience trade-off between all known and anticipated attacks. We demonstrate the application of SFLL to large designs (>100K gates) using a computer-aided design (CAD) framework that ensures attaining the desired security level at minimal implementation cost, 8%, 5%, and 0.5% for area, power, and delay, respectively. In addition to theoretical proofs and simulation confirmation of SFLL's security, we also report results from the silicon implementation of SFLL on an ARM Cortex-M0 microprocessor in 65<i>nm</i> technology.",
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"title": "Provably-Secure Logic Locking: From Theory To Practice",
"venue": "CCS",
"year": 2017
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"name": "Mehdi Tibouchi"
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"paperAbstract": "In this paper, we investigate the security of the BLISS lattice-based signature scheme, one of the most promising candidates for postquantum-secure signatures, against side-channel attacks. Several works have been devoted to its efficient implementation on various platforms, from desktop CPUs to microcontrollers and FPGAs, and more recent papers have also considered its security against certain types of physical attacks, notably fault injection and cache attacks. We turn to more traditional side-channel analysis, and describe several attacks that can yield a full key recovery.\n We first identify a serious source of leakage in the rejection sampling algorithm used during signature generation. Existing implementations of that rejection sampling step, which is essential for security, actually leak the \"relative norm\" of the secret key. We show how an extension of an algorithm due to Howgrave-Graham and Szydlo can be used to recover the key from that relative norm, at least when the absolute norm is easy to factor (which happens for a significant fraction of secret keys). We describe how this leakage can be exploited in practice both on an embedded device (an 8-bit AVR microcontroller) using <i>electromagnetic analysis</i> (EMA), and a desktop computer (recent Intel CPU running Linux) using <i>branch tracing</i>. The latter attack has been mounted against the open source VPN software strongSwan.\n We also show that other parts of the BLISS signing algorithm can leak secrets not just for a subset of secret keys, but for 100% of them. The BLISS Gaussian sampling algorithm in strongSwan is intrinsically variable time. This would be hard to exploit using a noisy source of leakage like EMA, but branch tracing allows to recover the <i>entire randomness</i> and hence the key: we show that a <i>single</i> execution of the strongSwan signature algorithm is actually sufficient for full key recovery. We also describe a more traditional side-channel attack on the sparse polynomial multiplications carried out in BLISS: classically, multiplications can be attacked using DPA; however, our target 8-bit AVR target implementation uses repeated shifted additions instead. Surprisingly, we manage to obtain a full key recovery in that setting using integer linear programming from a single EMA trace.",
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"http://eprint.iacr.org/2017/505",
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"https://eprint.iacr.org/2017/505.pdf",
"http://eprint.iacr.org/2017/583",
"http://doi.acm.org/10.1145/3133956.3134028"
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"title": "Side-Channel Attacks on BLISS Lattice-Based Signatures: Exploiting Branch Tracing against strongSwan and Electromagnetic Emanations in Microcontrollers",
"venue": "CCS",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Raghu Prabhakar"
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{
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"name": "Yaqi Zhang"
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{
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"name": "David Koeplinger"
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{
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"name": "Matthew Feldman"
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{
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"name": "Tian Zhao"
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{
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"name": "Stefan Hadjis"
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"name": "Ardavan Pedram"
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"name": "Christoforos E. Kozyrakis"
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"name": "Kunle Olukotun"
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],
"doi": "10.1145/3079856.3080256",
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"paperAbstract": "Reconfigurable architectures have gained popularity in recent years as they allow the design of energy-efficient accelerators. Fine-grain fabrics (e.g. FPGAs) have traditionally suffered from performance and power inefficiencies due to bit-level reconfigurable abstractions. Both fine-grain and coarse-grain architectures (e.g. CGRAs) traditionally require low level programming and suffer from long compilation times. We address both challenges with Plasticine, a new spatially reconfigurable architecture designed to efficiently execute applications composed of parallel patterns. Parallel patterns have emerged from recent research on parallel programming as powerful, high-level abstractions that can elegantly capture data locality, memory access patterns, and parallelism across a wide range of dense and sparse applications.\n We motivate Plasticine by first observing key application characteristics captured by parallel patterns that are amenable to hardware acceleration, such as hierarchical parallelism, data locality, memory access patterns, and control flow. Based on these observations, we architect Plasticine as a collection of Pattern Compute Units and Pattern Memory Units. Pattern Compute Units are multi-stage pipelines of reconfigurable SIMD functional units that can efficiently execute nested patterns. Data locality is exploited in Pattern Memory Units using banked scratchpad memories and configurable address decoders. Multiple on-chip address generators and scatter-gather engines make efficient use of DRAM bandwidth by supporting a large number of outstanding memory requests, memory coalescing, and burst mode for dense accesses. Plasticine has an area footprint of 113 mm2 in a 28nm process, and consumes a maximum power of 49 W at a 1 GHz clock. Using a cycle-accurate simulator, we demonstrate that Plasticine provides an improvement of up to 76.9x in performance-per-Watt over a conventional FPGA over a wide range of dense and sparse applications.",
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"http://platformlab.stanford.edu/Seminar%20Talks/retreat-2017/Raghu%20Prabhakar.pdf",
"http://csl.stanford.edu/~christos/publications/2017.plasticine.isca.pdf",
"http://doi.acm.org/10.1145/3079856.3080256"
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"title": "Plasticine: A reconfigurable architecture for parallel patterns",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
"1fe46b7df08ea13e52f86144de765f80e7567927": {
"authors": [
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],
"name": "Tsung Tai Yeh"
},
{
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"name": "Amit Sabne"
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{
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"name": "Putt Sakdhnagool"
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"name": "Rudolf Eigenmann"
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{
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],
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"paperAbstract": "Massively multithreaded GPUs achieve high throughput by running thousands of threads in parallel. To fully utilize the hardware, workloads spawn work to the GPU in bulk by launching large tasks, where each task is a kernel that contains thousands of threads that occupy the entire GPU.\n GPUs face severe underutilization and their performance benefits vanish if the tasks are narrow, i.e., they contain < 500 threads. Latency-sensitive applications in network, signal, and image processing that generate a large number of tasks with relatively small inputs are examples of such limited parallelism.\n This paper presents Pagoda, a runtime system that virtualizes GPU resources, using an OS-like daemon kernel called MasterKernel. Tasks are spawned from the CPU onto Pagoda as they become available, and are scheduled by the MasterKernel at the warp granularity. Experimental results demonstrate that Pagoda achieves a geometric mean speedup of 5.70x over PThreads running on a 20-core CPU, 1.51x over CUDA-HyperQ, and 1.69x over GeMTC, the state-of- the-art runtime GPU task scheduling system.",
"pdfUrls": [
"https://engineering.purdue.edu/tgrogers/papers/tsung.tai.ppopp.2017.pdf",
"http://dl.acm.org/citation.cfm?id=3018754"
],
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"title": "Pagoda: Fine-Grained GPU Resource Virtualization for Narrow Tasks",
"venue": "PPOPP",
"year": 2017
},
"1ff2cdb23079207c54a81cdf363af1b166aa1f6d": {
"authors": [
{
"ids": [
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"name": "Fanny Pascual"
},
{
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"name": "Krzysztof Rzadca"
}
],
"doi": "10.1007/978-3-319-64203-1_15",
"doiUrl": "https://doi.org/10.1007/978-3-319-64203-1_15",
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"Approximation algorithm",
"Baseline (configuration management)",
"Best, worst and average case",
"Circuit minimization for Boolean functions",
"Colocation centre",
"Combinatorial optimization",
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"Job shop scheduling",
"Makespan",
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"NP (complexity)",
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],
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"paperAbstract": "In data centers, up to dozens of tasks are colocated on a single physical machine. Machines are used more efficiently, but tasks\u2019 performance deteriorates, as colocated tasks compete for shared resources. As tasks are heterogeneous (CPU-, memory, networkor disk-intensive), the resulting performance dependencies are complex. We explore a new combinatorial optimization model that uses two parameters of a task \u2014 its size and its type \u2014 to characterize how a task influences the performance of the other tasks allocated to the same machine. We study the egalitarian optimization goal: maximizing the worst-off performance. This problem generalizes the classic makespan minimization on multiple processors (P ||Cmax). We prove that polynomially-solvable variants of multiprocessor scheduling become NP-hard and hard to approximate when the number of types is not constant. We propose a PTAS and a series of fast approximation algorithms when the number of types is constant. By simulation on instances derived from a trace of one of Google clusters, we show that our algorithms that take into account types lead to lower costs compared with P ||Cmax baseline. The notion of type enables us to model degeneration of performance caused by colocation using standard combinatorial optimization methods. Types add a layer of additional complexity. However, our results \u2014 approximation algorithms and good average-case performance \u2014 show that types can be handled efficiently.",
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"https://arxiv.org/pdf/1610.07339v2.pdf",
"http://arxiv.org/abs/1610.07339",
"https://arxiv.org/pdf/1610.07339v3.pdf",
"https://doi.org/10.1007/978-3-319-64203-1_15",
"http://www-poleia.lip6.fr/~pascualf/doc/europar2017.pdf",
"https://export.arxiv.org/pdf/1610.07339"
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"title": "Optimizing Egalitarian Performance in the Side-Effects Model of Colocation for Data Center Resource Management",
"venue": "Euro-Par",
"year": 2017
},
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"authors": [
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"name": "Weizhong Qiang"
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{
"ids": [
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"name": "Yong Cao"
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{
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"name": "Weiqi Dai"
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{
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"name": "Deqing Zou"
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{
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"name": "Hai Jin"
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{
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"name": "Benxi Liu"
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],
"doi": "10.1109/HPCC-SmartCity-DSS.2017.5",
"doiUrl": "https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.5",
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"journalName": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"paperAbstract": "A typical application normally includes the main program and some shared libraries. The main program can arbitrarily execute the code of those shared libraries due to the coexistence in the same memory space. This feature can be used by attackers to carry out code-reuse attacks. Meanwhile, the shared libraries are shared across multiple applications, which can help attackers to simplify the process of code-reuse attacks. Isolating shared library into a separate execution environment and restricting the access to this library is a promising countermeasure, while the existing isolation approaches need to either modify the main program, or break the shared feature of the library. In this paper, we present Libsec, an efficient and transparent approach to isolate shared libraries, without the need of source code of the main program or shared libraries. Libsec adopts commodity hardware virtualization extension to isolate shared libraries from the main program. Meanwhile, Libsec relies on static instrumentation and dynamic processing to provide interfaces for legitimate cross-domain invocations. By this way, Libsec can guarantee that the main program and shared libraries are executed in the corresponding execution environment respectively, while cross-domain invocation is only allowed via specific interfaces, thus preventing the main program from jumping directly to the shared library. We implement a prototype of Libsec in KVM. To demonstrate its effectiveness, we deploy it to some real-world applications and libraries, such as Nginx and OpenSSL libraries. Security evaluation shows that Libsec can prevent the attacker from utilizing the functions or instruction sequences of the shared library for code-reuse attack. Performance evaluation shows that the performance and space overhead caused by Libsec are appropriate.",
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"title": "Libsec: A Hardware Virtualization-Based Isolation for Shared Library",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
"year": 2017
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"authors": [
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"name": "Patrick Neubauer"
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{
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"doi": "10.1145/3136014.3136037",
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"paperAbstract": "The maintenance of modern systems often requires developers to perform complex and error-prone cognitive tasks, which are caused by the obscurity, redundancy, and irrelevancy of code, distracting from essential maintenance tasks. Typical maintenance scenarios include multiple branches of code in repositories, which involves dealing with branch-interdependent changes, and aspects in aspect-oriented development, which requires in-depth knowledge of behavior-interdependent changes. Thus, merging branched files as well as validating the behavior of statically composed code requires developers to conduct exhaustive individual introspection. \n In this work we present VirtualEdit for associative, commutative, and invertible model composition. It allows simultaneous editing of multiple model versions or variants through dynamically derived virtual models. We implemented the approach in terms of an open-source framework that enables multi-version editing and aspect-orientation by selectively focusing on specific parts of code, which are significant for a particular engineering task. \n The VirtualEdit framework is evaluated based on its application to the most popular publicly available Xtext-based languages. Our results indicate that VirtualEdit can be applied to existing languages with reasonably low effort.",
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"title": "Virtual textual model composition for supporting versioning and aspect-orientation",
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"year": 2017
},
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"authors": [
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"name": "Yupeng Zhang"
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{
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"name": "Daniel Genkin"
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{
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"name": "Jonathan Katz"
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{
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"name": "Dimitrios Papadopoulos"
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{
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"paperAbstract": "Cloud database systems such as Amazon RDS or Google Cloud SQLenable the outsourcing of a large database to a server who then responds to SQL queries. A natural problem here is to efficiently verify the correctness of responses returned by the (untrusted) server. In this paper we present vSQL, a novel cryptographic protocol for publicly verifiable SQL queries on dynamic databases. At a high level, our construction relies on two extensions of the CMT interactive-proof protocol [Cormode et al., 2012]: (i) supporting outsourced input via the use of a polynomial-delegation protocol with succinct proofs, and (ii) supporting auxiliary input (i.e., non-deterministic computation) efficiently. Compared to previous verifiable-computation systems based on interactive proofs, our construction has verification cost polylogarithmic in the auxiliary input (which for SQL queries can be as large as the database) rather than linear. In order to evaluate the performance and expressiveness of our scheme, we tested it on SQL queries based on the TPC-H benchmark on a database with 6 million rows and 13 columns. The server overhead in our scheme (which is typically the main bottleneck) is up to 120 times lower than previousapproaches based on succinct arguments of knowledge (SNARKs), and moreover we avoid the need for query-dependent pre-processing which is required by optimized SNARK-based schemes. In our construction, the server/client time and the communication cost are comparable to, and sometimessmaller than, those of existing customized solutions which only support specific queries.",
"pdfUrls": [
"https://eprint.iacr.org/2017/1145.pdf",
"https://doi.org/10.1109/SP.2017.43",
"https://www.cis.upenn.edu/~danielg3/papers/vsql.pdf",
"http://eprint.iacr.org/2017/1145",
"http://www.ece.umd.edu/~cpap/published/vSQl-oakland-17.pdf",
"https://obj.umiacs.umd.edu/papers_for_stories/vSQL_Zhang.pdf"
],
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"s2Url": "https://semanticscholar.org/paper/201f43d5b419070dc25cb173793a8227a075d32a",
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"title": "vSQL: Verifying Arbitrary SQL Queries over Dynamic Outsourced Databases",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"authors": [
{
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"name": "Emiliano Silvestri"
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{
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"name": "Simone Economo"
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{
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"name": "Pierangelo di Sanzo"
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{
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"paperAbstract": "The ever-increasing energy demands of modern High Performance Computing (HPC) platforms is undeniably one of the most critical aspects for the future design and evolution of such systems. The capability of managing their energy consumption not only allows for significant reduction in electricity costs but is also a step forward on the road towards the exascale. Powercapping is a widely studied technique that contributes to address this challenge by instantaneously setting and maintaining a predefined power threshold (power cap) that cannot be exceeded. However, the lack of a centralized mechanism responsible for efficiently allocating the available power among resources and jobs may ultimately yield to fragmentation, low system utilization and increased user waiting times. Additionally, power cap violations can lead to high risk scenarios and/or increase operational costs. This paper proposes to prevent such issues with the introduction of the Enhanced Power Adaptive Scheduling (E-PAS) algorithm. The E-PAS algorithm combines scheduling and resource management mechanisms, correlating estimated and real power consumption data in order to optimize the resource utilization of the platform under a predefined power cap. The algorithm has been implemented in the widely used open-source resource and job management system SLURM and is planned to be pushed in a future mainstream version. Its effectiveness has been evaluated through real-scale experiments respectively on an ARM- and an Intel-based cluster of comparable size. All experiments have been performed using synthetic workloads from a set of mini-applications.",
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"title": "IncBricks: Toward In-Network Computation with an In-Network Cache",
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"paperAbstract": "Researches affirm that coflow scheduling/routing substantially shortens the average application inner communication time in data center networks(DCNs). The commonly desirable critical features of existing coflow scheduling/routing framework includes (1) coflow scheduling, (2) coflow routing, and (3) per-flow rate-limiting. However, to provide the 3 features, existing frameworks require customized computing frameworks, customized operating systems, or specific external commercial monitoring frameworks on software-defined networking(SDN) switches. These requirements defer or even prohibit the deployment of coflow scheduling/routing in production DCNs. In this paper, we design a coflow scheduling and routing framework, MinCOF which has minimal requirements on hosts and switches for cloud storage area networks(SANs) based on OpenFlow SDN. MinCOF accommodates all critical features of coflow scheduling/routing from previous works. The deployability in production environment is especially taken into consideration. The OpenFlow architecture is capable of processing the traffic load in a cloud SAN. Not necessary requirements for hosts from existing frameworks are migrated to the mature commodity OpenFlow 1.3 Switch and our coflow scheduler. Transfer applications on hosts only need slight enhancements on their existing connection establishment and progress reporting functions. Evaluations reveal that MinCOF decreases the average coflow completion time (CCT) by 12.94% compared to the latest OpenFlow-based coflow scheduling and routing framework.",
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"paperAbstract": "Competitive and cooperative threading are widely used abstractions in computing. In competitive threading, threads are scheduled preemptively with the goal of minimizing response time, usually of interactive applications. In cooperative threading, threads are scheduled non-preemptively with the goal of maximizing throughput or minimizing the completion time, usually in compute-intensive applications, e.g. scientific computing, machine learning and AI. \n Although both of these forms of threading rely on the same abstraction of a thread, they have, to date, remained largely separate forms of computing. Motivated by the recent increase in the mainstream use of multicore computers, we propose a threading model that aims to unify competitive and cooperative threading. To this end, we extend the classic graph-based cost model for cooperative threading to allow for competitive threading, and describe how such a cost model may be used in a programming language by presenting a language and a corresponding cost semantics. Finally, we show that the cost model and the semantics are realizable by presenting an operational semantics for the language that specifies the behavior of an implementation, as well as an implementation and a small empirical evaluation.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3062341.3062370",
"http://reports-archive.adm.cs.cmu.edu/anon/2017/CMU-CS-17-107.pdf",
"http://www.cs.cmu.edu/~rwh/papers/resppar/pldi.pdf"
],
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"title": "Responsive parallel computation: bridging competitive and cooperative threading",
"venue": "PLDI",
"year": 2017
},
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"authors": [
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"name": "Seunghee Shin"
},
{
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"name": "Satish Kumar Tirukkovalluri"
},
{
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],
"name": "James Tuck"
},
{
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"name": "Yan Solihin"
}
],
"doi": "10.1145/3123939.3124539",
"doiUrl": "https://doi.org/10.1145/3123939.3124539",
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"Allocate-on-flush",
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"paperAbstract": "Emerging non-volatile memory (NVM) technologies, such as phase-change memory, spin-transfer torque magnetic memory, memristor, and 3D Xpoint, are encouraging the development of new architectures that support the challenges of persistent programming. An important remaining challenge is dealing with the high logging overheads introduced by durable transactions.\n In this paper, we propose a new logging approach, <i>Proteus</i> for durable transactions that achieves the favorable characteristics of both prior software and hardware approaches. Like software, it has no hardware constraint limiting the number of transactions or logs available to it, and like hardware, it has very low overhead. Our approach introduces two new instructions: log-load creates a log entry by loading the original data, and log-flush writes the log entry into the log. We add hardware support, primarily within the core, to manage the execution of these instructions and critical ordering requirements between logging operations and updates to data. We also propose a novel optimization at the memory controller that is enabled by a persistent write pending queue in the memory controller. We drop log updates that have not yet written back to NVMM by the time a transaction is considered durable.\n We implemented our design on a cycle accurate simulator, MarssX86, and compared it against state-of-the-art hardware logging, ATOM [19], and a software only approach. Our experiments show that <i>Proteus</i> improves performance by 1.44--1.47× depending on configuration, on average, compared to a system without hardware logging and 9--11% faster than ATOM. A significant advantage of our approach is dropping writes to the log when they are not needed. On average, ATOM makes 3.4× more writes to memory than our design.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3123939.3124539",
"http://nvmw.ucsd.edu/nvmw18-program/unzip/current/nvmw2018-final93.pdf"
],
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"sources": [
"DBLP"
],
"title": "Proteus: a flexible and fast software supported hardware logging approach for NVM",
"venue": "MICRO",
"year": 2017
},
"20fc7ec7834a055843ccb087c77656574a09bfb5": {
"authors": [
{
"ids": [
"1799329"
],
"name": "Jee Ho Ryoo"
},
{
"ids": [
"2543676"
],
"name": "Mitesh R. Meswani"
},
{
"ids": [
"2130920"
],
"name": "Reena Panda"
},
{
"ids": [
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],
"name": "Lizy Kurian John"
}
],
"doi": "10.1145/2967938.2974060",
"doiUrl": "https://doi.org/10.1145/2967938.2974060",
"entities": [
"Address space",
"Dynamic random-access memory",
"FM broadcast band",
"Flat memory model",
"Hot swapping",
"Interleaved memory",
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"journalName": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
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"paperAbstract": "In this paper, we present a flat address space organization called SILC-FM that allows subblocks from two pages to co-exist in an interleaved fashion in die-stacked DRAM. Data movement at subblocked granularity consumes less bandwidth compared to migrating the entire large block and prevents fetching useless subblocks that may never get accessed. SILC-FM can get more spatial locality hits than CAMEO and PoM due to page-level operation and interleaving blocks respectively. The interleaved subblock placement improves performance by 55% on average over a static placement scheme without data migration. We also selectively lock hot blocks to prevent them from being involved in the hardware swapping operations. Additional features such as locking, associativity and bandwidth balancing improve performance by 11%, 8%, and 8% respectively, resulting in a total of 82% performance improvement over no migration static placement scheme. Compared to the best state-of-the-art scheme, SILC-FM gets performance improvement of 36%.",
"pdfUrls": [
"http://ieeexplore.ieee.org/document/7756782/",
"http://doi.ieeecomputersociety.org/10.1109/HPCA.2017.20"
],
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"title": "SILC-FM: Subblocked InterLeaved Cache-Like Flat Memory Organization",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2016
},
"21158c013e6c723ed1e9b9033a49cbda7b8b7dd9": {
"authors": [
{
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],
"name": "Gordon V. Cormack"
},
{
"ids": [
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],
"name": "Maura R. Grossman"
}
],
"doi": "10.1145/3077136.3080812",
"doiUrl": "https://doi.org/10.1145/3077136.3080812",
"entities": [
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"Ground truth",
"Relevance",
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"paperAbstract": "Technology-assisted review (\"TAR\") systems seek to achieve \"total recall\"; that is, to approach, as nearly as possible, the ideal of 100% recall and 100% precision, while minimizing human review effort. The literature reports that TAR methods using relevance feedback can achieve considerably greater than the 65% recall and 65% precision reported by Voorhees as the \"practical upper bound on retrieval performance... since that is the level at which humans agree with one another\" (Variations in Relevance Judgments and the Measurement of Retrieval Effectiveness, 2000). This work argues that in order to build - as well as to, evaluate - TAR systems that approach 100% recall and 100% precision, it is necessary to model human assessment, not as absolute ground truth, but as an indirect indicator of the amorphous property known as \"relevance.\" The choice of model impacts both the evaluation of system effectiveness, as well as the simulation of relevance feedback. Models are presented that better fit available data than the infallible ground-truth model. These models suggest ways to improve TAR-system effectiveness so that hybrid human-computer systems can improve on both the accuracy and efficiency of human review alone. This hypothesis is tested by simulating TAR using two datasets: the TREC 4 AdHoc collection, and a dataset consisting of 401,960 email messages that were manually reviewed and classified by a single individual, Roger, in his official capacity as Senior State Records Archivist. The results using the TREC 4 data show that TAR achieves higher recall and higher precision than the assessments by either of two independent NIST assessors, and blind adjudication of the email dataset, conducted by Roger, more than two years after his original review, shows that he could have achieved the same recall and better precision, while reviewing substantially fewer than 401,960 emails, had he employed TAR in place of exhaustive manual review.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3077136.3080812"
],
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"sources": [
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],
"title": "Navigating Imprecision in Relevance Assessments on the Road to Total Recall: Roger and Me",
"venue": "SIGIR",
"year": 2017
},
"21164c79302a3182064ca3cbedb248c3ebd463e0": {
"authors": [
{
"ids": [
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],
"name": "Jerry Ajay"
},
{
"ids": [
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],
"name": "Chen Song"
},
{
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],
"name": "Aditya Singh Rathore"
},
{
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],
"name": "Chi Zhou"
},
{
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],
"name": "Wenyao Xu"
}
],
"doi": "10.1145/3037697.3037752",
"doiUrl": "https://doi.org/10.1145/3037697.3037752",
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"3D computer graphics",
"3D printing",
"Compiler",
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"paperAbstract": "As the next-generation manufacturing driven force, 3D printing technology is having a transformative effect on various industrial domains and has been widely applied in a broad spectrum of applications. It also progresses towards other versatile fields with portable battery-powered 3D printers working on a limited energy budget. While reducing manufacturing energy is an essential challenge in industrial sustainability and national economics, this growing trend motivates us to explore the energy consumption of the 3D printer for the purpose of energy efficiency. To this end, we perform an in-depth analysis of energy consumption in commercial, off-the-shelf 3D printers from an instruction-level perspective. We build an instruction-level energy model and an energy profiler to analyze the energy cost during the fabrication process. From the insights obtained by the energy profiler, we propose and implement a cross-layer energy optimization solution, called 3DGates, which spans the instruction-set, the compiler and the firmware. We evaluate 3DGates over 338 benchmarks on a 3D printer and achieve an overall energy reduction of 25%.",
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"title": "3DGates: An Instruction-Level Energy Analysis and Optimization of 3D Printers",
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"authors": [
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"name": "Yingjin Qian"
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"name": "Xi Li"
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{
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"name": "Shuichi Ihara"
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"name": "Lingfang Zeng"
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"title": "Why Your Encrypted Database Is Not Secure",
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"year": 2017
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"name": "Shaolei Ren"
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{
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"paperAbstract": "The power capacity of multi-tenant data centers is typically oversubscribed in order to increase the utilization of expensive power infrastructure. This practice can create dangerous situations and compromise data center availability if the designed power capacity is exceeded. This paper demonstrates that current safeguards are vulnerable to well-timed power attacks launched by malicious tenants (i.e., attackers). Further, we demonstrate that there is a physical side channel --- a thermal side channel due to hot air recirculation --- that contains information about the benign tenants' runtime power usage and can enable a malicious tenant to time power attacks effectively. In particular, we design a state-augmented Kalman filter to extract this information from the side channel and guide an attacker to use its maximum power at moments that coincide with the benign tenants' high power demand, thus overloading the shared power capacity. Our experimental results show that an attacker can capture 54% of all attack opportunities, significantly compromising the data center availability. Finally, we discuss a set of possible defense strategies to safeguard the data center infrastructure against power attacks.",
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"title": "Exploiting a Thermal Side Channel for Power Attacks in Multi-Tenant Data Centers",
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"paperAbstract": "Clusters equipped with accelerators such as graphics processing unit (GPU) and Many Integrated Core (MIC) are widely used. For such clusters, programmers write programs for their applications by combining MPI with one of the available accelerator programming models. In particular, OpenACC enables programmers to develop their applications easily, but with lower productivity owing to complex MPI programming. XcalableACC (XACC) is a new programming model, which is an "orthogonal" integration of a partitioned global address space (PGAS) language XcalableMP (XMP) and OpenACC. While XMP enables distributed-memory programming on both global-view and local-view models, OpenACC allows operations to be offloaded to a set of accelerators. In the local-view model, programmers can describe communication with the coarray features adopted from Fortran 2008, and we extend them to communication between accelerators. We have designed and implemented an XACC compiler for NVIDIA GPU and evaluated its performance and productivity by using two benchmarks, Himeno benchmark and NAS Parallel Benchmarks CG (NPB-CG). The performance of the XACC version with the Himeno benchmark and NPB-CG are over 85% and 97% in the local-view model against the MPI+OpenACC version, respectively. Moreover, using non-blocking communication makes the performance of local-view version over 89% with the Himeno benchmark. From the viewpoint of productivity, the local-view model provides an intuitive form of array assignment statement for communication.",
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"paperAbstract": "Container technology has been prevalent and widely-adopted in production environment considering the huge benefits to application packing, deploying and management. However, the deployment process is relatively slow by using conventional approaches. In large-scale concurrent deployments, resource contentions on the central image repository would aggravate such situation. In fact, it is observable that the image pulling operation is mainly responsible for the degraded performance. To this end, we propose Cider — a novel deployment system to enable rapid container deployment in a high concurrent and scalable manner at scale. Firstly, on-demand image data loading is proposed by altering the local Docker storage of worker nodes into all-nodes-sharing network storage. Also, the local copy-on-write layer for containers can ensure Cider to achieve the scalability whilst improving the cost-effectiveness during the holistic deployment. Experimental results reveal that Cider can shorten the overall deployment time by 85% and 62% on average when deploying one container and 100 concurrent containers respectively.",
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"https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.44"
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"title": "Cider: a Rapid Docker Container Deployment System through Sharing Network Storage",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
"year": 2017
},
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"name": "Jack Doerner"
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{
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"name": "Abhi Shelat"
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"doi": "10.1145/3133956.3133967",
"doiUrl": "https://doi.org/10.1145/3133956.3133967",
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"Byte",
"Computation",
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"paperAbstract": "We design and implement a Distributed Oblivious Random Access Memory (DORAM) data structure that is optimized for use in two-party secure computation protocols. We improve upon the access time of previous constructions by a factor of up to ten, their memory overhead by a factor of one hundred or more, and their initialization time by a factor of thousands. We are able to instantiate ORAMs that hold 2<sup>34</sup> bytes, and perform operations on them in seconds, which was not previously feasible with any implemented scheme.\n Unlike prior ORAM constructions based on hierarchical hashing, permutation, or trees, our Distributed ORAM is derived from the new Function Secret Sharing scheme introduced by Boyle, Gilboa and Ishai. This significantly reduces the amount of secure computation required to implement an ORAM access, albeit at the cost of <i>O</i>(<i>n</i>) efficient <i>local</i> memory operations.\n We implement our construction and find that, despite its poor <i>O</i>(<i>n</i>) asymptotic complexity, it still outperforms the fastest previously known constructions, Circuit ORAM and Square-root ORAM, for datasets that are 32 KiB or larger, and outperforms prior work on applications such as <i>stable matching</i> or <i>binary search</i> by factors of two to ten.",
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"http://doi.acm.org/10.1145/3133956.3133967",
"https://eprint.iacr.org/2017/827.pdf",
"http://eprint.iacr.org/2017/827",
"https://acmccs.github.io/papers/p523-doernerA.pdf"
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"title": "Scaling ORAM for Secure Computation",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Martin Maas"
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"name": "Krste Asanovic"
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"name": "John Kubiatowicz"
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"doi": "10.1145/3102980.3103003",
"doiUrl": "https://doi.org/10.1145/3102980.3103003",
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"Data center",
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"Runtime system",
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"title": "Return of the Runtimes: Rethinking the Language Runtime System for the Cloud 3.0 Era",
"venue": "HotOS",
"year": 2017
},
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"name": "Giorgis Georgakoudis"
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"name": "Ignacio Laguna"
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"name": "Dimitrios S. Nikolopoulos"
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"doi": "10.1145/3126908.3126972",
"doiUrl": "https://doi.org/10.1145/3126908.3126972",
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"paperAbstract": "Compiler-based fault injection (FI) has become a popular technique for resilience studies to understand the impact of soft errors in supercomputing systems. Compiler-based FI frameworks inject faults at a high intermediate-representation level. However, they are less accurate than machine code, binary-level FI because they lack access to all dynamic instructions, thus they fail to mimic certain fault manifestations. In this paper, we study the limitations of current practices in compiler-based FI and how they impact the interpretation of results in resilience studies.\n We propose REFINE, a novel framework that addresses these limitations, performing FI in a compiler backend. Our approach provides the portability and efficiency of compiler-based FI, while keeping accuracy comparable to binary-level FI methods. We demonstrate our approach in 14 HPC programs and show that, due to our unique design, its runtime overhead is significantly smaller than state-of-the-art compiler-based FI frameworks, reducing the time for large FI experiments.",
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"title": "REFINE: realistic fault injection via compiler-based instrumentation for accuracy, portability and speed",
"venue": "SC",
"year": 2017
},
"220eb711e0efd2c67759f169ff14ba3efc186bbe": {
"authors": [
{
"ids": [
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"name": "Giulio Malavolta"
},
{
"ids": [
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],
"name": "Pedro Moreno-Sanchez"
},
{
"ids": [
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"name": "Aniket Kate"
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{
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"name": "Matteo Maffei"
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{
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],
"doi": "10.1145/3133956.3134096",
"doiUrl": "https://doi.org/10.1145/3133956.3134096",
"entities": [
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"Blocking (computing)",
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"Concurrency (computer science)",
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"Hop",
"Non-blocking algorithm",
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"paperAbstract": "Permissionless blockchains protocols such as Bitcoin are inherently limited in transaction throughput and latency. Current efforts to address this key issue focus on off-chain payment channels that can be combined in a Payment-Channel Network (PCN) to enable an unlimited number of payments without requiring to access the blockchain other than to register the initial and final capacity of each channel. While this approach paves the way for low latency and high throughput of payments, its deployment in practice raises several privacy concerns as well as technical challenges related to the inherently concurrent nature of payments that have not been sufficiently studied so far. In this work, we lay the foundations for privacy and concurrency in PCNs, presenting a formal definition in the Universal Composability framework as well as practical and provably secure solutions. In particular, we present Fulgor and Rayo. Fulgor is the first payment protocol for PCNs that provides provable privacy guarantees for PCNs and is fully compatible with the Bitcoin scripting system. However, Fulgor is a blocking protocol and therefore prone to deadlocks of concurrent payments as in currently available PCNs. Instead, Rayo is the first protocol for PCNs that enforces non-blocking progress (i.e., at least one of the concurrent payments terminates). We show through a new impossibility result that non-blocking progress necessarily comes at the cost of weaker privacy. At the core of Fulgor and Rayo is Multi-Hop HTLC, a new smart contract, compatible with the Bitcoin scripting system, that provides conditional payments while reducing running time and communication overhead with respect to previous approaches. Our performance evaluation of Fulgor and Rayo shows that a payment with 10 intermediate users takes as few as 5 seconds, thereby demonstrating their feasibility to be deployed in practice.",
"pdfUrls": [
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"title": "Concurrency and Privacy with Payment-Channel Networks",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
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"paperAbstract": "Precedence and associativity declarations in systems like <pre>yacc</pre> resolve ambiguities in context-free grammars\u00c2 (CFGs) by specifying restrictions on allowed parses. However, they are special purpose and do not handle the grammatical restrictions that language designers need in order to resolve ambiguities like dangling <pre>else</pre>, the interactions between binary operators and functional <pre>if</pre> expressions in ML, and the interactions between object allocation and function calls in JavaScript. Often, language designers resort to restructuring their grammars in order to encode these restrictions, but this obfuscates the designerâ\u0080\u0099s intent and can make grammars more difficult to read, write, and maintain. \nIn this paper, we show how tree automata can modularly and concisely encode such restrictions. We do this by reinterpreting CFGs as tree automata and then intersecting them with tree automata encoding the desired restrictions. The results are then reinterpreted back into CFGs that encode the specified restrictions. This process can be used as a preprocessing step before other CFG manipulations and is well behaved. It performs well in practice and never introduces ambiguities or LR(<i>k</i>) conflicts.",
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"paperAbstract": "Social influence has attracted significant attention owing to the prevalence of social networks (SNs). In this paper, we study a new social influence problem, called <i>personalized social tags exploration</i> (PITEX), to help any user in the SN explore how she influences the network. Given a target user, it finds a size-k tag set that maximizes this user's social influence. We prove the problem is NP-hard to be approximated within any constant ratio. To solve it, we introduce a sampling-based framework, which has an approximation ratio of 1-ε over 1+ε with high probabilistic guarantee. To speedup the computation, we devise more efficient sampling techniques and propose best-effort exploration to quickly prune tag sets with small influence. To further enable instant exploration, we devise a novel index structure and develop effective pruning and materialization techniques. Experimental results on real large-scale datasets validate our theoretical findings and show high performances of our proposed methods.",
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"title": "Discovering Your Selling Points: Personalized Social Influential Tags Exploration",
"venue": "SIGMOD Conference",
"year": 2017
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"title": "Leakage-Abuse Attacks against Order-Revealing Encryption",
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"year": 2016
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"title": "Dealing with Performance Unpredictability in an Asymmetric Multicore Processor Cloud",
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"name": "Wenjie Zhang"
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"paperAbstract": "A sketch or synopsis of a large dataset captures vital properties of the original data while typically occupying much less space. In this paper, we consider the problem of computing a sketch of a massive data matrix <i>A</i> ∈ℜ<sup>nxd</sup>, which is distributed across a large number of <i>s</i> servers. Our goal is to output a matrix <i>B</i>∈ℜ<sup>ℓ x d</sup> which is significantly smaller than but still approximates <i>A</i> well in terms of <i>covariance error</i>, i.e., ||<i>A<sup>T</sup>A-B<sup>T</sup>B</i>||<sub>2</sub>||. Here, for a matrix <i>A</i>, ||<i>A</i>||<sub>2</sub>|| is the spectral norm of <i>A</i>, which is defined as the largest singular value of <i>A</i>. Following previous works, we call <i>B</i> a covariance sketch of <i>A</i>. We are mainly focused on minimizing the communication cost, which is arguably the most valuable resource in distributed computations. We show a gap between deterministic and randomized communication complexity for computing a covariance sketch. More specifically, we first prove a tight deterministic lower bound, then show how to bypass this lower bound using randomization. In <i>Principle Component Analysis</i> (PCA), the goal is to find a low-dimensional subspace that captures as much of the variance of a dataset as possible. Based on a well-known connection between covariance sketch and PCA, we give a new algorithm for distributed PCA with improved communication cost. Moreover, in our algorithms, each server only needs to make one pass over the data with limited working space.",
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"title": "Efficient Matrix Sketching over Distributed Data",
"venue": "PODS",
"year": 2017
},
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"doi": "10.1109/HiPC.2017.00052",
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"title": "Model checking copy phases of concurrent copying garbage collection with various memory models",
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"year": 2017
},
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"name": "Reza Shokri"
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"name": "Marco Stronati"
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"paperAbstract": "We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.",
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"title": "Membership Inference Attacks Against Machine Learning Models",
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"paperAbstract": "We describe the design, implementation, and evaluation of Occult (Observable Causal Consistency Using Lossy Timestamps), the first scalable, geo-replicated data store that provides causal consistency to its clients without exposing the system to the possibility of slowdown cascades, a key obstacle to the deployment of causal consistency at scale. Occult supports read/write transactions under PC-PSI, a variant of Parallel Snapshot Isolation that contributes to Occult\u2019s immunity to slowdown cascades by weakening how PSI replicates transactions committed at the same replica. While PSI insists that they all be totally ordered, PC-PSI simply requires total order Per Client session. Nonetheless, Occult guarantees that all transactions read from a causally consistent snapshot of the datastore without requiring any coordination in how transactions are asynchronously replicated.",
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"title": "I Can't Believe It's Not Causal! Scalable Causal Consistency with No Slowdown Cascades",
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"paperAbstract": "This paper describes an approach to designing, implementing, and formally verifying the functional correctness of an OS kernel, named Hyperkernel, with a high degree of proof automation and low proof burden. We base the design of Hyperkernel's interface on xv6, a Unix-like teaching operating system. Hyperkernel introduces three key ideas to achieve proof automation: it finitizes the kernel interface to avoid unbounded loops or recursion; it separates kernel and user address spaces to simplify reasoning about virtual memory; and it performs verification at the LLVM intermediate representation level to avoid modeling complicated C semantics.\n We have verified the implementation of Hyperkernel with the Z3 SMT solver, checking a total of 50 system calls and other trap handlers. Experience shows that Hyperkernel can avoid bugs similar to those found in xv6, and that the verification of Hyperkernel can be achieved with a low proof burden.",
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"title": "Hyperkernel: Push-Button Verification of an OS Kernel",
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"paperAbstract": "Software and hardware are increasingly being formally verified against specifications, but how can we verify the specifications themselves? This paper explores what it means to formally verify a specification. We solve three challenges: (1) How to create a secondary, higher-level specification that can be effectively reviewed by processor designers who are not experts in formal verification; (2) How to avoid common-mode failures between the specifications; and (3) How to automatically verify the two specifications against each other. \n One of the most important specifications for software verification is the processor specification since it defines the behaviour of machine code and of hardware protection features used by operating systems. We demonstrate our approach on ARM's v8-M Processor Specification, which is intended to improve the security of Internet of Things devices. Thus, we focus on establishing the security guarantees the architecture is intended to provide. Despite the fact that the ARM v8-M specification had previously been extensively tested, we found twelve bugs (including two security bugs) that have all been fixed by ARM.",
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"title": "Who guards the guards? formal validation of the Arm v8-m architecture specification",
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"name": "Chuanxiong Guo"
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"name": "Chengchen Hu"
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"name": "Zongpeng Li"
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"paperAbstract": "Web application performance heavily relies on the hit rate of DRAM key-value caches. Current DRAM caches statically partition memory across applications that share the cache. This results in under utilization and limits cache hit rates. We present Memshare, a DRAM key-value cache that dynamically manages memory across applications. Memshare provides a resource sharing model that guarantees reserved memory to different applications while dynamically pooling and sharing the remaining memory to optimize overall hit rate. Key-value caches are typically memory capacity bound, which leaves cache server CPU and memory bandwidth idle. Memshare leverages these resources with a logstructured design that allows it to provide better hit rates than conventional caches by dynamically re-partitioning memory among applications. We implemented Memshare and ran it on a week-long trace from a commercial memcached provider. Memshare increases the combined hit rate of the applications in the trace from 84.7% to 90.8%, and it reduces the total number of misses by 39.7% without significantly affecting cache throughput or latency. Even for single-tenant applications, Memshare increases the average hit rate of the state-of-the-art key-value cache by an additional 2.7%.",
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"paperAbstract": "The number of triangles in a graph is useful to deduce a plethora of important features of the network that the graph is modeling. However, finding the exact value of this number is computationally expensive. Hence, a number of approximation algorithms based on random sampling of edges, or wedges (adjacent edge pairs) have been proposed for estimating this value. We argue that for large sparse graphs with power-law degree distribution, random edge sampling requires sampling large number of edges before providing enough information for accurate estimation, and existing wedge sampling methods lead to biased samplings, which in turn lead to less accurate estimations. In this paper, we propose a hybrid algorithm between edge and wedge sampling that addresses the deficiencies of both approaches. We start with uniform edge sampling and then extend each selected edge to form a wedge that is more informative for estimating the overall triangle count. The core estimate we make is the number of triangles each sampled edge in the first phase participates in. This approach provides accurate approximations with very small sampling ratios, outperforming the state-of-the-art up to 8 times in sample size while providing estimations with 95% confidence.",
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"paperAbstract": "Currently, the attempt to choose the \"best\" content replica server for a client is carried out solely by CDNs. While CDNs have a decent view of load distribution and content placement, they receive little input from the clients themselves. We propose a hybrid solution, subnet assimilation, where the client participates in the server selection process while still leaving the final say to the CDN. Subnet assimilation allows clients to declare their own \"network location,\" different from the actual one, which in turn drives a CDN towards making better decisions. To demonstrate, we introduce Drongo, a client-side system, readily deployable on existing clients without any changes to the CDNs, that employs subnet assimilation to dramatically improve replica server selection. We implemented and extensively evaluated Drongo on a set of 429 clients spread across 177 countries and 6 major CDNs. We show that Drongo affects 69.93% of all clients, prompting better CDN replica choices which reduce the latency of affected requests by up to an order of magnitude and by 24.89% on average across six major providers, with Google's performance improving by 50% in the median case. Our results indicate that client participation holds great opportunities for the advancement of CDN performance.",
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"paperAbstract": "We consider the following question: what consistency model is appropriate for coordinating the actions of a replicated set of SDN controllers? We first argue that the conventional requirement of strong consistency, typically achieved through the use of Paxos or other consensus algorithms, is conceptually unnecessary to handle unplanned network updates. We present an alternate approach, based on the weaker notion of eventual correctness, and describe the design of a simple coordination layer (SCL) that can seamlessly turn a set of single-image SDN controllers (that obey certain properties) into a distributed SDN system that achieves this goal (whereas traditional consensus mechanisms do not). We then show through analysis and simulation that our approach provides faster responses to network events. While our primary focus is on handling unplanned network updates, our coordination layer also handles policy updates and other situations where consistency is warranted. Thus, contrary to the prevailing wisdom, we argue that distributed SDN control planes need only be slightly more complicated than single-image controllers.",
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"paperAbstract": "Centralized electrical storage system management in islanded micro-grids is treated both theoretically and experimentally. An overview of the types of distributed islands in Wi-Fi Long Distance networks is the starting point, followed by examples of WiLDNets and associated reliability problems. Presentation of an archetypical tree structure communication system serves to introduce a centralized management system that may be used to improve reliability of multi-community private networks. An existing Wi-Fi long distance testbed with distributed storage serves as an islanded micro-grid for experimental study. Centralized management algorithms are tested. The results are generalized to the case of large meshed communication system.",
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"paperAbstract": "Stencil-based applications such as CFD have succeeded in obtaining high performance on GPU supercomputers. The problem sizes of these applications are limited by the GPU device memory capacity, which is typically smaller than the host memory. On GPU supercomputers, a locality improvement technique using temporal blocking method with memory swapping between host and device enables large computation beyond the device memory capacity. However, because the loop management of temporal blocking with data movement across these memories increase programming difficulty, the applying this methodology to the real stencil applications demands substantially higher programming cost. Our high-productivity stencil framework automatically applies temporal blocking to boundary exchange required for stencil computation and supports automatic memory swapping provided by a MPI/CUDA wrapper library. The framework-based application for the airflow in an urban city maintains 80% performance even with the twice larger than the GPU memory capacity and have demonstrated good weak scalability on the TSUBAME 2.5 supercomputer.",
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"paperAbstract": "Almost all performance analysis tools in the HPC space perform some form of aggregation to compute summary information of a series of performance measurements, from summations to more complex operations like histograms. Aggregation not only reduces data volumes and consequently storage space requirements and overheads, but is also crucial to extract insights from recorded measurement data. In current tools, however, most aspects that control the aggregation, such as the data dimensions to be reduced, are hard-coded in the tool for a set of particular use cases identified by the tool developer and cannot be extended or modified by the user. This limits their flexibility and often results in users having to learn and use multiple tools with different aggregation options for their performance analysis needs.We present a novel approach for performance data aggregation based on a flexible key:value data model with user-defined attributes, where users can define custom aggregation schemes in a simple description language. This not only gives users the control to deploy the particular data aggregation they need, but also opens the door for aggregations along application-specific data dimensions that cannot be achieved with traditional profiling tools. We show how our approach can be applied for performance profiling at runtime, cross-process data aggregation, and interactive data analysis and demonstrate its functionality with several case studies driven by real world codes.",
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"paperAbstract": "Variation has been shown to exist across the cells within a modern DRAM chip. Prior work has studied and exploited several forms of variation, such as manufacturing-process- or temperature-induced variation. We empirically demonstrate a new form of variation that exists within a real DRAM chip, induced by the design and placement of different components in the DRAM chip: different regions in DRAM, based on their relative distances from the peripheral structures, require different minimum access latencies for reliable operation. In particular, we show that in most real DRAM chips, cells closer to the peripheral structures can be accessed much faster than cells that are farther. We call this phenomenon design-induced variation in DRAM. Our goals are to i) understand design-induced variation that exists in real, state-of-the-art DRAM chips, ii) exploit it to develop low-cost mechanisms that can dynamically find and use the lowest latency at which to operate a DRAM chip reliably, and, thus, iii) improve overall system performance while ensuring reliable system operation.\n To this end, we first experimentally demonstrate and analyze designed-induced variation in modern DRAM devices by testing and characterizing 96 DIMMs (768 DRAM chips). Our characterization identifies DRAM regions that are vulnerable to errors, if operated at lower latency, and finds consistency in their locations across a given DRAM chip generation, due to design-induced variation. Based on our extensive experimental analysis, we develop two mechanisms that reliably reduce DRAM latency. First, DIVA Profiling uses runtime profiling to dynamically identify the lowest DRAM latency that does not introduce failures. DIVA Profiling exploits design-induced variation and periodically profiles only the vulnerable regions to determine the lowest DRAM latency at low cost. It is the first mechanism to dynamically determine the lowest latency that can be used to operate DRAM reliably. DIVA Profiling reduces the latency of read/write requests by 35.1%/57.8%, respectively, at 55°C. Our second mechanism, DIVA Shuffling, shuffles data such that values stored in vulnerable regions are mapped to multiple error-correcting code (ECC) codewords. As a result, DIVA Shuffling can correct 26% more multi-bit errors than conventional ECC. Combined together, our two mechanisms reduce read/write latency by 40.0%/60.5%, which translates to an overall system performance improvement of 14.7%/13.7%/13.8% (in 2-/4-/8-core systems) across a variety of workloads, while ensuring reliable operation.",
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"https://people.inf.ethz.ch/omutlu/pub/DIVA-low-latency-DRAM_sigmetrics17-abstract.pdf",
"http://doi.acm.org/10.1145/3084464",
"http://doi.acm.org/10.1145/3078505.3078533",
"https://users.ece.cmu.edu/~saugatag/papers/17sigmetrics_diva.pdf",
"http://www.pdl.cmu.edu/PDL-FTP/NVM/17sigmetrics_diva.pdf",
"http://www.cs.toronto.edu/~pekhimenko/Papers/DIVA.pdf",
"http://www.cs.virginia.edu/~smk9u/DIVA_sigmetrics17-paper.pdf"
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"title": "Design-Induced Latency Variation in Modern DRAM Chips: Characterization, Analysis, and Latency Reduction Mechanisms",
"venue": "SIGMETRICS",
"year": 2017
},
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"ids": [
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"name": "Francisco J. Clemente-Castell\u00f3"
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"name": "Bogdan Nicolae"
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"name": "M. Mustafa Rafique"
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"name": "Rafael Mayo"
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"name": "Juan Carlos Fern\u00e1ndez"
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],
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"journalName": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
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"paperAbstract": "Hybrid cloud bursting (i.e., leasing temporary off-premise cloud resources to boost the overall capacity during peak utilization) is a popular and cost-effective way to deal with the increasing complexity of big data analytics. It is particularly promising for iterative MapReduce applications that reuse massive amounts of input data at each iteration, which compensates for the high overhead and cost of concurrent data transfers from the on-premise to the off-premise VMs over a weak inter-site link that is of limited capacity. In this paper we study how to combine various MapReduce data locality techniques designed for hybrid cloud bursting in order to achieve scalability for iterative MapReduce applications in a cost-effective fashion. This is a non trivial problem due to the complex interaction between the data movements over the weak link and the scheduling of computational tasks that have to adapt to the shifting data distribution. We show that using the right combination of techniques, iterative MapReduce applications can scale well in a hybrid cloud bursting scenario and come even close to the scalability observed in single sites.",
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"title": "Evaluation of Data Locality Strategies for Hybrid Cloud Bursting of Iterative MapReduce",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
"year": 2017
},
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"authors": [
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"name": "Frank Wang"
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"name": "Catherine Yun"
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"name": "Shafi Goldwasser"
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"name": "Vinod Vaikuntanathan"
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"paperAbstract": "Many online services let users query public datasets such as maps, flight prices, or restaurant reviews. Unfortunately, the queries to these services reveal highly sensitive information that can compromise users\u2019 privacy. This paper presents Splinter, a system that protects users\u2019 queries on public data and scales to realistic applications. A user splits her query into multiple parts and sends each part to a different provider that holds a copy of the data. As long as any one of the providers is honest and does not collude with the others, the providers cannot determine the query. Splinter uses and extends a new cryptographic primitive called Function Secret Sharing (FSS) that makes it up to an order of magnitude more efficient than prior systems based on Private Information Retrieval and garbled circuits. We develop protocols extending FSS to new types of queries, such as MAX and TOPK queries. We also provide an optimized implementation of FSS using AES-NI instructions and multicores. Splinter achieves end-to-end latencies below 1.6 seconds for realistic workloads including a Yelp clone, flight search, and map routing.",
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"http://eprint.iacr.org/2016/1148",
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"title": "Splinter: Practical Private Queries on Public Data",
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"name": "Glenn Reinman"
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"doi": "10.1109/HPCA.2017.19",
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"paperAbstract": "While emerging accelerator-centric architectures offer orders-of-magnitude performance and energy improvements, use cases and adoption can be limited by their rigid programming model. A unified virtual address space between the host CPU cores and customized accelerators can largely improve the programmability, which necessitates hardware support for address translation. However, supporting address translation for customized accelerators with low overhead is nontrivial. Prior studies either assume an infinite-sized TLB and zero page walk latency, or rely on a slow IOMMU for correctness and safety—which penalizes the overall system performance. To provide efficient address translation support for accelerator-centric architectures, we examine the memory access behavior of customized accelerators to drive the TLB augmentation and MMU designs. First, to support bulk transfers of consecutive data between the scratchpad memory of customized accelerators and the memory system, we present a relatively small private TLB design to provide low-latency caching of translations to each accelerator. Second, to compensate for the effects of the widely used data tiling techniques, we design a shared level-two TLB to serve private TLB misses on common virtual pages, eliminating duplicate page walks from accelerators working on neighboring data tiles that are mapped to the same physical page. This two-level TLB design effectively reduces page walks by 75.8% on average. Finally, instead of implementing a dedicated MMU which introduces additional hardware complexity, we propose simply leveraging the host per-core MMU for efficient page walk handling. This mechanism is based on our insight that the existing MMU cache in the CPU MMU satisfies the demand of customized accelerators with minimal overhead. Our evaluation demonstrates that the combined approach incurs only a 6.4% performance overhead compared to the ideal address translation.",
"pdfUrls": [
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"title": "Supporting Address Translation for Accelerator-Centric Architectures",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
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},
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"paperAbstract": "The blooming availability of traces for social, biological, and communication networks opens up unprecedented opportunities in analyzing diffusion processes in networks. However, the sheer sizes of the nowadays networks raise serious challenges in computational efficiency and scalability. In this paper, we propose a new hyper-graph sketching framework for influence dynamics in networks. The core of our sketching framework, called SKIS, is an efficient importance sampling algorithm that returns only non-singular reverse cascades in the network. Comparing to previously developed sketches like RIS and SKIM, our sketch significantly enhances estimation quality while substantially reducing processing time and memory-footprint. Further, we present general strategies of using SKIS to enhance existing algorithms for influence estimation and influence maximization which are motivated by practical applications like viral marketing. Using SKIS, wedesign high-quality influence oracles for seed sets with average estimation error up to 10x times smaller than those using RIS and 6x times smaller than SKIMs. In addition, our influence maximization using SKIS substantially improves the quality of solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x memory reduction for the fastest RIS-based DSSA algorithm, while maintaining the same theoretical guarantees.",
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"title": "Importance Sketching of Influence Dynamics in Billion-Scale Networks",
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"paperAbstract": "Simulation is a fast, controlled, and reproducible way to evaluate new algorithms for distributed computing platforms in a variety of conditions. However, the realism of simulations is rarely assessed, which critically questions the applicability of a whole range of findings. In this paper, we present our efforts to build platform models from application traces, to allow for the accurate simulation of file transfers across a distributed infrastructure. File transfers are key to performance, as the variability of file transfer times has important consequences on the dataflow of the application. We present a methodology to build realistic platform models from application traces and provide a quantitative evaluation of the accuracy of the derived simulations. Results show that the proposed models are able to correctly capture real-life variability and significantly outperform the state-of-the-art model.",
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"title": "Modeling Distributed Platforms from Application Traces for Realistic File Transfer Simulation",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
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"doi": "10.1145/3133956.3134072",
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"paperAbstract": "Given how the \"patching treadmill\" plays a central role for enabling sites to counter emergent security concerns, it behooves the security community to understand the patch development process and characteristics of the resulting fixes. Illumination of the nature of security patch development can inform us of shortcomings in existing remediation processes and provide insights for improving current practices. In this work we conduct a large-scale empirical study of security patches, investigating more than 4,000 bug fixes for over 3,000 vulnerabilities that affected a diverse set of 682 open-source software projects. For our analysis we draw upon the National Vulnerability Database, information scraped from relevant external references, affected software repositories, and their associated security fixes. Leveraging this diverse set of information, we conduct an analysis of various aspects of the patch development life cycle, including investigation into the duration of impact a vulnerability has on a code base, the timeliness of patch development, and the degree to which developers produce safe and reliable fixes. We then characterize the nature of security fixes in comparison to other non-security bug fixes, exploring the complexity of different types of patches and their impact on code bases.\n Among our findings we identify that: security patches have a lower footprint in code bases than non-security bug patches; a third of all security issues were introduced more than 3 years prior to remediation; attackers who monitor open-source repositories can often get a jump of weeks to months on targeting not-yet-patched systems prior to any public disclosure and patch distribution; nearly 5% of security fixes negatively impacted the associated software; and 7% failed to completely remedy the security hole they targeted.",
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"title": "A Large-Scale Empirical Study of Security Patches",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"name": "Abdelhafid Mazouz"
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"name": "David C. Wong"
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"name": "David J. Kuck"
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"doi": "10.1145/3030207.3030224",
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"paperAbstract": "This paper presents an empirical approach to measuring and modeling the energy consumption of multicore processors.The modeling approach allows us to find a breakdown of the energy consumption among a set of key hardware components, also called HW nodes. We explicitly model the front-end and the back-end in terms of the number of instructions executed. We also model the L1, L2 and L3 caches. Furthermore, we explicitly model the static and dynamic energy consumed by the the uncore and core components. From a software perspective, our methodology allows us to correlate energy to the executed code, which helps find opportunities for code optimization and tuning.\n We use binary analysis and hardware counters for performance characterization. Although, we use the on-chip counters (RAPL) for energy measurement, our methodology does not rely on a specific method for energy measurement. Thus, it is portable and easy to deploy in various computing environments. We validate our energy model using two Intel processors with a set of HPC codelets, where data sizes are varied to come from the L1, L2 and L3 caches and show 3% average modeling error. We present a comprehensive analysis and show energy consumption differences between kernels and relate those differences to the algorithms that are implemented. Finally, we discuss how vectorization leads to energy savings compared to non-vectorized codes.",
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"title": "An Incremental Methodology for Energy Measurement and Modeling",
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"year": 2017
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"name": "Vasily Tarasov"
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"name": "Erez Zadok"
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"paperAbstract": "Traditionally, file systems were implemented as part of OS kernels. However, as complexity of file systems grew, many new file systems began being developed in user space. Nowadays, user-space file systems are often used to prototype and evaluate new approaches to file system design. Low performance is considered the main disadvantage of user-space file systems but the extent of this problem has never been explored systematically. As a result, the topic of user-space file systems remains rather controversial: while some consider user-space file systems a toy not to be used in production, others develop full-fledged production file systems in user space. In this paper we analyze the design and implementation of the most widely known user-space file system framework\u2014FUSE\u2014and characterize its performance for a wide range of workloads. We instrumented FUSE to extract useful statistics and traces, which helped us analyze its performance bottlenecks and present our analysis results. Our experiments indicate that depending on the workload and hardware used, performance degradation caused by FUSE can be completely imperceptible or as high as \u201383% even when optimized; and relative CPU utilization can increase by 31%.",
"pdfUrls": [
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"https://www.usenix.org/system/files/conference/fast17/fast17-vangoor.pdf",
"https://www.usenix.org/conference/fast17/technical-sessions/presentation/vangoor",
"http://www.usenix.org./system/files/conference/fast17/fast17-vangoor.pdf",
"http://www.fsl.cs.sunysb.edu/docs/fuse/fuse-performance-fast17.pdf"
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"title": "To FUSE or Not to FUSE: Performance of User-Space File Systems",
"venue": "FAST",
"year": 2017
},
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"name": "Mihir Bellare"
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"name": "Viet Tung Hoang"
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"doi": "10.1145/3133956.3133995",
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"paperAbstract": "Deep and shallow convection calculations occupy significant times in atmosphere models. These calculations also present significant load imbalances due to varying cloud covers over different regions of the grid. In this work, we accelerate these calculations on Intel R \u00a9 Xeon PhiTM Coprocessor Systems. By employing dynamic scheduling in OpenMP, we demonstrate large reductions in load imbalance and about 10% increase in speedups. By careful categorization of data as private, firstprivate and shared, we minimize data copying overheads for the coprocessors. We identify regions of false sharing among threads and eliminate them by loop rearrangements. We also employ proportional partitioning of independent column computations across both the CPU and coprocessor cores based on the performance ratio of the computations on the heterogeneous resources. These techniques along with various vectorization strategies resulted in about 30% improvement in convection calculations.",
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"paperAbstract": "Software deobfuscation is a crucial activity in security analysis and especially in malware analysis. While standard static and dynamic approaches suffer from well-known shortcomings, Dynamic Symbolic Execution (DSE) has recently been proposed as an interesting alternative, more robust than staticanalysis and more complete than dynamic analysis. Yet, DSE addresses only certain kinds of questions encountered by a reverser, namely feasibility questions. Many issues arising during reverse, e.g., detecting protection schemes such as opaque predicates, fall into the category of infeasibility questions. We present Backward-Bounded DSE, a generic, precise, efficient and robust method for solving infeasibility questions. We demonstrate the benefit of the method for opaque predicates and call stack tampering, and give some insight for its usage for some other protection schemes. Especially, the technique has successfully been used on state-of-the-art packers as well as on the government-grade X-Tunnel malware – allowing its entire deobfuscation. Backward-Bounded DSE does not supersede existing DSE approaches, but rather complements them by addressing infeasibility questions in a scalable and precise manner. Following this line, we proposesparse disassembly, a combination of Backward-Bounded DSE and static disassembly able to enlarge dynamic disassembly in a guaranteed way, hence getting the best of dynamic and static disassembly. This work paves the way for robust, efficient and precise disassembly tools for heavily-obfuscated binaries.",
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"paperAbstract": "Graph <b>edge</b> partition models have recently become an appealing alternative to graph <b>vertex</b> partition models for distributed computing due to their flexibility in balancing loads and their performance in reducing communication cost [6, 16]. In this paper, we propose a simple yet effective graph edge partitioning algorithm. In practice, our algorithm provides good partition quality (and better than similar state-of-the-art edge partition approaches, at least for power-law graphs) while maintaining low partition overhead. In theory, previous work [6] showed that an approximation guarantee of <i>O</i>(<i>d<sub>max</sub></i>√ log <i>n</i> log <i>k</i>) apply to the graphs with <i>m</i>=Ω(<i>k</i><sup>2</sup>) edges (<i>k</i> is the number of partitions). We further rigorously proved that this approximation guarantee hold for all graphs.\n We show how our edge partition model can be applied to parallel computing. We draw our example from GPU program locality enhancement and demonstrate that the graph edge partition model does not only apply to distributed computing with many computer nodes, but also to parallel computing in a single computer node with a many-core processor.",
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"paperAbstract": "Free cloud-based services are powerful candidates for deploying ubiquitous encryption for messaging. In the case of email and increasingly chat, users expect the ability to store and search their messages persistently. Using data from one of the top three mail providers, we confirm that for a searchable encryption scheme to scale to millions of users, it should be highly IO-efficient (locality), and handle a very dynamic message corpi. We observe that existing solutions fail to achieve both properties simultaneously. We then design, build, and evaluate a provably secure Dynamic Searchable Symmetric Encryption (DSSE) scheme with significant reduction in IO cost compared to preceding works when used for email or other highly dynamic message corpi.",
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"paperAbstract": "Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide mobile traffic is however a complex and costly process that relies on dedicated probes. Some of these probes have limited precision or coverage, others gather tens of gigabytes of logs daily, which independently offer limited insights. Extracting fine-grained patterns involves expensive spatial aggregation of measurements, storage, and post-processing. In this paper, we propose a mobile traffic super-resolution technique that overcomes these problems by inferring narrowly localised traffic consumption from coarse measurements. We draw inspiration from image processing and design a deep-learning architecture tailored to mobile networking, which combines Zipper Network (ZipNet) and Generative Adversarial neural Network (GAN) models. This enables to uniquely capture spatio-temporal relations between traffic volume snapshots routinely monitored over broad coverage areas ('low-resolution') and the corresponding consumption at 0.05 km2 level ('high-resolution') usually obtained after intensive computation. Experiments we conduct with a real-world data set demonstrate that the proposed ZipNet(-GAN) infers traffic consumption with remarkable accuracy and up to 100X higher granularity as compared to standard probing, while outperforming existing data interpolation techniques. To our knowledge, this is the first time super-resolution concepts are applied to large-scale mobile traffic analysis and our solution is the first to infer fine-grained urban traffic patterns from coarse aggregates.",
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"paperAbstract": "This paper presents TumbleBit, a new unidirectional unlinkable payment hub that is fully compatible with today\u2019s Bitcoin protocol. TumbleBit allows parties to make fast, anonymous, off-blockchain payments through an untrusted intermediary called the Tumbler. TumbleBit\u2019s anonymity properties are similar to classic Chaumian eCash: no one, not even the Tumbler, can link a payment from its payer to its payee. Every payment made via TumbleBit is backed by bitcoins, and comes with a guarantee that Tumbler can neither violate anonymity, nor steal bitcoins, nor \u201cprint money\u201d by issuing payments to itself. We prove the security of TumbleBit using the real/ideal world paradigm and the random oracle model. Security follows from the standard RSA assumption and ECDSA unforgeability. We implement TumbleBit, mix payments from 800 users and show that TumbleBit\u2019s offblockchain payments can complete in seconds.",
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"title": "TumbleBit: An Untrusted Bitcoin-Compatible Anonymous Payment Hub",
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"paperAbstract": "Traffic shaping, including pacing and rate limiting, is fundamental to the correct and efficient operation of both datacenter and wide area networks. Sample use cases include policy-based bandwidth allocation to flow aggregates, rate-based congestion control algorithms, and packet pacing to avoid bursty transmissions that can overwhelm router buffers. Driven by the need to scale to millions of flows and to apply complex policies, traffic shaping is moving from network switches into the end hosts, typically implemented in software in the kernel networking stack.\n In this paper, we show that the performance overhead of end-host traffic shaping is substantial limits overall system scalability as we move to thousands of individual traffic classes per server. Measurements from production servers show that shaping at hosts consumes considerable CPU and memory, unnecessarily drops packets, suffers from head of line blocking and inaccuracy, and does not provide backpressure up the stack. We present Carousel, a framework that scales to tens of thousands of policies and flows per server, built from the synthesis of three key ideas: i) a single queue shaper using time as the basis for releasing packets, ii) fine-grained, just-in-time freeing of resources in higher layers coupled to actual packet departures, and iii) one shaper per CPU core, with lock-free coordination. Our production experience in serving video traffic at a Cloud service provider shows that Carousel shapes traffic accurately while improving overall machine CPU utilization by 8% (an improvement of 20% in the CPU utilization attributed to networking) relative to state-of-art deployments. It also conforms 10 times more accurately to target rates, and consumes two orders of magnitude less memory than existing approaches.",
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"https://www.cc.gatech.edu/~amsmti3/files/carousel_sigcomm_final.pdf",
"http://www.cc.gatech.edu/~amsmti3/files/carousel-sigcomm17.pdf",
"http://doi.acm.org/10.1145/3098822.3098852",
"http://conferences.sigcomm.org/sigcomm/2017/files/program/ts-9-4-carousel.pdf"
],
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"title": "Carousel: Scalable Traffic Shaping at End Hosts",
"venue": "SIGCOMM",
"year": 2017
},
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"name": "Xiangnan He"
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"paperAbstract": "Many predictive tasks of web applications need to model categorical variables, such as user IDs and demographics like genders and occupations. To apply standard machine learning techniques, these categorical predictors are always converted to a set of binary features via one-hot encoding, making the resultant feature vector highly sparse. To learn from such sparse data effectively, it is crucial to account for the interactions between features.\n <i>Factorization Machines</i> (FMs) are a popular solution for efficiently using the second-order feature interactions. However, FM models feature interactions in a linear way, which can be insufficient for capturing the non-linear and complex inherent structure of real-world data. While deep neural networks have recently been applied to learn non-linear feature interactions in industry, such as the <i>Wide&Deep</i> by Google and <i>DeepCross</i> by Microsoft, the deep structure meanwhile makes them difficult to train.\n In this paper, we propose a novel model <i>Neural Factorization Machine</i> (NFM) for prediction under sparse settings. NFM seamlessly combines the linearity of FM in modelling second-order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions. Conceptually, NFM is more expressive than FM since FM can be seen as a special case of NFM without hidden layers. Empirical results on two regression tasks show that with one hidden layer only, NFM significantly outperforms FM with a 7.3% relative improvement. Compared to the recent deep learning methods Wide&Deep and DeepCross, our NFM uses a shallower structure but offers better performance, being much easier to train and tune in practice.",
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"https://arxiv.org/pdf/1708.05027v1.pdf",
"http://www.comp.nus.edu.sg/~xiangnan/papers/sigir17-nfm.pdf",
"http://arxiv.org/abs/1708.05027",
"http://doi.acm.org/10.1145/3077136.3080777",
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"title": "Neural Factorization Machines for Sparse Predictive Analytics",
"venue": "SIGIR",
"year": 2017
},
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"paperAbstract": "The paper presents the first concurrency-optimal implementation of a binary search tree (BST). The implementation, based on a standard sequential implementation of an internal tree, ensures that every schedule is accepted, i.e., interleaving of steps of the sequential code, unless linearizability is violated. To ensure this property, we use a novel read-write locking scheme that protects tree edges in addition to nodes. Our implementation outperforms the state-of-the art BSTs on most basic workloads, which suggests that optimizing the set of accepted schedules of the sequential code can be an adequate design principle for efficient concurrent data structures.",
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"https://doi.org/10.1007/978-3-319-64203-1_42",
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"title": "A Concurrency-Optimal Binary Search Tree",
"venue": "Euro-Par",
"year": 2017
},
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"doi": "10.1145/3079856.3080253",
"doiUrl": "https://doi.org/10.1145/3079856.3080253",
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"paperAbstract": "Freedom from deadlock is one of the most important issues when designing routing algorithms in on-chip/off-chip networks. Many works have been developed upon Dally's theory proving that a network is deadlock-free if there is no cyclic dependency on the channel dependency graph. However, finding such acyclic graph has been very challenging, which limits Dally's theory to networks with a low number of channels. In this paper, we introduce three theorems that directly lead to routing algorithms with an acyclic channel dependency graph. We also propose the partitioning methodology, enabling a design to reach the maximum adaptiveness for the n-dimensional mesh and k-ary n-cube topologies with any given number of channels. In addition, deadlock-free routing algorithms can be derived ranging from maximally fully adaptive routing down to deterministic routing. The proposed theorems can drastically remove the difficulties of designing deadlock-free routing algorithms.",
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"title": "EbDa: A new theory on design and verification of deadlock-free interconnection networks",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
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"name": "Si Zhang"
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{
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"name": "Mehmet Yigit Yildirim"
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{
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{
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{
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"name": "Hasan Davulcu"
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{
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"name": "Jingrui He"
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],
"doi": "10.1145/3097983.3098015",
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"Cluster analysis",
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"http://www.public.asu.edu/~dzhou23/papers/KDD2017_HOSPLOC.pdf",
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"title": "A Local Algorithm for Structure-Preserving Graph Cut",
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"paperAbstract": "In sensitive applications, machines need to be periodically calibrated to ensure that they run to high standards. Creating an efficient schedule on these machines requires attention to two metrics: ensuring good throughput of the jobs, and ensuring that not too much cost is spent on machine calibration. In this paper we examine flow time as a metric for scheduling with calibrations. While previous papers guaranteed that jobs would meet a certain deadline, we relax that constraint to a tradeoff: we want to balance how long the average job waits with how many costly calibrations we need to perform.\n One advantage of this metric is that it allows for online schedules (where an algorithm is unaware of a job until it arrives). Thus we give two types of results. We give an efficient offline algorithm which gives the optimal schedule on a single machine for a set of jobs which are known ahead of time. We also give online algorithms which adapt to jobs as they come. Our online algorithms are constant competitive for unweighted jobs on single or multiple machines, and constant-competitive for weighted jobs on a single machine.",
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"title": "Minimizing Total Weighted Flow Time with Calibrations",
"venue": "SPAA",
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"paperAbstract": "Detecting fraudulent users in online social networks is a fundamental and urgent research problem as adversaries can use them to perform various malicious activities. Global social structure based methods, which are known as guilt-by-association, have been shown to be promising at detecting fraudulent users. However, existing guilt-by-association methods either assume symmetric (i.e., undirected) social links, which oversimplifies the asymmetric (i.e., directed) social structure of real-world online social networks, or only leverage labeled fraudulent users or labeled normal users (but not both) in the training dataset, which limits detection accuracies. In this work, we propose GANG, a guilt-by-association method on directed graphs, to detect fraudulent users in OSNs. GANG is based on a novel pairwise Markov Random Field that we design to capture the unique characteristics of the fraudulent-user-detection problem in directed OSNs. In the basic version of GANG, given a training dataset, we leverage Loopy Belief Propagation (LBP) to estimate the posterior probability distribution for each user and uses it to predict a user's label. However, the basic version is not scalable enough and not guaranteed to converge because it relies on LBP. Therefore, we further optimize GANG and our optimized version can be represented as a concise matrix form, with which we are able to derive conditions for convergence. We compare GANG with various existing guilt-by-association methods on a large-scale Twitter dataset and a large-scale Sina Weibo dataset with labeled fraudulent and normal users. Our results demonstrate that GANG substantially outperforms existing methods, and that the optimized version of GANG is significantly more efficient than the basic version.",
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"title": "GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
"year": 2017
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"paperAbstract": "Picking the right cloud configuration for recurring big data analytics jobs running in clouds is hard, because there can be tens of possible VM instance types and even more cluster sizes to pick from. Choosing poorly can significantly degrade performance and increase the cost to run a job by 2-3x on average, and as much as 12x in the worst-case. However, it is challenging to automatically identify the best configuration for a broad spectrum of applications and cloud configurations with low search cost. CherryPick is a system that leverages Bayesian Optimization to build performance models for various applications, and the models are just accurate enough to distinguish the best or close-to-the-best configuration from the rest with only a few test runs. Our experiments on five analytic applications in AWS EC2 show that CherryPick has a 45-90% chance to find optimal configurations, otherwise near-optimal, saving up to 75% search cost compared to existing solutions.",
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"title": "CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics",
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"paperAbstract": "Scalable in-memory key-value stores provide low-latency access times of a few microseconds and perform millions of operations per second per server. With all data in memory, these systems should provide a high level of reconfigurability. Ideally, they should scale up, scale down, and rebalance load more rapidly and flexibly than disk-based systems. Rapid reconfiguration is especially important in these systems since a) DRAM is expensive and b) they are the last defense against highly dynamic workloads that suffer from hot spots, skew, and unpredictable load. However, so far, work on in-memory key-value stores has generally focused on performance and availability, leaving reconfiguration as a secondary concern.\n We present Rocksteady, a live migration technique for the RAMCloud scale-out in-memory key-value store. It balances three competing goals: it migrates data quickly, it minimizes response time impact, and it allows arbitrary, fine-grained splits. Rocksteady migrates 758 MB/s between servers under high load while maintaining a median and 99.9th percentile latency of less than 40 and 250 μs, respectively, for concurrent operations without pauses, downtime, or risk to durability (compared to 6 and 45 μs during normal operation). To do this, it relies on pipelined and parallel replay and a lineagelike approach to fault-tolerance to defer re-replication costs during migration. Rocksteady allows RAMCloud to defer all repartitioning work until the moment of migration, giving it precise and timely control for load balancing.",
"pdfUrls": [
"http://www.flux.utah.edu/download?uid=257",
"http://doi.acm.org/10.1145/3132747.3132784",
"https://www.flux.utah.edu/download?slides=1&type=pdf&uid=257"
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"title": "Rocksteady: Fast Migration for Low-latency In-memory Storage",
"venue": "SOSP",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Gil Jae Lee"
},
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"name": "Jos\u00e9 A. B. Fortes"
}
],
"doi": "10.1109/ICAC.2017.45",
"doiUrl": "https://doi.org/10.1109/ICAC.2017.45",
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"journalName": "2017 IEEE International Conference on Autonomic Computing (ICAC)",
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"paperAbstract": "Many big-data processing applications use dataanalytics frameworks such as Apache Hadoop. Such frameworks have tunable configuration parameters set by experienced system administrators and/or application developers. However, tuning parameters manually can be hard and time-consuming because it requires domain-specific knowledge and understanding of complex inter-dependencies amongst parameters. Most of the frameworks seek efficient resource management by using slots or containers as resource units to be assigned to jobs or tasks, the maximum number of slots or containers in a system being part of the static configuration of the system. This static resource management has limited effectiveness in coping with jobs’ diversity and workload dynamics, even in the case of a single job. Seeking to improve performance (e.g., multiple-jobs makespan and job completion time) without modification of the framework, this paper proposes a hierarchical approach using a fuzzy-logic controller to dynamically adjust the number of concurrent jobs and additional controllers (one for each cluster node) to adjust the number of resource units assigned to jobs on each node. The fuzzylogic controller uses fuzzy rules based on a concave downward relationship between aggregate CPU usage and the number of concurrent jobs. The other controllers use a simple heuristic algorithm to adjust the number of resource units on the basis of resource usage by job tasks. A prototype of our approach was implemented for Apache Hadoop on a cluster running at CloudLab. The prototype was evaluated using realistic workloads generated by SWIM, a statistical workload injector for MapReduce. The evaluation shows that the proposed approach yields up to a 42% reduction of the jobs makespan that results from using Hadoop default settings.",
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"title": "Hierarchical Self-Tuning of Concurrency and Resource Units in Data-Analytics Frameworks",
"venue": "2017 IEEE International Conference on Autonomic Computing (ICAC)",
"year": 2017
},
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"authors": [
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"name": "Subarno Banerjee"
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"name": "Dipankar Das"
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"name": "Sasikanth Avancha"
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"name": "Ashok Jagannathan"
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"name": "Ajaya Durg"
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"name": "Dheemanth Nagaraj"
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"name": "Bharat Kaul"
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"name": "Pradeep Dubey"
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],
"doi": "10.1145/3079856.3080244",
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"paperAbstract": "Deep Neural Networks (DNNs) have demonstrated state-of-the-art performance on a broad range of tasks involving natural language, speech, image, and video processing, and are deployed in many real world applications. However, DNNs impose significant computational challenges owing to the complexity of the networks and the amount of data they process, both of which are projected to grow in the future. To improve the efficiency of DNNs, we propose ScaleDeep, a dense, scalable server architecture, whose processing, memory and interconnect subsystems are specialized to leverage the compute and communication characteristics of DNNs. While several DNN accelerator designs have been proposed in recent years, the key difference is that ScaleDeep primarily targets DNN training, as opposed to only inference or evaluation. The key architectural features from which ScaleDeep derives its efficiency are: (i) heterogeneous processing tiles and chips to match the wide diversity in computational characteristics (FLOPs and Bytes/FLOP ratio) that manifest at different levels of granularity in DNNs, (ii) a memory hierarchy and 3-tiered interconnect topology that is suited to the memory access and communication patterns in DNNs, (iii) a low-overhead synchronization mechanism based on hardware data-flow trackers, and (iv) methods to map DNNs to the proposed architecture that minimize data movement and improve core utilization through nested pipelining. We have developed a compiler to allow any DNN topology to be programmed onto ScaleDeep, and a detailed architectural simulator to estimate performance and energy. The simulator incorporates timing and power models of ScaleDeep's components based on synthesis to Intel's 14nm technology. We evaluate an embodiment of ScaleDeep with 7032 processing tiles that operates at 600 MHz and has a peak performance of 680 TFLOPs (single precision) and 1.35 PFLOPs (half-precision) at 1.4KW. Across 11 state-of-the-art DNNs containing 0.65M-14.9M neurons and 6.8M-145.9M weights, including winners from 5 years of the ImageNet competition, ScaleDeep demonstrates 6x-28x speedup at iso-power over the state-of-the-art performance on GPUs.",
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"title": "SCALEDEEP: A scalable compute architecture for learning and evaluating deep networks",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"authors": [
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"name": "Robert Utterback"
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{
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"name": "Kunal Agrawal"
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"doi": "10.1145/3018743.3018764",
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"paperAbstract": "Record-and-replay systems are useful tools for debugging non-deterministic parallel programs by first recording an execution and then replaying that execution to produce the same access pattern. Existing record-and-replay systems generally target thread-based execution models, and record the behaviors and interleavings of individual threads. Dynamic multithreaded languages and libraries, such as the Cilk family, OpenMP, TBB, etc., do not have a notion of threads. Instead, these languages provide a processor-oblivious model of programming, where programs expose task-parallelism using high-level constructs such as spawn/sync without regard to the number of threads/cores available to run the program. Thread-based record-and-replay would violate the processor-oblivious nature of these programs, as they incorporate the number of threads into the recorded information, constraining the replayed execution to the same number of threads.\n In this paper, we present a processor-oblivious record-and-replay scheme for such languages where record and replay can use different number of processors and both are scheduled using work stealing. We provide theoretical guarantees for our record and replay scheme --- namely that record is optimal for programs with one lock and replay is near-optimal for all cases. In addition, we implemented this scheme in the Cilk Plus runtime system and our evaluation indicates that processor-obliviousness does not cause substantial overheads.",
"pdfUrls": [
"http://dl.acm.org/citation.cfm?id=3018764",
"http://www.cse.wustl.edu/~angelee/papers/po-replay.pdf"
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"title": "Processor-Oblivious Record and Replay",
"venue": "PPOPP",
"year": 2017
},
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"authors": [
{
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],
"name": "Pandian Raju"
},
{
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"name": "Rohan Kadekodi"
},
{
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"name": "Vijay Chidambaram"
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{
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"name": "Ittai Abraham"
}
],
"doi": "10.1145/3132747.3132765",
"doiUrl": "https://doi.org/10.1145/3132747.3132765",
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"paperAbstract": "Key-value stores such as LevelDB and RocksDB offer excellent write throughput, but suffer high write amplification. The write amplification problem is due to the Log-Structured Merge Trees data structure that underlies these key-value stores. To remedy this problem, this paper presents a novel data structure that is inspired by Skip Lists, termed Fragmented Log-Structured Merge Trees (FLSM). FLSM introduces the notion of guards to organize logs, and avoids rewriting data in the same level. We build PebblesDB, a high-performance key-value store, by modifying HyperLevelDB to use the FLSM data structure. We evaluate PebblesDB using micro-benchmarks and show that for write-intensive workloads, PebblesDB reduces write amplification by 2.4-3x compared to RocksDB, while increasing write throughput by 6.7x. We modify two widely-used NoSQL stores, MongoDB and HyperDex, to use PebblesDB as their underlying storage engine. Evaluating these applications using the YCSB benchmark shows that throughput is increased by 18-105% when using PebblesDB (compared to their default storage engines) while write IO is decreased by 35-55%.",
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"paperAbstract": "Program analyses frequently track objects throughout a program, which requires reasoning about aliases. Most dataflow analysis frameworks, however, delegate the task of handling aliases to the analysis clients, which causes a number of problems. For instance, custom-made extensions for alias analysis are complex and cannot easily be reused. On the other hand, due to the complex interfaces involved, off-the-shelf alias analyses are hard to integrate precisely into clients. Lastly, for precision many clients require strong updates, and alias abstractions supporting strong updates are often relatively inefficient. \n In this paper, we present IDEal, an alias-aware extension to the framework for Interprocedural Distributive Environment (IDE) problems. IDEal relieves static-analysis authors completely of the burden of handling aliases by automatically resolving alias queries on-demand, both efficiently and precisely. IDEal supports a highly precise analysis using strong updates by resorting to an on-demand, flow-sensitive, and context-sensitive all-alias analysis. Yet, it achieves previously unseen efficiency by propagating aliases individually, creating highly reusable per-pointer summaries. \n We empirically evaluate IDEal by comparing TSf, a state-of-the-art typestate analysis, to TSal, an IDEal-based typestate analysis. Our experiments show that the individual propagation of aliases within IDEal enables TSal to propagate 10.4x fewer dataflow facts and analyze 10.3x fewer methods when compared to TSf. On the DaCapo benchmark suite, TSal is able to efficiently compute precise results.",
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"title": "IDEal: efficient and precise alias-aware dataflow analysis",
"venue": "PACMPL",
"year": 2017
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"paperAbstract": "Recent advances in formal verification techniques enabled the implementation of distributed systems with machine-checked proofs. While results are encouraging, the importance of distributed systems warrants a large scale evaluation of the results and verification practices.\n This paper thoroughly analyzes three state-of-the-art, formally verified implementations of distributed systems: Iron-Fleet, Verdi, and Chapar. Through code review and testing, we found a total of 16 bugs, many of which produce serious consequences, including crashing servers, returning incorrect results to clients, and invalidating verification guarantees. These bugs were caused by violations of a wide-range of assumptions on which the verified components relied. Our results revealed that these assumptions referred to a small fraction of the trusted computing base, mostly at the interface of verified and unverified components. Based on our observations, we have built a testing toolkit called PK, which focuses on testing these parts and is able to automate the detection of 13 (out of 16) bugs.",
"pdfUrls": [
"http://6826.csail.mit.edu/2017/papers/empirical-study.pdf",
"http://doi.acm.org/10.1145/3064176.3064183",
"http://locore.cs.washington.edu/papers/fonseca-dsbugs.pdf",
"http://homes.cs.washington.edu/~pfonseca/papers/eurosys2017-dsbugs.pdf"
],
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"s2Url": "https://semanticscholar.org/paper/2817df10c4ffe29482928cb97b8ee89d8560b4cd",
"sources": [
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"title": "An Empirical Study on the Correctness of Formally Verified Distributed Systems",
"venue": "EuroSys",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Hyunsub Song"
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"ids": [
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"name": "Young Je Moon"
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{
"ids": [
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"name": "Se Kwon Lee"
},
{
"ids": [
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"name": "Sam H. Noh"
}
],
"doi": "10.1109/ISPASS.2017.7975268",
"doiUrl": "https://doi.org/10.1109/ISPASS.2017.7975268",
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"paperAbstract": "The advent of Persistent Memory (PM), which is anticipated to have byte-addressable access latency in par with DRAM and yet nonvolatile, has stepped up interest in using PM as storage. Hence, PM storage targeted file systems are being developed under the premise that legacy file systems are suboptimal on memory bus attached PM-based storage. However, many years of time and effort are ingrained in legacy file systems that are now time-tested and mature. Simply scrapping them altogether may be unwarranted. In this paper, we look into how we can leverage the maturity ingrained in legacy file systems to the fullest, while, at the same time, reaping the high performance offered by PM. To this end, we first go through a thorough analysis of legacy Ext4 file systems, and compare it with NOVA, PMFS, and Ext4 with DAX extension, which are new PM file systems available in Linux. Based on these analyses, we then propose the Persistent Memory Adaptation Layer (PMAL) module that is lightweight (roughly 180 LoC) and can easily be integrated into legacy file systems to take advantage of PM storage. Using Ext4, we show that the performance of PMAL integrated Ext4 is in par with PM file systems for the Filebench and key-value store benchmarks.",
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"title": "PMAL: Enabling lightweight adaptation of legacy file systems on persistent memory systems",
"venue": "2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)",
"year": 2017
},
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"authors": [
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"name": "Taejoong Chung"
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{
"ids": [
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"name": "Roland van Rijswijk-Deij"
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"name": "David R. Choffnes"
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"name": "Dave Levin"
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"name": "Bruce M. Maggs"
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"name": "Alan Mislove"
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"name": "Christo Wilson"
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"doi": "10.1145/3131365.3131373",
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"paperAbstract": "The Domain Name System (DNS) provides a scalable, flexible name resolution service. Unfortunately, its unauthenticated architecture has become the basis for many security attacks. To address this, DNS Security Extensions (DNSSEC) were introduced in 1997. DNSSEC's deployment requires support from the top-level domain (TLD) registries and registrars, as well as participation by the organization that serves as the DNS operator. Unfortunately, DNSSEC has seen poor deployment thus far: despite being proposed nearly two decades ago, only 1% of .com, .net, and .org domains are properly signed.\n In this paper, we investigate the underlying reasons <i>why</i> DNSSEC adoption has been remarkably slow. We focus on registrars, as most TLD registries already support DNSSEC and registrars often serve as DNS operators for their customers. Our study uses large-scale, longitudinal DNS measurements to study DNSSEC adoption, coupled with experiences collected by trying to deploy DNSSEC on domains we purchased from leading domain name registrars and resellers. Overall, we find that a select few registrars are responsible for the (small) DNSSEC deployment today, and that many leading registrars do not support DNSSEC at all, or require customers to take cumbersome steps to deploy DNSSEC. Further frustrating deployment, many of the mechanisms for conveying DNSSEC information to registrars are error-prone or present security vulnerabilities. Finally, we find that using DNSSEC with third-party DNS operators such as Cloudflare requires the domain owner to take a number of steps that 40% of domain owners do not complete. Having identified several operational challenges for full DNSSEC deployment, we make recommendations to improve adoption.",
"pdfUrls": [
"https://conferences.sigcomm.org/imc/2017/papers/imc17-final53.pdf",
"http://doi.acm.org/10.1145/3131365.3131373",
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"title": "Understanding the role of registrars in DNSSEC deployment",
"venue": "IMC",
"year": 2017
},
"2834d13e15b530a15957c2ef13cabad689b86aa6": {
"authors": [
{
"ids": [
"1843219"
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"name": "Yuseok Jeon"
},
{
"ids": [
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"name": "Priyam Biswas"
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{
"ids": [
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"name": "Scott A. Carr"
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{
"ids": [
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"name": "Byoungyoung Lee"
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{
"ids": [
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"name": "Mathias Payer"
}
],
"doi": "10.1145/3133956.3134062",
"doiUrl": "https://doi.org/10.1145/3133956.3134062",
"entities": [
"Adobe Flash",
"Apache Xerces",
"C++",
"Class hierarchy",
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"Dangling pointer",
"Data structure",
"Firefox",
"Google Chrome",
"Optimizing compiler",
"PHP",
"Placement syntax",
"Qt (software)",
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"Type conversion",
"Type punning",
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],
"id": "2834d13e15b530a15957c2ef13cabad689b86aa6",
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"journalPages": "2373-2387",
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"paperAbstract": "Type confusion, often combined with use-after-free, is the main attack vector to compromise modern C++ software like browsers or virtual machines. Typecasting is a core principle that enables modularity in C++. For performance, most typecasts are only checked statically, i.e., the check only tests if a cast is allowed for the given type hierarchy, ignoring the actual runtime type of the object. Using an object of an incompatible base type instead of a derived type results in type confusion. Attackers abuse such type confusion issues to attack popular software products including Adobe Flash, PHP, Google Chrome, or Firefox. We propose to make all type checks explicit, replacing static checks with full runtime type checks. To minimize the performance impact of our mechanism HexType, we develop both low-overhead data structures and compiler optimizations. To maximize detection coverage, we handle specific object allocation patterns, e.g., placement new or reinterpret_cast which are not handled by other mechanisms. Our prototype results show that, compared to prior work, HexType has at least 1.1 -- 6.1 times higher coverage on Firefox benchmarks. For SPEC CPU2006 benchmarks with overhead, we show a 2 -- 33.4 times reduction in overhead. In addition, HexType discovered 4 new type confusion bugs in Qt and Apache Xerces-C++.",
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"http://hexhive.github.io/publications/files/17CCS.pdf",
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"title": "HexType: Efficient Detection of Type Confusion Errors for C++",
"venue": "CCS",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Shuang Chen"
},
{
"ids": [
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"name": "Shay GalOn"
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{
"ids": [
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"name": "Christina Delimitrou"
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{
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"name": "Srilatha Manne"
},
{
"ids": [
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],
"name": "Jos\u00e9 F. Mart\u00ednez"
}
],
"doi": "10.1109/IISWC.2017.8167770",
"doiUrl": "https://doi.org/10.1109/IISWC.2017.8167770",
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"paperAbstract": "Key-value stores (e.g., Memcached) and web servers (e.g., NGINX) are widely used by cloud providers. As interactive services, they have strict service-level objectives, with typical 99<sup>th</sup>-percentile tail latencies on the order of a few milliseconds. Unlike average latency, tail latency is more sensitive to changes in usage load and traffic patterns, system configurations, and resource availability. Understanding the sensitivity of tail latency to application and system factors is critical to efficiently design and manage systems for these latency-critical services. We present a comprehensive study of the impact a diverse set of application, hardware, and isolation configurations have on tail latency for two representative interactive services, Memcached and NGINX. Examined factors include input load, thread-level parallelism, request size, virtualization, and resource partitioning. We conduct this study on two server platforms with significant differences in terms of architecture and price points: an Intel Xeon and an ARM-based Cavium ThunderX server. Experimental results show that latency on both platforms is subject to changes of several orders of magnitude depending on application and system settings, with Cavium ThunderX being more sensitive to configuration parameters.",
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"http://www.csl.cornell.edu/~delimitrou/papers/2017.iiswc.bigsmall.pdf",
"https://sc2682cornell.github.io/pdf/iiswc17.pdf"
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"title": "Workload characterization of interactive cloud services on big and small server platforms",
"venue": "2017 IEEE International Symposium on Workload Characterization (IISWC)",
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},
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"name": "Beom Heyn Kim"
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{
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"name": "Sukwon Oh"
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"doi": "10.1145/3102980.3102994",
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"paperAbstract": "An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise.\n To help users tap the performance potential of systems, we present Best Config, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment.",
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"https://arxiv.org/pdf/1710.03439v1.pdf",
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"http://doi.acm.org/10.1145/3127479.3128605"
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"title": "BestConfig: tapping the performance potential of systems via automatic configuration tuning",
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"year": 2017
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"name": "Om Rameshwar Gatla"
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"name": "Mai Zheng"
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"title": "Understanding the Fault Resilience of File System Checkers",
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"paperAbstract": "Persistent spread measurement is to count the number of distinct elements that persist in each network flow for predefined time periods. It has many practical applications, including detecting long-term stealthy network activities in the background of normal-user activities, such as stealthy DDoS attack, stealthy network scan, or faked network trend, which cannot be detected by traditional flow cardinality measurement. With big network data, one challenge is to measure the persistent spreads of a massive number of flows without incurring too much memory overhead as such measurement may be performed at the line speed by network processors with fast but small on-chip memory. We propose a highly compact Virtual Intersection HyperLogLog (VI-HLL) architecture for this purpose. It achieves far better memory efficiency than the best prior work of V-Bitmap, and in the meantime drastically extends the measurement range. Theoretical analysis and extensive experiments demonstrate that VI-HLL provides good measurement accuracy even in very tight memory space of less than 1 bit per flow.",
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"paperAbstract": "Parametric polymorphism and inheritance are both important, extensively explored language mechanisms for providing code reuse and extensibility. But harmoniously integrating these apparently distinct mechanisms—and powerful recent forms of them, including type classes and family polymorphism—in a single language remains an elusive goal. In this paper, we show that a deep unification can be achieved by generalizing the semantics of interfaces and classes. The payoff is a significant increase in expressive power with little increase in programmer-visible complexity. Salient features of the new programming language include retroactive constraint modeling, underpinning both object-oriented programming and generic programming, and module-level inheritance with further-binding, allowing family polymorphism to be deployed at large scale. The resulting mechanism is syntactically light, and the more advanced features are transparent to the novice programmer. We describe the design of a programming language that incorporates this mechanism; using a core calculus, we show that the type system is sound. We demonstrate that this language is highly expressive by illustrating how to use it to implement highly extensible software and by showing that it can not only concisely model state-of-the-art features for code reuse, but also go beyond them.",
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"title": "Familia: unifying interfaces, type classes, and family polymorphism",
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"paperAbstract": "The network plays a key role in High-Performance Computing (HPC) system efficiency. Unfortunately, current HPC routing solutions are not application-aware, and therefore cannot deal with the sudden HPC traffic bursts and their resulting congestion peaks.To address this problem, we introduce Routing Keys, a scalable routing paradigm for HPC networks that decouples intra- and inter-application flow contention. Our Application Routing Key (ARK) algorithm proactively allows each self-aware application to route its flows according to a predetermined routing key, i.e., its own intra-application contention-free routing. In addition, in our Network Routing Key (NRK) algorithm, a centralized scheduler chooses between several routing keys for the communication phases of each application, and therefore reduces inter-application contention while maintaining intra-application contention-free routing and avoiding scalability issues. Using extensive evaluations, we show that both ARK and NRK significantly improve the communication runtime by up to 2.7x.",
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"paperAbstract": "We present a novel approach to proving the absence of timing channels. The idea is to partition the programâ\u0080\u0099s execution traces in such a way that each partition component is checked for timing attack resilience by a time complexity analysis and that per-component resilience implies the resilience of the whole program. We construct a partition by splitting the program traces at secret-independent branches. This ensures that any pair of traces with the same public input has a component containing both traces. Crucially, the per-component checks can be normal safety properties expressed in terms of a single execution. Our approach is thus in contrast to prior approaches, such as self-composition, that aim to reason about multiple (<i>k</i>â\u0089\u00a5 2) executions at once. \nWe formalize the above as an approach called quotient partitioning, generalized to any <i>k</i>-safety property, and prove it to be sound. A key feature of our approach is a demand-driven partitioning strategy that uses a regex-like notion called trails to identify sets of execution traces, particularly those influenced by tainted (or secret) data. We have applied our technique in a prototype implementation tool called Blazer, based on WALA, PPL, and the brics automaton library. We have proved timing-channel freedom of (or synthesized an attack specification for) 24 programs written in Java bytecode, including 6 classic examples from the literature and 6 examples extracted from the DARPA STAC challenge problems.",
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"http://doi.acm.org/10.1145/3062341.3062378",
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"https://www.paulgazzillo.com/papers/pldi17.pdf"
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"title": "Decomposition instead of self-composition for proving the absence of timing channels",
"venue": "PLDI",
"year": 2017
},
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"authors": [
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"name": "Matthew Poremba"
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"name": "Itir Akgun"
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"name": "Jieming Yin"
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{
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"name": "Onur Kayiran"
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"name": "Yuan Xie"
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"name": "Gabriel H. Loh"
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"paperAbstract": "High-performance computing, enterprise, and datacenter servers are driving demands for higher total memory capacity as well as memory performance. Memory \"cubes\" with high per-package capacity (from 3D integration) along with high-speed point-to-point interconnects provide a scalable memory system architecture with the potential to deliver both capacity and performance. Multiple such cubes connected together can form a \"Memory Network\" (MN), but the design space for such MNs is quite vast, including multiple topology types and multiple memory technologies per memory cube.\n In this work, we first analyze several MN topologies with different mixes of memory package technologies to understand the key tradeoffs and bottlenecks for such systems. We find that most of a MN's performance challenges arise from the interconnection network that binds the memory cubes together. In particular, arbitration schemes used to route through MNs, ratio of NVM to DRAM, and specific topologies used have dramatic impact on performance and energy results. Our initial analysis indicates that introducing non-volatile memory to the MN presents a unique tradeoff between memory array latency and network latency. We observe that placing NVM cubes in a specific order in the MN improves performance by reducing the network size/diameter up to a certain NVM to DRAM ratio. Novel MN topologies and arbitration schemes also provide performance and energy deltas by reducing the hop count of requests and response in the MN. Based on our analyses, we introduce three techniques to address MN latency issues: (1) Distance-based arbitration scheme to improve queuing latencies throughout the network, (2) skip-list topology, derived from the classic data structure, to improve network latency and link usage, and (3) the MetaCube, a denser memory cube that leverages advanced packaging technologies to improve latency by reducing MN size.",
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"title": "There and back again: Optimizing the interconnect in networks of memory cubes",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
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"name": "Yao Lu"
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"paperAbstract": "Machine learning models hosted in a cloud service are increasingly popular but risk privacy: clients sending prediction requests to the service need to disclose potentially sensitive information. In this paper, we explore the problem of privacy-preserving predictions: after each prediction, the server learns nothing about clients' input and clients learn nothing about the model.\n We present MiniONN, the first approach for transforming an existing neural network to an oblivious neural network supporting privacy-preserving predictions with reasonable efficiency. Unlike prior work, MiniONN requires no change to how models are trained. To this end, we design oblivious protocols for commonly used operations in neural network prediction models. We show that MiniONN outperforms existing work in terms of <i>response latency and message sizes</i>. We demonstrate the wide applicability of MiniONN by transforming several typical neural network models trained from standard datasets.",
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"title": "Oblivious Neural Network Predictions via MiniONN Transformations",
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"year": 2017
},
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{
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"name": "Roy Frings"
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{
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"name": "Ulrich R\u00fcde"
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"title": "Fully Resolved Simulations of Dune Formation in Riverbeds",
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"doi": "10.1145/3050748.3050755",
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"paperAbstract": "We introduce Sky, an extension to the VMM that gathers insights and information by intercepting system calls made by guest applications. We show how Sky gains three specific insights -- guest file-size information, metadata-data distinction, and file-content hints -- and uses said information to enhance virtualized-storage performance. By caching small files and metadata with higher priority, Sky reduces the runtime by 2.3 to 8.8 times for certain workloads. Sky also achieves 4.5 to 18.7 times reduction in the runtime of an open-source block-layer deduplication system by exploiting hints about file contents. Sky works underneath both Linux and FreeBSD guests, as well as under a range of file systems, thus enabling portable and general VMM-level optimization underneath a wide range of storage stacks.",
"pdfUrls": [
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"http://doi.acm.org/10.1145/3050748.3050755"
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"title": "Improving Virtualized Storage Performance with Sky",
"venue": "VEE",
"year": 2017
},
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"authors": [
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"name": "Sze Yiu Chau"
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{
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"name": "Omar Chowdhury"
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{
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"name": "Md. Endadul Hoque"
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{
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"name": "Cristina Nita-Rotaru"
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{
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"name": "Ninghui Li"
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],
"doi": "10.1109/SP.2017.40",
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"paperAbstract": "In the search for post-quantum secure alternatives to RSA and ECC, lattice-based cryptography appears to be an attractive and efficient option. A particularly interesting lattice-based signature scheme is BLISS, offering key and signature sizes in the range of RSA moduli. A range of works on efficient implementations of BLISS is available, and the scheme has seen a first real-world adoption in strongSwan, an IPsec-based VPN suite. In contrast, the implementation-security aspects of BLISS, and lattice-based cryptography in general, are still largely unexplored.\n At CHES 2016, Groot Bruinderink et al. presented the first side-channel attack on BLISS, thus proving that this topic cannot be neglected. Nevertheless, their attack has some limitations. First, the technique is demonstrated via a proof-of-concept experiment that was not performed under realistic attack settings. Furthermore, the attack does not apply to BLISS-B, an improved variant of BLISS and also the default option in strongSwan. This problem also applies to later works on implementation security of BLISS.\n In this work, we solve both of the above problems. We present a new side-channel key-recovery algorithm against both the original BLISS and the BLISS-B variant. Our key-recovery algorithm draws on a wide array of techniques, including learning-parity with noise, integer programs, maximimum likelihood tests, and a lattice-basis reduction. With each application of a technique, we reveal additional information on the secret key culminating in a complete key recovery.\n Finally, we show that cache attacks on post-quantum cryptography are not only possible, but also practical. We mount an asynchronous cache attack on the production-grade BLISS-B implementation of strongSwan. The attack recovers the secret signing key after observing roughly 6000 signature generations.",
"pdfUrls": [
"https://eprint.iacr.org/2017/490.pdf",
"http://eprint.iacr.org/2017/490",
"http://doi.acm.org/10.1145/3133956.3134023"
],
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"title": "To BLISS-B or not to be: Attacking strongSwan's Implementation of Post-Quantum Signatures",
"venue": "CCS",
"year": 2017
},
"2acc406cec7601f88934f321dced810ab0ee4024": {
"authors": [
{
"ids": [
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"name": "Ali Ziat"
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{
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"name": "Edouard Delasalles"
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{
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"name": "Ludovic Denoyer"
},
{
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"name": "Patrick Gallinari"
}
],
"doi": "10.1109/ICDM.2017.80",
"doiUrl": "https://doi.org/10.1109/ICDM.2017.80",
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"paperAbstract": "We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for forecasting time series of spatial processes, i.e. series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is evaluated and compared to state-of-the-art baselines, on a variety of forecasting problems representative of different application areas: epidemiology, geo-spatial statistics and car-traffic prediction. Besides these evaluations, we also describe experiments showing the ability of this approach to extract relevant spatial relations.",
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"http://doi.ieeecomputersociety.org/10.1109/ICDM.2017.80"
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"title": "Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
"year": 2017
},
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"authors": [
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"name": "Ilias Marinos"
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"name": "Robert N. M. Watson"
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"name": "Mark Handley"
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{
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"name": "Randall R. Stewart"
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"doi": "10.1145/3098822.3098844",
"doiUrl": "https://doi.org/10.1145/3098822.3098844",
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"paperAbstract": "Conventional operating systems used for video streaming employ an in-memory disk buffer cache to mask the high latency and low throughput of disks. However, data from Netflix servers show that this cache has a low hit rate, so does little to improve throughput. Latency is not the problem it once was either, due to PCIe-attached flash storage. With memory bandwidth increasingly becoming a bottleneck for video servers, especially when end-to-end encryption is considered, we revisit the interaction between storage and networking for video streaming servers in pursuit of higher performance.\n We show how to build high-performance userspace network services that saturate existing hardware while serving data directly from disks, with no need for a traditional disk buffer cache. Employing netmap, and developing a new diskmap service, which provides safe high-performance userspace direct I/O access to NVMe devices, we amortize system overheads by utilizing efficient batching of outstanding I/O requests, process-to-completion, and zerocopy operation. We demonstrate how a buffer-cache-free design is not only practical, but required in order to achieve efficient use of memory bandwidth on contemporary microarchitectures. Minimizing latency between DMA and CPU access by integrating storage and TCP control loops allows many operations to access only the last-level cache rather than bottle-necking on memory bandwidth. We illustrate the power of this design by building Atlas, a video streaming web server that outperforms state-of-the-art configurations, and achieves ~72Gbps of plaintext or encrypted network traffic using a fraction of the available CPU cores on commodity hardware.",
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"http://doi.acm.org/10.1145/3098822.3098844",
"http://www.cl.cam.ac.uk/~rnw24/papers/201708-sigcomm-diskcryptnet.pdf"
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"title": "Disk|Crypt|Net: rethinking the stack for high-performance video streaming",
"venue": "SIGCOMM",
"year": 2017
},
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"name": "Zhen Zheng"
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"name": "Chanyoung Oh"
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"name": "Jidong Zhai"
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"name": "Xipeng Shen"
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"name": "Youngmin Yi"
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"name": "Wenguang Chen"
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"doi": "10.1145/3123939.3123978",
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"paperAbstract": "Pipeline is an important programming pattern, while GPU, designed mostly for data-level parallel executions, lacks an efficient mechanism to support pipeline programming and executions. This paper provides a systematic examination of various existing pipeline execution models on GPU, and analyzes their strengths and weaknesses. To address their shortcomings, this paper then proposes three new execution models equipped with much improved controllability, including a hybrid model that is capable of getting the strengths of all. These insights ultimately lead to the development of a software programming framework named <i>VersaPipe.</i> With VersaPipe, users only need to write the operations for each pipeline stage. VersaPipe will then automatically assemble the stages into a hybrid execution model and configure it to achieve the best performance. Experiments on a set of pipeline benchmarks and a real-world face detection application show that VersaPipe produces up to 6.90X (2.88X on average) speedups over the original manual implementations.",
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"title": "Versapipe: a versatile programming framework for pipelined computing on GPU",
"venue": "MICRO",
"year": 2017
},
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"authors": [
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"name": "Jinshi Zhang"
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{
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"name": "Eddie Dong"
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{
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"name": "Jian Li"
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{
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"name": "Haibing Guan"
}
],
"doi": "10.1145/3050748.3050753",
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"paperAbstract": "Live migration of a virtual machine (VM) is a powerful technique with benefits of server maintenance, resource management, dynamic workload re-balance, etc. Modern research has effectively reduced the VM live migration (VMLM) time to dozens of milliseconds, but live migration still exhibits failures if it cannot terminate within the given time constraint. The ability to predict this type of failure can avoid wasting networking and computing resources on the VM migration, and the associated system performance degradation caused by wasting these resources. The cost of VM live migration highly depends on the application workload of the VM, which may undergo frequent changes. At the same time, the available system resources for VM migration can also change substantially and frequently. To account for these issues, we present a solution called MigVisor, which can accurately predict the behaviour of VM migration using working-set model. This can enable system managers to predict the migration cost and enhance the system management efficacy. The experimental results prove the design suitability and show that the MigVisor has a high prediction accuracy since the average relative error between the predicted value and the measured value is only 6.2%~9%.",
"pdfUrls": [
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"title": "MigVisor: Accurate Prediction of VM Live Migration Behavior using a Working-Set Pattern Model",
"venue": "VEE",
"year": 2017
},
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"authors": [
{
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"name": "Luhao Wang"
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{
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"name": "Shuang Chen"
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{
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"name": "Massoud Pedram"
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],
"doi": "10.1109/IGCC.2017.8323565",
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"entities": [
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"paperAbstract": "Aggressive network densification in next generation cellular networks is accompanied by an increase of the system energy consumption and calls for more advanced power management techniques in base stations. In this paper, we present a novel proactive and decentralized power management method for small cell base stations in a cache-enabled multitier heterogeneous cellular network. User contexts are utilized to drive the decision of dynamically switching a small cell base station between the active mode and the sleep mode to minimize the total energy consumption. The online control problem is formulated as a contextual multi-armed bandit problem. A variational inference based Bayesian neural network is proposed as the solution method, which implicitly finds a proper balance between exploration and exploitation. Experimental results show that the proposed solution can achieve up to 46.9% total energy reduction compared to baseline algorithms in the high density deployment scenario and has comparable performance to an offline optimal solution.",
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"paperAbstract": "This paper presents Canopy, Facebook's end-to-end performance tracing infrastructure. Canopy records causally related performance data across the end-to-end execution path of requests, including from browsers, mobile applications, and backend services. Canopy processes traces in near real-time, derives user-specified features, and outputs to performance datasets that aggregate across billions of requests. Using Canopy, Facebook engineers can query and analyze performance data in real-time. Canopy addresses three challenges we have encountered in scaling performance analysis: supporting the range of execution and performance models used by different components of the Facebook stack; supporting interactive ad-hoc analysis of performance data; and enabling deep customization by users, from sampling traces to extracting and visualizing features. Canopy currently records and processes over 1 billion traces per day. We discuss how Canopy has evolved to apply to a wide range of scenarios, and present case studies of its use in solving various performance challenges.",
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"paperAbstract": "High-performance architectures are over-provisioned with resources to extract the maximum achievable performance out of applications. Two sources of avoidable power dissipation are the leakage power from underutilized resources, along with clock power from the clock hierarchy that feeds these resources. Most reconfiguration mechanisms either focus solely on power gating execution resources alone or in addition, simply turn off the immediate clock tree segment which supplied the clock to those resources. These proposals neither attempt to gate further up the clock hierarchy nor do they involve the clock hierarchy in influencing the reconfiguration decisions. The primary contribution of CHARSTAR is optimizing reconfiguration mechanisms to become clock hierarchy aware. Resource gating decisions are cognizant of the power consumed by each node in the clock hierarchy and additionally, entire branches of the clock tree are greedily shut down whenever possible.\n The CHARSTAR design is further optimized for balanced spatio-temporal reconfiguration and also enables efficient joint control of resource and frequency scaling. The proposal is implemented by leveraging the inherent advantages of spatial architectures, utilizing a control mechanism driven by a lightweight offline trained neural predictor. CHARSTAR, when deployed on the CRIB tiled microarchitecture, improves processor energy efficiency by 20-25%, with efficiency improvements of roughly 2x in comparison to a naive power gating mechanism. Alternatively, it improves performance by 10-20% under varying power and energy constraints.",
"pdfUrls": [
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"title": "CHARSTAR: Clock hierarchy aware resource scaling in tiled architectures",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
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"authors": [
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"name": "Samuel Haney"
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"name": "Ashwin Machanavajjhala"
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"name": "John M. Abowd"
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"name": "Matthew Graham"
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"name": "Mark Kutzbach"
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"name": "Lars Vilhuber"
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"paperAbstract": "National statistical agencies around the world publish tabular summaries based on combined employer-employee (ER-EE) data. The privacy of both individuals and business establishments that feature in these data are protected by law in most countries. These data are currently released using a variety of statistical disclosure limitation (SDL) techniques that do not reveal the exact characteristics of particular employers and employees, but lack provable privacy guarantees limiting inferential disclosures.\n In this work, we present novel algorithms for releasing tabular summaries of linked ER-EE data with formal, provable guarantees of privacy. We show that state-of-the-art differentially private algorithms add too much noise for the output to be useful. Instead, we identify the privacy requirements mandated by current interpretations of the relevant laws, and formalize them using the Pufferfish framework. We then develop new privacy definitions that are customized to ER-EE data and satisfy the statutory privacy requirements. We implement the experiments in this paper on production data gathered by the U.S. Census Bureau. An empirical evaluation of utility for these data shows that for reasonable values of the privacy-loss parameter ε≥ 1, the additive error introduced by our provably private algorithms is comparable, and in some cases better, than the error introduced by existing SDL techniques that have no provable privacy guarantees. For some complex queries currently published, however, our algorithms do not have utility comparable to the existing traditional SDL algorithms. Those queries are fodder for future research.",
"pdfUrls": [
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"https://users.cs.duke.edu/~ashwin/pubs/Haney-UtilityCost-SIGMOD2017.pdf"
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"title": "Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics",
"venue": "SIGMOD Conference",
"year": 2017
},
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"name": "Alex Kogan"
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"name": "Yossi Lev"
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],
"doi": "10.1145/3087801.3087838",
"doiUrl": "https://doi.org/10.1145/3087801.3087838",
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"title": "Transactional Lock Elision Meets Combining",
"venue": "PODC",
"year": 2017
},
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"name": "Chong Zhou"
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"name": "Randy C. Paffenroth"
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"doi": "10.1145/3097983.3098052",
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"title": "Anomaly Detection with Robust Deep Autoencoders",
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"name": "Yizheng Chen"
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"paperAbstract": "Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks. We design and evaluate two novel graph attacks against a state-of-the-art network-level, graph-based detection system. Our work highlights areas in adversarial machine learning that have not yet been addressed, specifically: graph-based clustering techniques, and a global feature space where realistic attackers without perfect knowledge must be accounted for (by the defenders) in order to be practical. Even though less informed attackers can evade graph clustering with low cost, we show that some practical defenses are possible.",
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"http://doi.acm.org/10.1145/3133956.3134083"
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"title": "Practical Attacks Against Graph-based Clustering",
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"year": 2017
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"paperAbstract": "The problem of efficient concurrent memory reclamation in unmanaged languages such as C or C++ is one of the major challenges facing the parallelization of billions of lines of legacy code. Garbage collectors for C/C++ can be inefficient; thus, programmers are often forced to use finely-crafted concurrent memory reclamation techniques. These techniques can provide good performance, but require considerable programming effort to deploy, and have strict requirements, allowing the programmer very little room for error.\n In this work, we present Forkscan, a new conservative concurrent memory reclamation scheme which is fully automatic and surprisingly scalable. Forkscan's semantics place it between automatic garbage collectors (it requires the programmer to explicitly retire nodes before they can be reclaimed), and concurrent memory reclamation techniques (as it does not assume that nodes are completely unlinked from the data structure for correctness). Forkscan's implementation exploits these new semantics for efficiency: we leverage parallelism and optimized implementations of signaling and copy-on-write in modern operating systems to efficiently obtain and process consistent snapshots of memory that can be scanned concurrently with the normal program operation.\n Empirical evaluation on a range of classical concurrent data structure microbenchmarks shows that Forkscan can preserve the scalability of the original code, while maintaining an order of magnitude lower latency than automatic garbage collection, and demonstrating competitive performance with finely crafted memory reclamation techniques.",
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"title": "Forkscan: Conservative Memory Reclamation for Modern Operating Systems",
"venue": "EuroSys",
"year": 2017
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"name": "Ari Kobren"
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"name": "Nicholas Monath"
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"paperAbstract": "Many modern clustering methods scale well to a large number of data items, N , but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K\u2014a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivated by the desire for both accuracy and speed, our approach performs tree rotations for the sake of enhancing subtree purity and encouraging balancedness. We prove that, under a natural separability assumption, our non-greedy algorithm will produce trees with perfect dendrogram purity regardless of online data arrival order. Our experiments demonstrate that PERCH constructs more accurate trees than other tree-building clustering algorithms and scales well with both N and K, achieving a higher quality clustering than the strongest flat clustering competitor in nearly half the time.",
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"https://arxiv.org/pdf/1704.01858.pdf"
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"title": "An Online Hierarchical Algorithm for Extreme Clustering",
"venue": "ArXiv",
"year": 2017
},
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"name": "Nooshin Mirzadeh"
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"name": "Dmitrii Ustiugov"
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"paperAbstract": "The increasing demand for extracting value out of ever-growing data poses an ongoing challenge to system designers, a task only made trickier by the end of Dennard scaling. As the performance density of traditional CPU-centric architectures stagnates, advancing compute capabilities necessitates novel architectural approaches. Near-memory processing (NMP) architectures are reemerging as promising candidates to improve computing efficiency through tight coupling of logic and memory. NMP architectures are especially fitting for data analytics, as they provide immense bandwidth to memory-resident data and dramatically reduce data movement, the main source of energy consumption.\n Modern data analytics operators are optimized for CPU execution and hence rely on large caches and employ random memory accesses. In the context of NMP, such random accesses result in wasteful DRAM row buffer activations that account for a significant fraction of the total memory access energy. In addition, utilizing NMP's ample bandwidth with fine-grained random accesses requires complex hardware that cannot be accommodated under NMP's tight area and power constraints. Our thesis is that efficient NMP calls for an algorithm-hardware co-design that favors algorithms with sequential accesses to enable simple hardware that accesses memory in streams. We introduce an instance of such a co-designed NMP architecture for data analytics, the Mondrian Data Engine. Compared to a CPU-centric and a baseline NMP system, the Mondrian Data Engine improves the performance of basic data analytics operators by up to 49x and 5x, and efficiency by up to 28x and 5x, respectively.",
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"title": "The mondrian data engine",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
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"name": "Gu-Yeon Wei"
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"paperAbstract": "Recent work shows that dynamic memory allocation consumes nearly 7% of all cycles in Google datacenters. With the trend towards increased specialization of hardware, we propose Mallacc, an in-core hardware accelerator designed for broad use across a number of high-performance, modern memory allocators. The design of Mallacc is quite different from traditional throughput-oriented hardware accelerators. Because memory allocation requests tend to be very frequent, fast, and interspersed inside other application code, accelerators must be optimized for latency rather than throughput and area overheads must be kept to a bare minimum. Mallacc accelerates the three primary operations of a typical memory allocation request: size class computation, retrieval of a free memory block, and sampling of memory usage. Our results show that malloc latency can be reduced by up to 50% with a hardware cost of less than 1500 um2 of silicon area, less than 0.006% of a typical high-performance processor core.",
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"paperAbstract": "Matrix Factorization (MF) is a very popular method for recommendation systems. It assumes that the underneath rating matrix is low-rank. However, this assumption can be too restrictive to capture complex relationships and interactions among users and items. Recently, Local LOw-Rank Matrix Approximation (LLORMA) has been shown to be very successful in addressing this issue. It just assumes the rating matrix is composed of a number of low-rank submatrices constructed from subsets of similar users and items. Although LLORMA outperforms MF, how to construct such submatrices remains a big problem. Motivated by the availability of rich social connections in today's recommendation systems, we propose a novel framework, i.e., Social LOcal low-rank Matrix Approximation (SLOMA), to address this problem. To the best of our knowledge, SLOMA is the first work to incorporate social connections into the local low-rank framework. Furthermore, we enhance SLOMA by applying social regularization to submatrices factorization, denoted as SLOMA++. Therefore, the proposed model can benefit from both social recommendation and the local low-rank assumption. Experimental results from two real-world datasets, Yelp and Douban, demonstrate the superiority of the proposed models over LLORMA and MF.",
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"paperAbstract": "Emerging non-volatile main memory (NVMM) unlocks the performance potential of applications by storing persistent data in the main memory. Such applications require a lightweight persistent transactional memory (PTM) system, instead of a heavyweight filesystem or database, to have fast access to data. In a PTM system, the memory usage, both capacity and bandwidth, plays a key role in dictating performance and efficiency. Existing memory management mechanisms for PTMs generate high memory fragmentation, high write traffic and a large number of persist barriers, since data is first written to a log and then to the main data store. In this paper, we present a log-structured NVMM system that not only maintains NVMM in a compact manner but also reduces the write traffic and the number of persist barriers needed for executing transactions. All data allocations and modifications are appended to the log which becomes the location of the data. Further, we address a unique challenge of log-structured memory management by designing a tree-based address translation mechanism where access granularities are flexible and different from allocation granularities. Our results show that the new system enjoys up to 89.9% higher transaction throughput and up to 82.8% lower write traffic than a traditional PTM system.",
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"title": "Log-Structured Non-Volatile Main Memory",
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"title": "Circuit Treewidth, Sentential Decision, and Query Compilation",
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{
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"paperAbstract": "Amazon EC2 and Google Compute Engine (GCE) have recently introduced a new class of virtual machines called \"burstable\" instances that are cheaper than even the smallest traditional/regular instances. These lower prices come with reduced average capacity and increased variance. Using measurements from both EC2 and GCE, we identify key idiosyncrasies of resource capacity dynamism for burstable instances that set them apart from other instance types. Most importantly, certain resources for these instances appear to be regulated by deterministic token bucket like mechanisms. We find widely different types of disclosures by providers of the parameters governing these regulation mechanisms: full disclosure (e.g., CPU capacity for EC2 t2 instances), partial disclosure (e.g., CPU capacity and remote disk IO bandwidth for GCE shared-core instances), or no disclosure (network bandwidth for EC2 t2 instances). A tenant modeling these variations as random phenomena (as some recent work suggests) might make sub-optimal procurement and operation decisions. We present modeling techniques for a tenant to infer the properties of these regulation mechanisms via simple offline measurements. We also present two case studies of how certain memcached workloads might benefit from our modeling when operating on EC2 by: (i) augmenting cheap but low availability in-memory storage offered by spot instances with backup of popular content on burstable instances, and (ii) temporal multiplexing of multiple burstable instances to achieve the CPU or network bandwidth (and thereby throughput) equivalent of a more expensive regular EC2 instance.",
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"title": "Using Burstable Instances in the Public Cloud: Why, When and How?",
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"year": 2017
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"paperAbstract": "Nakamoto\u2019s famous blockchain protocol enables achieving consensus in a so-called permissionless setting\u2014anyone can join (or leave) the protocol execution, and the protocol instructions do not depend on the identities of the players. His ingenious protocol prevents \u201csybil attacks\u201d (where an adversary spawns any number of new players) by relying on computational puzzles (a.k.a. \u201cmoderately hard functions\u201d) introduced by Dwork and Naor (Crypto\u201992). Recent work by Garay et al (EuroCrypt\u201915) and Pass et al (manuscript, 2016) demonstrate that this protocol provably achieves consistency and liveness assuming a) honest players control a majority of the computational power in the network, b) the puzzle-hardness is appropriately set as a function of the maximum network delay and the total computational power of the network, and c) the computational puzzle is modeled as a random oracle. Assuming honest participation, however, is a strong assumption, especially in a setting where honest players are expected to perform a lot of work (to solve the computational puzzles). In Nakamoto\u2019s Bitcoin application of the blockchain protocol, players are incentivized to solve these puzzles by receiving rewards for every \u201cblock\u201d (of transactions) they contribute to the blockchain. An elegant work by Eyal and Sirer (FinancialCrypt\u201914), strengthening and formalizing an earlier attack discussed on the Bitcoin forum, demonstrates that a coalition controlling even a minority fraction of the computational power in the network can gain (close to) 2 times its \u201cfair share\u201d of the rewards (and transaction fees) by deviating from the protocol instructions. In contrast, in a fair protocol, one would expect that players controlling a \u03c6 fraction of the computational resources to reap a \u03c6 fraction of the rewards. In this work, we present a new blockchain protocol\u2014the FruitChain protocol\u2014which satisfies the same consistency and liveness properties as Nakamoto\u2019s protocol (assuming an honest majority of the computing power), and additionally is \u03b4-approximately fair : with overwhelming probability, any honest set of players controlling a \u03c6 fraction of computational power is guaranteed to get at least a fraction (1 \u2212 \u03b4)\u03c6 of the blocks (and thus rewards) in any \u03a9(\u03ba\u03b4 ) length segment of the chain (where \u03ba is the security parameter). As a consequence, if this blockchain protocol is used as the ledger underlying a cryptocurrency system, where rewards and transaction fees are evenly distributed among the miners of blocks in a length \u03ba segment of the chain, no coalition controlling less than a majority of the computing power can gain more than a factor (1 + 3\u03b4) by deviating from the protocol (i.e., honest participation is an n2 -coalition-safe 3\u03b4-Nash equilibrium). Finally, the FruitChain protocol enables decreasing the variance of mining rewards and as such significantly lessens (or even obliterates) the need for mining pools. \u2217Supported in part by NSF Award CNS-1217821, NSF Award CNS-1561209, AFOSR Award FA9550-15-1-0262, a Microsoft Faculty Fellowship, and a Google Faculty Research Award. \u2020Supported in part by NSF Award CNS-1314857, CNS-1514261, CNS-1544613, CNS-1561209, CNS-1601879, CNS1617676, an Office of Naval Research Young Investigator Program Award, a Packard Fellowship, a Sloan Fellowship, Google Faculty Research Awards, and a VMWare Research Award.",
"pdfUrls": [
"https://eprint.iacr.org/2016/916.pdf",
"http://doi.acm.org/10.1145/3087801.3087809",
"http://eprint.iacr.org/2016/916",
"http://eprint.iacr.org/2016/916.pdf"
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"title": "FruitChains: A Fair Blockchain",
"venue": "PODC",
"year": 2016
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"name": "Naga Praveen Katta"
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{
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"name": "Aditi Ghag"
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"paperAbstract": "Most datacenters still use Equal Cost Multi-Path (ECMP), which performs congestion-oblivious hashing of flows over multiple paths, leading to an uneven distribution of traffic. Alternatives to ECMP come with deployment challenges, as they require either changing the tenant VM network stacks (e.g., MPTCP) or replacing all of the switches (e.g., CONGA). We argue that the hypervisor provides a unique point for implementing load-balancing algorithms that are easy to deploy, while still reacting quickly to congestion. We propose Clove, a scalable load-balancer that (i) runs entirely in the hypervisor, requiring no modifications to tenant VM networking stacks or physical switches, and (ii) works on any topology and adapts quickly to topology changes and traffic shifts. Clove relies on standard ECMP in physical switches, discovers paths using a novel traceroute mechanism, uses software-based flowlet-switching, and continuously learns congestion (or path utilization) state using standard switch features. It then manipulates packet-header fields in the hypervisor switch to direct traffic over less congested paths. Clove achieves 1.5 to 7 times smaller flow-completion times at 70% network load than other load-balancing algorithms that work with existing hardware. Clove also captures some 80% of the performance gain of best-of-breed hardware-based load-balancing algorithms like CONGA that require new equipment.",
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"paperAbstract": "Dynamic scripting languages are becoming more and more widely adopted not only for fast prototyping but also for developing production-grade applications. They provide high-productivity programming environments featuring high levels of abstraction with powerful built-in functions, automatic memory management, object-oriented programming paradigm and dynamic typing. However, their flexible, dynamic type systems easily become the source of inefficiency in terms of instruction count, memory footprint, and energy consumption. This overhead makes it challenging to deploy these high-productivity programming technologies on emerging single-board computers for IoT applications. Addressing this challenge, this paper introduces Typed Architectures, a high-efficiency, low-cost execution substrate for dynamic scripting languages, where each data variable retains high-level type information at an ISA level. Typed Architectures calculate and check the dynamic type of each variable implicitly in hardware, rather than explicitly in software, hence significantly reducing instruction count for dynamic type checking. Besides, Typed Architectures introduce polymorphic instructions (e.g., xadd), which are bound to the correct native instruction at runtime within the pipeline (e.g., add or fadd) to efficiently implement polymorphic operators. Finally, Typed Architectures provide hardware support for flexible yet efficient type tag extraction and insertion, capturing common data layout patterns of tag-value pairs. Our evaluation using a fully synthesizable RISC-V RTL design on FPGA shows that Typed Architectures achieve geomean speedups of 11.2% and 9.9% with maximum speedups of 32.6% and 43.5% for two production-grade scripting engines for JavaScript and Lua, respectively. Moreover, Typed Architectures improve the energy-delay product (EDP) by 19.3% for JavaScript and 16.5% for Lua with an area overhead of 1.6% at a 40nm technology node.",
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"paperAbstract": "Scalable Networks-on-Chip (NoCs) have become the de facto interconnection mechanism in large scale Chip Multiprocessors. Not only are NoCs devouring a large fraction of the on-chip power budget but static NoC power consumption is becoming the dominant component as technology scales down. Hence reducing static NoC power consumption is critical for energy-efficient computing. Previous research has proposed to power-gate routers attached to inactive cores so as to save static power, but requires centralized control and global network knowledge. In this paper, we propose Fly-Over (FLOV), a light-weight distributed mechanism for power-gating routers, which encompasses FLOV router architecture, handshake protocols, and a partition-based dynamic routing algorithm to maintain network functionalities. With simple modifications to the baseline router architecture, FLOV can facilitate FLOV links over power-gated routers. Then we present two handshake protocols for FLOV routers, restricted FLOV that can power-gate routers under restricted conditions and generalized FLOV with more power saving capability. The proposed routing algorithm provides best-effort minimal path routing without the necessity for global network information. We evaluate our schemes using synthetic workloads as well as real workloads from PARSEC 2.1 benchmark suite. Our full system evaluations show that FLOV reduces the total and static energy consumption by 18% and 22% respectively, on average across several benchmarks, compared to state-of-the-art NoC power-gating mechanism while keeping the performance degradation minimal.",
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"http://engineering.tamu.edu/media/3427396/2016_3_1.pdf",
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"title": "Fly-Over: A Light-Weight Distributed Power-Gating Mechanism for Energy-Efficient Networks-on-Chip",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"paperAbstract": "Applications written to run on conventional operating systems typically depend on OS abstractions like processes, pipes, signals, sockets, and a shared file system. Porting these applications to the web currently requires extensive rewriting or hosting significant portions of code server-side because browsers present a nontraditional runtime environment that lacks OS functionality.\n This paper presents Browsix, a framework that bridges the considerable gap between conventional operating systems and the browser, enabling unmodified programs expecting a Unix-like environment to run directly in the browser. Browsix comprises two core parts: (1) a JavaScript-only system that makes core Unix features (including pipes, concurrent processes, signals, sockets, and a shared file system) available to web applications; and (2) extended JavaScript runtimes for C, C++, Go, and Node.js that support running programs written in these languages as processes in the browser. Browsix supports running a POSIX shell, making it straightforward to connect applications together via pipes.\n We illustrate Browsix's capabilities via case studies that demonstrate how it eases porting legacy applications to the browser and enables new functionality. We demonstrate a Browsix-enabled LaTeX editor that operates by executing unmodified versions of pdfLaTeX and BibTeX. This browser-only LaTeX editor can render documents in seconds, making it fast enough to be practical. We further demonstrate how Browsix lets us port a client-server application to run entirely in the browser for disconnected operation. Creating these applications required less than 50 lines of glue code and no code modifications, demonstrating how easily Browsix can be used to build sophisticated web applications from existing parts without modification.",
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"title": "Browsix: Bridging the Gap Between Unix and the Browser",
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},
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"name": "Yongkun Li"
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"name": "Yinlong Xu"
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"paperAbstract": "In hypervisor-based virtualization environments, translation lookaside buffers (TLBs) misses may induce twodimensional page table walks, which may incur a long access latency, and this issue becomes worse with ever increasing memory capacity. To reduce the overhead of TLB misses, large pages (e.g., 2M-pages) are widely supported in modern hardware platforms to reduce the number of page table entries. However, memory management with large pages can be inefficient in deduplication, leading to low utilization of memory, which is a precious resource for a variety of applications. To simultaneously enjoy benefits of high performance by accessing memory with large pages (e.g., 2M-pages) and high deduplication rate by managing memory with base pages (e.g., 4K-pages), we propose Smart Memory Deduplciation, or SmartMD in short, which is an adaptive and efficient management scheme for mixed-page memory. Specifically, we propose two lightweight schemes to accurately monitor pages\u2019 access frequency and repetition rate, and present a dynamic and adaptive conversion scheme to selectively split or reconstruct large pages. We implement a prototype system and conduct extensive experiments with various workloads. Experiment results show that SmartMD can simultaneously achieve high access performance similar to systems using large pages, and achieves a deduplication rate similar to that applying aggressive deduplication scheme (i.e., KSM) at the same time on base pages.",
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"title": "SmartMD: A High Performance Deduplication Engine with Mixed Pages",
"venue": "USENIX Annual Technical Conference",
"year": 2017
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"doi": "10.1145/3035918.3064049",
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"paperAbstract": "The advent of columnar data analytics engines fueled a series of optimizations on the scan operator. New designs include column-group storage, vectorized execution, shared scans, working directly over compressed data, and operating using SIMD and multi-core execution. Larger main memories and deeper cache hierarchies increase the efficiency of modern scans, prompting a revisit of the question of access path selection.\n In this paper, we compare modern sequential scans and secondary index scans. Through detailed analytical modeling and experimentation we show that while scans have become useful in more cases than before, both access paths are still useful, and so, access path selection (APS) is still required to achieve the best performance when considering variable workloads. We show how to perform access path selection. In particular, contrary to the way traditional systems choose between scans and secondary indexes, we find that in addition to the query selectivity, the underlying hardware, and the system design, modern optimizers also need to take into account <i>query concurrency</i>. We further discuss the implications of integrating access path selection in a modern analytical data system. We demonstrate, both theoretically and experimentally, that using the proposed model a system can quickly perform access path selection, outperforming solutions that rely on a single access path or traditional access path models. We outline a light-weight mechanism to integrate APS into main-memory analytical systems that does not interfere with low latency queries. We also use the APS model to explain how the division between sequential scan and secondary index scan has historically changed due to hardware and workload changes, which allows for future projections based on hardware advancements.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3035918.3064049",
"http://stratos.seas.harvard.edu/files/stratos/files/accespathselection.pdf"
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"title": "Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe?",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Nicola Cadenelli"
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{
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"name": "Jorda Polo"
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{
"ids": [
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"name": "David Carrera"
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],
"doi": "10.1109/HPCC-SmartCity-DSS.2017.57",
"doiUrl": "https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.57",
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"journalName": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"paperAbstract": "The emergence of Next Generation Sequencing (NGS) platforms has increased the throughput of genomic sequencing and in turn the amount of data that needs to be processed, requiring highly efficient computation for its analysis. In this context, modern architectures including accelerators and non-volatile memory are essential to enable the mass exploitation of these bioinformatics workloads. This paper presents a redesign of the main component of a state-of-the-art reference-free method for variant calling, SMUFIN, which has been adapted to make the most of GPUs and NVM devices. SMUFIN relies on counting the frequency of k-mers (substrings of length k) in DNA sequences, which also constitutes a well-known problem for many bioinformatics workloads, such as genome assembly. We propose techniques to improve the efficiency of k-mer counting and to scale-up workloads like SMUFIN that used to require 16 nodes of Marenostrum 3 to a single machine with a GPU and NVM drives. Results show that although the single machine is not able to improve the time to solution of 16 nodes, its CPU time is 7.5x shorter than the aggregate CPU time of the 16 nodes, with a reduction in energy consumption of 5.5x.",
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"paperAbstract": "This work studies the interplay between multithreaded cores and speculative parallelism (e.g., transactional memory or thread-level speculation). These techniques are often used together, yet they have been developed independently. This disconnect causes major performance pathologies: increasing the number of threads per core adds conflicts and wasted work, and puts pressure on speculative execution resources. These pathologies often squander the benefits of multithreading.We present speculation-aware multithreading (SAM), a simple policy that addresses these pathologies. By coordinating instruction dispatch and conflict resolution priorities, SAM focuses execution resources on work that is more likely to commit, avoiding aborts and using speculation resources more efficiently.We design SAM variants for in-order and out-of-order cores. SAM is cheap to implement and makes multithreaded cores much more beneficial on speculative parallel programs. We evaluate SAM on systems with up to 64 SMT cores. With SAM, 8-threaded cores outperform single-threaded cores by 2.33x on average, while a speculation-oblivious policy yields a 1.85x speedup. SAM also reduces wasted work by 52%.",
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"paperAbstract": "Query evaluation over probabilistic databases is known to be intractable in many cases, even in data complexity, i.e., when the query is fixed. Although some restrictions of the queries and instances[4] have been proposed to lower the complexity, these known tractable cases usually do not apply to combined complexity, i.e., when the query is not fixed. This leaves open the question of which query and instance languages ensure the tractability of probabilistic query evaluation in combined complexity.\n This paper proposes the first general study of the combined complexity of conjunctive query evaluation on probabilistic instances over binary signatures, which we can alternatively phrase as a probabilistic version of the graph homomorphism problem, or of a constraint satisfaction problem (CSP) variant. We study the complexity of this problem depending on whether instances and queries can use features such as edge labels, disconnectedness, branching, and edges in both directions. We show that the complexity landscape is surprisingly rich, using a variety of technical tools: automata-based compilation to d-DNNF lineages [4], ß-acyclic lineages, the <i>X</i>-property for tractable CSP[25], graded DAGs[28] and various coding techniques for hardness proofs.",
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"title": "Conjunctive Queries on Probabilistic Graphs: Combined Complexity",
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"paperAbstract": "The energy consumption of DRAM is a critical concern in modern computing systems. Improvements in manufacturing process technology have allowed DRAM vendors to lower the DRAM supply voltage conservatively, which reduces some of the DRAM energy consumption. We would like to reduce the DRAM supply voltage more aggressively, to further reduce energy. Aggressive supply voltage reduction requires a thorough understanding of the effect voltage scaling has on DRAM access latency and DRAM reliability.\n In this paper, we take a comprehensive approach to understanding and exploiting the latency and reliability characteristics of modern DRAM when the supply voltage is lowered below the nominal voltage level specified by DRAM standards. Using an FPGA-based testing platform, we perform an experimental study of 124 real DDR3L (low-voltage) DRAM chips manufactured recently by three major DRAM vendors. We find that reducing the supply voltage below a certain point introduces bit errors in the data, and we comprehensively characterize the behavior of these errors. We discover that these errors can be avoided by increasing the latency of three major DRAM operations (activation, restoration, and precharge). We perform detailed DRAM circuit simulations to validate and explain our experimental findings. We also characterize the various relationships between reduced supply voltage and error locations, stored data patterns, DRAM temperature, and data retention.\n Based on our observations, we propose a new DRAM energy reduction mechanism, called Voltron. The key idea of Voltron is to use a performance model to determine by how much we can reduce the supply voltage without introducing errors and without exceeding a user-specified threshold for performance loss. Our evaluations show that Voltron reduces the average DRAM and system energy consumption by 10.5% and 7.3%, respectively, while limiting the average system performance loss to only 1.8%, for a variety of memory-intensive quad-core workloads. We also show that Voltron significantly outperforms prior dynamic voltage and frequency scaling mechanisms for DRAM.",
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"http://doi.acm.org/10.1145/3084447"
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"title": "Understanding Reduced-Voltage Operation in Modern DRAM Devices: Experimental Characterization, Analysis, and Mechanisms",
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"year": 2017
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"title": "The Investigation of the ARMv7 and Intel Haswell Architectures Suitability for Performance and Energy-Aware Computing",
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"year": 2017
},
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"name": "Mahmut T. Kandemir"
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"paperAbstract": "Data access costs dominate the execution times of most parallel applications and they are expected to be even more important in the future. To address this, recent research has focused on Near Data Processing (NDP) as a new paradigm that tries to bring computation to data, instead of bringing data to computation (which is the norm in conventional computing). This paper explores the potential of compiler support in exploiting NDP in the context of emerging manycore systems. To that end, we propose a novel compiler algorithm that partitions the computations in a given loop nest into subcomputations and schedules the resulting subcomputations on different cores with the goal of reducing the distance-to-data on the on-chip network. An important characteristic of our approach is that it exploits NDP while taking advantage of data locality. Our experiments with 12 multithreaded applications running on a state-of-the-art commercial manycore system indicate that the proposed compiler-based approach significantly reduces data movements on the on-chip network by taking advantage of NDP, and these benefits lead to an average execution time improvement of 18.4%.",
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"http://doi.acm.org/10.1145/3123939.3123954",
"http://xzt102.github.io/publications/2017_MICRO_Xulong.pdf"
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"title": "Data movement aware computation partitioning",
"venue": "MICRO",
"year": 2017
},
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"authors": [
{
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"name": "Jayanth Kalyanasundaram"
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"name": "Yogesh L. Simmhan"
}
],
"doi": "10.1109/HiPC.2017.00032",
"doiUrl": "https://doi.org/10.1109/HiPC.2017.00032",
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"paperAbstract": "ARM processors have dominated the mobile device market in the last decade due to their favorable computing to energy ratio. In this age of Cloud data centers and Big Data analytics, the focus is increasingly on power efficient processing, rather than just high throughput computing. ARM's first commodity server-grade processor is the recent AMD A1100-series processor, based on a 64-bit ARM Cortex A57 architecture. In this paper, we study the performance and energy efficiency of a server based on this ARM64 CPU, relative to a comparable server running an AMD Opteron 3300-series x64 CPU, for Big Data workloads. Specifically, we study these for Intel's HiBench suite of web, query and machine learning benchmarks on Apache Hadoop v2.7 in a pseudo-distributed setup, for data sizes up to 20GB files, 5M web pages and 500M tuples. Our results show that the ARM64 server's runtime performance is comparable to the x64 server for integer-based workloads like Sort and Hive queries, and only lags behind for floating-point intensive benchmarks like PageRank, when they do not exploit data parallelism adequately. We also see that the ARM64 server takes 1/3rd the energy, and has an Energy Delay Product (EDP) that is 50-71% lower than the x64 server. These results hold promise for ARM64 data centers hosting Big Data workloads to reduce their operational costs, while opening up opportunities for further analysis.",
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"https://arxiv.org/pdf/1701.05996v1.pdf"
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"title": "ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads",
"venue": "2017 IEEE 24th International Conference on High Performance Computing (HiPC)",
"year": 2017
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"paperAbstract": "Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts in this area assume the intensity of the light emitted from LEDs is constant. This is not true any more when Smart Lighting is introduced to VLC in recent years, which requires the LEDs to adapt their brightness according to the intensity of the natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of the data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A new adaptive multiple pulse position modulation scheme is proposed to support fine-grained dimming levels to avoid flickering and at the same time, maximize the throughput under each dimming level. SmartVLC is implemented on low-cost commodity hardware and several real-life challenges in both hardware and software are addressed to make SmartVLC a robust realtime system. Comprehensive experiments are carried out to evaluate the performance of SmartVLC under multifaceted scenarios. The results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40% and 12% on average, respectively, without bringing any flickering to users.",
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"paperAbstract": "A central concern for an optimizing compiler is the design of its intermediate representation (IR) for code. The IR should make it easy to perform transformations, and should also afford efficient and precise static analysis. In this paper we study an aspect of IR design that has received little attention: the role of undefined behavior. The IR for every optimizing compiler we have looked at, including GCC, LLVM, Intel's, and Microsoft's, supports one or more forms of undefined behavior (UB), not only to reflect the semantics of UB-heavy programming languages such as C and C++, but also to model inherently unsafe low-level operations such as memory stores and to avoid over-constraining IR semantics to the point that desirable transformations become illegal. The current semantics of LLVM's IR fails to justify some cases of loop unswitching, global value numbering, and other important \"textbook\" optimizations, causing long-standing bugs. We present solutions to the problems we have identified in LLVM's IR and show that most optimizations currently in LLVM remain sound, and that some desirable new transformations become permissible. Our solutions do not degrade compile time or performance of generated code.",
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"paperAbstract": "Many robotic tasks require heavy computation, which can easily exceed the robot's onboard computer capability. A promising solution to address this challenge is outsourcing thecomputation to the cloud. However, exploiting the potential ofcloud resources in robotic software is difficult, because it in-volves complex code modification and extensive (re)configurationprocedures. Moreover, quality of service (QoS) such as timeliness, which is critical to robot's behavior, have to be considered. Inthis paper, we propose a transparent and QoS-aware softwareframework called Cloudroid for cloud robotic applications. Thisframework supports direct deployment of existing robotic soft-ware packages to the cloud, transparently transforming theminto Internet-accessible cloud services. And with the automati-cally generated service stubs, robotic applications can outsourcetheir computation to the cloud without any code modification. Furthermore, the robot and the cloud can cooperate to maintainthe specific QoS property such as request response time, evenin a highly dynamic and resource-competitive environment. Weevaluated Cloudroid based on a group of typical robotic scenariosand a set of software packages widely adopted in real-worldrobot practices. Results show that robots capability can beenhanced significantly without code modification and specific QoSobjectives can be guaranteed. In certain tasks, the "cloud + robot" setup shows improved performance in orders of magnitudecompared with the robot native setup.",
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"paperAbstract": "Cloud services are becoming increasingly globalized and data-center workloads are expanding exponentially. GPU and FPGA-based clouds have illustrated improvements in power and performance by accelerating compute-intensive workloads. ASIC-based clouds are a promising way to optimize the Total Cost of Ownership (TCO) of a given datacenter computation (e.g. YouTube transcoding) by reducing both energy consumption and marginal computation cost.\n The feasibility of an ASIC Cloud for a particular application is directly gated by the ability to manage the Non-Recurring Engineering (NRE) costs of designing and fabricating the ASIC, so that it is significantly lower (e.g. 2X) than the TCO of the best available alternative.\n In this paper, we show that technology node selection is a major tool for managing ASIC Cloud NRE, and allows the designer to trade off an accelerator's excess energy efficiency and cost performance for lower total cost.\n We explore NRE and cross-technology optimization of ASIC Clouds for four different applications: Bitcoin mining, YouTube-style video transcoding, Litecoin, and Deep Learning. We address these challenges and show large reductions in the NRE, potentially enabling ASIC Clouds to address a wider variety of datacenter workloads. Our results suggest that advanced nodes like 16nm will lead to sub-optimal TCO for many workloads, and that use of older nodes like 65nm can enable a greater diversity of ASIC Clouds.",
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"title": "Moonwalk: NRE Optimization in ASIC Clouds",
"venue": "ASPLOS",
"year": 2017
},
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"name": "Amrita Mathuriya"
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"name": "Ye Luo"
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"name": "Anouar Benali"
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"paperAbstract": "B-spline based orbital representations are widely used in Quantum Monte Carlo (QMC) simulations of solids, historically taking as much as 50% of the total run time. Random accesses to a large four-dimensional array make it challenging to efficiently utilize caches and wide vector units of modern CPUs. We present node-level optimizations of B-spline evaluations on multi/many-core shared memory processors. To increase SIMD efficiency and bandwidth utilization, we first apply data layout transformation from array-of-structures to structure-of-arrays (SoA). Then by blocking SoA objects, we optimize cache reuse and get sustained throughput for a range of problem sizes. We implement efficient nested threading in B-spline orbital evaluation kernels, paving the way towards enabling strong scaling of QMC simulations. These optimizations are portableon four distinct cache-coherent architectures and result in up to 5.6x performance enhancements on Intel Xeon Phi processor 7250P (KNL), 5.7x on Intel Xeon Phi coprocessor 7120P, 10x on an Intel Xeon processor E5v4 CPU and 9.5x on BlueGene/Q processor. Our nested threading implementation shows nearly ideal parallel efficiency on KNL up to 16 threads. We employ roofline performance analysis to model the impacts of our optimizations. This work combined with our current efforts of optimizing other QMC kernels, result in greater than 4.5x speedup of miniQMC on KNL.",
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"title": "Optimization and Parallelization of B-Spline Based Orbital Evaluations in QMC on Multi/Many-Core Shared Memory Processors",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"name": "Abbas Razaghpanah"
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"paperAbstract": "Middleboxes implement a variety of network management policies (e.g., prioritizing or blocking traffic) in their networks. While such policies can be beneficial (e.g., blocking malware) they also raise issues of network neutrality and freedom of speech when used for application-specific differentiation and censorship. There is a poor understanding of how such policies are implemented in practice, and how they can be evaded efficiently. As a result, most circumvention solutions are brittle, point solutions based on manual analysis.\n This paper presents the design and implementation of <i>lib•erate</i>, a tool for automatically identifying middlebox policies, reverse-engineering their implementations, and adaptively deploying custom circumvention techniques. Unlike previous work, our approach is application-agnostic, can be deployed unilaterally (i.e., only at one endpoint) on unmodified applications via a linked library or transparent proxy, and can adapt to changes to classifiers at runtime. We implemented a <i>lib•erate</i> prototype as a transparent proxy and evaluate it both in a testbed environment and in operational networks that throttle or block traffic based on DPI-based classifier rules, and show that our approach is effective across a wide range of middlebox deployments.",
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"https://people.cs.umass.edu/~phillipa/papers/imc2017_liberate_paper.pdf",
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"title": "Lib\u2022erate, (n): a Library for Exposing (traffic-classification) Rules and Avoiding Them Efficiently",
"venue": "IMC",
"year": 2017
},
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"paperAbstract": "With the end of Dennard scaling, architects have increasingly turned to special-purpose hardware accelerators to improve the performance and energy efficiency for some applications. Unfortunately, accelerators don't always live up to their expectations and may under-perform in some situations. Understanding the factors which effect the performance of an accelerator is crucial for both architects and programmers early in the design stage. Detailed models can be highly accurate, but often require low-level details which are not available until late in the design cycle. In contrast, simple analytical models can provide useful insights by abstracting away low-level system details.\n In this paper, we propose LogCA---a high-level performance model for hardware accelerators. LogCA helps both programmers and architects identify performance bounds and design bottlenecks early in the design cycle, and provide insight into which optimizations may alleviate these bottlenecks. We validate our model across a variety of kernels, ranging from sub-linear to super-linear complexities on both on-chip and off-chip accelerators. We also describe the utility of LogCA using two retrospective case studies. First, we discuss the evolution of interface design in SUN/Oracle's encryption accelerators. Second, we discuss the evolution of memory interface design in three different GPU architectures. In both cases, we show that the adopted design optimizations for these machines are similar to LogCA's suggested optimizations. We argue that architects and programmers can use insights from these retrospective studies for improving future designs.",
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"title": "LogCA: A high-level performance model for hardware accelerators",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"name": "Herv\u00e9 Yviquel"
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"paperAbstract": "Computation offloading is a programming model in which program fragments (e.g. hot loops) are annotated so that their execution is performed in dedicated hardware or accelerator devices. Although offloading has been extensively used to move computation to GPUs, through directive-based annotation standards like OpenMP, offloading computation to very large computer clusters can become a complex and cumbersome task. It typically requires mixing programming models (e.g. OpenMP and MPI) and languages (e.g. C/C++ and Scala), dealing with various access control mechanisms from different clouds (e.g. AWS and Azure), and integrating all this into a single application. This paper introduces the cloud as a computation offloading device. It integrates OpenMP directives, cloud based map-reduce Spark nodes and remote communication management such that the cloud appears to the programmer as yet another device available in its local computer. Experiments using LLVM, OpenMP 4.5 and Amazon EC2 show the viability of the proposed approach and enable a thorough analysis of the performance and costs involved in cloud offloading. The results show that although data transfers can impose overheads, cloud offloading can still achieve promising speedups of up to 86x in 256 cores for the 2MM benchmark using 1GB matrices.",
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"title": "The Cloud as an OpenMP Offloading Device",
"venue": "2017 46th International Conference on Parallel Processing (ICPP)",
"year": 2017
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"authors": [
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"name": "Shen Wang"
},
{
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"name": "Lifang He"
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{
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"name": "Bokai Cao"
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"name": "Chun-Ta Lu"
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"paperAbstract": "Cloud providers routinely schedule multiple applications per physical host to increase efficiency. The resulting interference on shared resources often leads to performance degradation and, more importantly, security vulnerabilities. Interference can leak important information ranging from a service's placement to confidential data, like private keys. We present Bolt, a practical system that accurately detects the type and characteristics of applications sharing a cloud platform based on the interference an adversary sees on shared resources. Bolt leverages online data mining techniques that only require 2-5 seconds for detection. In a multi-user study on EC2, Bolt correctly identifies the characteristics of 385 out of 436 diverse workloads. Extracting this information enables a wide spectrum of previously-impractical cloud attacks, including denial of service attacks (DoS) that increase tail latency by 140x, as well as resource freeing (RFA) and co-residency attacks. Finally, we show that while advanced isolation mechanisms, such as cache partitioning lower detection accuracy, they are insufficient to eliminate these vulnerabilities altogether. To do so, one must either disallow core sharing, or only allow it between threads of the same application, leading to significant inefficiencies and performance penalties.",
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"paperAbstract": "Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras, and electronic personal assistants. This paper introduces the Sparse CNN (SCNN) accelerator architecture, which improves performance and energy efficiency by exploiting the zero-valued weights that stem from network pruning during training and zero-valued activations that arise from the common ReLU operator. Specifically, SCNN employs a novel dataflow that enables maintaining the sparse weights and activations in a compressed encoding, which eliminates unnecessary data transfers and reduces storage requirements. Furthermore, the SCNN dataflow facilitates efficient delivery of those weights and activations to a multiplier array, where they are extensively reused; product accumulation is performed in a novel accumulator array. On contemporary neural networks, SCNN can improve both performance and energy by a factor of 2.7x and 2.3x, respectively, over a comparably provisioned dense CNN accelerator.",
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"title": "SCNN: An accelerator for compressed-sparse convolutional neural networks",
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"paperAbstract": "In this paper we describe a new, efficient predictive scheduling methodology for implementing computing infrastructure power savings using private clouds. Our approach, termed "QPRED," estimates the quantiles on the distribution of future machine usage so that unneeded machines may be powered down to save power. A cloud administrator sets a bound on the probability that all available machines will be powered down when a cloud request arrives. This target probability is the basis of a Service Level Agreement between the cloud administrator and all cloud users covering start-up delay resulting from power savings. Our results, validated using activity traces from several private clouds used in commercial production, indicate that QPRED successfully reduces power consumption substantially while maintaining the SLAs specified by the cloud administrator.",
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"title": "PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning",
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"paperAbstract": "A program can be viewed as a syntactic structure P (syntactic skeleton) parameterized by a collection of identifiers V (variable names). This paper introduces the skeletal program enumeration (SPE) problem: Given a syntactic skeleton P and a set of variables V , enumerate a set of programs P exhibiting all possible variable usage patterns within P. It proposes an effective realization of SPE for systematic, rigorous compiler testing by leveraging three important observations: (1) Programs with different variable usage patterns exhibit diverse control- and data-dependence, and help exploit different compiler optimizations; (2) most real compiler bugs were revealed by small tests (i.e., small-sized P) — this “small-scope” observation opens up SPE for practical compiler validation; and (3) SPE is exhaustive w.r.t. a given syntactic skeleton and variable set, offering a level of guarantee absent from all existing compiler testing techniques. \n \n The key challenge of SPE is how to eliminate the enormous amount of equivalent programs w.r.t. α-conversion. Our main technical contribution is a novel algorithm for computing the canonical (and smallest) set of all non-α-equivalent programs. To demonstrate its practical utility, we have applied the SPE technique to test C/C++ compilers using syntactic skeletons derived from their own regression test-suites. Our evaluation results are extremely encouraging. In less than six months, our approach has led to 217 confirmed GCC/Clang bug reports, 119 of which have already been fixed, and the majority are long latent despite extensive prior testing efforts. Our SPE algorithm also provides six orders of magnitude reduction. Moreover, in three weeks, our technique has found 29 CompCert crashing bugs and 42 bugs in two Scala optimizing compilers. These results demonstrate our SPE technique’s generality and further illustrate its effectiveness.",
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"title": "Automating the Application Data Placement in Hybrid Memory Systems",
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"paperAbstract": "We present methods for the effective application level reordering of non-blocking RDMA operations. We supplement out-of-order hardware delivery mechanisms with heuristics to account for the CPU side overhead of communication and for differences in network latency: a runtime scheduler takes into account message sizes, destination and concurrency and reorders operations to improve overall communication throughput. Results are validated on InfiniBand and Cray Aries networks, for SPMD and hybrid (SPMD+OpenMP) programming models. We show up to 5! potential speedup, with 30-50% more typical, for synthetic message patterns in microbenchmarks. We also obtain up to 33% improvement in the communication stages in application settings. While the design space is complex, the resulting scheduler is simple, both internally and at the application level interfaces. It also provides performance portability across networks and programming models. We believe these techniques can be easily retrofitted within any application or runtime framework that uses one-sided communication, e.g. using GASNet, MPI 3.0 RMA or low level APIs such as IBVerbs.",
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"paperAbstract": "We present MemEC, an erasure-coding-based in-memory key-value (KV) store that achieves high availability and fast recovery while keeping low data redundancy across storage servers. MemEC is specifically designed for workloads dominated by small objects. By encoding objects in entirety, MemEC is shown to incur 60% less storage redundancy for small objects than existing replication- and erasure-coding-based approaches. It also supports graceful transitions between decentralized requests in normal mode (i.e., no failures) and coordinated requests in degraded mode (i.e., with failures). We evaluate our MemEC prototype via testbed experiments under read-heavy and update-heavy YCSB workloads. We show that MemEC achieves high throughput and low latency in both normal and degraded modes, and supports fast transitions between the two modes.",
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"title": "Erasure coding for small objects in in-memory KV storage",
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"year": 2017
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"name": "Cheng Wang"
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"name": "Jianyu Jiang"
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"name": "Xusheng Chen"
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"name": "Ning Yi"
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"doi": "10.1145/3127479.3128609",
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"paperAbstract": "State machine replication (SMR) uses Paxos to enforce the same inputs for a program (e.g., Redis) replicated on a number of hosts, tolerating various types of failures. Unfortunately, traditional Paxos protocols incur prohibitive performance overhead on server programs due to their high consensus latency on TCP/IP. Worse, the consensus latency of extant Paxos protocols increases drastically when more concurrent client connections or hosts are added. This paper presents APUS, the first RDMA-based Paxos protocol that aims to be fast and scalable to client connections and hosts. APUS intercepts inbound socket calls of an unmodified server program, assigns a total order for all input requests, and uses fast RDMA primitives to replicate these requests concurrently.\n We evaluated APUS on nine widely-used server programs (e.g., Redis and MySQL). APUS incurred a mean overhead of 4.3% in response time and 4.2% in throughput. We integrated APUS with an SMR system Calvin. Our Calvin-APUS integration was 8.2X faster than the extant Calvin-ZooKeeper integration. The consensus latency of APUS outperformed an RDMA-based consensus protocol by 4.9X. APUS source code and raw results are released on github.com/hku-systems/apus.",
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"title": "APUS: fast and scalable paxos on RDMA",
"venue": "SoCC",
"year": 2017
},
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"authors": [
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"name": "John P. Rula"
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"name": "Fabi\u00e1n E. Bustamante"
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"name": "Moritz Steiner"
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"doi": "10.1145/3131365.3131402",
"doiUrl": "https://doi.org/10.1145/3131365.3131402",
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"paperAbstract": "The impressive growth of the mobile Internet has motivated several industry reports retelling the story in terms of number of devices or subscriptions sold per regions, or the increase in mobile traffic, both WiFi and cellular. Yet, despite the abundance of such reports, we still lack an understanding of the impact of cellular networks around the world.\n We present the first comprehensive analysis of global cellular networks. We describe an approach to accurately identify cellular network IP addresses using the Network Information API, a non-standard Javascript API in several mobile browsers, and show its effectiveness in a range cellular network configurations. We combine this approach with the vantage point of one of the world's largest CDNs, with servers located in 1,450 networks and clients distributed across across 245 countries, to characterize cellular access around the globe.\n We find that the majority of cellular networks exist as mixed networks (i.e., networks that share both fixed-line and cellular devices), requiring prefix - not ASN - level identification. We discover over 350 thousand /24 and 23 thousand /48 <i>cellular</i> IPv4 and IPv6 prefixes respectively. By utilizing addresses level traffic from the same CDN, we calculate the fraction of traffic coming from cellular addresses. Overall we find that cellular traffic comprises 16.2% of the CDN's global traffic, and that cellular traffic ranges widely in importance between countries, from capturing nearly 96% of all traffic in Ghana to just 12.1% in France.",
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"title": "Cell spotting: studying the role of cellular networks in the internet",
"venue": "IMC",
"year": 2017
},
"2fea3924ed086fd94ec56543e0136e0a18099de8": {
"authors": [
{
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"name": "Mohsen Ghaffari"
}
],
"doi": "10.1145/3087801.3087830",
"doiUrl": "https://doi.org/10.1145/3087801.3087830",
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"paperAbstract": "Computing a Maximal Independent Set (MIS) is a central problem in distributed graph algorithms. This paper presents an improved randomized distributed algorithm for computing an MIS in an allto-all communication distributed model, known as the congested clique model, defined as follows: Given a graphG = (V ,E), initially each node knows only its neighbors. Communication happens in synchronous rounds over a complete graph, and per round each node can send O (logn) bits to each other node. We present a randomized algorithm that computes an MIS in \u00d5 (log\u2206/ \u221a logn + 1) \u2264 \u00d5 ( \u221a log\u2206) rounds of congested clique, with high probability. Here \u2206 denotes the maximum degree in the graph. This improves quadratically on theO (log\u2206) algorithm of [Ghaffari, SODA\u201916]. The core technical novelty in this result is a certain local sparsification technique for MIS, which we believe to be of independent interest.",
"pdfUrls": [
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"http://groups.csail.mit.edu/tds/papers/Ghaffari/podc124.pdf"
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"title": "Distributed MIS via All-to-All Communication",
"venue": "PODC",
"year": 2017
},
"2ff7fdd38c05a7a763b7426a32cd036a312b2e43": {
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"name": "Tony Nowatzki"
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"name": "Vinay Gangadhar"
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{
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"name": "Newsha Ardalani"
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{
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"doi": "10.1145/3079856.3080255",
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"journalName": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
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"paperAbstract": "Demand for low-power data processing hardware continues to rise inexorably. Existing programmable and \"general purpose\" solutions (eg. SIMD, GPGPUs) are insufficient, as evidenced by the order-of-magnitude improvements and industry adoption of application and domain-specific accelerators in important areas like machine learning, computer vision and big data. The stark tradeoffs between efficiency and generality at these two extremes poses a difficult question: how could domain-specific hardware efficiency be achieved without domain-specific hardware solutions?\n In this work, we rely on the insight that \"acceleratable\" algorithms have broad common properties: high computational intensity with long phases, simple control patterns and dependences, and simple streaming memory access and reuse patterns. We define a general architecture (a hardware-software interface) which can more efficiently expresses program with these properties called stream-dataflow. The dataflow component of this architecture enables high concurrency, and the stream component enables communication and coordination at very-low power and area overhead. This paper explores the hardware and software implications, describes its detailed microarchitecture, and evaluates an implementation. Compared to a state-of-the-art domain specific accelerator (DianNao), and fixed-function accelerators for MachSuite, Softbrain can match their performance with only 2x power overhead on average.",
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"http://web.cs.ucla.edu/~tjn//papers/isca2017-stream-dataflow.pdf",
"http://research.cs.wisc.edu/vertical/talks/2017/isca17-stream-dataflow-talk"
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"title": "Stream-dataflow acceleration",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
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"name": "Xiaoye Li"
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"name": "Leandro Pfleger de Aguiar"
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"name": "Daniel Sadoc Menasch\u00e9"
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"doi": "10.1145/3084455",
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"paperAbstract": "Industrial Control Systems (ICS) are widely deployed in mission critical infrastructures such as manufacturing, energy, and transportation. The mission critical nature of ICS devices poses important security challenges for ICS vendors and asset owners. In particular, the patching of ICS devices is usually deferred to scheduled production outages so as to prevent potential operational disruption of critical systems. Unfortunately, anecdotal evidence suggests that ICS devices are riddled with security vulnerabilities that are not patched in a timely manner, which leaves them vulnerable to exploitation by hackers, nation states, and hacktivist organizations.\n In this paper, we present the results from our longitudinal measurement and characterization study of ICS patching behavior. Our study is based on IP scan data collected from Shodan over the duration of three years for more than 500 known industrial ICS protocols and products. Our longitudinal measurements reveal the impact of vulnerability disclosures on ICS patching. Our analysis of more than 100 thousand Internet-exposed ICS devices reveals that about 50% upgrade to newer patched versions within 60 days of a vulnerability disclosure. Based on our measurement and analysis, we further propose a variation of the Bass model to forecast the patching behavior of ICS devices. The evaluation shows that our proposed models have comparable prediction accuracy when contrasted against traditional ARIMA timeseries forecasting models, while requiring less parameters and being amenable to direct physical interpretation.",
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"http://doi.acm.org/10.1145/3078505.3078524",
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"title": "Characterizing and Modeling Patching Practices of Industrial Control Systems",
"venue": "SIGMETRICS",
"year": 2017
},
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"name": "Rishi Surendran"
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"name": "Francisco Martins"
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"name": "Vivek Sarkar"
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"name": "Vasco Thudichum Vasconcelos"
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"doi": "10.1145/3143359",
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"paperAbstract": "Futures are an elegant approach to expressing parallelism in functional programs. However, combining futures with imperative programming (as in C++ or in Java) can lead to pernicious bugs in the form of data races and deadlocks, as a consequence of uncontrolled data flow through mutable shared memory. \n In this paper we introduce the Known Joins (KJ) property for parallel programs with futures, and relate it to the Deadlock Freedom (DF) and the Data-Race Freedom (DRF) properties. Our paper offers two key theoretical results: 1) DRF implies KJ, and 2) KJ implies DF. These results show that data-race freedom is sufficient to guarantee deadlock freedom in programs with futures that only manipulate unsynchronized shared variables. To the best of our knowledge, these are the first theoretical results to establish sufficient conditions for deadlock freedom in imperative parallel programs with futures, and to characterize the subset of data races that can trigger deadlocks (those that violate the KJ property). \n From result 2), we developed a tool that avoids deadlocks in linear time and space when KJ holds, i.e., when there are no data races among references to futures. When KJ fails, the tool reports the data race and optionally falls back to a standard deadlock avoidance algorithm by cycle detection. Our tool verified a dataset of ∼2,300 student’s homework solutions and found one deadlocked program. The performance results obtained from our tool are very encouraging: a maximum slowdown of 1.06× on a 16-core machine, always outperforming deadlock avoidance via cycle-detection. Proofs of the two main results were formalized using the Coq proof assistant.",
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"title": "Deadlock avoidance in parallel programs with futures: why parallel tasks should not wait for strangers",
"venue": "PACMPL",
"year": 2017
},
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"authors": [
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"name": "Neeraja J. Yadwadkar"
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"name": "Bharath Hariharan"
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"name": "Joseph Gonzalez"
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"name": "Burton Smith"
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"paperAbstract": "Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of <i>accurately</i> and <i>economically</i> choosing the <i>best VM</i> for a given <i>workload</i> and <i>user goals.</i> To address the problem of optimal VM selection, we present PARIS, a data-driven system that uses a novel <i>hybrid</i> offline and online data collection and modeling framework to provide <i>accurate</i> performance estimates with <i>minimal</i> data collection. PARIS is able to predict workload performance for different user-specified metrics, and resulting costs for a wide range of VM types and workloads across multiple cloud providers. When compared to sophisticated baselines, including collaborative filtering and a linear interpolation model using measured workload performance on two VM types, PARIS produces significantly better estimates of performance. For instance, it reduces runtime prediction error by a factor of 4 for some workloads on both AWS and Azure. The increased accuracy translates into a 45% reduction in user cost while maintaining performance.",
"pdfUrls": [
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"title": "Selecting the best VM across multiple public clouds: a data-driven performance modeling approach",
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"paperAbstract": "The processors that drive embedded systems are getting smaller; meanwhile, the batteries used to provide power to those systems have stagnated. If we are to realize the dream of ubiquitous computing promised by the Internet of Things, processors must shed large, heavy, expensive, and high maintenance batteries and, instead, harvest energy from their environment. One challenge with this transition is that harvested energy is insufficient for continuous operation. Unfortunately, existing programs fail miserably when executed intermittently.\n This paper presents Clank: lightweight architectural support for correct and efficient execution of long-running applications on harvested energy---without programmer intervention or extreme hardware modifications. Clank is a set of hardware buffers and memory-access monitors that dynamically maintain idempotency. Essentially, Clank dynamically decomposes program execution into a stream of restartable sub-executions connected via lightweight checkpoints.\n To validate Clank's ability to correctly stretch program execution across frequent, random power cycles, and to explore the associated hardware and software overheads, we implement Clank in Verilog, formally verify it, and then add it to an ARM Cortex M0+ processor which we use to run a set of 23 embedded systems benchmarks. Experiments show run-time overheads as low as 2.5%, with run-time overheads of 6% for a version of Clank that adds 1.7% hardware. Clank minimizes checkpoints so much that re-execution time becomes the dominate contributor to run-time overhead.",
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"title": "Clank: Architectural support for intermittent computation",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"name": "Mallipeddi Hardhik"
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"name": "Dip Sankar Banerjee"
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"name": "Kiran Raj Ramamoorthy"
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"name": "Kishore Kothapalli"
},
{
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"name": "K. Srinathan"
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"doi": "10.1109/ICPP.2017.14",
"doiUrl": "https://doi.org/10.1109/ICPP.2017.14",
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"paperAbstract": "The architectural trend towards heterogeneity has pushed heterogeneous computing to the fore of parallel computing research. Heterogeneous algorithms, often carefully handcrafted, have been designed for several important problems from parallel computing such as sorting, graph algorithms, matrix computations, and the like. A majority of these algorithms follow a work partitioning approach where the input is divided into appropriate sized parts so that individual devices can process the “right” parts of the input. However, arriving at a good work partitioning is usually non-trivial and may require extensive empirical search. Such an extensive empirical search can potentially offset any gains accrued out of heterogeneous algorithms. Other recently proposed approaches too are in general inadequate.In this paper, we propose a simple and effective technique for work partitioning in the context of heterogeneous algorithms. Our technique is based on sampling and therefore can adapt to both the algorithm used and the input instance. Our technique is generic in its applicability as we will demonstrate in this paper. We validate our technique on three problems: finding the connected components of a graph (CC), multiplying two unstructured sparse matrices (spmm), and multiplying two scalefree sparse matrices. For these problems, we show that using our method, we can find the required threshold that is under 10% away from the best possible thresholds.",
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"title": "Nearly Balanced Work Partitioning for Heterogeneous Algorithms",
"venue": "2017 46th International Conference on Parallel Processing (ICPP)",
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"name": "Kai Wang"
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"name": "Calvin Lin"
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"paperAbstract": "This paper introduces a method of decoupling affine computations---a class of expressions that produces extremely regular values across SIMT threads---from the main execution stream, so that the affine computations can be performed with greater efficiency and with greater independence from the main execution stream. This decoupling has two benefits: (1) For compute-bound programs, it significantly reduces the dynamic warp instruction count; (2) for memory-bound workloads, it significantly reduces memory latency, since it acts as a non-speculative prefetcher for the data specified by the many memory address calculations that are affine computations.\n We evaluate our solution, known as Decoupled Affine Computation (DAC), using GPGPU-sim and a set of 29 GPGPU programs. We find that on average, DAC improves performance by 40% and reduces energy consumption by 20%. For the 11 compute-bound benchmarks, DAC improves performance by 34%, compared with 11% for the previous state-of-the-art. For the 18 memory-bound programs, DAC improves performance by an average of 44%, compared with 16% for state-of-the-art GPU prefetcher.",
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"title": "Decoupled affine computation for SIMT GPUs",
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"name": "Kenneth Raffenetti"
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"name": "Pavan Balaji"
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"doi": "10.1109/IPDPS.2017.18",
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"paperAbstract": "MPI allows applications to treat processes as a logical collection of integer ranks for each MPI communicator, while internally translating these logical ranks into actual network addresses. In current MPI implementations the management and lookup of such network addresses use memory sizes that are proportional to the number of processes in each communicator. In this paper, we propose a new mechanism, called AV-Rankmap, for managing such translation. AV-Rankmap takes advantage of logical patterns in rank-address mapping that most applications naturally tend to have, and it exploits the fact that some parts of network address structures are naturally more performance critical than others. It uses this information to compress the memory used for network address management. We demonstrate that AV-Rankmap can achieve performance similar to or better than that of other MPI implementations while using significantly less memory.",
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"doi": "10.1145/3030207.3030220",
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"paperAbstract": "Systems deployed in mobile environments are typically characterized by intermittent connectivity and asynchronous sending/reception of data. To create effective mobile systems for such environments, it is essential to guarantee acceptable levels of timeliness between sending and receiving mobile users. In order to provide QoS guarantees in different application scenarios and contexts, it is necessary to model the system performance by incorporating the intermittent connectivity. <i>Queueing Network Models</i> (QNMs) offer a simple modeling environment, which can be used to represent various application scenarios, and provide accurate analytical solutions for performance metrics, such as system response time. In this paper, we provide an analytical solution regarding the end-to-end response time between users sending and receiving data by modeling the intermittent connectivity of mobile users with QNMs. We utilize the <i>publish/subscribe</i> (pub/sub) middleware as the underlying communication infrastructure for mobile users. To represent the user's connections/disconnections, we model and solve analytically an ON/OFF queueing system by applying a mean value approach. Finally, we validate our model using simulations with real-world workload traces. The deviations between the performance results foreseen by the analytical model and the ones provided by the simulator are shown to be less than 5% for a variety of scenarios.",
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"title": "Timeliness Evaluation of Intermittent Mobile Connectivity over Pub/Sub Systems",
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"year": 2017
},
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"name": "Zhendong Su"
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"doi": "10.1145/3062341.3062383",
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"paperAbstract": "Achieving high code coverage is essential in testing, which gives us confidence in code quality. Testing floating-point code usually requires painstaking efforts in handling floating-point constraints, e.g., in symbolic execution. This paper turns the challenge of testing floating-point code into the opportunity of applying unconstrained programming --- the mathematical solution for calculating function minimum points over the entire search space. Our core insight is to derive a representing function from the floating-point program, any of whose minimum points is a test input guaranteed to exercise a new branch of the tested program. This guarantee allows us to achieve high coverage of the floating-point program by repeatedly minimizing the representing function. \n We have realized this approach in a tool called CoverMe and conducted an extensive evaluation of it on Sun's C math library. Our evaluation results show that CoverMe achieves, on average, 90.8% branch coverage in 6.9 seconds, drastically outperforming our compared tools: (1) Random testing, (2) AFL, a highly optimized, robust fuzzer released by Google, and (3) Austin, a state-of-the-art coverage-based testing tool designed to support floating-point code.",
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"title": "Cooper: Task Colocation with Cooperative Games",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
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"title": "An Analysis of Boosted Linear Classifiers on Noisy Data with Applications to Multiple-Instance Learning",
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"paperAbstract": "The development of positioning technologies has resulted in an increasing amount of mobility data being available. While bringing a lot of convenience to people's life, such availability also raises serious concerns about privacy. In this paper, we concentrate on one of the most sensitive information that can be inferred from mobility data, namely social relationships. We propose a novel social relation inference attack that relies on an advanced feature learning technique to automatically summarize users' mobility features. Compared to existing approaches, our attack is able to predict any two individuals' social relation, and it does not require the adversary to have any prior knowledge on existing social relations. These advantages significantly increase the applicability of our attack and the scope of the privacy assessment. Extensive experiments conducted on a large dataset demonstrate that our inference attack is effective, and achieves between 13% to 20% improvement over the best state-of-the-art scheme. We propose three defense mechanisms -- hiding, replacement and generalization -- and evaluate their effectiveness for mitigating the social link privacy risks stemming from mobility data sharing. Our experimental results show that both hiding and replacement mechanisms outperform generalization. Moreover, hiding and replacement achieve a comparable trade-off between utility and privacy, the former preserving better utility and the latter providing better privacy.",
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"http://doi.acm.org/10.1145/3133956.3133972",
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"title": "walk2friends: Inferring Social Links from Mobility Profiles",
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},
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"name": "Giulio Malavolta"
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"title": "SilentWhispers: Enforcing Security and Privacy in Decentralized Credit Networks",
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"paperAbstract": "Exploit mitigations, by themselves, do not stop determined and well-resourced adversaries from compromising vulnerable software through memory corruption. Multi-variant execution environments (MVEEs) add additional assurance by executing multiple, diversified copies (variants) of the same program in lockstep while monitoring their behavior for signs of attacks (divergence). While executing multiple copies of the same program requires additional computational resources, modern MVEEs run many workloads at near-native speed and can detect adversaries before they leak secrets or achieve persistence on the host system.\n Multi-threaded programs are challenging to execute in lockstep by an MVEE. If the threads in a set of variants are not scheduled in the exact same order, the variants will diverge from each other in terms of the system calls they make. While benign, such divergence undermines the MVEEs ability detect divergence caused by malicious program inputs. To address this problem, we developed an MVEE-specific synchronization scheme that lets us execute a set of multithreaded variants in lockstep without causing benign divergence. Our fully-fledged MVEE runs the PARSEC 2.1 and SPLASH-2x parallel benchmarks (with four worker threads per variant) with a slowdown of less than 15% relative to unprotected execution. Addressing this longstanding compatibility issue makes MVEEs a viable defense for a far greater range of realistic workloads.",
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"title": "Taming Parallelism in a Multi-Variant Execution Environment",
"venue": "EuroSys",
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},
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"name": "Matthew Wolf"
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"name": "George Ostrouchov"
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"name": "Tahsin M. Kur\u00e7"
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"name": "Qing Liu"
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"name": "Norbert Podhorszki"
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"name": "Melissa Romanus"
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"name": "Qian Sun"
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"name": "Choong-Seock Chang"
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],
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"paperAbstract": "Leader election is one of the basic problems in distributed computing. This is a symmetry breaking problem: all nodes of a network must agree on a single node, called the leader. If the nodes of the network have distinct labels, then such an agreement means that all nodes have to output the label of the elected leader. For anonymous networks, the task of leader election is formulated as follows: every node <i>v</i> of the network must output a simple path, which is coded as a sequence of port numbers, such that all these paths end at a common node, the leader. In this paper, we study deterministic leader election in arbitrary anonymous networks.\n It is well known that deterministic leader election is impossible in some networks, regardless of the allocated amount of time, even if nodes know the map of the network. This is due to possible symmetries in it. However, even in networks in which it is possible to elect a leader knowing the map, the task may be still impossible without any knowledge, regardless of the allocated time. On the other hand, for any network in which leader election is possible knowing the map, there is a minimum time, called the <i>election index</i>, in which this can be done. Informally, the election index of a network is the minimum depth at which views of all nodes are distinct. Our aim is to establish tradeoffs between the allocated time \\tau and the amount of information that has to be given <i>a priori</i> to the nodes to enable leader election in time \\tau in all networks for which leader election in this time is at all possible. Following the framework of <i>algorithms with advice</i>, this information (a single binary string) is provided to all nodes at the start by an oracle knowing the entire network. The length of this string is called the <i>size of advice</i>. For a given time \\tau allocated to leader election, we give upper and lower bounds on the minimum size of advice sufficient to perform leader election in time \\tau.\n We focus on the two sides of the time spectrum. For the smallest possible time, which is the election index of the network, we show that the minimum size of advice is linear in the size <i>n</i> of the network, up to polylogarithmic factors. On the other hand, we consider large values of time: larger than the diameter <i>D</i> by a summand, respectively, linear, polynomial, and exponential in the election index; for these values, we prove tight bounds on the minimum size of advice, up to multiplicative constants. We also show that constant advice is not sufficient for leader election in all graphs, regardless of the allocated time.",
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"http://arxiv.org/abs/1604.05023"
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"title": "Impact of Knowledge on Election Time in Anonymous Networks",
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"paperAbstract": "Recent advances in flash memory technology have reduced the cost-per-bit of flash storage devices, thereby enabling the development of large-capacity SSDs. However, two major concerns arise in designing SSDs. The first is the poor performance of random writes, and the second is the large size of the internal DRAM of an SSD. Although the previously proposed demand map loading technique can reduce the required DRAM size, the technique aggravates the poor random performance. We propose a novel address reshaping technique called sequentializing in host and randomizing in device (SHRD), which transforms random write requests into sequential write requests by assigning the address space of the reserved log area in the SSD. SHRD can restore the sequentially written data to the original location without requiring explicit copy operations by utilizing the address mapping scheme. We implement SHRD in a real SSD device and demonstrate the improved performance resulting from SHRD for various workloads1.",
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"paperAbstract": "The rise in popularity of the Android platform has resulted in an explosion of malware threats targeting it. As both Android malware and the operating system itself constantly evolve, it is very challenging to design robust malware mitigation techniques that can operate for long periods of time without the need for modifications or costly re-training. In this paper, we present MAMADROID, an Android malware detection system that relies on app behavior. MAMADROID builds a behavioral model, in the form of a Markov chain, from the sequence of abstracted API calls performed by an app, and uses it to extract features and perform classification. By abstracting calls to their packages or families, MAMADROID maintains resilience to API changes and keeps the feature set size manageable. We evaluate its accuracy on a dataset of 8.5K benign and 35.5K malicious apps collected over a period of six years, showing that it not only effectively detects malware (with up to 99% F-measure), but also that the model built by the system keeps its detection capabilities for long periods of time (on average, 87% and 73% F-measure, respectively, one and two years after training). Finally, we compare against DROIDAPIMINER, a state-of-the-art system that relies on the frequency of API calls performed by apps, showing that MAMADROID significantly outperforms it.",
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"paperAbstract": "Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect.\n Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on \"usefulness\" metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers.",
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"https://arxiv.org/pdf/1708.08151v1.pdf",
"http://arxiv.org/abs/1708.08151",
"https://arxiv.org/pdf/1708.08151v2.pdf",
"http://doi.acm.org/10.1145/3133956.3133990",
"https://users.cs.fiu.edu/~carbunar/teaching/cen5079/cen5079.2017/presentations/crowdturfing.pdf"
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"title": "Automated Crowdturfing Attacks and Defenses in Online Review Systems",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"name": "Yuxin Bai"
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"name": "Victor W. Lee"
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"name": "Engin Ipek"
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"doi": "10.1145/3037697.3037717",
"doiUrl": "https://doi.org/10.1145/3037697.3037717",
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"paperAbstract": "Conventional off-chip voltage regulators are typically bulky and slow, and are inefficient at exploiting system and workload variability using Dynamic Voltage and Frequency Scaling (DVFS). On-die integration of voltage regulators has the potential to increase the energy efficiency of computer systems by enabling power control at a fine granularity in both space and time. The energy conversion efficiency of on-chip regulators, however, is typically much lower than off-chip regulators, which results in significant energy losses. Fine-grained power control and high voltage regulator efficiency are difficult to achieve simultaneously, with either emerging on-chip or conventional off-chip regulators.\n A voltage conversion framework that relies on a hierarchy of off-chip switching regulators and on-chip linear regulators is proposed to enable fine-grained power control with a regulator efficiency greater than 90%. A DVFS control policy that is based on a reinforcement learning (RL) approach is developed to exploit the proposed framework. Per-core RL agents learn and improve their control policies independently, while retaining the ability to coordinate their actions to accomplish system level power management objectives. When evaluated on a mix of 14 parallel and 13 multiprogrammed workloads, the proposed voltage conversion framework achieves 18% greater energy efficiency than a conventional framework that uses on-chip switching regulators. Moreover, when the RL based DVFS control policy is used to control the proposed voltage conversion framework, the system achieves a 21% higher energy efficiency over a baseline oracle policy with coarse-grained power control capability.",
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"http://www.cs.rochester.edu/~ipek/asplos17.pdf",
"http://doi.acm.org/10.1145/3037697.3037717"
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"title": "Voltage Regulator Efficiency Aware Power Management",
"venue": "ASPLOS",
"year": 2017
},
"32274b8db3b9eb23799bf0b44181203afba1c080": {
"authors": [
{
"ids": [
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"name": "Li Ye"
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"name": "Hong Xie"
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{
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"name": "Weijie Wu"
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"name": "John C. S. Lui"
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"doi": "10.1109/ICDM.2017.65",
"doiUrl": "https://doi.org/10.1109/ICDM.2017.65",
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"paperAbstract": "Product bundling is widely adopted for information goods and online services because it can increase profit for companies. For example, cable companies often bundle Internet access and video streaming services together. However, it is challenging to obtain an optimal bundling strategy, not only because it is computationally expensive, but also that customers’ private information (e.g., valuations for products) is needed for the decision, and we need to infer it from accessible datasets. As customers’ purchasing data are getting richer due to the popularity of online shopping, doors are open for us to infer this information. This paper aims to address: (1) How to infer customers’ valuations from the purchasing data? (2) How to determine the optimal product bundle to maximize the profit? We first formulate a profit maximization framework to select the optimal bundle set. We show that finding the optimal bundle set is NPhard. We then identify key factors that impact the profitability of product bundling. These findings give us insights to develop a computationally efficient algorithm to approximate the optimal product bundle with a provable performance guarantee. To obtain the input of the bundling algorithm, we infer the distribution of customers’ valuations from their purchasing data, based on which we run our bundling algorithm and conduct experiments on an Amazon co-purchasing dataset. We extensively evaluate the accuracy of our inference and the bundling algorithm. Our results reveal conditions under which bundling is highly profitable and provide insights to guide the deployment of product bundling.",
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"http://www.cs.cuhk.hk/~cslui/PUBLICATION/ICDM17-Bundling.pdf",
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"title": "Mining Customer Valuations to Optimize Product Bundling Strategy",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
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"name": "Pedro Duarte"
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"name": "Pedro Tom\u00e1s"
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{
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"name": "Gabriel Falc\u00e3o Paiva Fernandes"
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"doi": "10.1145/3123939.3123953",
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"paperAbstract": "Applying advanced signal processing and artificial intelligence algorithms is often constrained by power and energy consumption limitations, in high performance and embedded, cyber-physical and super-computing devices and systems. Although Graphics Processing Units (GPUs) helped to mitigate the throughput-per-Watt performance problem in many compute-intensive applications, dealing more efficiently with the autonomy requirements of intelligent systems demands power-oriented customized architectures that are specially tuned for each application, preferably without manual redesign of the entire hardware and capable of supporting legacy code. Hence, this work proposes a new SCRATCH framework that aims at automatically identifying the specific requirements of each application kernel, regarding instruction set and computing unit demands, allowing for the generation of application-specific and FPGA-implementable trimmed-down GPU-inspired architectures. The work is based on an improved version of the original MIAOW system (here named MIAOW2.0), which is herein extended to support a set of 156 instructions and enhanced to provide a fast prefetch memory system and a dual-clock domain. Experimental results with 17 highly relevant benchmarks, using integer and floating-point arithmetic, demonstrate that we have been able to achieve an average of 140× speedup and 115× higher energy-efficiency levels (instructions-per-Joule) when compared to the original MIAOW system, and a 2.4× speedup and 2.1× energy-efficiency gains compared against our optimized version without pruning.",
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"title": "SCRATCH: an end-to-end application-aware soft-GPGPU architecture and trimming tool",
"venue": "MICRO",
"year": 2017
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"name": "Xinyue Liu"
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"name": "Yuanfang Song"
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"name": "Charu C. Aggarwal"
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{
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"name": "Yao Zhang"
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{
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"name": "Xiangnan Kong"
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"paperAbstract": "Recommender systems have attracted much attention in last decades, which can help the users explore new items in many applications. As a popular technique in recommender systems, item recommendation works by recommending items to users based on their historical interactions. Conventional item recommendation methods usually assume that users and items are stationary, which is not always the case in real-world applications. Many time-aware item recommendation models have been proposed to take the temporal effects into the considerations based on the absolute time stamps associated with observed interactions. We show that using absolute time to model temporal effects can be limited in some circumstances. In this work, we propose to model the temporal dynamics of both users and items in item recommendation based on their life cycles. This problem is very challenging to solve since the users and items can co-evolve in their life cycles and the sparseness of the data become more severe when we consider the life cycles of both users and items. A novel time-aware item recommendation model called BiCycle is proposed to address these challenges. BiCycle is designed based on two important observations: 1) correlated users or items usually share similar patterns in the similar stages of their life cycles. 2) user preferences and item characters can evolve gradually over different stages of their life cycles. Extensive experiments conducted on three real-world datasets demonstrate the proposed approach can significantly improve the performance of recommendation tasks by considering the inner life cycles of both users and items.",
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"paperAbstract": "In 2007, Shacham published a seminal paper on Return-Oriented Programming (ROP), the first systematic formulation of code reuse. The paper has been highly influential, profoundly shaping the way we still think about code reuse today: an attacker analyzes the <i>\"geometry\"</i> of victim binary code to locate gadgets and chains these to craft an exploit. This model has spurred much research, with a rapid progression of increasingly sophisticated code reuse attacks and defenses over time. After ten years, the common perception is that state-of-the-art code reuse defenses are effective in significantly raising the bar and making attacks exceedingly hard.\n In this paper, we challenge this perception and show that an attacker going beyond <i>\"geometry\"</i> (static analysis) and considering the <i>\"dynamics\"</i> (dynamic analysis) of a victim program can easily find function call gadgets even in the presence of state-of-the-art code-reuse defenses. To support our claims, we present Newton, a run-time gadget-discovery framework based on constraint-driven dynamic taint analysis. Newton can model a broad range of defenses by mapping their properties into simple, stackable, reusable constraints, and automatically generate gadgets that comply with these constraints. Using Newton, we systematically map and compare state-of-the-art defenses, demonstrating that even simple interactions with popular server programs are adequate for finding gadgets for all state-of-the-art code-reuse defenses. We conclude with an nginx case study, which shows that a Newton-enabled attacker can craft attacks which comply with the restrictions of advanced defenses, such as CPI and context-sensitive CFI.",
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"paperAbstract": "In technical support scams, cybercriminals attempt to convince users that their machines are infected with malware and are in need of their technical support. In this process, the victims are asked to provide scammers with remote access to their machines, who will then \u201cdiagnose the problem\u201d, before offering their support services which typically cost hundreds of dollars. Despite their conceptual simplicity, technical support scams are responsible for yearly losses of tens of millions of dollars from everyday users of the web. In this paper, we report on the first systematic study of technical support scams and the call centers hidden behind them. We identify malvertising as a major culprit for exposing users to technical support scams and use it to build an automated system capable of discovering, on a weekly basis, hundreds of phone numbers and domains operated by scammers. By allowing our system to run for more than 8 months we collect a large corpus of technical support scams and use it to provide insights on their prevalence, the abused infrastructure, the illicit profits, and the current evasion attempts of scammers. Finally, by setting up a controlled, IRB-approved, experiment where we interact with 60 different scammers, we experience first-hand their social engineering tactics, while collecting detailed statistics of the entire process. We explain how our findings can be used by law-enforcing agencies and propose technical and educational countermeasures for helping users avoid being victimized by technical support scams.",
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"paperAbstract": "Resource usage data, collected using tools such as TACC_Stats, capture the resource utilization by nodes within a high performance computing system. We present methods to analyze the resource usage data to understand the system performance and identify performance anomalies. The core idea is to model the data as a three-way tensor corresponding to the compute nodes, usage metrics, and time. Using the reconstruction error between the original tensor and the tensor reconstructed from a low rank tensor decomposition, as a scalar performance metric, enables us to monitor the performance of the system in an online fashion. This error statistic is then used for anomaly detection that relies on the assumption that the normal/routine behavior of the system can be captured using a low rank approximation of the original tensor. We evaluate the performance of the algorithm using information gathered from system logs and show that the performance anomalies identified by the proposed method correlates with critical errors reported in the system logs. Results are shown for data collected for 2013 from the Lonestar4 system at the Texas Advanced Computing Center (TACC).",
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"doi": "10.1145/3131365.3131384",
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"paperAbstract": "Understanding data plane health is essential to improving Internet reliability and usability. For instance, detecting disruptions in distant networks can identify repairable connectivity problems. Currently this task is difficult and time consuming as operators have poor visibility beyond their network's border. In this paper we leverage the diversity of RIPE Atlas traceroute measurements to solve the classic problem of monitoring in-network delays and get credible delay change estimations to monitor network conditions in the wild. We demonstrate a set of complementary methods to detect network disruptions and report them in near real time. The first method detects delay changes for intermediate links in traceroutes. Second, a packet forwarding model predicts traffic paths and identifies faulty routers and links in cases of packet loss. In addition, we define an alarm score that aggregates changes into a single value per AS in order to easily monitor its sanity, reducing the effect of uninteresting alarms. Using only existing public data we monitor hundreds of thousands of link delays while adding no burden to the network. We present three cases demonstrating that the proposed methods detect real disruptions and provide valuable insights, as well as surprising findings, on the location and impact of the identified events.",
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"paperAbstract": "Precise dynamic data race detectors provide strong correctness guarantees but have high overheads because they generally keep analysis state in a separate shadow location for each heap memory location, and they check (and potentially update) the corresponding shadow location on each heap access. The BigFoot dynamic data race detector uses a combination of static and dynamic analysis techniques to coalesce checks and compress shadow locations. With BigFoot, multiple accesses to an object or array often induce a single coalesced check that manipulates a single compressed shadow location, resulting in a performance improvement over FastTrack of 61%.",
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"paperAbstract": "Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data.\n We consider a malicious ML provider who supplies model-training code to the data holder, does \\emph{not} observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that \"memorize\" information about the training dataset in the model\\textemdash yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data.",
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"http://doi.acm.org/10.1145/3133956.3134077"
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"title": "Machine Learning Models that Remember Too Much",
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"year": 2017
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"name": "Xing Niu"
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"paperAbstract": "We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a framework for coupling biophysical models with medical image analysis. It provides solvers for an image-driven inverse brain tumor growth model and an image registration problem, the combination of which can eventually help in diagnosis and prognosis of brain tumors. The two main computational kernels of SIBIA are a Fast Fourier Transformation (FFT) implemented in the library AccFFT to discretize differential operators, and a cubic interpolation kernel for semi-Lagrangian based advection. We present efficiency and scalability results for the computational kernels, the inverse tumor solver and image registration on two x86 systems, <i>Lonestar 5</i> at the Texas Advanced Computing Center and <i>Hazel Hen</i> at the Stuttgart High Performance Computing Center. We showcase results that demonstrate that our solver can be used to solve registration problems of unprecedented scale, 4096<sup>3</sup> resulting in ∼ 200 billion unknowns---a problem size that is 64X larger than the state-of-the-art. For problem sizes of clinical interest, SIBIA is about 8X faster than the state-of-the-art.",
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"title": "A framework for scalable biophysics-based image analysis",
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"paperAbstract": "This paper proposes a technique for automatically learning semantic malware signatures for Android from very few samples of a malware family. The key idea underlying our technique is to look for a maximally suspicious common subgraph (MSCS) that is shared between all known instances of a malware family. An MSCS describes the shared functionality between multiple Android applications in terms of inter-component call relations and their semantic metadata (e.g., data-flow properties). Our approach identifies such maximally suspicious common subgraphs by reducing the problem to maximum satisfiability. Once a semantic signature is learned, our approach uses a combination of static analysis and a new approximate signature matching algorithm to determine whether an Android application matches the semantic signature characterizing a given malware family. We have implemented our approach in a tool called ASTROID and show that it has a number of advantages over state-of-theart malware detection techniques. First, we compare the semantic malware signatures automatically synthesized by ASTROID with manually-written signatures used in previous work and show that the signatures learned by ASTROID perform better in terms of accuracy as well as precision. Second, we compare ASTROID against two state-of-the-art malware detection tools and demonstrate its advantages in terms of interpretability and accuracy. Finally, we demonstrate that ASTROID\u2019s approximate signature matching algorithm is resistant to behavioral obfuscation and that it can be used to detect zero-day malware. In particular, we were able to find 22 instances of zero-day malware in Google Play that are not reported as malware by existing tools.",
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"title": "Automated Synthesis of Semantic Malware Signatures using Maximum Satisfiability",
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"doi": "10.1145/3133893",
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"paperAbstract": "This paper presents Fast Instrumentation Bias (FIB), a sound and complete dynamic data race detection algorithm that improves performance by reducing or eliminating the costs of analysis atomicity. In addition to checking for errors in target programs, dynamic data race detectors must introduce synchronization to guard against metadata races that may corrupt analysis state and compromise soundness or completeness. Pessimistic analysis synchronization can account for nontrivial performance overhead in a data race detector. \n The core contribution of FIB is a novel cooperative ownership-based synchronization protocol whose states and transitions are derived purely from preexisting analysis metadata and logic in a standard data race detection algorithm. By exploiting work already done by the analysis, FIB ensures atomicity of dynamic analysis actions with zero additional time or space cost in the common case. Analysis of temporally thread-local or read-shared accesses completes safely with no synchronization. Uncommon write-sharing transitions require synchronous cross-thread coordination to ensure common cases may proceed synchronization-free. \n We implemented FIB in the Jikes RVM Java virtual machine. Experimental evaluation shows that FIB eliminates nearly all instrumentation atomicity costs on programs where data often experience windows of thread-local access. Adaptive extensions to the ownership policy effectively eliminate high coordination costs of the core ownership protocol on programs with high rates of serialized sharing. FIB outperforms a naive pessimistic synchronization scheme by 50% on average. Compared to a tuned optimistic metadata synchronization scheme based on conventional fine-grained atomic compare-and-swap operations, FIB is competitive overall, and up to 17% faster on some programs. Overall, FIB effectively exploits latent analysis and program invariants to bring strong integrity guarantees to an otherwise unsynchronized data race detection algorithm at minimal cost.",
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"title": "Instrumentation bias for dynamic data race detection",
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"paperAbstract": "Intel SGX hardware enables applications to protect themselves from potentially-malicious OSes or hypervisors. In cloud computing and other systems, many users and applications could benefit from SGX. Unfortunately, current applications will not work out-of-the-box on SGX. Although previous work has shown that a library OS can execute unmodified applications on SGX, a belief has developed that a library OS will be ruinous for performance and TCB size, making application code modification an implicit prerequisite to adopting SGX. This paper demonstrates that these concerns are exaggerated, and that a fully-featured library OS can rapidly deploy unmodified applications on SGX with overheads comparable to applications modified to use \u201cshim\u201d layers. We present a port of Graphene to SGX, as well as a number of improvements to make the security benefits of SGX more usable, such as integrity support for dynamically-loaded libraries, and secure multi-process support. Graphene-SGX supports a wide range of unmodified applications, including Apache, GCC, and the R interpreter. The performance overheads of GrapheneSGX range from matching a Linux process to less than 2\u00d7 in most single-process cases; these overheads are largely attributable to current SGX hardware or missed opportunities to optimize Graphene internals, and are not necessarily fundamental to leaving the application unmodified. Graphene-SGX is open-source and has been used concurrently by other groups for SGX research.",
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"https://www.usenix.org/conference/atc17/technical-sessions/presentation/tsai",
"https://www.usenix.org/system/files/conference/atc17/atc17-tsai.pdf",
"http://www.cs.unc.edu/~porter/pubs/graphene-sgx.pdf"
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"title": "Graphene-SGX: A Practical Library OS for Unmodified Applications on SGX",
"venue": "USENIX Annual Technical Conference",
"year": 2017
},
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"name": "Daniel Huang"
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"name": "Jean-Baptiste Tristan"
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"paperAbstract": "The problem of probabilistic modeling and inference, at a high-level, can be viewed as constructing a (<i>model</i>, <i>query</i>, <i>inference</i>) tuple, where an <em>inference</em> algorithm implements a <em>query</em> on a <em>model</em>. Notably, the derivation of inference algorithms can be a difficult and error-prone task. Hence, researchers have explored how ideas from <em>probabilistic programming</em> can be applied. In the context of constructing these tuples, probabilistic programming can be seen as taking a language-based approach to probabilistic modeling and inference. For instance, by using (1) appropriate languages for expressing models and queries and (2) devising inference techniques that operate on encodings of models (and queries) as program expressions, the task of inference can be automated. \nIn this paper, we describe a compiler that transforms a probabilistic model written in a restricted modeling language and a query for posterior samples given observed data into a Markov Chain Monte Carlo (MCMC) inference algorithm that implements the query. The compiler uses a sequence of intermediate languages (ILs) that guide it in gradually and successively refining a declarative specification of a probabilistic model and the query into an executable MCMC inference algorithm. The compilation strategy produces composable MCMC algorithms for execution on a CPU or GPU.",
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"title": "Compiling Markov chain Monte Carlo algorithms for probabilistic modeling",
"venue": "PLDI",
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"paperAbstract": "Accelerated clusters, which are distributed memory systems equipped with accelerators, have been used in various fields. For accelerated clusters, programmers often implement their applications by a combination of MPI and CUDA (MPI+CUDA). However, the approach faces programming complexity issues. This paper introduces the XcalableACC (XACC) language, which is a hybrid model of XcalableMP (XMP) and OpenACC. While XMP is a directive-based language for distributed memory systems, OpenACC is also a directive-based language for accelerators. XACC enables programmers to develop applications on accelerated clusters with ease. To evaluate XACC performance and productivity levels, we implemented a lattice quantum chromodynamics (Lattice QCD) application using XACC on 64 compute nodes and 256 GPUs and found its performance was almost the same as that of MPI+CUDA. Moreover, we found that XACC requires much less change from the serial Lattice QCD code than MPI+CUDA to implement the parallel Lattice QCD code.",
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"paperAbstract": "Resource management of modern datacenters needs to consider multiple competing objectives that involve complex system interactions. In this work, Linear Temporal Logic (LTL) is adopted in describing such interactions by leveraging its ability to express complex properties. Further, LTL-based constraints are integrated with reinforcement learning according the recent progress on control synthesis theory. The LTL-constrained reinforcement learning facilitates desired balance among the competing objectives in managing resources for datacenters. The effectiveness of this new approach is demonstrated by two scenarios. In datacenter power management, the LTL-constrained manager reaches the best balance among power, performance and battery stress compared to the previous work and other alternative approaches. In multitenant job scheduling, 200 MapReduce jobs are emulated on the Amazon AWS cloud. The LTL-constrained scheduler achieves the best balance between system performance and fairness compared to several other methods including three Hadoop schedulers.",
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"paperAbstract": "TLS 1.3 is the next version of the Transport Layer Security (TLS) protocol. Its clean-slate design is a reaction both to the increasing demand for low-latency HTTPS connections and to a series of recent high-profile attacks on TLS. The hope is that a fresh protocol with modern cryptography will prevent legacy problems, the danger is that it will expose new kinds of attacks, or reintroduce old flaws that were fixed in previous versions of TLS. After 18 drafts, the protocol is nearing completion, and the working group has appealed to researchers to analyze the protocol before publication. This paper responds by presenting a comprehensive analysis of the TLS 1.3 Draft-18 protocol. We seek to answer three questions that have not been fully addressed in previous work on TLS 1.3: (1) Does TLS 1.3 prevent well-known attacks on TLS 1.2, such as Logjam or the Triple Handshake, even if it is run in parallel with TLS 1.2? (2) Can we mechanically verify the computational security of TLS 1.3 under standard (strong) assumptions on its cryptographic primitives? (3) How can we extend the guarantees of the TLS 1.3 protocol to the details of its implementations?To answer these questions, we propose a methodology for developing verified symbolic and computational models of TLS 1.3 hand-in-hand with a high-assurance reference implementation of the protocol. We present symbolic ProVerif models for various intermediate versions of TLS 1.3 and evaluate them against a rich class of attacks to reconstruct both known and previously unpublished vulnerabilities that influenced the current design of the protocol. We present a computational CryptoVerif model for TLS 1.3 Draft-18 and prove its security. We present RefTLS, an interoperable implementation of TLS 1.0-1.3 and automatically analyze its protocol core by extracting a ProVerif model from its typed JavaScript code.",
"pdfUrls": [
"http://prosecco.gforge.inria.fr/personal/bblanche/publications/BhargavanBlanchetKobeissiSP2017.pdf",
"https://suri.epfl.ch/talks/tls-blanchet.pdf",
"https://doi.org/10.1109/SP.2017.26",
"https://www.ieee-security.org/TC/SP2017/papers/234.pdf"
],
"pmid": "",
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"title": "Verified Models and Reference Implementations for the TLS 1.3 Standard Candidate",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Simon Kassing"
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"name": "Asaf Valadarsky"
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"ids": [
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"name": "Gal Shahaf"
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"name": "Michael Schapira"
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{
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"name": "Ankit Singla"
}
],
"doi": "10.1145/3098822.3098836",
"doiUrl": "https://doi.org/10.1145/3098822.3098836",
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"paperAbstract": "Recent studies have observed that large data center networks often have a few hotspots while most of the network is underutilized. Consequently, numerous data center network designs have explored the approach of identifying these communication hotspots in real-time and eliminating them by leveraging flexible optical or wireless connections to dynamically alter the network topology. These proposals are based on the premise that statically wired network topologies, which lack the opportunity for such online optimization, are fundamentally inefficient, and must be built at uniform full capacity to handle unpredictably skewed traffic.\n We show this assumption to be false. Our results establish that state-of-the-art static networks can also achieve the performance benefits claimed by dynamic, reconfigurable designs of the same cost: for the skewed traffic workloads used to make the case for dynamic networks, the evaluated static networks can achieve performance matching full-bandwidth fat-trees at two-thirds of the cost. Surprisingly, this can be accomplished even without relying on any form of online optimization, including the optimization of routing configuration in response to the traffic demands.\n Our results substantially lower the barriers for improving upon today's data centers by showing that a static, cabling-friendly topology built using commodity equipment yields superior performance when combined with well-understood routing methods.",
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"http://conferences.sigcomm.org/sigcomm/2017/files/program/ts-7-2-beyond-fat-tree.pdf",
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"http://doi.acm.org/10.1145/3098822.3098836"
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"title": "Beyond fat-trees without antennae, mirrors, and disco-balls",
"venue": "SIGCOMM",
"year": 2017
},
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"name": "Matt Fredrikson"
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{
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"name": "Jan Hoffmann"
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"doi": "10.1109/SP.2017.53",
"doiUrl": "https://doi.org/10.1109/SP.2017.53",
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"Amortized analysis",
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"paperAbstract": "Side channel attacks have been used to extract critical data such as encryption keys and confidential user data in a variety of adversarial settings. In practice, this threat is addressed by adhering to a constant-time programming discipline, which imposes strict constraints on the way in which programs are written. This introduces an additional hurdle for programmers faced with the already difficult task of writing secure code, highlighting the need for solutions that give the same source-level guarantees while supporting more natural programming models. We propose a novel type system for verifying that programs correctly implement constant-resource behavior. Our type system extends recent work on automatic amortized resource analysis (AARA), a set of techniques that automatically derive provable upper bounds on the resource consumption of programs. We devise new techniques that build on the potential method to achieve compositionality, precision, and automation. A strict global requirement that a program always maintains constant resource usage is too restrictive for most practical applications. It is sufficient to require that the program's resource behavior remain constant with respect to an attacker who is only allowed to observe part of the program's state and behavior. To account for this, our type system incorporates information flow tracking into its resource analysis. This allows our system to certify programs that need to violate the constant-time requirement in certain cases, as long as doing so does not leak confidential information to attackers. We formalize this guarantee by defining a new notion of resource-aware noninterference, and prove that our system enforces it. Finally, we show how our type inference algorithm can be used to synthesize a constant-time implementation from one that cannot be verified as secure, effectively repairing insecure programs automatically. We also show how a second novel AARA system that computes lower bounds on resource usage can be used to derive quantitative bounds on the amount of information that a program leaks through its resource use. We implemented each of these systems in Resource Aware ML, and show that it can be applied to verify constant-time behavior in a number of applicationsincluding encryption and decryption routines, database queries, and other resource-aware functionality.",
"pdfUrls": [
"https://arxiv.org/pdf/1801.01896v1.pdf",
"https://doi.org/10.1109/SP.2017.53",
"http://arxiv.org/abs/1801.01896",
"http://www.cs.cmu.edu/~mfredrik/papers/Ngo17Oakland.pdf",
"http://www.cs.cmu.edu/~janh/papers/NgoDFH2016.pdf"
],
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"title": "Verifying and Synthesizing Constant-Resource Implementations with Types",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
"33f95f238e12e1790ad880ec40cf6c63ea4a70dc": {
"authors": [
{
"ids": [
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"name": "Jialin Li"
},
{
"ids": [
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],
"name": "Ellis Michael"
},
{
"ids": [
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],
"name": "Dan R. K. Ports"
}
],
"doi": "10.1145/3132747.3132751",
"doiUrl": "https://doi.org/10.1145/3132747.3132751",
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"Concurrency (computer science)",
"Concurrency control",
"Consistency (database systems)",
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"Distributed transaction",
"Fault tolerance",
"Mercenary III: The Dion Crisis",
"Replication (computing)",
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"Throughput"
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"paperAbstract": "Distributed storage systems aim to provide strong consistency and isolation guarantees on an architecture that is partitioned across multiple shards for scalability and replicated for fault tolerance. Traditionally, achieving all of these goals has required an expensive combination of atomic commitment and replication protocols -- introducing extensive coordination overhead. Our system, Eris, takes a different approach. It moves a core piece of concurrency control functionality, which we term multi-sequencing, into the datacenter network itself. This network primitive takes on the responsibility for consistently ordering transactions, and a new lightweight transaction protocol ensures atomicity.\n The end result is that Eris avoids both replication and transaction coordination overhead: we show that it can process a large class of distributed transactions in a single round-trip from the client to the storage system without any explicit coordination between shards or replicas in the normal case. It provides atomicity, consistency, and fault tolerance with less than 10% overhead -- achieving throughput 3.6-35x higher and latency 72-80% lower than a conventional design on standard benchmarks.",
"pdfUrls": [
"https://homes.cs.washington.edu/~lijl/papers/eris-sosp17.pdf",
"https://syslab.cs.washington.edu/papers/eris-tr17.pdf",
"https://syslab.cs.washington.edu/papers/eris-sosp17.pdf",
"http://doi.acm.org/10.1145/3132747.3132751"
],
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"title": "Eris: Coordination-Free Consistent Transactions Using In-Network Concurrency Control",
"venue": "SOSP",
"year": 2017
},
"3416a5227b1e1956acfacba812081c2686367471": {
"authors": [
{
"ids": [
"40409510"
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"name": "Jeremy Andrus"
},
{
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],
"name": "Naser AlDuaij"
},
{
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],
"name": "Jason Nieh"
}
],
"doi": "10.1145/3135974.3135981",
"doiUrl": "https://doi.org/10.1145/3135974.3135981",
"entities": [
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"Application programming interface",
"Dynamic linker",
"Graphics processing unit",
"Pervasive informatics",
"Safari (web browser)",
"Smartphone",
"Tablet computer",
"Thread (computing)",
"Web content",
"WebKit",
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"paperAbstract": "Mobile apps make extensive use of GPUs on smartphones and tablets to access Web content. To support pervasive Web content, we introduce three key OS techniques for binary graphics compatibility necessary to build a real-world system to run iOS and Android apps together on the same smartphone or tablet. First diplomat usage patterns manage resources to bridge proprietary iOS and Android graphics implementations. Second, thread impersonation allows a single thread-specific context to be shared amongst multiple threads using multiple iOS and Android personas. Third, dynamic library replication allows multiple, independent instances of the same library to be loaded in a single process to support iOS apps on Android while using multiple graphics API versions at the same time. We use these techniques to build a system prototype, and demonstrate that it runs widely-used iOS apps, including apps such as Safari that use the popular GPU-accelerated WebKit framework, using a Google Nexus tablet running Android.",
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"http://doi.acm.org/10.1145/3135974.3135981"
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"title": "Binary compatible graphics support in Android for running iOS apps",
"venue": "Middleware",
"year": 2017
},
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"authors": [
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"name": "Nethanel Gelernter"
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{
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"name": "Senia Kalma"
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"name": "Bar Magnezi"
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{
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"name": "Hen Porcilan"
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],
"doi": "10.1109/SP.2017.9",
"doiUrl": "https://doi.org/10.1109/SP.2017.9",
"entities": [
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"Denial-of-service attack",
"Experiment",
"Man-in-the-middle attack",
"Mobile app",
"Mobile phone",
"Password",
"Recommender system",
"Self-service password reset",
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],
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"journalName": "2017 IEEE Symposium on Security and Privacy (SP)",
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"paperAbstract": "We present the password reset MitM (PRMitM) attack and show how it can be used to take over user accounts. The PRMitM attack exploits the similarity of the registration and password reset processes to launch a man in the middle (MitM) attack at the application level. The attacker initiates a password reset process with a website and forwards every challenge to the victim who either wishes to register in the attacking site or to access a particular resource on it. The attack has several variants, including exploitation of a password reset process that relies on the victim's mobile phone, using either SMS or phone call. We evaluated the PRMitM attacks on Google and Facebook users in several experiments, and found that their password reset process is vulnerable to the PRMitM attack. Other websites and some popular mobile applications are vulnerable as well. Although solutions seem trivial in some cases, our experiments show that the straightforward solutions are not as effective as expected. We designed and evaluated two secure password reset processes and evaluated them on users of Google and Facebook. Our results indicate a significant improvement in the security. Since millions of accounts are currently vulnerable to the PRMitM attack, we also present a list of recommendations for implementing and auditing the password reset process.",
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"https://www.ieee-security.org/TC/SP2017/papers/207.pdf",
"https://doi.org/10.1109/SP.2017.9"
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"title": "The Password Reset MitM Attack",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"authors": [
{
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"name": "Yuan Xiao"
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{
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"name": "Mengyuan Li"
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{
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],
"name": "Sanchuan Chen"
},
{
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"name": "Yinqian Zhang"
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],
"doi": "10.1145/3133956.3134016",
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"Adversary model",
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"Hockey Night in Canada",
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"paperAbstract": "Intel Software Guard Extension (SGX) offers software applications a shielded execution environment, dubbed <i>enclave</i>, to protect their confidentiality and integrity from malicious operating systems. As processors with this extended feature become commercially available, many new software applications are developed to enrich to the SGX-enabled ecosystem. One important primitive for these applications is a secure communication channel between the enclave and a remote trusted party. The SSL/TLS protocol, which is the <i>de facto</i> standard for protecting transport-layer network communications, has been broadly regarded a natural choice for such purposes. However, in this paper, we show that the marriage between SGX and SSL may not be smooth sailing.\n Particularly, we consider a category of side-channel attacks against SSL/TLS implementations in secure enclaves, which we call the control-flow inference attacks. In these attacks, the malicious operating system kernel may perform a powerful <i>man-in-the-kernel</i> attack to collect execution traces of the enclave programs at the page level, the cacheline level, or the branch level, while positioning itself in the middle of the two communicating parties. At the center of our work is a <i>differential analysis framework</i>, dubbed Stacco, to dynamically analyze the SSL/TLS implementations and detect vulnerabilities-discernible execution traces-that can be exploited as decryption oracles. Surprisingly, in spite of the prevailing constant-time programming paradigm adopted by many cryptographic libraries, we found exploitable vulnerabilities in the latest versions of all the SSL/TLS libraries we have examined.\n To validate the detected vulnerabilities, we developed a man-in-the-kernel adversary to demonstrate Bleichenbacher attacks against the latest OpenSSL library running in the SGX enclave (with the help of Graphene) and completely broke the PreMasterSecret encrypted by a 4096-bit RSA public key with only 57286 queries. We also conducted CBC padding oracle attacks against the latest GnuTLS running in Graphene-SGX and an open-source SGX implementation of mbedTLS (i.e., mbedTLS-SGX) that runs directly inside the enclave, and showed that it only needs 48388 and 25717 queries, respectively, to break one block of AES ciphertext. Empirical evaluation suggests these man-in-the-kernel attacks can be completed within 1 or 2 hours. Our results reveal the insufficient understanding of side-channel security in SGX settings, and our study will provoke discussions on the secure implementation and adoption of SSL/TLS in secure enclaves.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3133956.3134016",
"https://arxiv.org/pdf/1707.03473v1.pdf",
"http://arxiv.org/abs/1707.03473",
"https://arxiv.org/pdf/1707.03473v2.pdf"
],
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"title": "STACCO: Differentially Analyzing Side-Channel Traces for Detecting SSL/TLS Vulnerabilities in Secure Enclaves",
"venue": "CCS",
"year": 2017
},
"3470761bbe2c48b763a31a684abaeb8c99596b7c": {
"authors": [
{
"ids": [
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],
"name": "Yehuda Lindell"
},
{
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],
"name": "Ariel Nof"
}
],
"doi": "10.1145/3133956.3133999",
"doiUrl": "https://doi.org/10.1145/3133956.3133999",
"entities": [
"Adversary (cryptography)",
"Adversary model",
"Arithmetic circuit complexity",
"Compiler",
"Computation",
"Denial-of-service attack",
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"paperAbstract": "Protocols for secure multiparty computation enable a set of parties to compute a function of their inputs without revealing anything but the output. The security properties of the protocol must be preserved in the presence of adversarial behavior. The two classic adversary models considered are <i>semi-honest</i> (where the adversary follows the protocol specification but tries to learn more than allowed by examining the protocol transcript) and <i>malicious</i> (where the adversary may follow any arbitrary attack strategy). Protocols for semi-honest adversaries are often far more efficient, but in many cases the security guarantees are not strong enough.\n In this paper, we present a new efficient method for \"compiling\" a large class of protocols that are secure in the presence of semi-honest adversaries into protocols that are secure in the presence of malicious adversaries. Our method assumes an honest majority (i.e., that <i>t</i><<i>n</i>/2 where <i>t</i> is the number of corrupted parties and <i>n</i> is the number of parties overall), and is applicable to many semi-honest protocols based on secret-sharing. In order to achieve high efficiency, our protocol is <i>secure with abort</i> and does not achieve fairness, meaning that the adversary may receive output while the honest parties do not.\n We present a number of instantiations of our compiler, and obtain protocol variants that are very efficient for both a small and large number of parties. We implemented our protocol variants and ran extensive experiments to compare them with each other. Our results show that secure computation with an honest majority can be practical, even with security in the presence of malicious adversaries. For example, we securely compute a large arithmetic circuit of depth 20 with 1,000,000 multiplication gates, in approximately 0.5 seconds with three parties, and approximately 29 seconds with 50 parties, and just under 1 minute with 90 parties.",
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"title": "A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
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"name": "Thomas O'Brien"
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"paperAbstract": "In this paper, we present a data acquisition and analysis framework for materials-to-devices processes, named 4CeeD, that focuses on the immense potential of capturing, accurately curating, correlating, and coordinating materials-to-devices digital data in a real-time and trusted manner before fully archiving and publishing them for wide access and sharing. In particular, 4CeeD consists of novel services: a curation service for collecting data from microscopes and fabrication instruments, curating, and wrapping of data with extensive metadata in real-time and in a trusted manner, and a cloud-based coordination service for storing data, extracting meta-data, analyzing and finding correlations among the data. Our evaluation results show that our novel cloud framework can help researchers significantly save time and cost spent on experiments, and is efficient in dealing with high-volume and fast-changing workload of heterogeneous types of experimental data.",
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"title": "4CeeD: Real-Time Data Acquisition and Analysis Framework for Material-Related Cyber-Physical Environments",
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"name": "Srinivas Devadas"
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"doi": "10.1145/3132747.3132755",
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"paperAbstract": "Atom is an anonymous messaging system that protects against traffic-analysis attacks. Unlike many prior systems, each Atom server touches only a small fraction of the total messages routed through the network. As a result, the system's capacity scales near-linearly with the number of servers. At the same time, each Atom user benefits from \"best possible\" anonymity: a user is anonymous among all honest users of the system, even against an active adversary who monitors the entire network, a portion of the system's servers, and any number of malicious users. The architectural ideas behind Atom have been known in theory, but putting them into practice requires new techniques for (1) avoiding heavy general-purpose multi-party computation protocols, (2) defeating active attacks by malicious servers at minimal performance cost, and (3) handling server failure and churn.\n Atom is most suitable for sending a large number of short messages, as in a microblogging application or a high-security communication bootstrapping (\"dialing\") for private messaging systems. We show that, on a heterogeneous network of 1,024 servers, Atom can transit a million Tweet-length messages in 28 minutes. This is over 23x faster than prior systems with similar privacy guarantees.",
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"title": "Atom: Horizontally Scaling Strong Anonymity",
"venue": "SOSP",
"year": 2017
},
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"name": "Alena Naiakshina"
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{
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"name": "Anastasia Danilova"
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"name": "Christian Tiefenau"
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"name": "Marco Herzog"
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"name": "Sergej Dechand"
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"name": "Matthew Smith"
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],
"doi": "10.1145/3133956.3134082",
"doiUrl": "https://doi.org/10.1145/3133956.3134082",
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"paperAbstract": "Passwords are still a mainstay of various security systems, as well as the cause of many usability issues. For end-users, many of these issues have been studied extensively, highlighting problems and informing design decisions for better policies and motivating research into alternatives. However, end-users are not the only ones who have usability problems with passwords! Developers who are tasked with writing the code by which passwords are stored must do so securely. Yet history has shown that this complex task often fails due to human error with catastrophic results. While an end-user who selects a bad password can have dire consequences, the consequences of a developer who forgets to hash and salt a password database can lead to far larger problems. In this paper we present a first qualitative usability study with 20 computer science students to discover how developers deal with password storage and to inform research into aiding developers in the creation of secure password systems.",
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"http://arxiv.org/abs/1708.08759",
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"https://acmccs.github.io/papers/p311-naiakshinaA.pdf",
"https://arxiv.org/pdf/1708.08759v1.pdf",
"http://doi.acm.org/10.1145/3133956.3134082",
"https://users.cs.fiu.edu/~carbunar/teaching/cis5374/cis5374.2017/slides/storage.pdf",
"http://net.cs.uni-bonn.de/fileadmin/user_upload/danilova/API_Usability_Developer_1_.pdf"
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"title": "Why Do Developers Get Password Storage Wrong?: A Qualitative Usability Study",
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"paperAbstract": "Existing distributed asynchronous graph processing systems employ checkpointing to capture globally consistent snapshots and rollback all machines to most recent checkpoint to recover from machine failures. In this paper we argue that recovery in distributed asynchronous graph processing does not require the entire execution state to be rolled back to a globally consistent state due to the relaxed asynchronous execution semantics. We define the properties required in the recovered state for it to be usable for correct asynchronous processing and develop CoRAL, a lightweight checkpointing and recovery algorithm. First, this algorithm carries out confined recovery that only rolls back graph execution states of the failed machines to affect recovery. Second, it relies upon lightweight checkpoints that capture locally consistent snapshots with a reduced peak network bandwidth requirement. Our experiments using real-world graphs show that our technique recovers from failures and finishes processing 1.5x to 3.2x faster compared to the traditional asynchronous checkpointing and recovery mechanism when failures impact 1 to 6 machines of a 16 machine cluster. Moreover, capturing locally consistent snapshots significantly reduces intermittent high peak bandwidth usage required to save the snapshots -- the average reduction in 99th percentile bandwidth ranges from 22% to 51% while 1 to 6 snapshot replicas are being maintained.",
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"https://people.csail.mit.edu/jshun/6886-s18/papers/Coral.pdf",
"http://www.cs.sfu.ca/~keval/contents/talks/CoRAL-ASPLOS17.pdf",
"http://www.cs.ucr.edu/~kvora001/contents/papers/asplos17-coral.pdf",
"http://www.cs.sfu.ca/~keval/contents/papers/coral-asplos17.pdf",
"http://www.cs.ucr.edu/~gupta/research/Publications/Comp/asplos17-coral.pdf",
"http://doi.acm.org/10.1145/3037697.3037747"
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"title": "CoRAL: Confined Recovery in Distributed Asynchronous Graph Processing",
"venue": "ASPLOS",
"year": 2017
},
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"name": "Joshua S. Auerbach"
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{
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{
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"name": "Louis Mandel"
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"name": "Avraham Shinnar"
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{
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"name": "J\u00e9r\u00f4me Sim\u00e9on"
}
],
"doi": "10.1145/3035918.3035961",
"doiUrl": "https://doi.org/10.1145/3035918.3035961",
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"paperAbstract": "Algebras based on combinators, i.e., variable-free, have been proposed as a better representation for query compilation and optimization. A key benefit of combinators is that they avoid the need to handle variable shadowing or accidental capture during rewrites. This simplifies both the optimizer specification and its correctness analysis, but the environment from the source language has to be reified as records, which can lead to more complex query plans.\n This paper proposes <b>NRA<sup>e</sup></b>, an extension of a combinators-based nested relational algebra <b>(NRA)</b> with built-in support for environments. We show that it can naturally encode an equivalent <b>NRA</b> with lambda terms and that all optimizations on <b>NRA</b> carry over to <b>NRA<sup>e</sup></b>. This extension provides an elegant way to represent views in query plans, and can radically simplify compilation and optimization for source languages with rich environment manipulations.\n We have specified a query compiler using the Coq proof assistant with <b>NRA<sup>e</sup></b> at its heart. Most of the compiler, including the query optimizer, is accompanied by a (machine-checked) correctness proof. The implementation is automatically extracted from the specification, resulting in a query compiler with a verified core.",
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"title": "Handling Environments in a Nested Relational Algebra with Combinators and an Implementation in a Verified Query Compiler",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
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"name": "Debankur Mukherjee"
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"name": "Souvik Dhara"
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"name": "Sem C. Borst"
},
{
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"name": "Johan van Leeuwaarden"
}
],
"doi": "10.1145/3084463",
"doiUrl": "https://doi.org/10.1145/3084463",
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"Autoscaling",
"Centralisation",
"Comparison of raster-to-vector conversion software",
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"Elasticity (cloud computing)",
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"Idle (CPU)",
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"Scalability",
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"journalName": "POMACS",
"journalPages": "25:1-25:28",
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"paperAbstract": "A fundamental challenge in large-scale cloud networks and data centers is to achieve highly efficient server utilization and limit energy consumption, while providing excellent user-perceived performance in the presence of uncertain and time-varying demand patterns. Auto-scaling provides a popular paradigm for automatically adjusting service capacity in response to demand while meeting performance targets, and queue-driven auto-scaling techniques have been widely investigated in the literature. In typical data center architectures and cloud environments however, no centralized queue is maintained, and load balancing algorithms immediately distribute incoming tasks among parallel queues. In these distributed settings with vast numbers of servers, centralized queue-driven auto-scaling techniques involve a substantial communication overhead and major implementation burden, or may not even be viable at all.\n Motivated by the above issues, we propose a joint auto-scaling and load balancing scheme which does not require any global queue length information or explicit knowledge of system parameters, and yet provides provably near-optimal service elasticity. We establish the fluid-level dynamics for the proposed scheme in a regime where the total traffic volume and nominal service capacity grow large in proportion. The fluid-limit results show that the proposed scheme achieves asymptotic optimality in terms of user-perceived delay performance as well as energy consumption. Specifically, we prove that both the waiting time of tasks and the relative energy portion consumed by idle servers vanish in the limit. At the same time, the proposed scheme operates in a distributed fashion and involves only constant communication overhead per task, thus ensuring scalability in massive data center operations. Extensive simulation experiments corroborate the fluid-limit results, and demonstrate that the proposed scheme can match the user performance and energy consumption of state-of-the-art approaches that do take full advantage of a centralized queue.",
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"http://doi.acm.org/10.1145/3078505.3078532",
"http://doi.acm.org/10.1145/3084463",
"https://arxiv.org/pdf/1703.08373v1.pdf",
"http://arxiv.org/abs/1703.08373"
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"sources": [
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"title": "Optimal Service Elasticity in Large-Scale Distributed Systems",
"venue": "SIGMETRICS",
"year": 2017
},
"35634a173dc77967a0c10d0578f10fbf808e0972": {
"authors": [
{
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"name": "Jiepu Jiang"
},
{
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"name": "Daqing He"
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{
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"name": "James Allan"
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],
"doi": "10.1145/3077136.3080840",
"doiUrl": "https://doi.org/10.1145/3077136.3080840",
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"Human\u2013computer interaction",
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"Usability testing",
"User experience",
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],
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"paperAbstract": "To address concerns of TREC-style relevance judgments, we explore two improvements. The first one seeks to make relevance judgments contextual, collecting in situ feedback of users in an interactive search session and embracing usefulness as the primary judgment criterion. The second one collects multidimensional assessments to complement relevance or usefulness judgments, with four distinct alternative aspects examined in this paper - novelty, understandability, reliability, and effort.\n We evaluate different types of judgments by correlating them with six user experience measures collected from a lab user study. Results show that switching from TREC-style relevance criteria to usefulness is fruitful, but in situ judgments do not exhibit clear benefits over the judgments collected without context. In contrast, combining relevance or usefulness with the four alternative judgments consistently improves the correlation with user experience measures, suggesting future IR systems should adopt multi-aspect search result judgments in development and evaluation.\n We further examine implicit feedback techniques for predicting these judgments. We find that click dwell time, a popular indicator of search result quality, is able to predict some but not all dimensions of the judgments. We enrich the current implicit feedback methods using post-click user interaction in a search session and achieve better prediction for all six dimensions of judgments.",
"pdfUrls": [
"http://ciir-publications.cs.umass.edu/pub/web/getpdf.php?id=1272",
"http://doi.acm.org/10.1145/3077136.3080840"
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"s2Url": "https://semanticscholar.org/paper/35634a173dc77967a0c10d0578f10fbf808e0972",
"sources": [
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"title": "Comparing In Situ and Multidimensional Relevance Judgments",
"venue": "SIGIR",
"year": 2017
},
"358371444e037519ba8837bed0437985d4dc582e": {
"authors": [
{
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"name": "Milad Nasr"
},
{
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"name": "Amir Houmansadr"
},
{
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"name": "Arya Mazumdar"
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],
"doi": "10.1145/3133956.3134074",
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"paperAbstract": "Traffic analysis is the practice of inferring sensitive information from communication patterns, particularly packet timings and packet sizes. Traffic analysis is increasingly becoming relevant to security and privacy with the growing use of encryption and other evasion techniques that render content-based analysis of network traffic impossible. The literature has investigated traffic analysis for various application scenarios, from tracking stepping stone cybercriminals to compromising anonymity systems.\n The major challenge to existing traffic analysis mechanisms is scaling to today's exploding volumes of network traffic, i.e., they impose high storage, communications, and computation overheads. In this paper, we aim at addressing this scalability issue by introducing a new direction for traffic analysis, which we call \\emph{compressive traffic analysis}. The core idea of compressive traffic analysis is to compress traffic features, and perform traffic analysis operations on such compressed features instead of on raw traffic features (therefore, improving the storage, communications, and computation overheads of traffic analysis due to using smaller numbers of features). To compress traffic features, compressive traffic analysis leverages linear projection algorithms from compressed sensing, an active area within signal processing. We show that these algorithms offer unique properties that enable compressing network traffic features while preserving the performance of traffic analysis compared to traditional mechanisms.\n We introduce the idea of compressive traffic analysis as a new generic framework for scalable traffic analysis. We then apply compressive traffic analysis to two widely studied classes of traffic analysis, namely, flow correlation and website fingerprinting. We show that the compressive versions of state-of-the-art flow correlation and website fingerprinting schemes\\textemdash significantly\\textemdash outperform their non-compressive (traditional) alternatives, e.g., the compressive version of Houmansadr et al. [44]'s flow correlation is two orders of magnitude faster, and the compressive version of Wang et al. [77] fingerprinting system runs about 13 times faster. We believe that our study is a major step towards scaling traffic analysis.",
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"http://doi.acm.org/10.1145/3133956.3134074",
"https://www.freehaven.net/anonbib/cache/compressive-ccs2017.pdf",
"http://people.cs.umass.edu/~milad/papers/compress_CCS.pdf",
"http://people.cs.umass.edu/~amir/papers/CCS17-CompressiveTA.pdf"
],
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"title": "Compressive Traffic Analysis: A New Paradigm for Scalable Traffic Analysis",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Wouter B. de Vries"
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"name": "Ricardo de Oliveira Schmidt"
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"name": "Wes Hardaker"
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"name": "John S. Heidemann"
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"name": "Pieter-Tjerk de Boer"
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"name": "Aiko Pras"
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],
"doi": "10.1145/3131365.3131371",
"doiUrl": "https://doi.org/10.1145/3131365.3131371",
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"paperAbstract": "IP anycast provides DNS operators and CDNs with automatic fail-over and reduced latency by breaking the Internet into <i>catchments</i>, each served by a different anycast site. Unfortunately, <i>understanding</i> and <i>predicting</i> changes to catchments as anycast sites are added or removed has been challenging. Current tools such as RIPE Atlas or commercial equivalents map from thousands of vantage points (VPs), but their coverage can be inconsistent around the globe. This paper proposes <i>Verfploeter</i>, a new method that maps anycast catchments using active probing. Verfploeter provides around 3.8M passive VPs, 430x the 9k physical VPs in RIPE Atlas, providing coverage of the vast majority of networks around the globe. We then add load information from prior service logs to provide calibrated predictions of anycast changes. Verfploeter has been used to evaluate the new anycast deployment for B-Root, and we also report its use of a nine-site anycast testbed. We show that the greater coverage made possible by Verfploeter's active probing is necessary to see routing differences in regions that have sparse coverage from RIPE Atlas, like South America and China.",
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"http://doi.acm.org/10.1145/3131365.3131371",
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"https://conferences.sigcomm.org/imc/2017/slides/Broad%20and%20load-aware%20anycast%20mapping%20with%20verfploeter_v4.pdf",
"http://ftp.isi.edu/~johnh/PAPERS/Vries17b.pdf",
"https://wbdv.nl/files/Vries17b.pdf"
],
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"title": "Broad and load-aware anycast mapping with verfploeter",
"venue": "IMC",
"year": 2017
},
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"name": "Goran Flegar"
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"name": "Enrique S. Quintana-Ort\u00ed"
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"title": "Balanced CSR Sparse Matrix-Vector Product on Graphics Processors",
"venue": "Euro-Par",
"year": 2017
},
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"name": "Zhen Cao"
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"name": "Dean Hildebrand"
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"name": "Erez Zadok"
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"paperAbstract": "Ensuring stable performance for storage stacks is important, especially with the growth in popularity of hosted services where customers expect QoS guarantees. The same requirement arises from benchmarking settings as well. One would expect that repeated, carefully controlled experiments might yield nearly identical performance results\u2014but we found otherwise. We therefore undertook a study to characterize the amount of variability in benchmarking modern storage stacks. In this paper we report on the techniques used and the results of this study. We conducted many experiments using several popular workloads, file systems, and storage devices\u2014and varied many parameters across the entire storage stack. In over 25% of the sampled configurations, we uncovered variations higher than 10% in storage performance between runs. We analyzed these variations and found that there was no single root cause: it often changed with the workload, hardware, or software configuration in the storage stack. In several of those cases we were able to fix the cause of variation and reduce it to acceptable levels. We believe our observations in benchmarking will also shed some light on addressing stability issues in production systems.",
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"title": "On the Performance Variation in Modern Storage Stacks",
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"name": "Zili Shao"
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"paperAbstract": "In recent years, flash-based key-value cache systems have raised high interest in industry, such as Facebook\u2019s McDipper and Twitter\u2019s Fatcache. These cache systems typically use commercial SSDs to store and manage key-value cache data in flash. Such a practice, though simple, is inefficient due to the huge semantic gap between the key-value cache manager and the underlying flash devices. In this paper, we advocate to reconsider the cache system design and directly open device-level details of the underlying flash storage for key-value caching. This co-design approach bridges the semantic gap and well connects the two layers together, which allows us to leverage both the domain knowledge of key-value caches and the unique device properties. In this way, we can maximize the efficiency of key-value caching on flash devices while minimizing its weakness. We implemented a prototype, called DIDACache, based on the Open-Channel SSD platform. Our experiments on real hardware show that we can significantly increase the throughput by 35.5%, reduce the latency by 23.6%, and remove unnecessary erase operations by 28%.",
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"title": "DIDACache: A Deep Integration of Device and Application for Flash Based Key-Value Caching",
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},
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"name": "Ao Ren"
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"name": "Zhe Li"
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"name": "Caiwen Ding"
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{
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"name": "Qinru Qiu"
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"name": "Yanzhi Wang"
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"name": "Ji Li"
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"name": "Xuehai Qian"
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"name": "Bo Yuan"
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],
"doi": "10.1145/3037697.3037746",
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"paperAbstract": "With the recent advance of wearable devices and Internet of Things (IoTs), it becomes attractive to implement the Deep Convolutional Neural Networks (DCNNs) in embedded and portable systems. Currently, executing the software-based DCNNs requires high-performance servers, restricting the widespread deployment on embedded and mobile IoT devices. To overcome this obstacle, considerable research efforts have been made to develop highly-parallel and specialized DCNN accelerators using GPGPUs, FPGAs or ASICs.\n Stochastic Computing (SC), which uses a bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has high potential for implementing DCNNs with high scalability and ultra-low hardware footprint. Since multiplications and additions can be calculated using AND gates and multiplexers in SC, significant reductions in power (energy) and hardware footprint can be achieved compared to the conventional binary arithmetic implementations. The tremendous savings in power (energy) and hardware resources allow immense design space for enhancing scalability and robustness for hardware DCNNs.\n This paper presents SC-DCNN, the first comprehensive design and optimization framework of SC-based DCNNs, using a bottom-up approach. We first present the designs of function blocks that perform the basic operations in DCNN, including inner product, pooling, and activation function. Then we propose four designs of feature extraction blocks, which are in charge of extracting features from input feature maps, by connecting different basic function blocks with joint optimization. Moreover, the efficient weight storage methods are proposed to reduce the area and power (energy) consumption. Putting all together, with feature extraction blocks carefully selected, SC-DCNN is holistically optimized to minimize area and power (energy) consumption while maintaining high network accuracy. Experimental results demonstrate that the LeNet5 implemented in SC-DCNN consumes only 17 <i>mm</i><sup>2</sup> area and 1.53 W power, achieves throughput of 781250 images/s, area efficiency of 45946 images/s/<i>mm</i><sup>2</sup>, and energy efficiency of 510734 images/J.",
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"http://arxiv.org/abs/1611.05939",
"https://arxiv.org/pdf/1611.05939v1.pdf",
"http://doi.acm.org/10.1145/3037697.3037746",
"http://alchem.usc.edu/portal/static/download/sc_dcnn.pdf"
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"title": "SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing",
"venue": "ASPLOS",
"year": 2017
},
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"authors": [
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"name": "Yan Chen"
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"name": "Ashwin Machanavajjhala"
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"name": "Michael Hay"
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"doi": "10.1145/3133956.3134102",
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"paperAbstract": "Individuals are continually observed by an ever-increasing number of sensors that make up the Internet of Things. The resulting streams of data, which are analyzed in real time, can reveal sensitive personal information about individuals. Hence, there is an urgent need for stream processing solutions that can analyze these data in real time with provable guarantees of privacy and low error.\n We present PeGaSus, a new algorithm for differentially private stream processing. Unlike prior work that has focused on answering individual queries over streams, our algorithm is the first that can simultaneously support a variety of stream processing tasks -- counts, sliding windows, event monitoring -- over multiple resolutions of the stream. PeGaSus uses a <i>Perturber</i> to release noisy counts, a data-adaptive <i>Perturber</i> to identify stable uniform regions in the stream, and a query specific <i>Smoother</i>, which combines the outputs of the <i>Perturber</i> and <i>Grouper</i> to answer queries with low error. In a comprehensive study using a WiFi access point dataset, we empirically show that PeGaSus can answer continuous queries with lower error than the previous state-of-the-art algorithms, even those specialized to particular query types.",
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"https://users.cs.duke.edu/~ashwin/pubs/Chen-PeGaSus-CCS2017-final.pdf",
"http://doi.acm.org/10.1145/3133956.3134102",
"http://people.cs.umass.edu/~miklau/assets/pubs/dp/Chen17PeGaSus.pdf",
"https://acmccs.github.io/papers/p1375-chenA.pdf"
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"title": "PeGaSus: Data-Adaptive Differentially Private Stream Processing",
"venue": "CCS",
"year": 2017
},
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"doi": "10.1145/3133900",
"doiUrl": "https://doi.org/10.1145/3133900",
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"paperAbstract": "Series of traversals of tree structures arise in numerous contexts: abstract syntax tree traversals in compiler passes, rendering traversals of the DOM in web browsers, kd-tree traversals in computational simulation codes. In each of these settings, a tree is traversed multiple times to compute various values and modify various portions of the tree. While it is relatively easy to write these traversals as separate small updates to the tree, for efficiency reasons, traversals are often manually fused to reduce the number of times that each portion of the tree is traversed: by performing multiple operations on the tree simultaneously, each node of the tree can be visited fewer times, increasing opportunities for optimization and decreasing cache pressure and other overheads. This fusion process is often done manually, requiring careful understanding of how each of traversals of the tree interact. This paper presents an automatic approach to traversal fusion: tree traversals can be written independently, and then our framework analyzes the dependences between the traversals to determine how they can be fused to reduce the number of visits to each node in the tree. A critical aspect of our framework is that it exploits two opportunities to increase the amount of fusion: i) it automatically integrates code motion, and ii) it supports partial fusion, where portions of one traversal can be fused with another, allowing for a reduction in node visits without requiring that two traversals be fully fused. We implement our framework in Clang, and show across several case studies that we can successfully fuse complex tree traversals, reducing the overall number of traversals and substantially improving locality and performance.",
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"title": "TreeFuser: a framework for analyzing and fusing general recursive tree traversals",
"venue": "PACMPL",
"year": 2017
},
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"authors": [
{
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"name": "Jiyan Sun"
},
{
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"name": "Yan Zhang"
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{
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"name": "Xin Wang"
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{
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"name": "Shihan Xiao"
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{
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"name": "Zhen Xu"
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{
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"name": "Hongjing Wu"
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{
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"name": "Xin Chen"
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{
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"name": "Yanni Han"
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"doi": "10.1109/IPDPS.2017.40",
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"paperAbstract": "Multi-path TCP has recently shown great potential to take advantage of the rich path diversity in data center networks (DCN) to increase transmission throughput. However, the small flows, which take a large fraction of data center traffic, will easily get a timeout when split onto multiple paths. Moreover, the dynamic congestions and node failures in DCN will exacerbate the reorder problem of parallel multi-path transmissions for large flows. In this paper, we propose DC2-MTCP (Data Center Coded Multi-path TCP), which employs a fast and light-weight coding method to address the above challenges while maintaining the benefit of parallel multi-path transmissions. To meet the high flow performance in DCN, we insert a very low ratio of coded packets with a careful selection of the packets to be coded. We further present a progressive decoding algorithm to decode the packets online with a low time complexity. Extensive ns2-based simulations show that with two orders of magnitude lower coding delay, DC2-MTCP can reduce on average 40% flow completion time for small flows and increase 30% flow throughput for large flows compared to the peer schemes in varying network conditions.",
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"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
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"paperAbstract": "Distributed property testing in networks has been introduced by Brakerski and Patt-Shamir (2011), with the objective of detecting the presence of large dense sub-networks in a distributed manner. Recently, Censor-Hillel et al. (2016) have shown how to detect 3-cycles in a constant number of rounds by a distributed algorithm. In a follow up work, Fraigniaud et al. (2016) have shown how to detect 4-cycles in a constant number of rounds as well. However, the techniques in these latter works were shown not to generalize to larger cycles C<sub>k</sub> with k ≥ 5. In this paper, we completely settle the problem of cycle detection, by establishing the following result. For every k ≥ 3, there exists a distributed property testing algorithm for C<sub>k</sub>-freeness, performing in a constant number of rounds. All these results hold in the classical congest/ model for distributed network computing. Our algorithm is 1-sided error. Its round-complexity is O(1/ε) where ε ∈(0,1) is the property testing parameter measuring the gap between legal and illegal instances.",
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"paperAbstract": "Tasks coupled in an in situ workflow may not process data at the same speed, potentially causing overflows in the communication channel between them. To prevent this problem, software infrastructures for in situ workflows usually impose a strict FIFO policy that has the side-effect of slowing down faster tasks to the speed of the slower ones. This may not be the desired behavior; for example, a scientist may prefer to drop older data in the communication channel in order to visualize the latest snapshot of a simulation. In this paper, we present Manala, a flexible flow control library designed to manage the flow of messages between a producer and a consumer in an in situ workflow. Manala intercepts messages from the producer, stores them, and selects the message to forward to the consumer depending on the flow control policy. The library is designed to ease the creation of new flow control policies and buffering mechanisms. We demonstrate with three examples how changing the flow control policy between tasks can influence the performance and results of scientific workflows. The first example focuses on materials science with LAMMPS and a synthetic diffraction analysis code. The second example is an interactive visualization scenario with Gromacs as the producer and Damaris/Viz as consumer. Our third example studies different strategies to perform an asynchronous checkpoint with Gromacs.",
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"paperAbstract": "Non-Uniform Memory Access (NUMA) architecture has become the dominant architecture and is widely used in virtualization platforms. In NUMA-based cloud computing platform, arbitrary topology of vCPUs and memory may cause significant performance degradation for VMs, which introduces great challenges for virtual machine monitors (VMMs) to efficiently manage the vCPUs and memory. Previous studies mainly sample the characteristics of the vCPUs to indicate the optimizing strategies to reduce the NUMA overheads in virtualization platforms. But the typical periodical sampling methods have some deviations with the real vCPU characteristics. This leads to the inaccurate sampling and scheduling decisions for the optimizing strategies. Motivated by the inaccuracy in sampling methods and scheduling decisions, we propose a fine-grained scheduler, named vScope, which makes accurate scheduling decisions according to the guest OS processes in the vCPUs, to improve the performance of memory-intensive workloads in cloud platforms. In vScope, the VMM identifies the guest OS processes in the vCPUs and calculates the NUMA affinity of each process from the PMU data. At the end of vCPU's scheduling cycle, the scheduler appropriately schedules the vCPUs to their local NUMA node to alleviate the unnecessary NUMA overhead. We implement vScope in Xen-4.5.1 VMM and evaluate its effectiveness with some memory-intensive benchmarks. The experimental results shows that vScope can achieve up to 11.5% performance improvement for these workloads when compared with the Credit scheduler in Xen. Moreover, vScope only introduces limited overhead into the system.",
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"paperAbstract": "This paper presents natural synthesis, which generalizes the proof-theoretic synthesis technique to support very expressive logic theories. This approach leverages the natural proof methodology and reduces an intractable, unbounded-size synthesis problem to a tractable, bounded-size synthesis problem, which is amenable to be handled by modern inductive synthesis engines. The synthesized program admits a natural proof and is a provably-correct solution to the original synthesis problem. We explore the natural synthesis approach in the domain of imperative data-structure manipulations and present a novel syntax-guided synthesizer based on natural synthesis. The input to our system is a program template together with a rich functional specification that the synthesized program must meet. Our system automatically produces a program implementation along with necessary proof artifacts, namely loop invariants and ranking functions, and guarantees the total correctness with a natural proof. Experiments show that our natural synthesizer can efficiently produce provably-correct implementations for sorted lists and binary search trees. To our knowledge, this is the first system that can automatically synthesize these programs, their functional correctness and their termination in tandem from bare-bones control flow skeletons.",
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"title": "Natural synthesis of provably-correct data-structure manipulations",
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"year": 2017
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"paperAbstract": "Increasing test collection sizes and limited judgment budgets create measurement challenges for IR batch evaluations, challenges that are greater when using deep effectiveness metrics than when using shallow metrics, because of the increased likelihood that unjudged documents will be encountered. Here we study the problem of metric score adjustment, with the goal of accurately estimating system performance when using deep metrics and limited judgment sets, assuming that dynamic score adjustment is required per topic due to the variability in the number of relevant documents. We seek to induce system orderings that are as close as is possible to the orderings that would arise if full judgments were available. Starting with depth-based pooling, and no prior knowledge of sampling probabilities, the first phase of our two-stage process computes a background gain for each document based on rank-level statistics. The second stage then accounts for the distributional variance of relevant documents. We also exploit the frequency statistics of pooled relevant documents in order to determine a threshold for dynamically determining the set of topics to be adjusted. Taken together, our results show that: (i) better score estimates can be achieved when compared to previous work; (ii) by setting a global threshold, we are able to adapt our methods to different collections; and (iii) the proposed estimation methods reliably approximate the system orderings achieved when many more relevance judgments are available. We also consider pools generated by a two-strata sampling approach.",
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"title": "Can Deep Effectiveness Metrics Be Evaluated Using Shallow Judgment Pools?",
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"paperAbstract": "Training machine learning (ML) models with large datasets can incur significant resource contention on shared clusters. This training typically involves many iterations that continually improve the quality of the model. Yet in exploratory settings, better models can be obtained faster by directing resources to jobs with the most potential for improvement. We describe SLAQ, a cluster scheduling system for approximate ML training jobs that aims to maximize the overall job quality.\n When allocating cluster resources, SLAQ explores the quality-runtime trade-offs across multiple jobs to maximize system-wide quality improvement. To do so, SLAQ leverages the iterative nature of ML training algorithms, by collecting quality and resource usage information from concurrent jobs, and then generating highly-tailored quality-improvement predictions for future iterations. Experiments show that SLAQ achieves an average quality improvement of up to 73% and an average delay reduction of up to 44% on a large set of ML training jobs, compared to resource fairness schedulers.",
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"https://arxiv.org/pdf/1802.04819v1.pdf",
"http://www.cs.princeton.edu/~haoyuz/publications/slaq-slides.pdf",
"http://arxiv.org/abs/1802.04819"
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"title": "SLAQ: Quality-Driven Scheduling for Distributed Machine Learning",
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},
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"name": "Jiamin Huang"
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"name": "Barzan Mozafari"
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{
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"name": "Thomas F. Wenisch"
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"doi": "10.1145/3064176.3064179",
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"paperAbstract": "Most software profiling tools quantify average performance and rely on a program's control flow graph to organize and report results. However, in interactive server applications, performance predictability is often an equally important measure. Moreover, the end user is often concerned with the performance of a semantically defined interval of execution, such as a request or transaction, which may not directly map to any single function in the call graph, especially in high-performance applications that use asynchrony or event-based programming. It is difficult to distinguish functionality that lies on the critical path of a semantic interval from other activity (e.g., periodic logging or side operations) that may nevertheless appear prominent in a conventional profile. Existing profilers lack the ability to (i) aggregate results for a semantic interval and (ii) attribute its performance variance to individual functions.\n We propose a profiler called VProfiler that, given the source code of a software system and programmer annotations indicating the start and end of semantic intervals of interest, is able to identify the dominant sources of latency variance in a semantic context. Using a novel abstraction, called a variance tree, VProfiler analyzes the thread interleaving and deconstructs overall latency variance into variances and covariances of the execution time of individual functions. It then aggregates latency variance along a backwards path of dependence relationships among threads from the end of an interval to its start. We evaluate VProfiler's effectiveness on three popular open-source projects (MySQL, Postgres, and Apache Web Server). By identifying a few culprit functions in these complex code bases, VProfiler allows us to eliminate 27%--82% of the overall latency variance of these systems with a modest programming effort.",
"pdfUrls": [
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"sources": [
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"title": "Statistical Analysis of Latency Through Semantic Profiling",
"venue": "EuroSys",
"year": 2017
},
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"authors": [
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"name": "Kei Davis"
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{
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"name": "Dorian C. Arnold"
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{
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"name": "Randal S. Baker"
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{
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"name": "Robert W. Robey"
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{
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"name": "Patrick S. McCormick"
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{
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"name": "Jon A. Dahl"
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"name": "R. Joe Zerr"
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"name": "Florian Weik"
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{
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"doi": "10.1109/IPDPS.2017.13",
"doiUrl": "https://doi.org/10.1109/IPDPS.2017.13",
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"paperAbstract": "Hybrid parallel program models that combine message passing and multithreading (MP+MT) are becoming more popular, extending the basic message passing (MP) model that uses single-threaded processes for both inter- and intra-node parallelism. A consequence is that coupled parallel applications increasingly comprise MP libraries together with MP+MT libraries with differing preferred degrees of threading, resulting in thread-level heterogeneity. Retroactively matching threading levels between independently developed and maintained libraries is difficult; the challenge is exacerbated because contemporary parallel job launchers provide only static resource binding policies over entire application executions. A standard approach for accommodating thread-level heterogeneity is to under-subscribe compute resources such that the library with the highest degree of threading per process has one processing element per thread. This results in libraries with fewer threads per process utilizing only a fraction of the available compute resources. We present and evaluate a novel approach for accommodating thread-level heterogeneity. Our approach enables full utilization of all available compute resources throughout an application's execution by providing programmable facilities to dynamically reconfigure runtime environments for compute phases with differing threading factors and memory affinities. We show that our approach can improve overall application performance by up to 5.8x in real-world production codes. Furthermore, the practicality and utility of our approach has been demonstrated by continuous production use for over one year, and by more recent incorporation into a number of production codes.",
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"https://doi.org/10.1109/IPDPS.2017.13"
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"title": "Accommodating Thread-Level Heterogeneity in Coupled Parallel Applications",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
"37676b91fdf1e4b7ff5edeb230e3dce67033d717": {
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],
"name": "Pawel Garncarek"
},
{
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],
"name": "Tomasz Jurdzinski"
},
{
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"name": "Krzysztof Lorys"
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],
"doi": "10.1109/IPDPS.2017.105",
"doiUrl": "https://doi.org/10.1109/IPDPS.2017.105",
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"paperAbstract": "We consider the problem of scheduling packets of different lengths via k directed parallel communication links. The links are prone to simultaneous errors --- if an error occurs, all links are affected. Dynamic packet arrivals and errors are modelled by a worst-case adversary. The goal is to optimize competitive throughput of online scheduling algorithms. Two types of failures are considered: jamming, when currently scheduled packets are simply not delivered, and crashes, when additionally the channel scheduler crashes losing its current state. For the former, milder type of failures, we prove an upper bound on competitive throughput of 3/4 - 1/(4k) for odd values of k, and 3/4 - 1/(4k+4) for even values of k. On constructive side, we design an online algorithm that, for packets of two different lengths, matches the upper bound on competitive throughput. To compare, scheduling on independent channels, that is, when adversary could cause errors on each channel independently, reaches throughput of 1/2. This shows that scheduling under simultaneous jamming is provably more efficient than scheduling under channel-independent jamming. In the setting with crash failures we prove a general upper bound for competitive throughput of (√5-1)/2 and design an algorithm achieving it for packets of two different lengths. This result has two interesting implications. First, simultaneous crashes are significantly stronger than simultaneous jamming. Second, due to the above mentioned upper bound of 1/2 on throughput under channel-independenterrors, scheduling under simultaneous crashes is significantly stronger than channel-independent crashes, similarly as in the case of jamming errors.",
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"title": "Fault-Tolerant Online Packet Scheduling on Parallel Channels",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
"376c5f9084d8721aed49db7a2fbe693a6b5670ed": {
"authors": [
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{
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"name": "Gudula R\u00fcnger"
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{
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"name": "Matthias Stachowski"
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],
"doi": "10.1109/IGCC.2017.8323578",
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"paperAbstract": "Energy efficiency is considered to be a critical concern for modern hardware and a variety of hardware features have been developed to improve the energy balance for executing applications. This article focuses on the dynamic voltage frequency scaling (DVFS) technique, which is available for many platforms, including CPUs and GPUs. Analytical models for capturing the energy efficiency are considered and it is investigated whether such an analytical model is able to support an a priori selection of the operational frequency that leads to a near optimal energy consumption for the application code to be executed. Also the energy-delay product (EDP) is investigated, weighting the power against the square of execution time. The experimental evaluation is performed on the basis of the multi-threaded PARSEC benchmarks. We show that the operational frequency selected according to the analytical models leads to an energy consumption that is near the minimum energy consumption over all frequencies available.",
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"paperAbstract": "Owing to increasing demand of faster and larger DRAM system, the DRAM system accounts for a large portion of the total power consumption of computing systems. As memory traffic and DRAM bandwidth grow, the row activation and I/O power consumptions are becoming major contributors to total DRAM power consumption. Thus, reducing row activation and I/O power consumptions has big potential for improving the power and energy efficiency of the computing systems. To this end, we propose a partial row activation scheme for memory writes, in which DRAM is re-architected to mitigate row overfetching problem of modern DRAMs and to reduce row activation power consumption. In addition, accompanying I/O power consumption in memory writes is also reduced by transferring only a part of cache line data that must be written to partially opened rows. In our proposed scheme, partial rows ranging from a one-eighth row to a full row can be activated to minimize row activation granularity for memory writes and the full bandwidth of the conventional DRAM can be maintained for memory reads. Our partial row activation scheme is shown to reduce total DRAM power consumption by up to 32% and 23% on average, which outperforms previously proposed schemes in DRAM power saving with almost no performance loss.",
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"http://doi.ieeecomputersociety.org/10.1109/HPCA.2017.35"
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"title": "Partial Row Activation for Low-Power DRAM System",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
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"paperAbstract": "Software projects that use a compiled language are built hundreds of thousands of times during their lifespan. Hence, the compiler is invoked over and over again on an incrementally changing source base. As previous work has shown, up to 97 percent of these invocations are redundant and do not lead to an altered compilation result. In order to avoid such redundant builds, many developers use caching tools that are based on textual hashing of the source files. However, these tools fail in the presence of modifications that leave the compilation result unchanged. Especially for C projects, where module-interface definitions are imported textually with the C preprocessor, modifications to header files lead to many redundant compilations. In this paper, we present the cHash approach and compiler extension to quickly detect modifications on the language level that will not lead to a changed compilation result. By calculating a hash over the abstract syntax tree, we achieve a high precision at comparatively low costs. While cHash is light-weight and build system agnostic, it can cancel 80 percent of all compiler invocations early and reduce the build-time of incremental builds by up to 51 percent. In comparison to the state-of-the-art CCache tool, cHash is at least 30 percent more precise in detecting redundant compilations.",
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"https://www4.cs.fau.de/Publications/2017/dietrich_17_atc.pdf",
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"title": "cHash: Detection of Redundant Compilations via AST Hashing",
"venue": "USENIX Annual Technical Conference",
"year": 2017
},
"3783bde154bf6e7db5447fe50f8fccb3afb0ab34": {
"authors": [
{
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"name": "Priya Govindan"
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{
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"name": "Morteza Monemizadeh"
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"name": "S. Muthukrishnan"
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],
"doi": "10.1145/3034786.3056118",
"doiUrl": "https://doi.org/10.1145/3034786.3056118",
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"paperAbstract": "We consider publication settings with positive user feedback, such as, users publishing tweets and other users retweeting them, friends posting photos and others liking them or even authors publishing research papers and others citing these publications. A well-accepted notion of \"impact\" for users in these settings is the <i>H-Index</i>: Query rewriting through link analysis of the click graph. PVLDB, 1(1):408--421, 2008., which is the largest <i>k</i> such that at least <i>k</i> publications have k or more (positive) feedback.\n We study how to calculate H-index on large streams of user publications and feedback. If all the items can be stored, H-index of a user can be computed by sorting. We focus on the streaming setting where as is typical, we do not have space to store all the items.\n We present the first known streaming algorithm for computing the H-index of a user in the cash register streaming model using space poly(1/ε,log(1/δ),log<i>n</i>); this algorithm provides an additive ε approximation. For the aggregated model where feedback for a publication is collated, we present streaming algorithms that use much less space, either only dependent on ε and even a small constant. We also address the problem of finding \"heavy hitters\" users in H-index without estimating everyones? H-index. We present randomized streaming algorithms for finding 1 + ε approximation to heavy hitters that uses space poly(1/ε,log(1/δ),log<i>n</i>) and succeeds with probability at least 1 -- δ. Again, this is the first sublinear space algorithm for this problem, despite extensive research on heavy hitters in general. Our work initiates study of streaming algorithms for problems that estimate impact or identify impactful users.",
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"http://eden.rutgers.edu/~priyagn/priya/papers/streaminghindex_PODS2017.pdf",
"http://doi.acm.org/10.1145/3034786.3056118"
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"title": "Streaming Algorithms for Measuring H-Impact",
"venue": "PODS",
"year": 2017
},
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"authors": [
{
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"name": "Mattijs Jonker"
},
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"name": "Alistair King"
},
{
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"name": "Johannes Krupp"
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{
"ids": [
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"name": "Christian Rossow"
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{
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"name": "Anna Sperotto"
},
{
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"name": "Alberto Dainotti"
}
],
"doi": "10.1145/3131365.3131383",
"doiUrl": "https://doi.org/10.1145/3131365.3131383",
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"paperAbstract": "Denial-of-Service attacks have rapidly increased in terms of frequency and intensity, steadily becoming one of the biggest threats to Internet stability and reliability. However, a rigorous comprehensive characterization of this phenomenon, and of countermeasures to mitigate the associated risks, faces many infrastructure and analytic challenges. We make progress toward this goal, by introducing and applying a new framework to enable a macroscopic characterization of attacks, attack targets, and DDoS Protection Services (DPSs). Our analysis leverages data from four independent global Internet measurement infrastructures over the last two years: backscatter traffic to a large network telescope; logs from amplification honeypots; a DNS measurement platform covering 60% of the current namespace; and a DNS-based data set focusing on DPS adoption. Our results reveal the massive scale of the DoS problem, including an eye-opening statistic that one-third of all / 24 networks recently estimated to be active on the Internet have suffered at least one DoS attack over the last two years. We also discovered that often targets are simultaneously hit by different types of attacks. In our data, Web servers were the most prominent attack target; an average of 3% of the Web sites in .com, .net, and .org were involved with attacks, daily. Finally, we shed light on factors influencing migration to a DPS.",
"pdfUrls": [
"https://conferences.sigcomm.org/imc/2017/slides/imc2017_jonkerm.pdf",
"https://www.caida.org/publications/presentations/2017/millions_targets_under_attack_imc/millions_targets_under_attack_imc.pdf",
"http://doi.acm.org/10.1145/3131365.3131383",
"https://conferences.sigcomm.org/imc/2017/papers/imc17-103.pdf"
],
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"title": "Millions of targets under attack: a macroscopic characterization of the DoS ecosystem",
"venue": "IMC",
"year": 2017
},
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"authors": [
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"name": "Yujin Kwon"
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{
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"name": "Dohyun Kim"
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"name": "Yunmok Son"
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"name": "Eugene Y. Vasserman"
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{
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"name": "Yongdae Kim"
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"doi": "10.1145/3133956.3134019",
"doiUrl": "https://doi.org/10.1145/3133956.3134019",
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"Cryptography",
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"Fork (software development)",
"Nash equilibrium",
"Pools of Darkness"
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"paperAbstract": "In the Bitcoin system, participants are rewarded for solving cryptographic puzzles. In order to receive more consistent rewards over time, some participants organize mining pools and split the rewards from the pool in proportion to each participant's contribution. However, several attacks threaten the ability to participate in pools. The block withholding (BWH) attack makes the pool reward system unfair by letting malicious participants receive unearned wages while only pretending to contribute work. When two pools launch BWH attacks against each other, they encounter the miner's dilemma: in a Nash equilibrium, the revenue of both pools is diminished. In another attack called selfish mining, an attacker can unfairly earn extra rewards by deliberately generating forks.\n In this paper, we propose a novel attack called a fork after withholding (FAW) attack. FAW is not just another attack. The reward for an FAW attacker is always equal to or greater than that for a BWH attacker, and it is usable up to four times more often per pool than in BWH attack. When considering multiple pools --- the current state of the Bitcoin network -- the extra reward for an FAW attack is about 56% more than that for a BWH attack. Furthermore, when two pools execute FAW attacks on each other, the miner's dilemma may not hold: under certain circumstances, the larger pool can consistently win. More importantly, an FAW attack, while using intentional forks, does not suffer from practicality issues, unlike selfish mining. We also discuss partial countermeasures against the FAW attack, but finding a cheap and efficient countermeasure remains an open problem. As a result, we expect to see FAW attacks among mining pools.",
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"http://doi.acm.org/10.1145/3133956.3134019",
"https://syssec.kaist.ac.kr/pub/2017/kwon_ccs_2017.pdf",
"http://arxiv.org/abs/1708.09790",
"https://arxiv.org/pdf/1708.09790v1.pdf",
"http://people.cs.georgetown.edu/~clay/classes/fall2017/835/papers/Be_Selfish_and_Avoid_Dilemmas.pdf",
"http://diyhpl.us/~bryan/papers2/bitcoin/Be%20selfish%20and%20avoid%20dilemmas:%20Fork%20after%20withholding%20(FAW)%20attacks%20on%20bitcoin%20-%202017.pdf"
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"title": "Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin",
"venue": "CCS",
"year": 2017
},
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"name": "David Swasey"
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"name": "Deepak Garg"
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"name": "Derek Dreyer"
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],
"doi": "10.1145/3133913",
"doiUrl": "https://doi.org/10.1145/3133913",
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"Cryptographic protocol",
"Formal specification",
"Formal verification",
"Immutable object",
"Object-capability model",
"Programmer",
"Proof assistant",
"Separation logic",
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"paperAbstract": "In scenarios such as web programming, where code is linked together from multiple sources, <em>object capability patterns</em> (OCPs) provide an essential safeguard, enabling programmers to protect the private state of their objects from corruption by unknown and untrusted code. However, the benefits of OCPs in terms of program verification have never been properly formalized. In this paper, building on the recently developed Iris framework for concurrent separation logic, we develop OCPL, the first program logic for compositionally specifying and verifying OCPs in a language with closures, mutable state, and concurrency. The key idea of OCPL is to account for the interface between verified and untrusted code by adopting a well-known idea from the literature on security protocol verification, namely <em>robust safety</em>. Programs that export only properly wrapped values to their environment can be proven robustly safe, meaning that their untrusted environment cannot violate their internal invariants. We use OCPL to give the first general, compositional, and machine-checked specs for several commonly-used OCPsâ\u0080\u0094including the <em>dynamic sealing</em>, <em>membrane</em>, and <em>caretaker</em> patternsâ\u0080\u0094which we then use to verify robust safety for representative client code. All our results are fully mechanized in the Coq proof assistant.",
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"http://doi.acm.org/10.1145/3133913",
"https://people.mpi-sws.org/~swasey/papers/ocpl/ocpl-20170802.pdf",
"https://people.mpi-sws.org/~dreyer/papers/ocpl/paper.pdf",
"https://people.mpi-sws.org/~dg/papers/oopsla17-obj.pdf"
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"title": "Robust and compositional verification of object capability patterns",
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"year": 2017
},
"37c5fc44b36b5dc1bdb361b1e08caf83f109b7ce": {
"authors": [
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"paperAbstract": "Ethernet is being expanded to many industrial automation applications, and the demand for high availability drives the adoption of High Availability Seamless Redundancy (HSR) due to its capability to achieve zero recovery time. A major challenge of HSR is long delay of large HSR network due to its ring architecture. A solution to this issue is to use QuadBox to design multiple rings which raise many design questions because of different architecture options. Our approach is to apply the concept of SS7 signaling to classify the links as A, B, C, and D from which we present a flexible and scalable architecture. We developed a simulation model to validate this architecture, and the results show that one-way delay is not negatively affected by network growth. Another contribution of this paper is to use a central controller, like Software Defined Network (SDN), to create MAC forwarding tables on individual HSR nodes. The proposed scheme prevents flooding and broadcast storm in this loop topology.",
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"name": "Prashanth Menon"
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"name": "Todd C. Mowry"
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"paperAbstract": "Heterogenous chip multiprocessors (Het-CMPs) offer a combination of large Out-of-Order (<i>OoO</i>) cores optimized for high single-threaded performance and small In-Order (<i>InO</i>) cores optimized for low-energy and area costs. Due to practical constraints, CMP designers must choose to either optimize for total system throughput by utilizing many <i>InO</i> cores or maximize single-thread execution with fewer <i>OoO</i> cores. We propose <i>Mirage Cores</i>, a novel Het-CMP design where clusters of <i>InO</i> cores are architected around an <i>OoO</i> in a manner that optimizes for both throughput and single-thread performance. The insight behind <i>Mirage Cores</i> is that <i>InO</i> cores can achieve near-<i>OoO</i> performance if they are provided with the dynamic instruction schedule of an <i>OoO</i> core. To leverage this, <i>Mirage Cores</i> employs an <i>OoO</i> core as an optimal instruction schedule generator as well as a high-performance alternative for all neighboring <i>InO</i> cores. We also develop intelligent runtime schedulers which orchestrate the arbitration and migration of applications between the <i>InO</i> cores and the central <i>OoO.</i> Fast and timely transfer of dynamic schedules from the <i>OoO</i> to <i>InO</i> allows <i>Mirage Cores</i> to create the appearance of all <i>OoO</i> cores to the user using underlying In-Order hardware.\n Overall, with an 8 <i>InO</i> per <i>OoO</i> configuration, <i>Mirage Cores</i> can achieve on average 84% of the performance of a CMP with 8 <i>OoO</i> cores, a 28% increase relative to current systems, while conserving 55% of energy and 25% of area costs. We find that we can scale the design to around 12 <i>InOs</i> per <i>OoO</i> before starvation for the <i>OoO</i> starts to hamper system performance.",
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"title": "Mirage cores: the illusion of many out-of-order cores using in-order hardware",
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"name": "Pavlos Petoumenos"
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"name": "Zheng Wang"
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{
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"name": "Hugh Leather"
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"doi": "10.1109/PACT.2017.24",
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"paperAbstract": "Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and diversity of modern hardware and software. Machine learning is aproven technique for learning such heuristics, but its success is bound by thequality of the features used. These features must be hand crafted by developersthrough a combination of expert domain knowledge and trial and error. This makesthe quality of the final model directly dependent on the skill and availabletime of the system architect.Our work introduces a better way for building heuristics. We develop a deepneural network that learns heuristics over raw code, entirely without using codefeatures. The neural network simultaneously constructs appropriaterepresentations of the code and learns how best to optimize, removing the needfor manual feature creation. Further, we show that our neural nets can transferlearning from one optimization problem to another, improving the accuracy of newmodels, without the help of human experts.We compare the effectiveness of our automatically generated heuristics againstones with features hand-picked by experts. We examine two challenging tasks:predicting optimal mapping for heterogeneous parallelism and GPU threadcoarsening factors. In 89% of the cases, the quality of our fully automaticheuristics matches or surpasses that of state-of-the-art predictive models usinghand-crafted features, providing on average 14% and 12% more performance withno human effort expended on designing features.",
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"title": "End-to-End Deep Learning of Optimization Heuristics",
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},
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"paperAbstract": "The Resource Public Key Infrastructure (RPKI) binds IP address blocks to owners\u2019 public keys. RPKI enables routers to perform Route Origin Validation (ROV), thus preventing devastating attacks such as IP prefix hijacking. Yet, despite extensive effort, RPKI\u2019s deployment is frustratingly sluggish, leaving the Internet largely insecure. We tackle fundamental questions regarding today\u2019s RPKI\u2019s deployment and security: What is the adoption status of RPKI and ROV? What are the implications for global security of partial adoption? What are the root-causes for slow adoption? How can deployment be pushed forward? We address these questions through a combination of empirical analyses, a survey of over 100 network practitioners, and extensive simulations. Our main contributions include the following. We present the first study measuring ROV enforcement, revealing disappointingly low adoption at the core of the Internet. We show, in contrast, that without almost ubiquitous ROV adoption by large ISPs significant security benefits cannot be attained. We next expose a critical security vulnerability: about a third of RPKI authorizations issued for IP prefixes do not protect the prefix from hijacking attacks. We examine potential reasons for scarce adoption of RPKI and ROV, including human error in issuing RPKI certificates and inter-organization dependencies, and present recommendations for addressing these challenges.",
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"http://eprint.iacr.org/2016/1010",
"http://eprint.iacr.org/2016/1010.pdf",
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"title": "Are We There Yet? On RPKI's Deployment and Security",
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"doi": "10.1109/SP.2017.18",
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"paperAbstract": "In recent years, researchers have shown that unwanted web tracking is on the rise, as advertisers are trying to capitalize on users' online activity, using increasingly intrusive and sophisticated techniques. Among these, browser fingerprinting has received the most attention since it allows trackers to uniquely identify users despite the clearing of cookies and the use of a browser's private mode. In this paper, we investigate and quantify the fingerprintability of browser extensions, such as, AdBlock and Ghostery. We show that an extension's organic activity in a page's DOM can be used to infer its presence, and develop XHound, the first fully automated system for fingerprinting browser extensions. By applying XHound to the 10,000 most popular Google Chrome extensions, we find that a significant fraction of popular browser extensions are fingerprintable and could thus be used to supplement existing fingerprinting methods. Moreover, by surveying the installed extensions of 854 users, we discover that many users tend to install different sets of fingerprintable browser extensions and could thus be uniquely, or near-uniquely identifiable by extension-based fingerprinting. We use XHound's results to build a proof-of-concept extension-fingerprinting script and show that trackers can fingerprint tens of extensions in just a few seconds. Finally, we describe why the fingerprinting of extensions is more intrusive than the fingerprinting of other browser and system properties, and sketch two different approaches towards defending against extension-based fingerprinting.",
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"title": "XHOUND: Quantifying the Fingerprintability of Browser Extensions",
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"doi": "10.1109/HPCC-SmartCity-DSS.2017.58",
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"paperAbstract": "Parametric polymorphism is one of the linchpins of modern typed programming, but it comes with a real performance penalty. We describe this penalty; offer a principled way to reason about it (kinds as calling conventions); and propose levity polymorphism. This new form of polymorphism allows abstractions over calling conventions; we detail and verify restrictions that are necessary in order to compile levity-polymorphic functions. Levity polymorphism has created new opportunities in Haskell, including the ability to generalize nearly half of the type classes in GHC's standard library.",
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"title": "Levity polymorphism",
"venue": "PLDI",
"year": 2017
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"authors": [
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"name": "Costas Busch"
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"name": "Maurice Herlihy"
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"name": "Miroslav Popovic"
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"name": "Gokarna Sharma"
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"paperAbstract": "We investigate scheduling algorithms for distributed transactional memory systems where transactions residing at nodes of a communication graph operate on shared, mobile objects. A transaction requests the objects it needs, executes once those objects have been assembled, and then possibly forwards those objects to other waiting transactions. Minimizing execution time in this model is known to be NP-hard for arbitrary communication graphs, and also hard to approximate within any factor smaller than the size of the graph. Nevertheless, networks on chips, multi-core systems, and clusters are not arbitrary. Here, we explore efficient execution schedules in specialized graphs likely to arise in practice: Clique, Line, Grid, Cluster, Hypercube, Butterfly, and Star. In most cases, when individual transactions request k objects, we obtain solutions close to a factor O(k) from optimal, yielding near-optimal solutions for constant k. These execution times approximate the TSP tour lengths of the objects in the graph. We show that for general networks, even for two objects (k=2), it is impossible to obtain execution time close to the objects' optimal TSP tour lengths, which is why it is useful to consider more realistic network models. To our knowledge, this is the first attempt to obtain provably fast schedules for distributed transactional memory.",
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"title": "Fast Scheduling in Distributed Transactional Memory",
"venue": "SPAA",
"year": 2017
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"authors": [
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"name": "Gottfried Herold"
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"name": "Max Hoffmann"
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"name": "Michael Kloo\u00df"
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"name": "Carla R\u00e0fols"
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"name": "Andy Rupp"
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],
"doi": "10.1145/3133956.3134068",
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"paperAbstract": "Bilinear groups form the algebraic setting for a multitude of important cryptographic protocols including anonymous credentials, e-cash, e-voting, e-coupon, and loyalty systems. It is typical of such crypto protocols that participating parties need to repeatedly verify that certain equations over bilinear groups are satisfied, e.g., to check that computed signatures are valid, commitments can be opened, or non-interactive zero-knowledge proofs verify correctly. Depending on the form and number of equations this part can quickly become a performance bottleneck due to the costly evaluation of the bilinear map.\n To ease this burden on the verifier, batch verification techniques have been proposed that allow to combine and check multiple equations probabilistically using less operations than checking each equation individually.\n In this work, we revisit the batch verification problem and existing standard techniques. We introduce a new technique which, in contrast to previous work, enables us to fully exploit the structure of certain systems of equations. Equations of the appropriate form naturally appear in many protocols, e.g., due to the use of Groth-Sahai proofs.\n The beauty of our technique is that the underlying idea is pretty simple: we observe that many systems of equations can alternatively be viewed as a single equation of products of polynomials for which probabilistic polynomial identity testing following Schwartz-Zippel can be applied. Comparisons show that our approach can lead to significant improvements in terms of the number of pairing evaluations. Indeed, for the BeleniosRF voting system presented at CCS 2016, we can reduce the number of pairings (required for ballot verification) from <i>4k+140</i>, as originally reported by Chaidos et al., to <i>k+7</i>. As our implementation and benchmarks demonstrate, this may reduce the verification runtime to only 5% to 13% of the original runtime.",
"pdfUrls": [
"https://eprint.iacr.org/2017/802.pdf",
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"http://doi.acm.org/10.1145/3133956.3134068"
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"title": "New Techniques for Structural Batch Verification in Bilinear Groups with Applications to Groth-Sahai Proofs",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
"38d3ff3b608a334b12400b031c7cc483923bc629": {
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"name": "Jonathan Lifflander"
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{
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"name": "Sriram Krishnamoorthy"
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"doi": "10.1145/3062341.3062385",
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"paperAbstract": "We present an approach to optimize the cache locality for recursive programs by dynamically splicing---recursively interleaving---the execution of distinct function invocations. By utilizing data effect annotations, we identify concurrency and data reuse opportunities across function invocations and interleave them to reduce reuse distance. We present algorithms that efficiently track effects in recursive programs, detect interference and dependencies, and interleave execution of function invocations using user-level (non-kernel) lightweight threads. To enable multi-core execution, a program is parallelized using a nested fork/join programming model. Our cache optimization strategy is designed to work in the context of a random work stealing scheduler. We present an implementation using the MIT Cilk framework that demonstrates significant improvements in sequential and parallel performance, competitive with a state-of-the-art compile-time optimizer for loop programs and a domain-specific optimizer for stencil programs.",
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"http://doi.acm.org/10.1145/3062341.3062385"
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"sources": [
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],
"title": "Cache locality optimization for recursive programs",
"venue": "PLDI",
"year": 2017
},
"38f84c67235d17ea9a85136dacf57c14b5b3f5b0": {
"authors": [
{
"ids": [
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],
"name": "Qilong Gu"
},
{
"ids": [
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],
"name": "Joshua Trzasko"
},
{
"ids": [
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],
"name": "Arindam Banerjee"
}
],
"doi": "10.1109/ICDM.2017.23",
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"id": "38f84c67235d17ea9a85136dacf57c14b5b3f5b0",
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"paperAbstract": "We consider the problem of modeling data matrices with locally low rank (LLR) structure, a generalization of the popular low rank structure widely used in a variety of real world application domains ranging from medical imaging to recommendation systems. While LLR modeling has been found to be promising in real world application domains, limited progress has been made on the design of scalable algorithms for such structures. In this paper, we consider a convex relaxation of LLR structure, and propose an efficient algorithm based on dual projected gradient descent (D-PGD) for computing the proximal operator. While the original problem is non-smooth, so that primal (sub)gradient algorithms will be slow, we show that the proposed D-PGD algorithm has geometrical convergence rate. We present several practical ways to further speed up the computations, including acceleration and approximate SVD computations. With experiments on both synthetic and real data from MRI (magnetic resonance imaging) denoising, we illustrate the superior performance of the proposed D-PGD algorithm compared to several baselines.",
"pdfUrls": [
"http://www-users.cs.umn.edu/~banerjee/papers/17/llr-main.pdf",
"http://doi.ieeecomputersociety.org/10.1109/ICDM.2017.23"
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"title": "Scalable Algorithms for Locally Low-Rank Matrix Modeling",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
"year": 2017
},
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"authors": [
{
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"name": "Anshuman Dutt"
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{
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"name": "Vivek R. Narasayya"
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{
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"name": "Surajit Chaudhuri"
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"doi": "10.1145/3035918.3064040",
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"paperAbstract": "Parametric query optimization (PQO) deals with the problem of finding and reusing a relatively small number of plans that can achieve good plan quality across multiple instances of a parameterized query. An ideal solution to PQO would process query instances <i>online</i> and ensure (a) tight, bounded cost sub-optimality for each instance, (b) low optimization overheads, and (c) only a small number of plans need to be stored. Existing solutions to online PQO however, fall short on at least one of the above metrics. We propose a plan re-costing based approach that enables us to perform well on all three metrics. We empirically show the effectiveness of our technique on industry benchmark and real-world query workloads with our modified version of the Microsoft SQL Server query optimizer.",
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"title": "Leveraging Re-costing for Online Optimization of Parameterized Queries with Guarantees",
"venue": "SIGMOD Conference",
"year": 2017
},
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"name": "Savvas Zannettou"
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"name": "Tristan Caulfield"
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"name": "Emiliano De Cristofaro"
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"name": "Nicolas Kourtellis"
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"name": "Ilias Leontiadis"
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"name": "Gianluca Stringhini"
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"doi": "10.1145/3131365.3131390",
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"paperAbstract": "As the number and the diversity of news outlets on the Web grows, so does the opportunity for \"alternative\" sources of information to emerge. Using large social networks like Twitter and Facebook, misleading, false, or agenda-driven information can quickly and seamlessly spread online, deceiving people or influencing their opinions. Also, the increased engagement of tightly knit communities, such as Reddit and 4chan, further compounds the problem, as their users initiate and propagate alternative information, not only within their own communities, but also to different ones as well as various social media. In fact, these platforms have become an important piece of the modern information ecosystem, which, thus far, has not been studied as a whole.\n In this paper, we begin to fill this gap by studying mainstream and alternative news shared on Twitter, Reddit, and 4chan. By analyzing millions of posts around several axes, we measure how mainstream and alternative news flows between these platforms. Our results indicate that alt-right communities within 4chan and Reddit can have a surprising level of influence on Twitter, providing evidence that \"fringe\" communities often succeed in spreading alternative news to mainstream social networks and the greater Web.",
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"http://www0.cs.ucl.ac.uk/staff/G.Stringhini/papers/centipede-IMC2017.pdf",
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"https://conferences.sigcomm.org/imc/2017/papers/imc17-final145.pdf",
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"title": "The web centipede: understanding how web communities influence each other through the lens of mainstream and alternative news sources",
"venue": "IMC",
"year": 2017
},
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"name": "Janne H. Korhonen"
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"name": "Christopher Purcell"
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"name": "Jukka Suomela"
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"name": "Przemyslaw Uznanski"
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"paperAbstract": "LCLs or locally checkable labelling problems (e.g. maximal independent set, maximal matching, and vertex colouring) in the LOCAL model of computation are very well-understood in cycles (toroidal 1-dimensional grids): every problem has a complexity of O(1), \u0398(log\u2217 n), or \u0398(n), and the design of optimal algorithms can be fully automated. This work develops the complexity theory of LCL problems for toroidal 2-dimensional grids. The complexity classes are the same as in the 1-dimensional case: O(1), \u0398(log\u2217 n), and \u0398(n). However, given an LCL problem it is undecidable whether its complexity is \u0398(log\u2217 n) or \u0398(n) in 2-dimensional grids. Nevertheless, if we correctly guess that the complexity of a problem is \u0398(log\u2217 n), we can completely automate the design of optimal algorithms. For any problem we can find an algorithm that is of a normal form A\u2032 \u25e6 Sk, where A\u2032 is a finite function, Sk is an algorithm for finding a maximal independent set in kth power of the grid, and k is a constant. Finally, partially with the help of automated design tools, we classify the complexity of several concrete LCL problems related to colourings and orientations. ar X iv :1 70 2. 05 45 6v 2 [ cs .D C ] 2 4 M ay 2 01 7",
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"http://arxiv.org/abs/1702.05456",
"http://users.ics.aalto.fi/suomela/doc/grid-lcl.pdf",
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"https://arxiv.org/pdf/1702.05456v2.pdf",
"http://www.tik.ee.ethz.ch/file/081e2ca2a419c214ac2b73c940e5ff6a/grid-lcl.pdf",
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"title": "LCL Problems on Grids",
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"year": 2017
},
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"name": "Aaron Johnson"
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"doi": "10.1145/3133956.3134034",
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"paperAbstract": "This paper introduces a cryptographic protocol for efficiently aggregating a count of unique items across a set of data parties privately - that is, without exposing any information other than the count. Our protocol allows for more secure and useful statistics gathering in privacy-preserving distributed systems such as anonymity networks; for example, it allows operators of anonymity networks such as Tor to securely answer the questions: <i>how many unique users are using the distributed service?</i> and <i>how many hidden services are being accessed?</i>. We formally prove the correctness and security of our protocol in the Universal Composability framework against an active adversary that compromises all but one of the aggregation parties. We also show that the protocol provides security against adaptive corruption of the data parties, which prevents them from being victims of targeted compromise. To ensure <i>safe</i> measurements, we also show how the output can satisfy differential privacy.\n We present a proof-of-concept implementation of the private set-union cardinality protocol (PSC) and use it to demonstrate that PSC operates with low computational overhead and reasonable bandwidth. In particular, for reasonable deployment sizes, the protocol run at timescales smaller than the typical measurement period would be and thus is suitable for distributed measurement.",
"pdfUrls": [
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"title": "Distributed Measurement with Private Set-Union Cardinality",
"venue": "CCS",
"year": 2017
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"paperAbstract": "In Total Store Order memory consistency (TSO), loads can be speculatively reordered to improve performance. If a load-load reordering is seen by other cores, speculative loads must be squashed and re-executed. In architectures with an unordered interconnection network and directory coherence, this has been the established view for decades. We show, for the first time, that it is not necessary to squash and re-execute speculatively reordered loads in TSO when their reordering is seen. Instead, the reordering can be hidden form other cores by the coherence protocol. The implication is that we can irrevocably bind speculative loads. This allows us to commit reordered loads out-of-order without having to wait (for the loads to become non-speculative) or without having to checkpoint committed state (and rollback if needed), just to ensure correctness in the rare case of some core seeing the reordering. We show that by exposing a reordering to the coherence layer and by appropriately modifying a typical directory protocol we can successfully hide load-load reordering without perceptible performance cost and without deadlock. Our solution is cost-effective and increases the performance of out-of-order commit by a sizable margin, compared to the base case where memory operations are not allowed to commit if the consistency model could be violated.",
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"http://doi.acm.org/10.1145/3079856.3080220"
],
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"title": "Non-speculative load-load reordering in TSO",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
"398e280e5afaeab753ca567eb137e31bd7f55e9b": {
"authors": [
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"name": "Tyler Szepesi"
},
{
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"name": "Benjamin Cassell"
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{
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"name": "Tim Brecht"
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{
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"name": "Derek L. Eager"
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{
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"name": "Jim Summers"
},
{
"ids": [
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],
"name": "Bernard Wong"
}
],
"doi": "10.1145/3030207.3030231",
"doiUrl": "https://doi.org/10.1145/3030207.3030231",
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"FreeBSD",
"Hypertext Transfer Protocol",
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"paperAbstract": "Video streaming applications generate a large fraction of Internet traffic. Much of this content is delivered over HTTP using standard web servers. Unlike other types of web workloads, HTTP video streaming workloads are typically disk bound, and therefore an important problem is that of optimizing disk access.\n In this paper we design, implement and evaluate Libception, an application-level shim library that implements techniques for improving disk I/O efficiency. Web servers can achieve the benefits of these techniques simply by linking with Libception, without the need to modify source code. In contrast to making kernel changes or attempting to optimize kernel tuning, Libception provides a portable and relatively simple setting in which techniques for optimizing I/O in HTTP video streaming servers can be implemented and evaluated.\n We report experimental results evaluating the efficacy of the aggressive prefetching and disk I/O serialization techniques currently implemented in Libception, for three web servers (Apache, nginx and the userver) and two operating systems (FreeBSD, Linux). We find that on FreeBSD, video streaming throughput with all three web servers can be doubled by linking with Libception. On Linux, performance similar to that provided with Libception was eventually obtained by examining the kernel source to understand and tune kernel parameters. With the default kernel parameter settings, however, and regardless of which Linux disk scheduler is selected, we find that use of Libception can approximately double throughput. We find that both aggressive prefetching and serialization are necessary to achieve these benefits.",
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"https://www.cs.usask.ca/faculty/eager/icpe-libception-2017.pdf",
"http://doi.acm.org/10.1145/3030207.3030231"
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"title": "Using Libception to Understand and Improve HTTP Streaming Video Server Throughput",
"venue": "ICPE",
"year": 2017
},
"39a1fb4cb03f5fada6e485a1d0dc6a723a17ed22": {
"authors": [
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"name": "Terry Ching-Hsiang Hsu"
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"name": "Helge Br\u00fcgner"
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"name": "Indrajit Roy"
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"name": "Kimberly Keeton"
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{
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"name": "Patrick Th. Eugster"
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],
"doi": "10.1145/3064176.3064204",
"doiUrl": "https://doi.org/10.1145/3064176.3064204",
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"paperAbstract": "Non-volatile memory technologies, such as memristor and phase-change memory, will allow programs to persist data with regular memory instructions. Liberated from the overhead to serialize and deserialize data to storage devices, programs can aim for high performance and still be crash fault-tolerant. Unfortunately, to leverage non-volatile memory, existing systems require hardware changes or extensive program modifications.\n We present NVthreads, a programming model and runtime that adds persistence to existing multi-threaded C/C++ programs. NVthreads is a drop-in replacement for the pthreads library and requires only tens of lines of program changes to leverage non-volatile memory. NVthreads infers consistent states via synchronization points, uses the process memory to buffer uncommitted changes, and logs writes to ensure a program's data is recoverable even after a crash. NVthreads' page level mechanisms result in good performance: applications that use NVthreads can be more than 2× faster than state-of-the-art systems that favor fine-grained tracking of writes. After a failure, iterative applications that use NVthreads gain speedups by resuming execution.",
"pdfUrls": [
"http://nvmw.ucsd.edu/nvmw18-program/unzip/current/nvmw2018-final60.pdf",
"http://nvmw.ucsd.edu/nvmw18-program/unzip/current/nvmw2018-paper60-presentations-slides.pdf",
"http://doi.acm.org/10.1145/3064176.3064204"
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"title": "NVthreads: Practical Persistence for Multi-threaded Applications",
"venue": "EuroSys",
"year": 2017
},
"39a68607cfc1cf0acdfcdbea208b8f7cc78041ac": {
"authors": [
{
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"name": "David Eppstein"
},
{
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],
"name": "Michael T. Goodrich"
},
{
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],
"name": "Michael Mitzenmacher"
},
{
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"name": "Manuel R. Torres"
}
],
"doi": "10.1145/3034786.3056115",
"doiUrl": "https://doi.org/10.1145/3034786.3056115",
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"paperAbstract": "We introduce new dynamic set intersection data structures, which we call <i><b>2-3 cuckoo</b></i> filters and hash tables. These structures differ from the standard cuckoo hash tables and cuckoo filters in that they choose two out of three locations to store each item, instead of one out of two, ensuring that any item in an intersection of two structures will have at least one common location in both structures. We demonstrate the utility of these structures by using them in improved algorithms for listing triangles and answering set intersection queries in internal or external memory. For a graph <i>G</i> of <i>n</i> vertices and <i>m</i> edges, our internal-memory triangle listing algorithm runs in <i>O</i>(<i>m</i>⌈(α(<i>G</i>)log <i>w</i>)/<i>w</i>⌉ + <i>k</i>) expected time, where α(<i>G</i>) is the arboricity of <i>G, w</i> is the number of bits in a machine word, and <i>k</i> is the number of output triangles. Our external-memory algorithm uses <i>O</i>(sort(<i>n</i>,α(<i>G</i>))+ sort(<i>m</i>⌈(α(<i>G</i>)log <i>w</i>)/<i>w</i>⌉) + sort(<i>k</i>)) expected number of I/Os.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3034786.3056115",
"http://www.ics.uci.edu/~goodrich/teach/graph/notes/GoodrichPODS.pdf"
],
"pmid": "",
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"s2Url": "https://semanticscholar.org/paper/39a68607cfc1cf0acdfcdbea208b8f7cc78041ac",
"sources": [
"DBLP"
],
"title": "2-3 Cuckoo Filters for Faster Triangle Listing and Set Intersection",
"venue": "PODS",
"year": 2017
},
"39d4e7c9f4342104ffc1931d4aa1817646ca3ab5": {
"authors": [
{
"ids": [
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],
"name": "Hiroshi Sasaki"
},
{
"ids": [
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],
"name": "Fang-Hsiang Su"
},
{
"ids": [
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],
"name": "Teruo Tanimoto"
},
{
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],
"name": "Simha Sethumadhavan"
}
],
"doi": "10.1109/IISWC.2017.8167771",
"doiUrl": "https://doi.org/10.1109/IISWC.2017.8167771",
"entities": [
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"Customer relationship management",
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"journalName": "2017 IEEE International Symposium on Workload Characterization (IISWC)",
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"paperAbstract": "Designing and optimizing computer systems require deep understanding of the underlying system. Historically many important observations that led to the development of essential hardware and software optimizations were driven by empirical studies of program behavior. In this paper we report an interesting property of dynamic program execution by viewing it as a changing (or social) network. In a program social network, two instructions are friends if there is a producer-consumer relationship between them. One prominent result is that the outdegree of instructions follow heavy tails or power law distributions, i.e., a few instructions produce values for many instructions while most instructions do so for very few instructions. In other words, the number of instruction dependencies is highly skewed. In this paper we investigate this curious phenomenon. By analyzing a large set of workloads under different compilers, compilation options, ISAs and inputs we find that the dependence skew is widespread, suggesting that it is fundamental. We also observe that the skew is fractal across time and space. Finally, we describe conditions under which skew emerges within programs and provide evidence that suggests that the heavy-tailed distributions are a unique program property.",
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"paperAbstract": "Storage-class memory (SCM) combines the benefits of a solid-state memory, such as high-performance and robustness, with the archival capabilities and low cost of conventional hard-disk magnetic storage. Among candidate solid-state nonvolatile memory technologies that could potentially be used to construct SCM, flash memory is a well-established technology and have been widely used in commercially available SCM incarnations. Flash-based SCM enables much better tradeoffs between performance, space and power than disk-based systems. However, write endurance is a significant challenge for a flash-based SCM (each act of writing a bit may slightly damage a cell, so one flash cell can be written 10^4-10^5 times, depending on the flash technology, before it becomes unusable). This is a well-documented problem and has received a lot of attention by manufactures that are using some combination of write reduction and wear-leveling techniques for achieving longer lifetime. In an effort to improve flash lifetime, first, by quantifying data longevity in an SCM, we show that a majority of the data stored in a solid-state SCM do not require long retention times provided by flash memory (i.e., up to 10 years in modern devices); second, by exploiting retention time relaxation, we propose a novel mechanism, called Dense-SLC (D-SLC), which enables us perform multiple writes into a cell during each erase cycle for lifetime extension; and finally, we discuss the required changes in the flash management software (FTL) in order to use D-SLC mechanism for extending the lifetime of the solid-state part of an SCM. Using an extensive simulation-based analysis of an SLC flash-based SCM, we demonstrate that D-SLC is able to significantly improve device lifetime (between 5.1X and 8.6X) with no performance overhead and also very small changes at the FTL software.",
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"title": "Exploiting Data Longevity for Enhancing the Lifetime of Flash-based Storage Class Memory",
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"paperAbstract": "Almost all real-world social networks are dynamic and evolving with time, where new links may form and old links may drop, largely determined by the homophily of social actors (i.e., nodes in the network). Meanwhile, (latent) properties of social actors, such as their opinions, are changing along the time, partially due to social influence received from the network, which will in turn affect the network structure. Social network evolution and node property migration are usually treated as two orthogonal problems, and have been studied separately. In this paper, we propose a co-evolution model that closes the loop by modeling the two phenomena together, which contains two major components: (1) a network generative model when the node property is known; and (2) a property migration model when the social network structure is known. Simulation shows that our model has several nice properties: (1) it can model a broad range of phenomena such as opinion convergence (i.e., herding) and community-based opinion divergence; and (2) it allows to control the evolution via a set of factors such as social influence scope, opinion leader, and noise level. Finally, the usefulness of our model is demonstrated by an application of co-sponsorship prediction for legislative bills in Congress, which outperforms several state-of-the-art baselines.",
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"title": "The Co-Evolution Model for Social Network Evolving and Opinion Migration",
"venue": "KDD",
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"paperAbstract": "We consider the edge partitioning problem that partitions the edges of an input graph into multiple balanced components, while minimizing the total number of vertices replicated (one vertex might appear in more than one partition). This problem is critical in minimizing communication costs and running time for several large-scale distributed graph computation platforms (e.g., PowerGraph, Spark GraphX). We first prove that this problem is NP-hard, and then present a new partitioning heuristic with polynomial running time. We provide a worst-case upper bound of replication factor for our heuristic on general graphs. To our knowledge, we are the first to provide such bound for edge partitioning algorithms on general graphs. Applying this bound to random power-law graphs greatly improves the previous bounds of expected replication factor. Extensive experiments demonstrated that our partitioning algorithm consistently produces much smaller replication factors on various benchmark data sets than the state-of-the-art. When deployed in the production graph engine, PowerGraph, in average it reduces replication factor, communication, and running time by 54%, 66%, and 21%, respectively.",
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"title": "Graph Edge Partitioning via Neighborhood Heuristic",
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"paperAbstract": "Similarity search is a critical primitive for a wide variety of applications including natural language processing, content-based search, machine learning, computer vision, databases, robotics, and recommendation systems. At its core, similarity search is implemented using the k-nearest neighbors (kNN) algorithm, where computation consists of highly parallel distance calculations and a global top-k sort. In contemporary von-Neumann architectures, kNN is bottlenecked by data movement which limits throughput and latency. In this paper, we present and evaluate a novel automata-based algorithm for kNN on the Micron Automata Processor (AP), which is a non-von Neumann near-data processing architecture. By employing near-data processing, the AP minimizes the data movement bottleneck and is able to achieve better performance. Unlike prior work in the automata processing space, our work combines temporal encodings with automata design to augment the space of applications for the AP. We evaluate our design's performance on the AP and compare to state-of-the-art CPU, GPU, and FPGA implementations; we show that the current generation of AP hardware can achieve over 50x speedup over CPUs while maintaining competitive energy efficiency gains. We also propose several automata optimization techniques and simple architectural extensions that highlight the potential of the AP hardware.",
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"https://export.arxiv.org/pdf/1608.03175",
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"title": "Similarity Search on Automata Processors",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"authors": [
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"name": "Zhongjie Wang"
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"name": "Yue Cao"
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"name": "Zhiyun Qian"
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"name": "Chengyu Song"
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"name": "Srikanth V. Krishnamurthy"
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],
"doi": "10.1145/3131365.3131374",
"doiUrl": "https://doi.org/10.1145/3131365.3131374",
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"paperAbstract": "Understanding the behaviors of, and evading state-level Internet-scale censorship systems such as the Great Firewall (GFW) of China, has emerged as a research problem of great interest. One line of evasion is the development of techniques that leverage the possibility that the TCP state maintained on the GFW may not represent the state at end-hosts. In this paper we undertake, arguably, the most extensive measurement study on TCP-level GFW evasion techniques, with several vantage points within and outside China, and with clients subscribed to multiple ISPs. We find that the state-of-the art evasion techniques are no longer very effective on the GFW. Our study further reveals that the primary reason that causes these failures is the evolution of GFW over time. In addition, other factors such as the presence of middleboxes on the route from the client to the server also contribute to previously unexpected behaviors.\n Our measurement study leads us to new understandings of the GFW and new evasion techniques. Evaluations of our new evasion strategies show that our new techniques provide much higher success rates of (compared to prior schemes) ≈ 90% or higher. Our results further validate our new understandings of the GFW's evolved behaviors. We also develop a measurement-driven tool INTANG, that systematically looks for and finds the best strategy that works with a server and network path. Our measurements show that INTANG can yield near perfect evasion rates and is extremely effective in aiding various protocols such as HTTP, DNS over TCP, and Tor in evading the GFW.",
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"http://www.cs.ucr.edu/~krish/imc17.pdf"
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"title": "Your state is not mine: a closer look at evading stateful internet censorship",
"venue": "IMC",
"year": 2017
},
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"name": "Huan Feng"
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"name": "Kassem Fawaz"
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"name": "Kang G. Shin"
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"doi": "10.1145/3117811.3117823",
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"paperAbstract": "Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the current trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Alexa, Siri, and Google Now, have become our everyday fixtures, especially when/where touch interfaces are inconvenient or even dangerous to use, such as driving or exercising. The open nature of the voice channel makes voice assistants difficult to secure, and hence exposed to various threats as demonstrated by security researchers. To defend against these threats, we present VAuth, the first system that provides continuous authentication for voice assistants. VAuth is designed to fit in widely-adopted wearable devices, such as eyeglasses, earphones/buds and necklaces, where it collects the body-surface vibrations of the user and matches it with the speech signal received by the voice assistant's microphone. VAuth guarantees the voice assistant to execute only the commands that originate from the voice of the owner. We have evaluated VAuth with 18 users and 30 voice commands and find it to achieve 97% detection accuracy and less than 0.1% false positive rate, regardless of VAuth's position on the body and the user's language, accent or mobility. VAuth successfully thwarts various practical attacks, such as replay attacks, mangled voice attacks, or impersonation attacks. It also incurs low energy and latency overheads and is compatible with most voice assistants.",
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"https://arxiv.org/pdf/1701.04507v1.pdf",
"https://kabru.eecs.umich.edu/wordpress/wp-content/uploads/continuous-authentication-voice.pdf",
"http://arxiv.org/abs/1701.04507"
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"title": "Continuous Authentication for Voice Assistants",
"venue": "MobiCom",
"year": 2017
},
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"name": "Hao Wang"
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"name": "Xuebin Zhang"
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{
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"name": "Ning Zheng"
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"name": "Shafa Dahandeh"
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"paperAbstract": "This paper presents a simple yet effective design solution to facilitate technology scaling for hard disk drives (HDDs) being deployed in data centers. Emerging magnetic recording technologies improve storage areal density mainly through reducing the track pitch, which however makes HDDs subject to higher read retry rates. More frequent HDD read retries could cause intolerable tail latency for large-scale systems such as data centers. To reduce the occurrence of costly read retry, one intuitive solution is to apply erasure coding locally on each HDD or JBOD (just a bunch of disks). To be practically viable, local erasure coding must have very low coding redundancy, which demands very long codeword length (e.g., one codeword spans hundreds of 4kB sectors) and hence large file size. This makes local erasure coding mainly suitable for data center applications. This paper contends that local erasure coding should be implemented transparently within filesystems, and accordingly presents a basic design framework and elaborates on important design issues. Meanwhile, this paper derives the mathematical formulations for estimating its effect on reducing HDD read tail latency. Using Reed-Solomon (RS) based erasure codes as test vehicles, we carried out detailed analysis and experiments to evaluate its implementation feasibility and effectiveness. We integrated the developed design solution into ext4 to further demonstrate its feasibility and quantitatively measure its impact on average speed performance of various big data benchmarks.",
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"title": "Facilitating Magnetic Recording Technology Scaling for Data Center Hard Disk Drives through Filesystem-Level Transparent Local Erasure Coding",
"venue": "FAST",
"year": 2017
},
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"authors": [
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"name": "Aditya Sundarrajan"
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"name": "Mingdong Feng"
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{
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"name": "Mangesh Kasbekar"
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{
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"name": "Ramesh K. Sitaraman"
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"doi": "10.1145/3143361.3143368",
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"paperAbstract": "Modern CDNs cache and deliver a highly-diverse set of traffic classes, including web pages, images, videos and software downloads. It is economically advantageous for a CDN to cache and deliver all traffic classes using a shared distributed cache server infrastructure. However, such sharing of cache resources across multiple traffic classes poses significant cache provisioning challenges that are the focus of this paper.\n Managing a vast shared caching infrastructure requires careful modeling of user request sequences for each traffic class. Using extensive traces from Akamai's CDN, we show how each traffic class has drastically different object access patterns, object size distributions, and cache resource requirements. We introduce the notion of a footprint descriptor that is a succinct representation of the cache requirements of a request sequence. Leveraging novel connections to Fourier analysis, we develop a footprint descriptor calculus that allows us to predict the cache requirements when different traffic classes are added, subtracted and scaled to within a prediction error of 2.5%. We integrated our footprint calculus in the cache provisioning operations of the production CDN and show how it is used to solve key challenges in cache sizing, traffic mixing, and cache partitioning.",
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"title": "Footprint Descriptors: Theory and Practice of Cache Provisioning in a Global CDN",
"venue": "CoNEXT",
"year": 2017
},
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"authors": [
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"name": "Panagiotis Papadopoulos"
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"name": "Pablo Rodriguez"
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"name": "Nicolas Kourtellis"
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"title": "Contextual Motifs: Increasing the Utility of Motifs using Contextual Data",
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"paperAbstract": "Page protection is often used to achieve memory access monitoring in many applications, dealing with program-analysis, checkpoint-based failure recovery, and garbage collection in managed runtime systems. Typically, low overhead access monitoring is limited by the relatively large page-level granularity of memory management unit hardware support for virtual memory protection. In this paper, we improve upon traditional page-level mechanisms by additionally using hardware support for virtualization in order to achieve fine and flexible granularities that can be smaller than a page. We first introduce a memory allocator based on page protection that can achieve fine-grained monitoring. Second, we explain how virtualization hardware support can be used to achieve dynamic adjustment of the monitoring granularity. In all, we propose a process-level virtual machine to achieve dynamic and fine-grained monitoring. Any application can run on our process-level virtual machine without modification. Experimental results for an incremental checkpoint tool provide a use-case to demonstrate our work. Comparing with traditional page-based checkpoint, our work can effectively reduce the amount of checkpoint data and improve performance.",
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"title": "Flexible Page-level Memory Access Monitoring Based on Virtualization Hardware",
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"paperAbstract": "In cache-based side channel attacks, a spy that shares a cache with a victim probes cache locations to extract information on the victim's access patterns. For example, in evict+reload, the spy repeatedly evicts and then reloads a probe address, checking if the victim has accessed the address in between the two operations. While there are many proposals to combat these cache attacks, they all have limitations: they either hurt performance, require programmer intervention, or can only defend against some types of attacks.\n This paper makes the following observation for an environment with an inclusive cache hierarchy: when the spy evicts the probe address from the shared cache, the address will also be evicted from the private cache of the victim process, creating an inclusion victim. Consequently, to disable cache attacks, this paper proposes to alter the line replacement algorithm of the shared cache, to prevent a process from creating inclusion victims in the caches of cores running other processes. By enforcing this rule, the spy cannot evict the probe address from the shared cache and, hence, cannot glimpse any information on the victim's access patterns. We call our proposal SHARP (Secure Hierarchy-Aware cache Replacement Policy). SHARP efficiently defends against all existing cross-core shared-cache attacks, needs only minimal hardware modifications, and requires no code modifications. We implement SHARP in a cycle-level full-system simulator. We show that it protects against real-world attacks, and that it introduces negligible average performance degradation.",
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"http://doi.acm.org/10.1145/3079856.3080222"
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"title": "Secure Hierarchy-Aware Cache Replacement Policy (SHARP): Defending Against Cache-Based Side Channel Atacks",
"venue": "ISCA",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Shuang Song"
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{
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"name": "Yizhen Wang"
},
{
"ids": [
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"name": "Kamalika Chaudhuri"
}
],
"doi": "10.1145/3035918.3064025",
"doiUrl": "https://doi.org/10.1145/3035918.3064025",
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"Database",
"Differential privacy",
"Information privacy",
"Personally identifiable information",
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"paperAbstract": "Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical activity of single subjects across time. However, differential privacy, the current gold standard in data privacy, does not adequately address privacy issues in this kind of data.\n This work looks at a recent generalization of differential privacy, called Pufferfish, that can be used to address privacy in correlated data. The main challenge in applying Pufferfish is a lack of suitable mechanisms. We provide the first mechanism -- the Wasserstein Mechanism -- which applies to any general Pufferfish framework. Since this mechanism may be computationally inefficient, we provide an additional mechanism that applies to some practical cases such as physical activity measurements across time, and is computationally efficient. Our experimental evaluations indicate that this mechanism provides privacy and utility for synthetic as well as real data in two separate domains.",
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"http://doi.acm.org/10.1145/3035918.3064025",
"https://simons.berkeley.edu/sites/default/files/docs/5679/simonsuncertaintyincomputationworkshop.pdf",
"http://cseweb.ucsd.edu/~shs037/pufferfish_slides.pdf"
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"title": "Pufferfish Privacy Mechanisms for Correlated Data",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
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"name": "Martin L. Kersten"
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"name": "Lefteris Sidirourgos"
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"paperAbstract": "Big Data comes with huge challenges. Its volume and velocity makes handling, curating, and analytical processing a costly affair. Even to simply \u201clook at\u201d the data within an a priori defined budget and with a guaranteed interactive response time might be impossible to achieve. Commonly applied scale-out approaches will hit the technology and monetary wall soon, if not done so already. Likewise, blindly rejecting data when the channels are full, or reducing the data resolution at the source, might lead to loss of valuable observations. An army of well-educated database administrators or full software stack architects might deal with these challenges albeit at substantial cost. This calls for a mostly knobless DBMS with a fundamental change in database management. Data rotting has been proposed as a direction to find a solution [10, 11]. For the sake of storage management and responsiveness, it lets the DBMS semi-autonomously rot away data. Rotting is based on the systems own unwillingness to keep old data as easily accessible as fresh data. This paper sheds more light on the opportunities and potential impacts of this radical departure in data management. Specifically, we study the case where a DBMS selectively forgets tuples (by marking them inactive) under various amnesia scenarios and with different implementation strategies. Our ultimate goal is to use the findings of this study to morph an existing data management engine to serve demanding big data scientific applications with well-chosen built-in data amnesia algorithms.",
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"title": "A Database System with Amnesia",
"venue": "CIDR",
"year": 2017
},
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"authors": [
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"name": "Robert O'Callahan"
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"name": "Chris Jones"
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{
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"name": "Nathan Froyd"
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{
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"name": "Kyle Huey"
},
{
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],
"name": "Albert Noll"
},
{
"ids": [
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"name": "Nimrod Partush"
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],
"doi": "",
"doiUrl": "",
"entities": [
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"Debugger",
"Debugging",
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"Linux",
"Linux",
"Linux for PlayStation 2",
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"paperAbstract": "The ability to record and replay program executions with low overhead enables many applications, such as reverse-execution debugging, debugging of hard-toreproduce test failures, and \u201cblack box\u201d forensic analysis of failures in deployed systems. Existing record-andreplay approaches limit deployability by recording an entire virtual machine (heavyweight), modifying the OS kernel (adding deployment and maintenance costs), requiring pervasive code instrumentation (imposing significant performance and complexity overhead), or modifying compilers and runtime systems (limiting generality). We investigated whether it is possible to build a practical record-and-replay system avoiding all these issues. The answer turns out to be yes \u2014 if the CPU and operating system meet certain non-obvious constraints. Fortunately modern Intel CPUs, Linux kernels and user-space frameworks do meet these constraints, although this has only become true recently. With some novel optimizations, our system RR records and replays real-world lowparallelism workloads with low overhead, with an entirely user-space implementation, using stock hardware, compilers, runtimes and operating systems. RR forms the basis of an open-source reverse-execution debugger seeing significant use in practice. We present the design and implementation of RR, describe its performance on a variety of workloads, and identify constraints on hardware and operating system design required to support our approach.",
"pdfUrls": [
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"title": "Engineering Record and Replay for Deployability",
"venue": "USENIX Annual Technical Conference",
"year": 2017
},
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"name": "Yujie An"
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{
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"name": "Quentin F. Stout"
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"paperAbstract": "We give efficient algorithms to solve fundamental data movement problems on mesh-connected computers augmented with limited global bandwidth. Adding a small amount of global bandwidth makes a practical design that combines aspects of mesh and fully connected models to achieve the benefits of each. We give algorithms for sorting, finding the median, finding a spanning tree, and determining various graph properties to show that the small amount of global communication can significantly reduce the time, and that concurrent read helps even more. Most of these algorithms are optimal. We also extend our results to mesh-connected computers with row and column buses.",
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"title": "Optimal Algorithms for a Mesh-Connected Computer with Limited Additional Global Bandwidth",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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{
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{
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"doi": "10.1145/3077136.3080774",
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"paperAbstract": "Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may suggest attractive localities within close proximity to users' current location. Considering that many adults use more than three social networks simultaneously, it is reasonable to leverage on this rapidly growing multi-source social media data to boost venue recommendation performance. Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output. Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C<sup>3</sup>R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new cross-source user community detection approach, which utilizes both inter-source relationship and the ability of sources to complement each other. To fully utilize multi-source multi-view data, we process user-generated content by employing state-of-the-art text, image, and location processing techniques. Our experimental results demonstrate the superiority of our multi-source framework over state-of-the-art baselines and different data source combinations. In addition, we suggest a new approach for automatic construction of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge.",
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"title": "Cross-Domain Recommendation via Clustering on Multi-Layer Graphs",
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"name": "Zhiqiang Zuo"
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"name": "Guoqing Xu"
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"doi": "10.1145/3037697.3037744",
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"paperAbstract": "There is more than a decade-long history of using static analysis to find bugs in systems such as Linux. Most of the existing static analyses developed for these systems are simple checkers that find bugs based on pattern matching. Despite the presence of many sophisticated interprocedural analyses, few of them have been employed to improve checkers for systems code due to their complex implementations and poor scalability. In this paper, we revisit the scalability problem of interprocedural static analysis from a \"Big Data\" perspective. That is, we turn sophisticated code analysis into Big Data analytics and leverage novel data processing techniques to solve this traditional programming language problem. We develop Graspan, a disk-based parallel graph system that uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.\n We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations. Moreover, we show that these analyses can be used to augment the existing checkers; these augmented checkers uncovered 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.",
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"title": "Graspan: A Single-machine Disk-based Graph System for Interprocedural Static Analyses of Large-scale Systems Code",
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"year": 2017
},
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"name": "Wenfei Fan"
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"name": "Chunming Hu"
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"name": "Chao Tian"
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"doi": "10.1145/3035918.3035944",
"doiUrl": "https://doi.org/10.1145/3035918.3035944",
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"paperAbstract": "The incremental problem for a class Q of graph queries aims to compute, given a query <i>Q</i> in 'Q, graph <i>G</i>, output <i>Q(G)</i> and updates Δ <i>G</i> to <i>G</i> as input, changes Δ <i>O</i> to <i>Q(G)</i> such that <i>Q</i>(<i>G</i> ⊕ Δ <i>G</i>) = <i>Q</i>(<i>G</i>) ⊕ Δ <i>O</i>. It is called <i>bounded</i> if its cost can be expressed as a polynomial function in the sizes of <i>Q</i>, Δ <i>G</i> and Δ <i>O</i>. It is to reduce computations on possibly big <i>G</i> to small Δ <i>G</i> and Δ <i>O</i>. No matter how desirable, however, our first results are negative: for common graph queries such as graph traversal, connectivity, keyword search and pattern matching, their incremental problems are unbounded.\n In light of the negative results, we propose two characterizations for the effectiveness of incremental computation: (a) <i>localizable</i>, if its cost is decided by small neighbors of nodes in Δ <i>G</i> instead of the entire <i>G</i>; and (b) <i>bounded relative</i> to a batch algorithm <i>T</i>, if the cost is determined by the sizes of Δ <i>G</i> and changes to the affected area that is necessarily checked by <i>T</i>. We show that the incremental computations above are either localizable or relatively bounded, by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize batch algorithms by minimizing unnecessary recomputation. Using real-life graphs, we experimentally verify the effectiveness of our algorithms.",
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"title": "Incremental Graph Computations: Doable and Undoable",
"venue": "SIGMOD Conference",
"year": 2017
},
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"name": "Wenfei Fan"
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"name": "Ping Lu"
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"doi": "10.1145/3034786.3056114",
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"paperAbstract": "This paper proposes a class of dependencies for graphs, referred to as <i>graph entity dependencies</i> (<b>GEDs</b>). A <b>GED</b> is a combination of a graph pattern and an attribute dependency. In a uniform format, <b>GEDs</b> express graph functional dependencies with constant literals to catch inconsistencies, and keys carrying <b>id</b> literals to identify entities in a graph.\n We revise the chase for <b>GEDs</b> and prove its Church-Rosser property. We characterize <b>GED</b> satisfiability and implication, and establish the complexity of these problems and the validation problem for <b>GEDs</b>, in the presence and absence of constant literals and <b>id</b> literals. We also develop a sound and complete axiom system for finite implication of <b>GEDs</b>. In addition, we extend <b>GEDs</b> with built-in predicates or disjunctions, to strike a balance between the expressive power and complexity. We settle the complexity of the satisfiability, implication and validation problems for the extensions.",
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"http://homepages.inf.ed.ac.uk/wenfei/papers/pods17.pdf"
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"title": "Dependencies for Graphs",
"venue": "PODS",
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"paperAbstract": "\u0091e rapid adoption of smartphones with di\u0082erent types of advanced sensors has led to an increasing trend in the usage of mobile crowdsensing applications, e.g., to create hyperlocal weather maps. However, the high energy consumption of crowdsensing, chie\u0083y due to expensive network communication, has been found to be detrimental to the wide-spread adoption. We propose a framework, called Sense-Aid, that can provide energy-e\u0081cient mobile crowdsensing service, coexisting with the cellular network. \u0091ere are two key innovations in Sense-Aid beyond prior work (Piggyback Crowdsensing-Sensys13)\u2014the middleware running on the cellular network edge to orchestrate multiple devices present in geographical proximity to suppress redundant data collection and communication. It understands the state of each device (radio state, ba\u008bery state, etc.) to decide which ones should be selected for crowdsensing activities at any point in time. It also provides a simple programming abstraction to help with the development of crowdsensing applications. We show the bene\u0080t of Sense-Aid by conducting a user study consisting of 60 students in our campus, compared to a baseline periodic data collection method and Piggyback Crowdsensing. We \u0080nd that energy saving is 93.3% for Sense-Aid compared with Piggyback Crowdsensing in a representative case which requires 2 devices to provide barometric values within an area of a circle whose radius is 1 kilometer and requires periodic data collection every 5 minutes for a 90-minute test. \u0091e selection algorithm of Sense-Aid also ensures reasonable fairness in the use of the di\u0082erent devices.",
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"title": "Sense-Aid: A Framework for Enabling Network as a Service for Participatory Sensing",
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"year": 2017
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"doi": "10.1145/3127479.3132244",
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"paperAbstract": "In a cloud data center, a single physical machine simultaneously executes dozens of highly heterogeneous tasks. Such colocation results in more efficient utilization of machines, but, when tasks' requirements exceed available resources, some of the tasks might be throttled down or preempted. We analyze version 2.1 of the Google cluster trace that shows short-term (1 second) task CPU usage. Contrary to the assumptions taken by many theoretical studies, we demonstrate that the empirical distributions do not follow any single distribution. However, high percentiles of the total processor usage (summed over at least 10 tasks) can be reasonably estimated by the Gaussian distribution. We use this result for a probabilistic fit test, called the Gaussian Percentile Approximation (GPA), for standard bin-packing algorithms. To check whether a new task will fit into a machine, GPA checks whether the resulting distribution's percentile corresponding to the requested service level objective, SLO is still below the machine's capacity. In our simulation experiments, GPA resulted in colocations exceeding the machines' capacity with a frequency similar to the requested SLO.",
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"title": "SLO-aware colocation of data center tasks based on instantaneous processor requirements",
"venue": "SoCC",
"year": 2017
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"paperAbstract": "Consider a random preferential attachment model <i>G</i>(<i>p</i>) for network evolution that allows both node and edge arrivals. Starting with an arbitrary nonempty graph <i>G</i><sub>0</sub>, at each time step, there are two possible events: with probability <i>p</i> > 0 a new node arrives and a new edge is added between the new node and an existing node, and with probability 1 - <i>p</i> a new edge is added between two existing nodes. In both cases, the involved existing nodes are chosen at random according to preferential attachment, i.e., with probability proportional to their degree. <i>G</i>(<i>p</i>) is known to generate <i>power law networks</i>, i.e., the fraction of nodes with degree <i>k</i> is proportional to <i>k</i>-β. Here β=(4-p)/(2-<i>p</i>) is in the range (2,3].\n Denoting the number of nodes of degree <i>k</i> at time <i>t</i> by <i>m</i><i><sub>k,t</sub></i>, we significantly improve some long-standing results. In particular, we show that <i>m</i><i><sub>k,t</sub></i> is concentrated around its mean with a deviation of <i>O</i>(√<i>t</i>), which is independent of <i>k</i>. We also tightly bound the expectation E<i>m<sub>k,t</sub></i> with an additive error of <i>O</i>(1/<i>k</i>), which is independent of <i>t</i>. These new bounds allow us to tightly estimate <i>m<sub>k,t</sub></i> for a considerably larger <i>k</i> values than before. This, in turn, enables us to estimate other important quantities, e.g., the size of the <i>k</i>-rich club, namely, the set of all nodes with a degree at least <i>k</i>.\n Finally, we introduce a new generalized model, <i>G</i>(<i>p<sub>t</sub>, r<sub>t</sub>, q<sub>t</sub></i>), which extends <i>G</i>(<i>p</i>) by allowing also <i>time-varying</i> probabilities for node and edge arrivals, as well as the formation of new components. We show that the extended model can produce power law networks with any exponent β in the range (1,∞). Furthermore, the concentration bounds established for <i>m<sub>k,t</sub></i> in <i>G</i>(<i>p</i>) also apply in <i>G</i>(<i>p<sub>t</sub>, r<sub>t</sub>, q<sub>t</sub></i>).",
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"title": "Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks",
"venue": "KDD",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Danyang Zhuo"
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"name": "Monia Ghobadi"
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"name": "Ratul Mahajan"
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"name": "Klaus-Tycho F\u00f6rster"
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"name": "Arvind Krishnamurthy"
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],
"doi": "10.1145/3098822.3098849",
"doiUrl": "https://doi.org/10.1145/3098822.3098849",
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"paperAbstract": "We take a comprehensive look at packet corruption in data center networks, which leads to packet losses and application performance degradation. By studying 350K links across 15 production data centers, we find that the extent of corruption losses is significant and that its characteristics differ markedly from congestion losses. Corruption impacts fewer links than congestion, but imposes a heavier loss rate; and unlike congestion, corruption rate on a link is stable over time and is not correlated with its utilization.\n Based on these observations, we developed CorrOpt, a system to mitigate corruption. To minimize corruption losses, it intelligently selects which corrupting links can be safely disabled, while ensuring that each top-of-rack switch has a minimum number of paths to reach other switches. CorrOpt also recommends specific actions (e.g., replace cables, clean connectors) to repair disabled links, based on our analysis of common symptoms of different root causes of corruption. Our recommendation engine has been deployed in over seventy data centers of a large cloud provider. Our analysis shows that, compared to current state of the art, CorrOpt can reduce corruption losses by three to six orders of magnitude and improve repair accuracy by 60%.",
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"http://doi.acm.org/10.1145/3098822.3098849",
"https://www.microsoft.com/en-us/research/wp-content/uploads/2017/06/CorrOpt_SIGCOMM2017.pdf",
"https://homes.cs.washington.edu/~arvind/papers/corropt.pdf",
"http://conferences.sigcomm.org/sigcomm/2017/files/program/ts-9-1-CorrOpt.pdf"
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"title": "Understanding and Mitigating Packet Corruption in Data Center Networks",
"venue": "SIGCOMM",
"year": 2017
},
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"name": "Daniele Venzano"
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"paperAbstract": "Video cameras are pervasively deployed for security and smart city scenarios, with millions of them in large cities worldwide. Achieving the potential of these cameras requires efficiently analyzing the live videos in realtime. We describe VideoStorm, a video analytics system that processes thousands of video analytics queries on live video streams over large clusters. Given the high costs of vision processing, resource management is crucial. We consider two key characteristics of video analytics: resource-quality tradeoff with multi-dimensional configurations, and variety in quality and lag goals. VideoStorm\u2019s offline profiler generates query resourcequality profile, while its online scheduler allocates resources to queries to maximize performance on quality and lag, in contrast to the commonly used fair sharing of resources in clusters. Deployment on an Azure cluster of 101 machines shows improvement by as much as 80% in quality of real-world queries and 7\u00d7 better lag, processing video from operational traffic cameras.",
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"paperAbstract": "Network measurement remains a missing piece in today's software packet processing platforms. Sketches provide a promising building block for filling this void by monitoring every packet with fixed-size memory and bounded errors. However, our analysis shows that existing sketch-based measurement solutions suffer from severe performance drops under high traffic load. Although sketches are efficiently designed, applying them in network measurement inevitably incurs heavy computational overhead.\n We present SketchVisor, a robust network measurement framework for software packet processing. It augments sketch-based measurement in the data plane with a fast path, which is activated under high traffic load to provide high-performance local measurement with slight accuracy degradations. It further recovers accurate network-wide measurement results via compressive sensing. We have built a SketchVisor prototype on top of Open vSwitch. Extensive testbed experiments show that SketchVisor achieves high throughput and high accuracy for a wide range of network measurement tasks and microbenchmarks.",
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"title": "SketchVisor: Robust Network Measurement for Software Packet Processing",
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"year": 2017
},
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"paperAbstract": "Benchmarking is a widely-used technique to quantify the performance of software systems. However, the design and implementation of a benchmarking study can face several challenges. In particular, the time required to perform a benchmarking study can quickly spiral out of control, owing to the number of distinct variables to systematically examine. In this paper, we propose <b>IRIS</b>, an <b>I</b>te<b>R</b>ative and <b>I</b>ntelligent Experiment Selection methodology, to maximize the information gain while minimizing the duration of the benchmarking process. IRIS selects the region to place the next experiment point based on the variability of both dependent, i.e., response, and independent variables in that region. It aims to identify a performance function that minimizes the response variable prediction error for a constant and limited experimentation budget. We evaluate IRIS for a wide selection of experimental, simulated and synthetic systems with one, two and three independent variables. Considering a limited experimentation budget, the results show IRIS is able to reduce the performance function prediction error up to 4.3 times compared to equal distance experiment point selection. Moreover, we show that the error reduction can further improve through system-specific parameter tuning. Analysis of the error distributions obtained with IRIS reveals that the technique is particularly effective in regions where the response variable is sensitive to changes in the independent variables.",
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"title": "IRIS: Iterative and Intelligent Experiment Selection",
"venue": "ICPE",
"year": 2017
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"paperAbstract": "The wide availability of tracking devices has drastically increased the role of geolocation in social networks, resulting in new commercial applications; for example, marketers can identify current trending topics within a region of interest and focus their products accordingly. In this paper we study a basic analytics query on geotagged data, namely: <i>given a spatiotemporal region, find the most frequent terms among the social posts in that region</i>. While there has been prior work on keyword search on spatial data (find the objects nearest to the query point that contain the query keywords), and on group keyword search on spatial data (retrieving groups of objects), our problem is different in that it returns keywords and aggregated frequencies as output, instead of having the keyword as input. Moreover, we differ from works addressing the streamed version of this query in that we operate on large, disk resident data and we provide exact answers. We propose an index structure and algorithms to efficiently answer such top-k spatiotemporal range queries, which we refer as <i>Top-k Frequent Spatiotemporal Terms (kFST)</i> queries. Our index structure employs an R-tree augmented by top-k sorted term lists (STLs), where a key challenge is to balance the size of the index to achieve faster execution and smaller space requirements. We theoretically study and experimentally validate the ideal length of the stored term lists, and perform detailed experiments to evaluate the performance of the proposed methods compared to baselines on real datasets.",
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"title": "Efficient Computation of Top-k Frequent Terms over Spatio-temporal Ranges",
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"paperAbstract": "Efficiently programming shared-memory machines is a difficult challenge because mapping application threads onto the memory hierarchy has a strong impact on the performance. However, optimizing such thread placement is difficult: architectures become increasingly complex and application behavior changes with implementations and input parameters, e.g problem size and number of threads. In this work, we propose a fully automatic, abstracted and portable affinity module. It produces and implements an optimized affinity strategy that combines knowledge about application characteristics and the platform topology. Implemented in theback-end of our runtime system (ORWL), our approach was used to enhance the performance and the scalability of several unmodified ORWL-coded applications: matrix multiplication, a 2D stencil (Livermore Kernel 23), and a video tracking real world application. On two SMP machines with quite different hardware characteristics, our tests show spectacular performance improvements for these unmodified application codes due to a dramatic decrease of cache misses and pipeline stalls. A comparison to reference implementations using OpenMP confirms this performance gain of almost one order of magnitude.",
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"paperAbstract": "Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as <i>bridge users</i>. In this work, we address the problem of <i>cross-domain social recommendation</i>, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before.\n Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel <i>Neural Social Collaborative Ranking</i> (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method.",
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"title": "Item Silk Road: Recommending Items from Information Domains to Social Users",
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"paperAbstract": "Software systems with quality of service (QoS), such as database management systems and web servers, are ubiquitous. Such systems must meet strict performance requirements. Instrumentation is a useful technique for the analysis and debugging of QoS systems. Dynamic binary instrumentation (DBI) extracts runtime information to comprehend system's behavior and detect performance bottlenecks. However, existing DBI tools are intrusive; adding unacceptable delay to the program execution. Such delay alters the performance requirements and degrades the overall quality and the user experience of the system. Moreover, the delay may change the system behavior, thus, producing misleading run-time information.This paper presents QDIME, a QoS-aware dynamic binary instrumentation technique that respects system's performance requirements. QDIME takes a user-defined QoS threshold as an input and periodically gathers QoS feedback from the system under analysis to decide its instrumentation budget.We implemented QDIME on top of PIN, a popular DBI framework. We evaluated QDIME with Gzip, MySQL server, Apache HTTP server, and Redis. The experiments show that QDIME respects the user-defined QoS threshold and, thus, improves the performance of the monitored application by manifolds. QDIME is able to provide up to 100% instrumentation coverage with an average of 92% when compared to PIN. Moreover, QDIME reduces the slow-down factor of the instrumented application by 1.41, 5.67, and 10.26 folds for Sys-trace, Call-trace, and Branch-profile respectively. A release of QDIME is available for download at https://github.com/pansy-arafa/qdime.",
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"title": "QDIME: QoS-Aware Dynamic Binary Instrumentation",
"venue": "2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)",
"year": 2017
},
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"doi": "10.1145/3098822.3098840",
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"paperAbstract": "Small RTTs (~tens of microseconds), bursty flow arrivals, and a large number of concurrent flows (thousands) in datacenters bring fundamental challenges to congestion control as they either force a flow to send at most one packet per RTT or induce a large queue build-up. The widespread use of shallow buffered switches also makes the problem more challenging with hosts generating many flows in bursts. In addition, as link speeds increase, algorithms that gradually probe for bandwidth take a long time to reach the fair-share. An ideal datacenter congestion control must provide 1) zero data loss, 2) fast convergence, 3) low buffer occupancy, and 4) high utilization. However, these requirements present conflicting goals.\n This paper presents a new radical approach, called ExpressPass, an end-to-end credit-scheduled, delay-bounded congestion control for datacenters. ExpressPass uses credit packets to control congestion even before sending data packets, which enables us to achieve bounded delay and fast convergence. It gracefully handles bursty flow arrivals. We implement ExpressPass using commodity switches and provide evaluations using testbed experiments and simulations. ExpressPass converges up to 80 times faster than DCTCP in 10 Gbps links, and the gap increases as link speeds become faster. It greatly improves performance under heavy incast workloads and significantly reduces the flow completion times, especially, for small and medium size flows compared to RCP, DCTCP, HULL, and DX under realistic workloads.",
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"title": "Credit-Scheduled Delay-Bounded Congestion Control for Datacenters",
"venue": "SIGCOMM",
"year": 2017
},
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"name": "Hanghang Tong"
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{
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"name": "Yong Wang"
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"name": "Conglei Shi"
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"name": "Nan Cao"
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"paperAbstract": "\u008ce part-whole relationship routinely \u0080nds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent e\u0082ect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. \u008ce proposed method o\u0082ers two distinct advantages over the existing work. First (Model Generality), we formulate joint part-whole outcome prediction as a generic optimization problem, which is able to encode a variety of complex relationships between the outcome of the whole and parts, beyond the linear independence assumption. Second (Algorithm E\u0081cacy), we propose an e\u0082ective and e\u0081cient block coordinate descent algorithm, which is able to \u0080nd the coordinate-wise optimum with a linear complexity in both time and space. Extensive empirical evaluations on real-world datasets demonstrate that the proposed PAROLE (1) leads to consistent prediction performance improvement by modeling the non-linear part-whole relationship as well as part-part interdependency, and (2) scales linearly in terms of the size of the training dataset.",
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"title": "Is the Whole Greater Than the Sum of Its Parts?",
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"year": 2017
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{
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"name": "Jiang Ming"
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{
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"paperAbstract": "Cryptographic functions have been commonly abused by malware developers to hide malicious behaviors, disguise destructive payloads, and bypass network-based firewalls. Now-infamous crypto-ransomware even encrypts victim's computer documents until a ransom is paid. Therefore, detecting cryptographic functions in binary code is an appealing approach to complement existing malware defense and forensics. However, pervasive control and data obfuscation schemes make cryptographic function identification a challenging work. Existing detection methods are either brittle to work on obfuscated binaries or ad hoc in that they can only identify specific cryptographic functions. In this paper, we propose a novel technique called bit-precise symbolic loop mapping to identify cryptographic functions in obfuscated binary code. Our trace-based approach captures the semantics of possible cryptographic algorithms with bit-precise symbolic execution in a loop. Then we perform guided fuzzing to efficiently match boolean formulas with known reference implementations. We have developed a prototype called CryptoHunt and evaluated it with a set of obfuscated synthetic examples, well-known cryptographic libraries, and malware. Compared with the existing tools, CryptoHunt is a general approach to detecting commonly used cryptographic functions such as TEA, AES, RC4, MD5, and RSA under different control and data obfuscation scheme combinations.",
"pdfUrls": [
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"https://doi.org/10.1109/SP.2017.56"
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"title": "Cryptographic Function Detection in Obfuscated Binaries via Bit-Precise Symbolic Loop Mapping",
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"year": 2017
},
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"paperAbstract": "Carrier sensing is a key mechanism that enables decentralized sharing of unlicensed spectrum. However, carrier sensing in its current form is fundamentally unsuitable when devices transmit at different power levels, a scenario increasingly common given the diversity of Wi-Fi APs in the market and the need for WiFi\u2019s co-existence with new upcoming standards such as LAA/LWA. The primary contribution of this paper is a novel carrier sensing mechanism \u2013 skip correlation \u2013 that extends carrier sensing to accommodate multiple transmit power levels. Through an FPGA based implementation on the WARP platform, we demonstrate the effectiveness of our technique in a variety of scenarios including support for backward compatibility.",
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"paperAbstract": "Blockchains and more general distributed ledgers are becoming increasingly popular as efficient, reliable, and persistent records of data and transactions. Unfortunately, they ensure reliability and correctness by making all data public, raising confidentiality concerns that eliminate many potential uses.\n In this paper we present <i>Solidus</i>, a protocol for confidential transactions on public blockchains, such as those required for asset transfers with on-chain settlement. Solidus operates in a framework based on real-world financial institutions: a modest number of banks each maintain a large number of user accounts. Within this framework, Solidus hides both transaction values and the transaction graph (i.e., the identities of transacting entities) while maintaining the public verifiability that makes blockchains so appealing. To achieve strong confidentiality of this kind, we introduce the concept of a <i>Publicly-Verifiable Oblivious RAM Machine</i> (PVORM). We present a set of formal security definitions for both PVORM and Solidus and show that our constructions are secure. Finally, we implement Solidus and present a set of benchmarks indicating that the system is efficient in practice.",
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"https://eprint.iacr.org/2017/317.pdf"
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"title": "Solidus: Confidential Distributed Ledger Transactions via PVORM",
"venue": "CCS",
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"name": "Xun Gong"
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"paperAbstract": "Presilicon simulation is one of the key toolsets for computer architects to evaluate and optimize their future designs. As Graphics Processing Units (GPUs) have become the platform of choice in many computing communities due to their impressive processing capabilities, computer architecture researchers need a simulation framework that allows them to quantitatively consider design tradeoffs. In this paper, we present the Multi2Sim Kepler simulator framework, a new detailed GPU microarchitecture performance simulator that supports NVIDIA's Kepler shader assembly (SASS) code execution. The toolset provides a disassembler, a functional simulator and a detailed cycle-based simulator. We provide insight into the architecture of the NVIDIA Kepler GPU, describing the details of the streaming multiprocessor, front-end and instruction pipelines. We compare the performance of this new simulator against an NVIDIA K20X, a high-end Kepler device. We also evaluate the performance of NVIDIA's CUDA benchmark suite on our GPU performance simulator.",
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"paperAbstract": "Cloud instances are usually virtual machines hosted on shared hardware. Containers are often used to deploy services in cloud instances. However, excessive consumption of shared hardware resources by some VMs may lead to unpredictability in the performance of containers running in co-located VMs. Existing techniques to detect performance interference in applications are either too expensive in terms of profiling or applicable only from the perspective of the infrastructure owner. In this paper, we propose Sherlock, a lightweight subscriber-centric mechanism to detect performance interference and estimate its impact on cloud services. Sherlock does not require access to hardware counters and can work on unmodified clouds without any support from the cloud provider or changes to the hypervisor. Sherlock uses a simple profiling technique which is performed only for a short duration before deployment. When interference from co-resident VMs is observed, Sherlock notifies the cloud subscriber, so that any remedial actions can be taken. We also define a metric IScore, which is an estimate of the impact of interference on a service. Experiments on the real-world web benchmark CloudSuite show that our approach is able to detect interference with accuracy ranging from 89% to 98.4%, and with very less false positives ( 8%).",
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"title": "Sherlock: Lightweight Detection of Performance Interference in Containerized Cloud Services",
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"paperAbstract": "Systems for processing big data---e.g., Hadoop, Spark, and massively parallel databases---need to run workloads on behalf of multiple tenants simultaneously. The abundant disk-based storage in these systems is usually complemented by a smaller, but much faster, <i>cache</i>. Cache is a precious resource: Tenants who get to use the cache can see two orders of magnitude performance improvement. Cache is also a limited and hence shared resource: Unlike a resource like a CPU core which can be used by only one tenant at a time, a cached data item can be accessed by multiple tenants at the same time. Cache, therefore, has to be shared by a multi-tenancy-aware policy across tenants, each having a unique set of priorities and workload characteristics.\n In this paper, we develop cache allocation strategies that speed up the overall workload while being <i>fair</i> to each tenant. We build a novel fairness model targeted at the shared resource setting that incorporates not only the more standard concepts of Pareto-efficiency and sharing incentive, but we also define envy freeness via the notion of <i>core</i> from cooperative game theory. Our cache management platform, ROBUS, uses randomization over small time batches, and we develop a proportionally fair allocation mechanism that satisfies the core property in expectation. We show that this algorithm and related fair algorithms can be approximated to arbitrary precision in polynomial time. We evaluate these algorithms on a ROBUS prototype implemented on Spark with RDD store used as cache. Our evaluation on an industry-standard workload shows that our algorithms score high on both performance and fairness metrics across a wide variety of practical multi-tenant setups.",
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"title": "ROBUS: Fair Cache Allocation for Data-parallel Workloads",
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"year": 2017
},
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"name": "Amelie Chi Zhou"
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"doi": "10.1109/CLUSTER.2017.73",
"doiUrl": "https://doi.org/10.1109/CLUSTER.2017.73",
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"paperAbstract": "Burst Buffer is an effective solution for reducing the data transfer time and the I/O interference in HPC systems. Extending Burst Buffers (BBs) to handle Big Data applications is challenging because BBs must account for the large data inputs of Big Data applications and the performance guarantees of HPC applications - which are considered as first-class citizens in HPC systems. Existing BBs focus on only intermediate data of Big Data applications and incur a high performance degradation of both Big Data and HPC applications. We present Eley, a burst buffer solution that helps to accelerate the performance of Big Data applications while guaranteeing the performance of HPC applications. In order to improve the performance of Big Data applications, Eley employs a prefetching technique that fetches the input data of these applications to be stored close to computing nodes thus reducing the latency of reading data inputs. Moreover, Eley is equipped with a full delay operator to guarantee the performance of HPC applications - as they are running independently on a HPC system. The experimental results show the effectiveness of Eley in obtaining shorter execution time of Big Data applications (shorter map phase) while guaranteeing the performance of HPC applications.",
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"paperAbstract": "Nowadays, with the increasing burst of newly generated data everyday, as well as the vast expanding needs for corresponding data analyses, grand challenges have been brought to big data computing platforms. Computing resources in a single cluster are often not able to fulfill the computing capability needs. The requests of distributed computing resources are dramatically arising. In addition, with increasing popularity of cloud computing platforms, many organizations with data security concerns are more favor to hybrid cloud, a multi-cluster environment composed by both public cloud and private cloud in purpose of keeping sensitive data local. All these scenarios show great necessity of migrating big data computing to multi-cluster environment. In this paper, we present a hierarchical multi-cluster big data computing framework built upon Apache Spark. Our framework supports combination of heterogeneous Spark computing clusters. With an integrated controller within the framework, it also facilitates ability for submitting, monitoring, executing of Spark workflow. Our experimental results show that the proposed framework not only enables possibility of distributing Spark workflow throughout multiple clusters, but also provides significant performance improvement compared to single cluster environment by optimizing utilization of multi-cluster computing resources.",
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"doi": "10.1145/3064176.3064207",
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"paperAbstract": "The combination of the explosive growth in digital data and the need to preserve much of this data in the long term has made it an imperative to find a more cost-effective way than HDD arrays and more easily accessible way than tape libraries to store massive amounts of data. While modern optical discs are capable of guaranteeing more than 50-year data preservation without migration, individual optical disks' lack of the performance and capacity relative to HDDs or tapes has significantly limited their use in datacenters. This paper presents a Rack-scale Optical disc library System, or ROS in short, that provides a PB-level total capacity and inline accessibility on thousands of optical discs built within a 42U Rack. A rotatable roller and robotic arm separating and fetching the discs are designed to improve disc placement density and simplify the mechanical structure. A hierarchical storage system based on SSD, hard disks and optical discs are presented to hide the delay of mechanical operation. On the other hand, an optical library file system is proposed to schedule mechanical operation and organize data on the tiered storage with a POSIX user interface to provide an illusion of inline data accessibility. We evaluate ROS on a few key performance metrics including operation delays of the mechanical structure and software overhead in a prototype PB-level ROS system. The results show that ROS stacked on Samba and FUSE can provide almost 323MB/s read and 236MB/s write throughput, about 53ms file write and 15ms read latency via 10GbE network for external users, exhibiting its inline accessibility. Besides, ROS is able to effectively hide and virtualize internal complex operational behaviors and be easily deployable in datacenters.",
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"year": 2017
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"name": "Zhaoxia Deng"
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"paperAbstract": "Most architectures are designed to mitigate the usually undesirable phenomenon of device wearout. We take a contrarian view and harness this phenomenon to create hardware security mechanisms that resist attacks by statistically enforcing an upper bound on hardware uses, and consequently attacks. For example, let us assume that a user may log into a smartphone a maximum of 50 times a day for 5 years, resulting in approximately 91,250 legitimate uses. If we assume at least 8-character passwords and we require login (and retrieval of the storage decryption key) to traverse hardware that wears out in 91,250 uses, then an adversary has a negligible chance of successful brute-force attack before the hardware wears out, even assuming real-world password cracking by professionals. M-way replication of our hardware and periodic re-encryption of storage can increase the daily usage bound by a factor of M.\n The key challenge is to achieve practical statistical bounds on both minimum and maximum uses for an architecture, given that individual devices can vary widely in wearout characteristics. We introduce techniques for architecturally controlling these bounds and perform a design space exploration for three use cases: a limited-use connection, a limited-use targeting system and one-time pads. These techniques include decision trees, parallel structures, Shamir's secret-sharing mechanism, Reed-Solomon codes, and module replication. We explore the cost in area, energy and latency of using these techniques to achieve system-level usage targets given device-level wearout distributions. With redundant encoding, for example, we can improve exponential sensitivity to device lifetime variation to linear sensitivity, reducing the total number of NEMS devices by 4 orders of magnitude to about 0.8 million for limited-use connections (compared with 4 billion if without redundant encoding).",
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"title": "Lemonade from lemons: Harnessing device wearout to create limited-use security architectures",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
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"title": "Quantifying the Potential Benefits of On-chip Near-Data Computing in Manycore Processors",
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"year": 2017
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"title": "Abridging source code",
"venue": "PACMPL",
"year": 2017
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"name": "Biplab Kumar Saha"
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{
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"name": "Tiffany A. Connors"
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{
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"name": "Saami Rahman"
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{
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],
"doi": "10.1109/HPCC-SmartCity-DSS.2017.3",
"doiUrl": "https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.3",
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"paperAbstract": "Recent interest in machine-learning based methods has produced many sophisticated models for performance modeling and optimization. These models tend to be sensitive to parameters of the underlying architecture and hence yield the highest prediction accuracy when trained on the target platform. Training a classifier, however, is a fairly involved process and requires knowledge of statistics and machine learning that the end users of such models may not possess. This paper presents a new framework for automatically generating machine-learning based performance models. A tool-chain is developed that provides automated mechanisms for sample generation, dynamic feature extraction, feature selection, data labeling, validation and hyper parameter tuning. We describe the design and implementation of this system and demonstrate its efficacy by developing a learning heuristic for register allocation in GPU kernels. Results show that auto-generated models can predict register thresholds that lead to integer factor performance improvements over kernels produced by state-of-the-art optimizing compilers.",
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"title": "A Machine Learning Approach to Automatic Creation of Architecture-Sensitive Performance Heuristics",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"name": "Christopher M. Chambreau"
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"paperAbstract": "Debugging intermittently occurring bugs within MPI applications is challenging, and message races, a condition in which two or more sends race to match with a receive, are one of the common root causes. Many debugging tools have been proposed to help programmers resolve them, but their runtime interference perturbs the timing such that subtle races often cannot be reproduced with debugging tools. We present novel noise injection techniques to expose message races even under a tool's control. We first formalize this race problem in the context of non-deterministic parallel applications and use this analysis to determine an effective noise-injection strategy to uncover them. We codified these techniques in NINJA (Noise INJection Agent) that exposes these races without modification to the application. Our evaluations on synthetic cases as well as a real-world bug in Hypre-2.10.1 show that NINJA significantly helps expose races.",
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"year": 2017
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"paperAbstract": "We propose <i>indexes of queries</i>, a novel mechanism for supporting efficient, expressive, and information-theoretically private single-round queries over multi-server PIR databases. Our approach decouples the way that users construct their requests for data from the physical layout of the remote data store, thereby enabling users to fetch data using \"contextual\" queries that specify <i>which</i> data they seek, as opposed to \"positional\" queries that specify <i>where</i> those data happen to reside. For example, an open-access eprint repository could employ indexes of queries to let researchers fetch academic articles via PIR queries such as for \"this year's 5 most cited papers about PIR\" or \"the 3 most recently posted papers about PIR\". Our basic approach is compatible with any PIR protocol in the ubiquitous \"vector-matrix\" model for PIR, though the most sophisticated and useful of our constructions rely on some nice algebraic properties of Goldberg's IT-PIR protocol (Oakland 2007). We have implemented our techniques as an extension to Percy++, an open-source implementation of Goldberg's IT-PIR protocol. Our experiments indicate that the new techniques can greatly improve not only utility for private information retrievers but also efficiency for private information retrievers and servers alike.",
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"http://homes.soic.indiana.edu/henry/publications/ccs17.pdf"
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"title": "Querying for Queries: Indexes of Queries for Efficient and Expressive IT-PIR",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
"3f67d1b9a3fa7c68fb58dcbe9d4d2d3532824f01": {
"authors": [
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"name": "Ambika Kaul"
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"name": "Saket Maheshwary"
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"name": "Vikram Pudi"
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],
"doi": "10.1109/ICDM.2017.31",
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"title": "AutoLearn - Automated Feature Generation and Selection",
"venue": "ICDM",
"year": 2017
},
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"doi": "10.1145/3078468.3078484",
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"paperAbstract": "Misconfigurations in the storage systems can lead to business losses due to system downtime with substantial people resources invested into troubleshooting. Hence, faster troubleshooting of software misconfigurations has been critically important for the customers as well as the vendors.\n This paper introduces a framework and a tool called <i>Dexter,</i> which embraces the recent trend of viewing systems as data to derive the troubleshooting clues. Dexter provides quick insights into the problem root cause and possible resolution by solely using the storage system logs. This differentiates Dexter from other previously known approaches which complement log analysis with source code analysis, execution traces etc.. Furthermore, Dexter analyzes command history logs from the sick system after it has been healed and predicts the exact command(s) which resolved the problem. Dexter's approach is simple and can be applied to other software systems with diagnostic logs for immediate problem detection without any pre-trained models.\n Evaluation on 600 real customer support cases shows 90% accuracy in root causing and over 65% accuracy in finding an exact resolution for the misconfiguration problem. Results show up to 60% noise reduction in system logs and at least 10x savings in case resolution times, bringing down the troubleshooting times from <i>days</i> to <i>minutes</i> at times. Dexter runs 24x7 in the NetApp's® support data center.\n The paper also presents insights from study on thousands of real customer support cases over thousands of deployed systems over the period of 1.5 years. These investigations uncover facts that cause potential delays in customer case resolutions and influence Dexter's design.",
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"http://doi.acm.org/10.1145/3078468.3078484"
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"title": "Dexter: faster troubleshooting of misconfiguration cases using system logs",
"venue": "SYSTOR",
"year": 2017
},
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"name": "Sourav Sikdar"
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"name": "Kia Teymourian"
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"doi": "10.1145/3127479.3129248",
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"paperAbstract": "Many cloud-based data management and analytics systems support complex objects. Dataflow platforms such as Spark and Flink allow programmers to manipulate sets consisting of objects from a host programming language (often Java). Document databases such as MongoDB make use of hierarchical interchange formats---most popularly JSON---which embody a data model where individual records can themselves contain sets of records. Systems such as Dremel and AsterixDB allow complex nesting of data structures.\n Clearly, no system designer would expect a system that stores JSON objects as text to perform at the same level as a system based upon a custom-built physical data model. The question we ask is: How significant is the performance hit associated with choosing a particular physical implementation? Is the choice going to result in a negligible performance cost, or one that is debilitating? Unfortunately, there does not exist a scientific study of the effect of physical complex model implementation on system performance in the literature. Hence it is difficult for a system designer to fully understand performance implications of such choices. This paper is an attempt to remedy that.",
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"title": "An experimental comparison of complex object implementations for big data systems",
"venue": "SoCC",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Xuchao Zhang"
},
{
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],
"name": "Liang Zhao"
},
{
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"name": "Arnold P. Boedihardjo"
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{
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"name": "Chang-Tien Lu"
}
],
"doi": "10.1109/ICDM.2017.72",
"doiUrl": "https://doi.org/10.1109/ICDM.2017.72",
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"paperAbstract": "In today's era of big data, robust least-squares regression becomes a more challenging problem when considering the adversarial corruption along with explosive growth of datasets. Traditional robust methods can handle the noise but suffer from several challenges when applied in huge dataset including 1) computational infeasibility of handling an entire dataset at once, 2) existence of heterogeneously distributed corruption, and 3) difficulty in corruption estimation when data cannot be entirely loaded. This paper proposes online and distributed robust regression approaches, both of which can concurrently address all the above challenges. Specifically, the distributed algorithm optimizes the regression coefficients of each data block via heuristic hard thresholding and combines all the estimates in a distributed robust consolidation. Furthermore, an online version of the distributed algorithm is proposed to incrementally update the existing estimates with new incoming data. We also prove that our algorithms benefit from strong robustness guarantees in terms of regression coefficient recovery with a constant upper bound on the error of state-of-the-art batch methods. Extensive experiments on synthetic and real datasets demonstrate that our approaches are superior to those of existing methods in effectiveness, with competitive efficiency.",
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"paperAbstract": "Task-based parallel programming frameworks offer compelling productivity and performance benefits for modern chip multi-processors (CMPs). At the same time, CMPs also provide packed-SIMD units to exploit fine-grain data parallelism. Two fundamental challenges make using packed-SIMD units with task-parallel programs particularly difficult: (1) the intra-core parallel abstraction gap; and (2) inefficient execution of irregular tasks. To address these challenges, we propose augmenting CMPs with intra-core <i>loop-task accelerators</i> (LTAs). We introduce a lightweight hint in the instruction set to elegantly encode loop-task execution and an LTA microarchitectural template that can be configured at design time for different amounts of spatial/temporal decoupling to efficiently execute both regular and irregular loop tasks. Compared to an in-order CMP baseline, CMP+LTA results in an average speedup of 4.2X (1.8X area normalized) and similar energy efficiency. Compared to an out-of-order CMP baseline, CMP+LTA results in an average speedup of 2.3X (1.5X area normalized) and also improves energy efficiency by 3.2X. Our work suggests augmenting CMPs with lightweight LTAs can improve performance and efficiency on both regular and irregular loop-task parallel programs with minimal software changes.",
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"title": "Using intra-core loop-task accelerators to improve the productivity and performance of task-based parallel programs",
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"year": 2017
},
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"paperAbstract": "This paper presents a storage system that can hide the presence of hidden data alongside a larger volume of public data. Encryption allows a user to hide the contents of data, but not the fact that sensitive data is present. Under duress, the owner of high-value data can be coerced by a powerful adversary to disclose decryption keys. Thus, private users and corporations have an interest in hiding the very presence of some sensitive data, alongside a larger body of less sensitive data (e.g., the operating system and other benign files); this property is called plausible deniability. Existing plausible deniability systems do not fulfill all of the following requirements: (1) resistance to multiple snapshot attacks where an attacker compares the state of the device over time; (2) ensuring that hidden data won't be destroyed when the public volume is modified by a user unaware of the hidden data; and (3) disguising writes to secret data as normal system operations on public data.\n We explain why existing solutions do not meet all these requirements and present the Ever-Changing Disk (ECD), a generic scheme for plausible deniability storage systems that meets all of these requirements. An ECD stores hidden data inside a large volume of pseudorandom data. Portions of this volume are periodically migrated in a log-structured manner. Hidden writes can then be interchanged with normal firmware operations. The expected access patterns and time until hidden data is overwritten are completely predictable, and insensitive to whether data is hidden. Users control the rate of internal data migration (R), trading write bandwidth to hidden data for longevity of the hidden data. For a typical 2TB disk and setting of R, a user preserves hidden data by entering her secret key every few days or weeks.",
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"title": "Preserving Hidden Data with an Ever-Changing Disk",
"venue": "HotOS",
"year": 2017
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"title": "Authenticated Garbling and Efficient Maliciously Secure Two-Party Computation",
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"paperAbstract": "With exploding traffic stuffing existing network infra-structure, today's telecommunication and cloud service providers resort to Network Function Virtualization (NFV) for greater agility and economics. Pioneer service provider such as AT&T proposes to adopt container in NFV to achieve shorter Virtualized Network Function (VNF) provisioning time and better runtime performance. However, we characterize typical NFV work-loads on the containers and find that the performance is unsatisfactory. We observe that the shared host OS net-work stack is the main bottleneck, where the traffic flow processing involves a large amount of intermediate memory buffers and results in significant last level cache pollution. Existing OS memory allocation policies fail to exploit the locality and data sharing information among buffers. In this paper, we propose NetContainer, a software framework that achieves fine-grained hardware resource management for containerized NFV platform. NetContainer employs a cache access overheads guided page coloring scheme to coordinately address the inter-flow cache access overheads and intra-flow cache access overheads. It maps the memory buffer pages that manifest low cache access overheads (across a flow or among the flows) to the same last level cache partition. NetContainer exploits a footprint theory based method to estimate the cache access overheads and a Min-Cost Max-Flow model to guide the memory buffer mappings. We implement the NetContainer in Linux kernel and extensively evaluate it with real NFV workloads. Exper-imental results show that NetContainer outperforms conventional page coloring-based memory allocator by 48% in terms of successful call rate.",
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"title": "Towards \"Full Containerization\" in Containerized Network Function Virtualization",
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"name": "Samuel Ranellucci"
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"name": "Jonathan Katz"
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"paperAbstract": "We propose a new, constant-round protocol for multi-party computation of boolean circuits that is secure against an arbitrary number of malicious corruptions. At a high level, we extend and generalize recent work of Wang et al. in the two-party setting. Namely, we design an efficient preprocessing phase that allows the parties to generate authenticated information; we then show how to use this information to distributively construct a single \"authenticated\" garbled circuit that is evaluated by one party.\n Our resulting protocol improves upon the state-of-the-art both asymptotically and concretely. We validate these claims via several experiments demonstrating both the efficiency and scalability of our protocol: <ul><li><b>Efficiency:</b> For three-party computation over a LAN, our protocol requires only 95 ms to evaluate AES. This is roughly a 700X improvement over the best prior work, and only 2.5X slower than the best known result in the two-party setting. In general, for n-party computation our protocol improves upon prior work (which was never implemented) by a factor of more than 230n, e.g., an improvement of 3 orders of magnitude for 5-party computation.</li> <li><b>Scalability:</b> We successfully executed our protocol with a large number of parties located all over the world, computing (for example) AES with 128 parties across 5 continents in under 3 minutes. Our work represents the largest-scale demonstration of secure computation to date.</li></ul>",
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"title": "Global-Scale Secure Multiparty Computation",
"venue": "CCS",
"year": 2017
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"name": "El Mahdi El Mhamdi"
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"name": "Rachid Guerraoui"
}
],
"doi": "10.1109/IPDPS.2017.66",
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"journalName": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
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"paperAbstract": "Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks are now appealing solutions, cannot afford any additional learning at run-time. In this paper, we view a multilayer neural network as a distributed system of which neurons can fail independently, and we evaluate its robustness in the absence of any (recovery) learning phase. We give tight bounds on the number of neurons that can fail without harming the result of a computation. To determine our bounds, we leverage the fact that neuralactivation functions are Lipschitz-continuous. Our bound isgiven in the form of quantity, we call the Forward ErrorPropagation, computing this quantity only requires looking atthe topology of the network, while experimentally assessingthe robustness of a network requires the costly experiment oflooking at all the possible inputs and testing all the possibleconfigurations of the network corresponding to different failuresituations, facing a discouraging combinatorial explosion. We distinguish the case of neurons that can fail and stop their activity (crashed neurons) from the case of neurons that can fail by transmitting arbitrary values (Byzantine neurons). In the crash case, our bound involves the number of neuronsper layer, the Lipschitz constant of the neural activationfunction, the number of failing neurons, the synaptic weightsand the depth of the layer where the failure occurred. In thecase of Byzantine failures, our bound involves, in addition, thesynaptic transmission capacity. Interestingly, as we show inthe paper, our bound can easily be extended to the case wheresynapses can fail. We present three applications of our results. The first is aquantification of the effect of memory cost reduction on theaccuracy of a neural network. The second is a quantification ofthe amount of information any neuron needs from its precedinglayer, enabling thereby a boosting scheme that prevents neuronsfrom waiting for unnecessary signals. Our third applicationis a quantification of the trade-off between neural networksrobustness and learning cost.",
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"http://infoscience.epfl.ch/record/217561/files/When_Neurons_Fail.pdf",
"https://arxiv.org/pdf/1706.08884v1.pdf",
"https://doi.org/10.1109/IPDPS.2017.66",
"http://arxiv.org/abs/1706.08884"
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"title": "When Neurons Fail",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"authors": [
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"name": "Yaacov Y. Weiss"
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"doi": "10.1145/3034786.3056112",
"doiUrl": "https://doi.org/10.1145/3034786.3056112",
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"paperAbstract": "This paper investigates the problem of reverse engineering, i.e., learning, select-project-join (SPJ) queries from a user-provided example set, containing positive and negative tuples. The goal is then to determine whether there exists a query returning all the positive tuples, but none of the negative tuples, and furthermore, to find such a query, if it exists. These are called the satisfiability and learning problems, respectively. The ability to solve these problems is an important step in simplifying the querying process for non-expert users.\n This paper thoroughly investigates the satisfiability and learning problems in a variety of settings. In particular, we consider several classes of queries, which allow different combinations of the operators select, project and join. In addition, we compare the complexity of satisfiability and learning, when the query is, or is not, of bounded size. We note that bounded-size queries are of particular interest, as they can be used to avoid over-fitting (i.e., tailoring a query precisely to only the seen examples).\n In order to fully understand the underlying factors which make satisfiability and learning (in)tractable, we consider different components of the problem, namely, the size of a query to be learned, the size of the schema and the number of examples. We study the complexity of our problems, when considering these as part of the input, as constants or as parameters (i.e., as in parameterized complexity analysis). Depending on the setting, the complexity of satisfiability and learning can vary significantly. Among other results, our analysis also provides new problems that are complete for W[3], for which few natural problems are known. Finally, by considering a variety of settings, we derive insight on how the different facets of our problem interplay with the size of the database, thereby providing the theoretical foundations necessary for a future implementation of query learning from examples.",
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"title": "Reverse Engineering SPJ-Queries from Examples",
"venue": "PODS",
"year": 2017
},
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"name": "Ralph Keusch"
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"name": "Johannes Lengler"
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"Greedy algorithm",
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"paperAbstract": "The algorithmic small-world phenomenon, empirically established by Milgram\u2019s letter forwarding experiments from the 60s [59], was theoretically explained by Kleinberg in 2000 [46]. However, from today\u2019s perspective his model has several severe shortcomings that limit the applicability to real-world networks. In order to give a more convincing explanation of the algorithmic small-world phenomenon, we study decentralized greedy routing in a more flexible random graph model (geometric inhomogeneous random graphs) which overcomes all previous shortcomings. Apart from exhibiting good properties in theory, it has also been extensively experimentally validated that this model reasonably captures real-world networks. In this model, the greedy routing protocol is purely distributed as each vertex only needs to know information about its direct neighbors. We prove that it succeeds with constant probability, and in case of success almost surely finds an almost shortest path of length \u0398(log log n), where our bound is tight including the leading constant. Moreover, we study natural local patching methods which augment greedy routing by backtracking and which do not require any global knowledge. We show that such methods can ensure success probability 1 in an asymptotically tight number of steps. These results also address the question of Krioukov et al. [51] whether there are efficient local routing protocols for the internet graph. There were promising experimental studies, but the question remained unsolved theoretically. Our results give for the first time a rigorous and analytical affirmative answer. \u2217Max-Planck-Institute for Informatics, Saarbr\u00fccken, Germany, kbringma@mpi-inf.mpg.de \u2020Department of Computer Science, ETH Zurich, Switzerland, rkeusch@inf.ethz.ch \u2021Department of Computer Science, ETH Zurich, Switzerland, lenglerj@inf.ethz.ch \u00a7Department of Computer Science, University of Freiburg, Germany, yannic.maus@cs.uni-freiburg.de \u00b6Department of Computer Science, University of Freiburg, Germany, armolla@cs.uni-freiburg.de ar X iv :1 61 2. 05 53 9v 3 [ cs .S I] 1 9 Fe b 20 17",
"pdfUrls": [
"https://arxiv.org/pdf/1612.05539v2.pdf",
"https://arxiv.org/pdf/1612.05539v3.pdf",
"https://arxiv.org/pdf/1612.05539v1.pdf"
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"title": "Greedy Routing and the Algorithmic Small-World Phenomenom",
"venue": "",
"year": 2016
},
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"name": "Haoyuan Zhang"
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"name": "Huang Li"
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"name": "Bruno C. d. S. Oliveira"
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"doi": "10.1145/3136014.3136016",
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"paperAbstract": "Over the years a lot of effort has been put on solving extensibility problems, while retaining important software engineering properties such as modular type-safety and separate compilation. Most previous work focused on operations that traverse and process extensible Abstract Syntax Tree (AST) structures. However, there is almost no work on operations that <em>build</em> such extensible ASTs, including <em>parsing</em>. \nThis paper investigates solutions for the problem of <em>modular parsing</em>. We focus on <em>semantic</em> modularity and not just <em>syntactic</em> modularity. That is, the solutions should not only allow complete parsers to be built out of modular parsing components, but also enable the parsing components to be <em>modularly type-checked</em> and <em>separately compiled</em>. We present a technique based on parser combinators that enables modular parsing. Interestingly, the modularity requirements for modular parsing rule out several existing parser combinator approaches, which rely on some non-modular techniques. We show that Packrat parsing techniques, provide solutions for such modularity problems, and enable reasonable performance in a modular setting. Extensibility is achieved using <em>multiple inheritance</em> and <em>Object Algebras</em>. To evaluate the approach we conduct a case study based on the â\u0080\u009cTypes and Programming Languagesâ\u0080\u009d interpreters. The case study shows the effectiveness at reusing parsing code from existing interpreters, and the total parsing code is 69% shorter than an existing code base using a non-modular parsing approach.",
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"http://doi.acm.org/10.1145/3136014.3136016",
"http://i.cs.hku.hk/~bruno/papers/sle17.pdf"
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"title": "Type-safe modular parsing",
"venue": "SLE",
"year": 2017
},
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"authors": [
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"name": "Dmitrii Kuvaiskii"
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"name": "Oleksii Oleksenko"
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"name": "Sergei Arnautov"
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"name": "Bohdan Trach"
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"name": "Pramod Bhatotia"
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"name": "Christof Fetzer"
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],
"doi": "10.1145/3064176.3064192",
"doiUrl": "https://doi.org/10.1145/3064176.3064192",
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"paperAbstract": "Shielded execution based on Intel SGX provides strong security guarantees for legacy applications running on untrusted platforms. However, memory safety attacks such as Heartbleed can render the confidentiality and integrity properties of shielded execution completely ineffective. To prevent these attacks, the state-of-the-art memory-safety approaches can be used in the context of shielded execution.\n In this work, we first showcase that two prominent software- and hardware-based defenses, AddressSanitizer and Intel MPX respectively, are impractical for shielded execution due to high performance and memory overheads. This motivated our design of SGXBounds---an efficient memory-safety approach for shielded execution exploiting the architectural features of Intel SGX. Our design is based on a simple combination of tagged pointers and compact memory layout.\n We implemented SGXBounds based on the LLVM compiler framework targeting unmodified multithreaded applications. Our evaluation using Phoenix, PARSEC, and RIPE benchmark suites shows that SGXBounds has performance and memory overheads of 17% and 0.1% respectively, while providing security guarantees similar to AddressSanitizer and Intel MPX. We have obtained similar results with SPEC CPU2006 and four real-world case studies: SQLite, Memcached, Apache, and Nginx.",
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"title": "SGXBOUNDS: Memory Safety for Shielded Execution",
"venue": "EuroSys",
"year": 2017
},
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"paperAbstract": "In today's databases, previous query answers <i>rarely</i> benefit answering future queries. For the first time, to the best of our <i>knowledge</i>, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries.\n We call this novel idea---learning from past query answers---<i>Database Learning</i>. We exploit <i>the principle of maximum entropy</i> to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that database learning supports 73.7% of these queries, speeding them up by up to 23.0x for the same accuracy level compared to existing AQP systems.",
"pdfUrls": [
"http://web.eecs.umich.edu/~mozafari/php/data/uploads/dbl_techreport.pdf",
"http://web.eecs.umich.edu/~mozafari/php/data/uploads/sigmod_2017_dbl.pdf",
"http://arxiv.org/abs/1703.05468",
"http://web.eecs.umich.edu/~michjc/papers/ypark_sigmod17.pdf",
"http://doi.acm.org/10.1145/3035918.3064013",
"https://arxiv.org/pdf/1703.05468v2.pdf",
"https://arxiv.org/pdf/1703.05468v1.pdf",
"http://yongjoopark.com/resources/dbl-sub.pdf"
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"title": "Database Learning: Toward a Database that Becomes Smarter Every Time",
"venue": "SIGMOD Conference",
"year": 2017
},
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"name": "Jordi Wolfson-Pou"
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"name": "Edmond Chow"
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"doi": "10.1145/3126908.3126966",
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"paperAbstract": "We present a new algorithm, the Distributed Southwell method, as a competitor to Block Jacobi for preconditioning and multigrid smoothing. It is based on the Southwell iterative method, which is sequential, where only the equation with the largest residual is relaxed per iteration. The Parallel Southwell method extends this idea by relaxing equation <i>i</i> if it has the largest residual among all the equations coupled to variable <i>i</i>. Since communication is required for processes to exchange residuals, this method in distributed memory can be expensive. Distributed Southwell uses a novel scheme to reduce this communication of residuals while avoiding deadlock. Using test problems from the SuiteSparse Matrix Collection, we show that Distributed Southwell requires less communication to reach the same accuracy when compared to Parallel Southwell. Additionally, we show that the convergence of Distributed Southwell does not degrade like that of Block Jacobi when the number of processes is increased.",
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"http://doi.acm.org/10.1145/3126908.3126966"
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"title": "Distributed southwell: an iterative method with low communication costs",
"venue": "SC",
"year": 2017
},
"40dc09f5fbd3776c3f34adedc7a4718307ace0d6": {
"authors": [
{
"ids": [
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"name": "Jian Huang"
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{
"ids": [
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"name": "Jun Xu"
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{
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"name": "Xinyu Xing"
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{
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"name": "Peng Liu"
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{
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"name": "Moinuddin K. Qureshi"
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"doi": "10.1145/3133956.3134035",
"doiUrl": "https://doi.org/10.1145/3133956.3134035",
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"paperAbstract": "Encryption ransomware is a malicious software that stealthily encrypts user files and demands a ransom to provide access to these files. Several prior studies have developed systems to detect ransomware by monitoring the activities that typically occur during a ransomware attack. Unfortunately, by the time the ransomware is detected, some files already undergo encryption and the user is still required to pay a ransom to access those files. Furthermore, ransomware variants can obtain kernel privilege, which allows them to terminate software-based defense systems, such as anti-virus. While periodic backups have been explored as a means to mitigate ransomware, such backups incur storage overheads and are still vulnerable as ransomware can obtain kernel privilege to stop or destroy backups. Ideally, we would like to defend against ransomware without relying on software-based solutions and without incurring the storage overheads of backups.\n To that end, this paper proposes FlashGuard, a ransomware tolerant Solid State Drive (SSD) which has a firmware-level recovery system that allows quick and effective recovery from encryption ransomware without relying on explicit backups. FlashGuard leverages the observation that the existing SSD already performs out-of-place writes in order to mitigate the long erase latency of flash memories. Therefore, when a page is updated or deleted, the older copy of that page is anyway present in the SSD. FlashGuard slightly modifies the garbage collection mechanism of the SSD to retain the copies of the data encrypted by ransomware and ensure effective data recovery. Our experiments with 1,447 manually labeled ransomware samples show that FlashGuard can efficiently restore files encrypted by ransomware. In addition, we demonstrate that FlashGuard has a negligible impact on the performance and lifetime of the SSD.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3133956.3134035",
"http://memlab.ece.gatech.edu/papers/CCS_2017_1.pdf",
"http://memlab.ece.gatech.edu/slides/CCS_2017_1_slides.pdf"
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"title": "FlashGuard: Leveraging Intrinsic Flash Properties to Defend Against Encryption Ransomware",
"venue": "CCS",
"year": 2017
},
"40f137ea7a002685fb9cbf0fb04086e96904619c": {
"authors": [
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"name": "Vasileios Giotsas"
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{
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"name": "Christoph Dietzel"
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"name": "Georgios Smaragdakis"
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"name": "Anja Feldmann"
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"name": "Arthur W. Berger"
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"name": "Emile Aben"
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],
"doi": "10.1145/3098822.3098855",
"doiUrl": "https://doi.org/10.1145/3098822.3098855",
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"paperAbstract": "Peering infrastructures, namely, colocation facilities and Internet exchange points, are located in every major city, have hundreds of network members, and support hundreds of thousands of interconnections around the globe. These infrastructures are well provisioned and managed, but outages have to be expected, e.g., due to power failures, human errors, attacks, and natural disasters. However, little is known about the frequency and impact of outages at these critical infrastructures with high peering concentration.\n In this paper, we develop a novel and lightweight methodology for detecting peering infrastructure outages. Our methodology relies on the observation that BGP communities, announced with routing updates, are an excellent and yet unexplored source of information allowing us to pinpoint outage locations with high accuracy. We build and operate a system that can locate the epicenter of infrastructure outages at the level of a building and track the reaction of networks in near real-time. Our analysis unveils four times as many outages as compared to those publicly reported over the past five years. Moreover, we show that such outages have significant impact on remote networks and peering infrastructures. Our study provides a unique view of the Internet's behavior under stress that often goes unreported.",
"pdfUrls": [
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"http://people.csail.mit.edu/gsmaragd/publications/SIGCOMM2017/SIGCOMM2017.pdf",
"http://doi.acm.org/10.1145/3098822.3098855",
"http://conferences.sigcomm.org/sigcomm/2017/files/program/ts-10-3-outages-in-wild.pdf",
"http://people.csail.mit.edu/gsmaragd/publications/SIGCOMM2017/SIGCOMM2017-presentation.pdf",
"http://people.csail.mit.edu/awberger/papers/Detecting_Peering_Infrastructure_Outages_in_the_Wild.pdf",
"http://www.caida.org/publications/presentations/2017/detecting_peering_infrastructure_outages_ucla/detecting_peering_infrastructure_outages_ucla.pdf",
"http://www.caida.org/publications/papers/2017/detecting_peering_infrastructure_outages/detecting_peering_infrastructure_outages.pdf"
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"title": "Detecting Peering Infrastructure Outages in the Wild",
"venue": "SIGCOMM",
"year": 2017
},
"40f196e21a289394c4354961116587b8accba45e": {
"authors": [
{
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"name": "Mingzhe Hao"
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{
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"name": "Huaicheng Li"
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{
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"name": "Michael Hao Tong"
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{
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"name": "Chrisma Pakha"
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"name": "Riza O. Suminto"
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"name": "Cesar A. Stuardo"
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"name": "Haryadi S. Gunawi"
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"doi": "10.1145/3132747.3132774",
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"paperAbstract": "MittOS provides operating system support to cut millisecond-level tail latencies for data-parallel applications. In MittOS, we advocate a new principle that operating system should quickly reject IOs that cannot be promptly served. To achieve this, MittOS exposes a fast rejecting SLO-aware interface wherein applications can provide their SLOs (e.g., IO deadlines). If MittOS predicts that the IO SLOs cannot be met, MittOS will promptly return EBUSY signal, allowing the application to failover (retry) to another less-busy node without waiting. We build MittOS within the storage stack (disk, SSD, and OS cache managements), but the principle is extensible to CPU and runtime memory managements as well. MittOS' no-wait approach helps reduce IO completion time up to 35% compared to wait-then-speculate approaches.",
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"title": "MittOS: Supporting Millisecond Tail Tolerance with Fast Rejecting SLO-Aware OS Interface",
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"paperAbstract": "We present a new language construct, filtered iterators, for robust input processing. Filtered iterators are designed to eliminate many common input processing errors while enabling robust continued execution. The design is inspired by (1) observed common input processing errors and (2) successful strategies implemented by human developers fixing input processing errors. Filtered iterators decompose inputs into input units and atomically and automatically discard units that trigger errors. Statistically significant results from a developer study demonstrate the effectiveness of filtered iterators in enabling developers to produce robust input processing code without common input processing defects.",
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"http://doi.acm.org/10.1145/3136014.3136030"
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"title": "Robust programs with filtered iterators",
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"name": "Chuan Lei"
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"paperAbstract": "Event processing applications from financial fraud detection to health care analytics continuously execute event queries with Kleene closure to extract event sequences of arbitrary, statically unknown length, called Complete Event Trends (CETs). Due to common event sub-sequences in CETs, either the responsiveness is delayed by repeated computations or an exorbitant amount of memory is required to store partial results. To overcome these limitations, we define the CET graph to compactly encode all CETs matched by a query. Based on the graph, we define the spectrum of CET detection algorithms from CPU-optimal to memory-optimal. We find the middle ground between these two extremes by partitioning the graph into time-centric graphlets and caching partial CETs per graphlet to enable effective reuse of these intermediate results. We reveal cost monotonicity properties of the search space of graph partitioning plans. Our CET optimizer leverages these properties to prune significant portions of the search to produce a partitioning plan with minimal CPU costs yet within the given memory limit. Our experimental study demonstrates that our CET detection solution achieves up to 42--fold speed-up even under rigid memory constraints compared to the state-of-the-art techniques in diverse scenarios.",
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"title": "Complete Event Trend Detection in High-Rate Event Streams",
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"year": 2017
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"name": "Liwei Wu"
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"name": "Cho-Jui Hsieh"
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"paperAbstract": "In this paper, we consider the Collaborative Ranking (CR) problem for recommendation systems. Given a set of pairwise preferences between items for each user, collaborative ranking can be used to rank un-rated items for each user, and this ranking can be naturally used for recommendation. It is observed that collaborative ranking algorithms usually achieve better performance since they directly minimize the ranking loss; however, they are rarely used in practice due to the poor scalability. All the existing CR algorithms have time complexity at least <i>O</i>(|Ω|<i>r</i>) per iteration, where <i>r</i> is the target rank and |Ω| is number of pairs which grows quadratically with number of ratings per user. For example, the Netflix data contains totally 20 billion rating pairs, and at this scale all the current algorithms have to work with significant subsampling, resulting in poor prediction on testing data.\n In this paper, we propose a new collaborative ranking algorithm called Primal-CR that reduces the time complexity to <i>O</i>(|Ω|+<i>d</i><sub>1</sub> |<i>d</i><sub>2</sub> <i>r</i>), where <i>d</i><sub>1</sub> is number of users and |<i>d</i><sub>2</sub> is the averaged number of items rated by a user. Note that <i>d</i><sub>1</sub> |d<sub>2</sub> is strictly smaller and often much smaller than |Ω|.\n Furthermore, by exploiting the fact that most data is in the form of numerical ratings instead of pairwise comparisons, we propose Primal-CR++ with <i>O</i>(<i>d</i><sub>1</sub>|<i>d</i><sub>2</sub> (<i>r</i>+ log |<i>d</i><sub>2</sub>)) time complexity. Both algorithms have better theoretical time complexity than existing approaches and also outperform existing approaches in terms of NDCG and pairwise error on real data sets. To the best of our knowledge, this is the first collaborative ranking algorithm capable of working on the full Netflix dataset using all the 20 billion rating pairs, and this leads to a model with much better recommendation compared with previous models trained on subsamples. Finally, compared with classical matrix factorization algorithm which also requires <i>O</i>(<i>d</i><sub>1</sub><i>d</i><sub>2</sub><i>r</i>) time, our algorithm has almost the same efficiency while making much better recommendations since we consider the ranking loss.",
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"http://www.stat.ucdavis.edu/~chohsieh/rf/KDD_Collaborative_Ranking.pdf",
"http://doi.acm.org/10.1145/3097983.3098071"
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"title": "Large-scale Collaborative Ranking in Near-Linear Time",
"venue": "KDD",
"year": 2017
},
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"authors": [
{
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"name": "Dongyao Chen"
},
{
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"name": "Kang G. Shin"
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{
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"name": "Yurong Jiang"
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{
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"name": "Kyu-Han Kim"
}
],
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"paperAbstract": "We present a smartphone-based application, called LocBLE, for enabling users to estimate the location of nearby Bluetooth low energy (BLE) beacons. In contrast to existing BLE beacon-based proximity applications that can only show coarse-grained (immediate, near, and far) distance estimation, LocBLE's fine-grained estimation can enhance human-environment interactions.\n LocBLE has three salient features in estimating location from BLE beacon signals. First, it is adaptive to dynamic signal propagation environments by learning the environmental changes directly from the received signal strength (RSS). Second, it performs sensor-fusion for location estimation by utilizing motion sensor data and RSS readings from a smartphone. Finally, LocBLE improves location tracking accuracy with novel on-line calibration on a set of beacons nearby. We have built a prototype of LocBLE on smartphones and evaluated it on commodity proximity-enabled beacons. Our experimental results demonstrate that LocBLE achieves an average of 1.8m and 1.2m accuracies in locating indoor and outdoor BLE beacons, respectively.",
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"https://kabru.eecs.umich.edu/wordpress/wp-content/uploads/LocBLE_conext.pdf",
"http://doi.acm.org/10.1145/3143361.3143385"
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"title": "Locating and Tracking BLE Beacons with Smartphones",
"venue": "CoNEXT",
"year": 2017
},
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"name": "Rakesh Pandey"
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"name": "Aryabartta Sahu"
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"doi": "10.1109/HPCC-SmartCity-DSS.2017.43",
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"title": "Efficient Mapping of Multi-threaded Applications onto 3D Stacked Chip-Multiprocessor",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"title": "Hypercallbacks: Decoupling Policy Decisions and Execution",
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"paperAbstract": "Many systems run rich analytics on sensitive data in the cloud, but are prone to data breaches. Hardware enclaves promise data confidentiality and secure execution of arbitrary computation, yet still suffer from access pattern leakage. We propose Opaque, a distributed data analytics platform supporting a wide range of queries while providing strong security guarantees. Opaque introduces new distributed oblivious relational operators that hide access patterns, and new query planning techniques to optimize these new operators. Opaque is implemented on Spark SQL with few changes to the underlying system. Opaque provides data encryption, authentication and computation verification with a performance ranging from 52% faster to 3.3x slower as compared to vanilla Spark SQL; obliviousness comes with a 1.6\u201346x overhead. Opaque provides an improvement of three orders of magnitude over state-of-the-art oblivious protocols, and our query optimization techniques improve performance by 2\u20135x.",
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"paperAbstract": "Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of scientific experiments. We present CamFlow, a whole-system provenance capture mechanism that integrates easily into a PaaS offering. While there have been several prior whole-system provenance systems that captured a comprehensive, systemic and ubiquitous record of a system's behavior, none have been widely adopted. They either A) impose too much overhead, B) are designed for long-outdated kernel releases and are hard to port to current systems, C) generate too much data, or D) are designed for a single system. CamFlow addresses these shortcoming by: 1) leveraging the latest kernel design advances to achieve efficiency; 2) using a self-contained, easily maintainable implementation relying on a Linux Security Module, NetFilter, and other existing kernel facilities; 3) providing a mechanism to tailor the captured provenance data to the needs of the application; and 4) making it easy to integrate provenance across distributed systems. The provenance we capture is streamed and consumed by tenant-built auditor applications. We illustrate the usability of our implementation by describing three such applications: demonstrating compliance with data regulations; performing fault/intrusion detection; and implementing data loss prevention. We also show how CamFlow can be leveraged to capture meaningful provenance without modifying existing applications.",
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"title": "Practical whole-system provenance capture",
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"year": 2017
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"paperAbstract": "This paper introduces PapyrusKV, a parallel embedded key-value store (KVS) for distributed high-performance computing (HPC) architectures that offer potentially massive pools of nonvolatile memory (NVM). PapyrusKV stores keys with their values in arbitrary byte arrays across multiple NVMs in a distributed system. PapyrusKV provides standard KVS operations such as put, get, and delete. More importantly, PapyrusKV provides advanced features for HPC such as dynamic consistency control, zero-copy workflow, and asynchronous checkpoint/restart. Beyond filesystems, PapyrusKV provides HPC programmers with a high-level interface to exploit distributed NVM in the system, and it transparently organizes data to achieve high performance. Also, it allows HPC applications to specialize PapyrusKV to meet their specific requirements. We empirically evaluate PapyrusKV on three HPC systems with real NVM devices: OLCF's Summitdev, TACC's Stampede, and NERSC's Cori. Our results show that PapyrusKV can offer high performance, scalability, and portability across these various distributed NVM architectures.",
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"title": "PapyrusKV: a high-performance parallel key-value store for distributed NVM architectures",
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"year": 2017
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"doi": "10.1145/3132747.3132779",
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"paperAbstract": "It is challenging to simultaneously achieve multicore scalability and high disk throughput in a file system. For example, even for commutative operations like creating different files in the same directory, current file systems introduce cache-line conflicts when updating an in-memory copy of the on-disk directory block, which limits scalability.\n ScaleFS is a novel file system design that decouples the in-memory file system from the on-disk file system using per-core operation logs. This design facilitates the use of highly concurrent data structures for the in-memory representation, which allows commutative operations to proceed without cache conflicts and hence scale perfectly. ScaleFS logs operations in a per-core log so that it can delay propagating updates to the disk representation (and the cache-line conflicts involved in doing so) until an fsync. The fsync call merges the per-core logs and applies the operations to disk. ScaleFS uses several techniques to perform the merge correctly while achieving good performance: timestamped linearization points to order updates without introducing cache-line conflicts, absorption of logged operations, and dependency tracking across operations.\n Experiments with a prototype of ScaleFS show that its implementation has no cache conflicts for 99% of test cases of commutative operations generated by Commuter, scales well on an 80-core machine, and provides on-disk performance that is comparable to that of Linux ext4.",
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"title": "Scaling a file system to many cores using an operation log",
"venue": "SOSP",
"year": 2017
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"name": "Muhammed Nufail Farooqi"
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{
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"name": "Didem Unat"
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{
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"name": "Tan Nguyen"
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{
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"name": "Weiqun Zhang"
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{
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"name": "Ann S. Almgren"
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"name": "John Shalf"
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"doi": "10.1007/978-3-319-64203-1_49",
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"title": "Nonintrusive AMR Asynchrony for Communication Optimization",
"venue": "Euro-Par",
"year": 2017
},
"41fe6f6d4c0947a0b078bd52a9fea0821b539381": {
"authors": [
{
"ids": [
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"name": "Kai Zhang"
},
{
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"name": "Chuanren Liu"
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{
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"name": "Jie Zhang"
},
{
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"name": "Hui Xiong"
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{
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"name": "Eric P. Xing"
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{
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"name": "Jieping Ye"
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],
"doi": "10.1145/3097983.3098050",
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"paperAbstract": "Matrix sketching is aimed at finding compact representations of a matrix while simultaneously preserving most of its properties, which is a fundamental building block in modern scientific computing. Randomized algorithms represent state-of-the-art and have attracted huge interest from the fields of machine learning, data mining, and theoretic computer science. However, it still requires the use of the entire input matrix in producing desired factorizations, which can be a major computational and memory bottleneck in truly large problems. In this paper, we uncover an interesting theoretic connection between matrix low-rank decomposition and lossy signal compression, based on which a cascaded compression sampling framework is devised to approximate an <i>m</i>-by-<i>n</i> matrix in only <i>O</i>(<i>m</i>+<i>n</i>) time and space. Indeed, the proposed method accesses only a small number of matrix rows and columns, which significantly improves the memory footprint. Meanwhile, by sequentially teaming two rounds of approximation procedures and upgrading the sampling strategy from a uniform probability to more sophisticated, encoding-orientated sampling, significant algorithmic boosting is achieved to uncover more granular structures in the data. Empirical results on a wide spectrum of real-world, large-scale matrices show that by taking only linear time and space, the accuracy of our method rivals those state-of-the-art randomized algorithms consuming a quadratic, <i>O</i>(<i>mn</i>), amount of resources.",
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"title": "Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling",
"venue": "KDD",
"year": 2017
},
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"name": "Leonid Barenboim"
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"name": "Michael Elkin"
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{
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"doi": "10.1145/3087801.3087812",
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"title": "Deterministic Distributed (Delta + o(Delta))-Edge-Coloring, and Vertex-Coloring of Graphs with Bounded Diversity",
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"paperAbstract": "Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a given seed node and are faster than traditional global graph clustering methods because their runtime does not depend on the size of the input graph. However, current local graph partitioning methods are not designed to account for the higher-order structures crucial to the network, nor can they effectively handle directed networks. Here we introduce a new class of local graph clustering methods that address these issues by incorporating higher-order network information captured by small subgraphs, also called network motifs. We develop the Motif-based Approximate Personalized PageRank (MAPPR) algorithm that finds clusters containing a seed node with minimal \\emph{motif conductance}, a generalization of the conductance metric for network motifs. We generalize existing theory to prove the fast running time (independent of the size of the graph) and obtain theoretical guarantees on the cluster quality (in terms of motif conductance). We also develop a theory of node neighborhoods for finding sets that have small motif conductance, and apply these results to the case of finding good seed nodes to use as input to the MAPPR algorithm. Experimental validation on community detection tasks in both synthetic and real-world networks, shows that our new framework MAPPR outperforms the current edge-based personalized PageRank methodology.",
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"http://doi.acm.org/10.1145/3097983.3098069",
"http://www.cs.cornell.edu/~arb/papers/local-higher-order-kdd-2017.pdf"
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"title": "Local Higher-Order Graph Clustering",
"venue": "KDD",
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{
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"title": "Energy consumption in Java: An early experience",
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"paperAbstract": "Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or <i>clusters</i>. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (<i>i.e.</i>, walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call <i>Toeplitz Inverse Covariance-based Clustering</i> (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.",
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"paperAbstract": "Algorand is a new cryptocurrency that confirms transactions with latency on the order of a minute while scaling to many users. Algorand ensures that users never have divergent views of confirmed transactions, even if some of the users are malicious and the network is temporarily partitioned. In contrast, existing cryptocurrencies allow for temporary forks and therefore require a long time, on the order of an hour, to confirm transactions with high confidence.\n Algorand uses a new Byzantine Agreement (BA) protocol to reach consensus among users on the next set of transactions. To scale the consensus to many users, Algorand uses a novel mechanism based on Verifiable Random Functions that allows users to privately check whether they are selected to participate in the BA to agree on the next set of transactions, and to include a proof of their selection in their network messages. In Algorand's BA protocol, users do not keep any private state except for their private keys, which allows Algorand to replace participants immediately after they send a message. This mitigates targeted attacks on chosen participants after their identity is revealed.\n We implement Algorand and evaluate its performance on 1,000 EC2 virtual machines, simulating up to 500,000 users. Experimental results show that Algorand confirms transactions in under a minute, achieves 125x Bitcoin's throughput, and incurs almost no penalty for scaling to more users.",
"pdfUrls": [
"http://people.csail.mit.edu/nickolai/papers/gilad-algorand.pdf",
"http://people.csail.mit.edu/nickolai/papers/gilad-algorand-eprint.pdf",
"http://www.mit.edu/~yossigi/Algorand.pdf",
"https://eprint.iacr.org/2017/454.pdf",
"http://eprint.iacr.org/2017/454",
"https://web.eecs.umich.edu/~manosk/assets/papers/algorand.pdf",
"http://doi.acm.org/10.1145/3132747.3132757"
],
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"s2Url": "https://semanticscholar.org/paper/424ada88740ee0d2f1218becdba14ab38aa37804",
"sources": [
"DBLP"
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"title": "Algorand: Scaling Byzantine Agreements for Cryptocurrencies",
"venue": "SOSP",
"year": 2017
},
"4261e50b407355c9dfc0394741a2fae78fea3d43": {
"authors": [
{
"ids": [
"2681668"
],
"name": "Spenser Bauman"
},
{
"ids": [
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],
"name": "Carl Friedrich Bolz-Tereick"
},
{
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],
"name": "Jeremy G. Siek"
},
{
"ids": [
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],
"name": "Sam Tobin-Hochstadt"
}
],
"doi": "10.1145/3133878",
"doiUrl": "https://doi.org/10.1145/3133878",
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"Compiler",
"Data structure",
"Gradual typing",
"Just-in-time compilation",
"Program optimization",
"Programmer",
"PyPy",
"Racket",
"Run time (program lifecycle phase)",
"Symposium on Principles of Programming Languages",
"Tracing just-in-time compilation",
"Typing"
],
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"paperAbstract": "While gradual typing has proven itself attractive to programmers, many systems have avoided sound gradual typing due to the run time overhead of enforcement. In the context of sound gradual typing, both anecdotal and systematic evidence has suggested that run time costs are quite high, and often unacceptable, casting doubt on the viability of soundness as an approach. \nWe show that these overheads are not fundamental, and that with appropriate improvements, <b>just-in-time compilers can greatly reduce the overhead of sound gradual typing</b>. Our study takes benchmarks published in a recent paper on gradual typing performance in Typed Racket (Takikawa et al., POPL 2016) and evaluates them using a experimental tracing JIT compiler for Racket, called Pycket. On typical benchmarks, Pycket is able to eliminate more than 90% of the gradual typing overhead. While our current results are not the final word in optimizing gradual typing, we show that the situation is not dire, and where more work is needed. \nPycketâ\u0080\u0099s performance comes from several sources, which we detail and measure individually. First, we apply a sophisticated tracing JIT compiler and optimizer, automatically generated in Pycket using the RPython framework originally created for PyPy. Second, we focus our optimization efforts on the challenges posed by run time checks, implemented in Racket by <em>chaperones and impersonators</em>. We introduce representation improvements, including a novel use of <em>hidden classes</em> to optimize these data structures.",
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"http://doi.acm.org/10.1145/3133878"
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"title": "Sound gradual typing: only mostly dead",
"venue": "PACMPL",
"year": 2017
},
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"name": "Seher Acer"
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"name": "R. Oguz Selvitopi"
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"name": "Cevdet Aykanat"
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"title": "Addressing Volume and Latency Overheads in 1D-parallel Sparse Matrix-Vector Multiplication",
"venue": "Euro-Par",
"year": 2017
},
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"authors": [
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"name": "Su Yong Kim"
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"name": "Sangho Lee"
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"name": "Insu Yun"
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{
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"name": "Wen Xu"
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"name": "Byoungyoung Lee"
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{
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"name": "Youngtae Yun"
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"name": "Taesoo Kim"
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],
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"Microsoft Windows",
"Operating system",
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"Test automation",
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],
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"paperAbstract": "Discovering the security vulnerabilities of commercial off-the-shelf (COTS) operating systems (OSes) is challenging because they not only are huge and complex, but also lack detailed debug information. Concolic testing, which generates all feasible inputs of a program by using symbolic execution and tests the program with the generated inputs, is one of the most promising approaches to solve this problem. Unfortunately, the state-of-the-art concolic testing tools do not scale well for testing COTS OSes because of state explosion. Indeed, they often fail to find a single bug (or crash) in COTS OSes despite their long execution time. In this paper, we propose CAB-FUZZ (Context-Aware and Boundary-focused), a practical concolic testing tool to quickly explore interesting paths that are highly likely triggering real bugs without debug information. First, CAB-FUZZ prioritizes the boundary states of arrays and loops, inspired by the fact that many vulnerabilities originate from a lack of proper boundary checks. Second, CAB-FUZZ exploits real programs interacting with COTS OSes to construct proper contexts to explore deep and complex kernel states without debug information. We applied CAB-FUZZ to Windows 7 and Windows Server 2008 and found 21 undisclosed unique crashes, including two local privilege escalation vulnerabilities (CVE2015-6098 and CVE-2016-0040) and one information disclosure vulnerability in a cryptography driver (CVE2016-7219). CAB-FUZZ found vulnerabilities that are non-trivial to discover; five vulnerabilities have existed for 14 years, and we could trigger them even in the initial version of Windows XP (August 2001).",
"pdfUrls": [
"https://www.usenix.org/sites/default/files/conference/protected-files/atc17_slides_lee.pdf",
"https://taesoo.gtisc.gatech.edu/pubs/2017/kim:cab-fuzz.pdf",
"https://www.usenix.org/conference/atc17/technical-sessions/presentation/kim",
"https://taesoo.gtisc.gatech.edu/pubs/2017/kim:cab-fuzz-slides.pdf",
"https://www.usenix.org/system/files/conference/atc17/atc17-kim.pdf"
],
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"sources": [
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"title": "CAB-Fuzz: Practical Concolic Testing Techniques for COTS Operating Systems",
"venue": "USENIX Annual Technical Conference",
"year": 2017
},
"4284153a0bf0aa3d0f94ad3113f4d117e4767bef": {
"authors": [
{
"ids": [
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"name": "Matteo Campanelli"
},
{
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"name": "Rosario Gennaro"
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{
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"name": "Steven Goldfeder"
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{
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"name": "Luca Nizzardo"
}
],
"doi": "10.1145/3133956.3134060",
"doiUrl": "https://doi.org/10.1145/3133956.3134060",
"entities": [
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"paperAbstract": "Zero Knowledge Contingent Payment (ZKCP) protocols allow fair exchange of sold goods and payments over the Bitcoin network. In this paper we point out two main shortcomings of current proposals for ZKCP, and propose ways to address them.\n First we show an attack that allows a buyer to learn partial information about the digital good being sold, without paying for it. This break in the zero-knowledge condition of ZKCP is due to the fact that in the protocols we attack, the buyer is allowed to choose common parameters that normally should be selected by a trusted third party. We implemented and tested this attack: we present code that learns, without paying, the value of a Sudoku cell in the \"Pay-to-Sudoku\" ZKCP implementation. We also present ways to fix this attack that do not require a trusted third party.\n Second, we show that ZKCP are not suited for the purchase of digital services} rather than goods. Current constructions of ZKCP do not allow a seller to receive payments after proving that a certain service has been rendered, but only for the sale of a specific digital good. We define the notion of Zero-Knowledge Contingent Service Payment (ZKCSP) protocols and construct two new protocols, for either public or private verification. We implemented our ZKCSP protocols for Proofs of Retrievability, where a client pays the server for providing a proof that the client's data is correctly stored by the server.We also implement a secure ZKCP protocol for \"Pay-to-Sudoku\" via our ZKCSP protocol, which does not require a trusted third party.\n A side product of our implementation effort is a new optimized circuit for SHA256 with less than a quarter than the number of AND gates of the best previously publicly available one. Our new SHA256 circuit may be of independent use for circuit-based MPC and FHE protocols that require SHA256 circuits.",
"pdfUrls": [
"http://stevengoldfeder.com/papers/ZKCSP.pdf",
"http://doi.acm.org/10.1145/3133956.3134060",
"http://eprint.iacr.org/2017/566",
"http://diyhpl.us/~bryan/papers2/bitcoin/Zero-knowledge%20contingent%20payments%20revisited:%20Attacks%20and%20payments%20for%20services.pdf"
],
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"sources": [
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"title": "Zero-Knowledge Contingent Payments Revisited: Attacks and Payments for Services",
"venue": "CCS",
"year": 2017
},
"429b2866bd20df130eceb147034a2dbcd026637c": {
"authors": [
{
"ids": [
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"name": "Yong Zhao"
},
{
"ids": [
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"name": "Kun Suo"
},
{
"ids": [
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"name": "Luwei Cheng"
},
{
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"name": "Jia Rao"
}
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"doi": "10.1145/3135974.3135975",
"doiUrl": "https://doi.org/10.1145/3135974.3135975",
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"paperAbstract": "Many distributed systems require coordination between the components involved. With the steady growth of such systems, the probability of failures increases, which necessitates scalable fault-tolerant agreement protocols. The most common practical agreement protocol, for such scenarios, is leader-based atomic broadcast. In this work, we propose AllConcur, a distributed system that provides agreement through a leaderless concurrent atomic broadcast algorithm, thus, not suffering from the bottleneck of a central coordinator. In AllConcur, all components exchange messages concurrently through a logical overlay network that employs early termination to minimize the agreement latency. Our implementation of AllConcur supports standard sockets-based TCP as well as high-performance InfiniBand Verbs communications. AllConcur can handle up to 135 million requests per second and achieves 17x higher throughput than today's standard leader-based protocols, such as Libpaxos. Thus, AllConcur is highly competitive with regard to existing solutions and, due to its decentralized approach, enables hitherto unattainable system designs in a variety of fields.",
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"title": "AllConcur: Leaderless Concurrent Atomic Broadcast",
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"name": "Jung Ho Ahn"
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"doi": "10.1109/IISWC.2017.8167762",
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"paperAbstract": "As servers are equipped with more memory modules each with larger capacity, main-memory systems are now the second highest energy-consuming component in big-memory servers and their energy consumption even becomes comparable to processors in some servers. Meanwhile, it is critical for big-memory servers and their main-memory systems to offer high energy efficiency. Prior work exploited mobile LPDDR devices' advantages (lower power than DDR devices) while attempting to surmount their limitations (longer latency, lower bandwidth, or both). However, we demonstrate that such main memory architectures (based on the latest LPDDR4 devices) are no longer effective. This is because the power consumption of present DDR4 devices has substantially decreased by adopting the strength of mobile and graphics memory whereas LPDDR4 has sacrificed energy efficiency and focused more on increasing data transfer rates; we also exhibit that the power consumption of DDR4 devices can substantially vary across manufacturers. Moreover, investigating a new energy-saving feature of DDR4 devices in depth, we show that activating this feature often hurts overall energy efficiency of servers due to its performance penalties.",
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"paperAbstract": "We consider load balancing in a network of caching servers delivering contents to end users. Randomized load balancing via the so-called power of two choices is a well-known approach in parallel and distributed systems that reduces network imbalance. In this paper, we propose a randomized load balancing scheme which simultaneously considers cache size limitation and proximity in the server redirection process. Since the memory limitation and the proximity constraint cause correlation in the server selection process, we may not benefit from the power of two choices in general. However, we prove that in certain regimes, in terms of memory limitation and proximity constraint, our scheme results in the maximum load of order Θ(log log n) (here n is the number of servers and requests), and at the same time, leads to a low communication cost. This is an exponential improvement in the maximum load compared to the scheme which assigns each request to the nearest available replica. Finally, we investigate our scheme performance by extensive simulations.",
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"https://arxiv.org/pdf/1610.05961v2.pdf"
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"title": "Proximity-Aware Balanced Allocations in Cache Networks",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"paperAbstract": "Bounded exhaustive testing is an effective methodology for detecting bugs in a wide range of applications. A well-known approach for bounded exhaustive testing is Korat. It generates all test inputs, up to a given small size, based on a formal specification that is written as an executable predicate and characterizes properties of desired inputs. Korat uses the predicate's executions on candidate inputs to implement a backtracking search based on pruning to systematically explore the space of all possible inputs and generate only those that satisfy the specification. \n This paper presents a novel approach for speeding up test generation for bounded exhaustive testing using Korat. The novelty of our approach is two-fold. One, we introduce a new technique for writing the specification predicate based on an abstract representation of candidate inputs, so that the predicate executes directly on these abstract structures and each execution has a lower cost. Two, we use the abstract representation as the basis to define the first technique for utilizing GPUs for systematic test generation using executable predicates. Moreover, we present a suite of optimizations that enable effective utilization of the computational resources offered by modern GPUs. We use our prototype tool KoratG to experimentally evaluate our approach using a suite of 7 data structures that were used in prior studies on bounded exhaustive testing. Our results show that our abstract representation can speed up test generation by 5.68 times on a standard CPU, while execution on a GPU speeds up the execution, on average, by 17.46 times.",
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"year": 2017
},
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"paperAbstract": "Routing algorithms can improve network performance by maximizing routing adaptiveness but can be problematic in the presence of endpoint congestion. Tree-saturation is a well-known behavior caused by endpoint congestion. Adaptive routing can, however, spread the congestion and result in thick branches of the congestion tree -- creating Head-of-Line (HoL) blocking and degrading performance. In this work, we identify how ignoring virtual channels (VCs) and their occupancy during adaptive routing results in congestion trees with thick branches as congestion is spread to all VCs. To address this limitation, we propose Footprint routing algorithm -- a new adaptive routing algorithm that minimizes the size of the congestion tree, both in terms of the number of nodes in the congestion tree as well as branch thickness. Footprint achieves this by regulating adaptiveness by requiring packets to follow the path of prior packets to the same destination if the network is congested instead of forking a new path or VC. Thus, the congestion tree is dynamically kept as slim as possible and reduces HoL blocking or congestion spreading while maintaining high adaptivity and maximizing VC buffer utilization. We evaluate the proposed Footprint routing algorithm against other adaptive routing algorithms and our simulation results show that the network saturation throughput can be improved by up to 43% (58%) compared with the fully adaptive routing (partially adaptive routing) algorithms.",
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"title": "Footprint: Regulating routing adaptiveness in Networks-on-Chip",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"name": "Gabriel Kliot"
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"title": "Geo-distribution of actor-based services",
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"name": "Jingyuan Chen"
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"title": "Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention",
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"year": 2017
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"journalName": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
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"paperAbstract": "Modern DRAM-based systems suffer from significant energy and latency penalties due to conservative DRAM refresh standards. Volatile DRAM cells can retain information across a wide distribution of times ranging from milliseconds to many minutes, but each cell is currently refreshed every 64ms to account for the extreme tail end of the retention time distribution, leading to a high refresh overhead. Due to poor DRAM technology scaling, this problem is expected to get worse in future device generations. Hence, the current approach of refreshing all cells with the worst-case refresh rate must be replaced with a more intelligent design.\n Many prior works propose reducing the refresh overhead by extending the default refresh interval to a higher value, which we refer to as the target refresh interval, across parts or all of a DRAM chip. These proposals handle the small set of failing cells that cannot retain data throughout the entire extended refresh interval via retention failure mitigation mechanisms (e.g., error correcting codes or bit-repair mechanisms). This set of failing cells is discovered via retention failure profiling, which is currently a brute-force process that writes a set of known data to DRAM, disables refresh and waits for the duration of the target refresh interval, and then checks for retention failures across the DRAM chip. We show that this brute-force approach is too slow and is detrimental to system execution, especially with frequent online profiling.\n This paper presents reach profiling, a new methodology for retention failure profiling based on the key observation that an overwhelming majority of failing DRAM cells at a target refresh interval fail more reliably at both longer refresh intervals and higher temperatures. Using 368 state-of-the-art LPDDR4 DRAM chips from three major vendors, we conduct a thorough experimental characterization of the complex set of tradeoffs inherent in the profiling process. We identify three key metrics to guide design choices for retention failure profiling and mitigation mechanisms: coverage, false positive rate, and runtime. We propose reach profiling, a new retention failure profiling mechanism whose key idea is to profile failing cells at a longer refresh interval and/or higher temperature relative to the target conditions in order to maximize failure coverage while minimizing the false positive rate and profiling runtime. We thoroughly explore the tradeoffs associated with reach profiling and show that there is significant room for improvement in DRAM retention failure profiling beyond the brute-force approach. We show with experimental data that on average, by profiling at 250ms above the target refresh interval, our first implementation of reach profiling (called REAPER) can attain greater than 99% coverage of failing DRAM cells with less than a 50% false positive rate while running 2.5x faster than the brute-force approach. In addition, our end-to-end evaluations show that REAPER enables significant system performance improvement and DRAM power reduction, outperforming the brute-force approach and enabling high-performance operation at longer refresh intervals that were previously unreasonable to employ due to the high associated profiling overhead.",
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"http://doi.acm.org/10.1145/3079856.3080242",
"https://people.inf.ethz.ch/omutlu/pub/reaper-dram-retention-profiling-lpddr4_isca17.pdf"
],
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"s2Url": "https://semanticscholar.org/paper/447f492235719d7c2b061b95d818f928d6cbdac5",
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"title": "The reach profiler (REAPER): Enabling the mitigation of DRAM retention failures via profiling at aggressive conditions",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
"44a86aa5b47e158619d2cb815b6dc99201e8f099": {
"authors": [
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"ids": [
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"name": "Wenhao Wang"
},
{
"ids": [
"3044625"
],
"name": "Xiaoyang Xu"
},
{
"ids": [
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],
"name": "Kevin W. Hamlen"
}
],
"doi": "10.1145/3133956.3133986",
"doiUrl": "https://doi.org/10.1145/3133956.3133986",
"entities": [
"Component-based software engineering",
"Confused deputy problem",
"Control flow",
"Control-flow integrity",
"Fault detection and isolation",
"Immutable object",
"Sandbox (computer security)",
"Scalability",
"Virtual method table"
],
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"paperAbstract": "Object flow integrity (OFI) augments control-flow integrity (CFI) and software fault isolation (SFI) protections with secure, first-class support for binary object exchange across inter-module trust boundaries. This extends both source-aware and source-free CFI and SFI technologies to a large class of previously unsupported software: those containing immutable system modules with large, object-oriented APIs---which are particularly common in component-based, event-driven consumer software. It also helps to protect these inter-module object exchanges against confused deputy-assisted vtable corruption and counterfeit object-oriented programming attacks.\n A prototype implementation for Microsoft Component Object Model demonstrates that OFI is scalable to large interfaces on the order of tens of thousands of methods, and exhibits low overheads of under 1% for some common-case applications. Significant elements of the implementation are synthesized automatically through a principled design inspired by type-based contracts.",
"pdfUrls": [
"http://utdallas.edu/~hamlen/wang17ccs.pdf",
"http://doi.acm.org/10.1145/3133956.3133986"
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"sources": [
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"title": "Object Flow Integrity",
"venue": "CCS",
"year": 2017
},
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"name": "Briland Hitaj"
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"name": "Giuseppe Ateniese"
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"name": "Fernando P\u00e9rez-Cruz"
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],
"doi": "10.1145/3133956.3134012",
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"paperAbstract": "Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases.\n Its success is due to a combination of recent algorithmic breakthroughs, increasingly powerful computers, and access to significant amounts of data.\n Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15.\n Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a <i>distributed, federated, or decentralized deep learning</i> approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level differential privacy applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).",
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"http://arxiv.org/abs/1702.07464",
"https://arxiv.org/pdf/1702.07464v3.pdf",
"https://arxiv.org/pdf/1702.07464v1.pdf",
"http://doi.acm.org/10.1145/3133956.3134012",
"https://arxiv.org/pdf/1702.07464v2.pdf"
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"title": "Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning",
"venue": "CCS",
"year": 2017
},
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"name": "Sergei Arnautov"
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"name": "Christof Fetzer"
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"name": "Bohdan Trach"
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"paperAbstract": "With the spreading of multi-core architectures, operating systems and applications are becoming increasingly more concurrent and their scalability is often limited by the primitives used to synchronize the different hardware threads. In this paper, we address the problem of how to optimize the throughput of a system with multiple producer and consumer threads. Such applications typically synchronize their threads via multi-producer/multi-consumer FIFO queues, but existing solutions have poor scalability, as we could observe when designing a secure application framework that requires high-throughput communication between many concurrent threads. In our target system, however, the items enqueued by different producers do not necessarily need to be FIFO ordered. Hence, we propose a fast FIFO queue, FFQ, that aims at maximizing throughput by specializing the algorithm for single-producer/multiple-consumer settings: each producer has its own queue from which multiple consumers can concurrently dequeue. Furthermore, while we provide a wait-free interface for producers, we limit ourselves to lock-free consumers to eliminate the need for helping. We also propose a multi-producer variant to show which synchronization operations we were able to remove by focusing on a single producer variant. Our evaluation analyses the performance using micro-benchmarks and compares our results with other state-of-the-art solutions: FFQ exhibits excellent performance and scalability.",
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"title": "FFQ: A Fast Single-Producer/Multiple-Consumer Concurrent FIFO Queue",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
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},
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"name": "Liqiang Nie"
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"name": "Xiang Wang"
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"name": "Richang Hong"
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"name": "Tat-Seng Chua"
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],
"doi": "10.1145/3077136.3080773",
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"paperAbstract": "Many professional organizations produce regular reports of social indicators to monitor social progress. Despite their reasonable results and societal value, early efforts on social indicator computing suffer from three problems: 1) labor-intensive data gathering, 2) insufficient data, and 3) expert-relied data fusion. Towards this end, we present a novel graph-based multi-channel ranking scheme for social indicator computation by exploring the rich multi-channel Web data. For each channel, this scheme presents the semi-structured and unstructured data with simple graphs and hypergraphs, respectively. It then groups the channels into different clusters according to their correlations. After that, it uses a unified model to learn the cluster-wise common spaces, perform ranking separately upon each space, and fuse these rankings to produce the final one. We take Chinese university ranking as a case study and validate our scheme over a real-world dataset. It is worth emphasizing that our scheme is applicable to computation of other social indicators, such as Educational attainment.",
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"title": "Computational Social Indicators: A Case Study of Chinese University Ranking",
"venue": "SIGIR",
"year": 2017
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"doiUrl": "https://doi.org/10.1145/3133929",
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"paperAbstract": "Providing better supports for debugging type errors has been an active research area in the last three decades. Numerous approaches from different perspectives have been developed. Most approaches work well under certain conditions only, for example, when type errors are caused by single leaves and when type annotations are correct. However, the research community is still unaware of which conditions hold in practice and what the real debugging situations look like. We address this problem with a study of 3 program data sets, which were written in different years, using different compilers, and were of diverse sizes. They include more than 55,000 programs, among which more than 2,700 are ill typed. We investigated all the ill-typed programs, and our results indicate that current error debugging support is far from sufficient in practice since only about 35% of all type errors were caused by single leaves. In addition, type annotations cannot always be trusted in error debuggers since about 30% of the time type errors were caused by wrong type annotations. Our study also provides many insights about the debugging behaviors of students in functional programming, which could be exploited for developing more effective error debuggers.",
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"title": "How type errors were fixed and what students did?",
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"year": 2017
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"paperAbstract": "This paper presents an algorithm based fault tolerance method to harden three two-sided matrix factorizations against soft errors: reduction to Hessenberg form, tridiagonal form, and bidiagonal form. These two sided factorizations are usually the prerequisites to computing eigenvalues/eigenvectors and singular value decomposition. Algorithm based fault tolerance has been shown to work on three main one-sided matrix factorizations: LU, Cholesky, and QR, but extending it to cover two sided factorizations is non-trivial because there are no obvious \\textit{offline, problem} specific maintenance of checksums. We thus develop an \\textit{online, algorithm} specific checksum scheme and show how to systematically adapt the two sided factorization algorithms used in LAPACK and ScaLAPACK packages to introduce the algorithm based fault tolerance.\n The resulting ABFT scheme can detect and correct arithmetic errors \\textit{continuously} during the factorizations that allow timely error handling. Detailed analysis and experiments are conducted to show the cost and the gain in resilience. We demonstrate that our scheme covers a significant portion of the operations of the factorizations. Our checksum scheme achieves high error detection coverage and error correction coverage compared to the state of the art, with low overhead and high scalability.",
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"title": "Silent Data Corruption Resilient Two-sided Matrix Factorizations",
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"year": 2017
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"paperAbstract": "Most load balancing techniques implemented in current data centers tend to rely on a mapping from packets to server IP addresses through a hash value calculated from the flow five-tuple. The hash calculation allows extremely fast packet forwarding and provides flow `stickiness', meaning that all packets belonging to the same flow get dispatched to the same server. Unfortunately, such static hashing may not yield an optimal degree of load balancing, e.g. due to variations in server processing speeds or traffic patterns. On the other hand, dynamic schemes, such as the Join-the-Shortest-Queue (JSQ) scheme, provide a natural way to mitigate load imbalances, but at the expense of stickiness violation.In the present paper we examine the fundamental trade-off between stickiness violation and packet-level latency performance in large-scale data centers. We establish that stringent flow stickiness carries a significant performance penalty in terms of packet-level delay. Moreover, relaxing the stickiness requirement by a minuscule amount is highly effective in clipping the tail of the latency distribution. We further propose a bin-based load balancing scheme that achieves a good balance among scalability, stickiness violation and packet-level delay performance. Extensive simulation experiments corroborate the analytical results and validate the effectiveness of the bin-based load balancing scheme.",
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"paperAbstract": "List comprehensions provide a powerful abstraction mechanism for expressing computations over ordered collections of data declaratively without having to use explicit iteration constructs. This paper puts forth <em>effectful comprehensions</em> as an elegant way to describe list comprehensions that incorporate loop-carried state. This is motivated by operations such as compression/decompression and serialization/deserialization that are common in log/data processing pipelines and require loop-carried state when processing an input stream of data. \nWe build on the underlying theory of <em>symbolic transducers</em> to fuse pipelines of effectful comprehensions into a single representation, from which efficient code can be generated. Using background theory reasoning with an SMT solver, our fusion and subsequent reachability based branch elimination algorithms can significantly reduce the complexity of the fused pipelines. Our implementation shows significant speedups over reasonable hand-written code (3.4\u00c3\u0097, on average) and traditionally fused version of the pipeline (2.6\u00c3\u0097, on average) for a variety of examples, including scenarios for extracting fields with regular expressions, processing XML with XPath, and running queries over encoded data.",
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"name": "Elie Bursztein"
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"paperAbstract": "As HTTPS deployment grows, middlebox and antivirus products are increasingly intercepting TLS connections to retain visibility into network traffic. In this work, we present a comprehensive study on the prevalence and impact of HTTPS interception. First, we show that web servers can detect interception by identifying a mismatch between the HTTP User-Agent header and TLS client behavior. We characterize the TLS handshakes of major browsers and popular interception products, which we use to build a set of heuristics to detect interception and identify the responsible product. We deploy these heuristics at three large network providers: (1) Mozilla Firefox update servers, (2) a set of popular e-commerce sites, and (3) the Cloudflare content distribution network. We find more than an order of magnitude more interception than previously estimated and with dramatic impact on connection security. To understand why security suffers, we investigate popular middleboxes and clientside security software, finding that nearly all reduce connection security and many introduce severe vulnerabilities. Drawing on our measurements, we conclude with a discussion on recent proposals to safely monitor HTTPS and recommendations for the security community. I . I N T R O D U C T I O N When it comes to HTTPS, the security community is working at cross purposes. On the one hand, we are striving to harden and ubiquitously deploy HTTPS in order to provide strong endto-end connection security [5], [20], [22], [23], [34], [51]. At the same time, middlebox and antivirus products increasingly intercept (i.e., terminate and re-initiate) HTTPS connections in an attempt to detect and block malicious content that uses the protocol to avoid inspection [6], [12], [15], [27]. Previous work has found that some specific HTTPS interception products dramatically reduce connection security [7], [12], [58]; however, the broader security impact of such interception remains unclear. In this paper, we conduct the first comprehensive study of HTTPS interception in the wild, quantifying both its prevalence in traffic to major services and its effects on real-world security. We begin by introducing a novel technique for passively detecting HTTPS interception based on handshake characteristics. HTTPS interception products typically function as transparent proxies: they terminate the browser\u2019s TLS connection, inspect the HTTP plaintext, and relay the HTTP data over a new TLS connection to the destination server. We show that web servers can detect such interception by identifying a mismatch between the HTTP User-Agent header and the behavior of the TLS client. TLS implementations display varied support (and preference order) for cipher suites, extensions, elliptic curves, compression methods, and signature algorithms. We characterize these variations for major browsers and popular interception products in order to construct heuristics for detecting interception and identifying the responsible product. Next, we assess the prevalence and impact of HTTPS interception by applying our heuristics to nearly eight billion connection handshakes. In order to avoid the bias inherent in any single network vantage point, we analyzed connections for one week at three major Internet services: (1) Mozilla Firefox update servers, (2) a set of popular e-commerce websites, and (3) the Cloudflare content distribution network. These providers serve different types of content and populations of users, and we find differing rates of interception: 4.0% of Firefox update connections, 6.2% of e-commerce connections, and 10.9% of U.S. Cloudflare connections were intercepted. While these rates vary by vantage point, all are more than an order of magnitude higher than previous estimates [27], [46]. To quantify the real-world security impact of the observed interception, we establish a grading scale based on the TLS features advertised by each client. By applying the metric to unmodified browser handshakes and to the intercepted connections seen at each vantage point, we calculate the change in security for intercepted connections. While for some older clients, proxies increased connection security, these improvements were modest compared to the vulnerabilities introduced: 97% of Firefox, 32% of e-commerce, and 54% of Cloudflare connections that were intercepted became less secure. Alarmingly, not only did intercepted connections use weaker cryptographic algorithms, but 10\u201340% advertised support for known-broken ciphers that would allow an active man-in-the-middle attacker to later intercept, downgrade, and decrypt the connection. A large number of these severely broken connections were due to network-based middleboxes rather than client-side security software: 62% of middlebox connections were less secure and an astounding 58% had severe vulnerabilities enabling later interception. Finally, we attempt to understand why such a large number of intercepted connections are vulnerable by testing the security of a range of popular corporate middleboxes, antivirus products, and other software known to intercept TLS. The default settings for eleven of the twelve corporate middleboxes we evaluated expose connections to known attacks, and five introduce severe vulnerabilities (e.g., incorrectly validate certificates). Similarly, 18 of the 20 client-side security products we tested reduce Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author\u2019s employer if the paper was prepared within the scope of employment. NDSS\u201917, 26 February\u20131 March, 2017, San Diego, CA, USA Internet Society, ISBN 1-891562-46-0 http://dx.doi.org/10.14722/ndss.2017.23456 connection security, and half introduce severe vulnerabilities. In some cases, manufacturers attempted to customize libraries or re-implement TLS, introducing negligent vulnerabilities. In other cases, products shipped with libraries that were years out of date. Across the board, companies are struggling to correctly deploy the base TLS protocol, let alone implement modern HTTPS security features. Our results indicate that HTTPS interception has become startlingly widespread, and that interception products as a class have a dramatically negative impact on connection security. We hope that shedding light on this state of affairs will motivate improvements to existing products, advance work on recent proposals for safely intercepting HTTPS [26], [38], [44], [54], and prompt discussion on long-term solutions. I I . B A C K G R O U N D In this section, we provide a brief background on HTTPS interception and describe the aspects of HTTP and TLS that are relevant to our fingerprinting techniques. We refer the reader to RFC 5280 [14] for a detailed description of TLS.",
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"paperAbstract": "Historically, improvements in GPU-based high performance computing have been tightly coupled to transistor scaling. As Moore's law slows down, and the number of transistors per die no longer grows at historical rates, the performance curve of single monolithic GPUs will ultimately plateau. However, the need for higher performing GPUs continues to exist in many domains. To address this need, in this paper we demonstrate that package-level integration of multiple GPU modules to build larger logical GPUs can enable continuous performance scaling beyond Moore's law. Specifically, we propose partitioning GPUs into easily manufacturable basic GPU Modules (GPMs), and integrating them on package using high bandwidth and power efficient signaling technologies. We lay out the details and evaluate the feasibility of a basic Multi-Chip-Module GPU (MCM-GPU) design. We then propose three architectural optimizations that significantly improve GPM data locality and minimize the sensitivity on inter-GPM bandwidth. Our evaluation shows that the optimized MCM-GPU achieves 22.8% speedup and 5x inter-GPM bandwidth reduction when compared to the basic MCM-GPU architecture. Most importantly, the optimized MCM-GPU design is 45.5% faster than the largest implementable monolithic GPU, and performs within 10% of a hypothetical (and unbuildable) monolithic GPU. Lastly we show that our optimized MCM-GPU is 26.8% faster than an equally equipped Multi-GPU system with the same total number of SMs and DRAM bandwidth.",
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"title": "Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning",
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"doi": "10.1145/3097983.3098030",
"doiUrl": "https://doi.org/10.1145/3097983.3098030",
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"title": "Tracking the Dynamics in Crowdfunding",
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"year": 2017
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"title": "Efficient Dynamic Pinning of Parallelized Applications by Reinforcement Learning with Applications",
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"year": 2017
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"title": "Provably Efficient Scheduling of Cache-oblivious Wavefront Algorithms",
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"year": 2017
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"paperAbstract": "Emerging cloud markets like spot markets and batch computing services scale up services at the granularity of whole VMs. In this paper, we observe that GPU workloads underutilize GPU device memory, leading us to explore the benefits of reallocating heterogeneous GPUs within existing VMs. We outline approaches for upgrading and downgrading GPUs for OpenCL GPGPU workloads, and show how to minimize the chance of cloud operator VM termination by maximizing the heterogeneous environments in which applications can run.",
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"paperAbstract": "Intel Software Guard extensions (SGX) enable secure and trusted execution of user code in an isolated enclave to protect against a powerful adversary. Unfortunately, running I/O-intensive, memory-demanding server applications in enclaves leads to significant performance degradation. Such applications put a substantial load on the in-enclave system call and secure paging mechanisms, which turn out to be the main reason for the application slowdown. In addition to the high direct cost of thousands-of-cycles long SGX management instructions, these mechanisms incur the high indirect cost of enclave exits due to associated TLB flushes and processor state pollution.\n We tackle these performance issues in Eleos by enabling exit-less system calls and exit-less paging in enclaves. Eleos introduces a novel Secure User-managed Virtual Memory (SUVM) abstraction that implements application-level paging inside the enclave. SUVM eliminates the overheads of enclave exits due to paging, and enables new optimizations such as sub-page granularity of accesses.\n We thoroughly evaluate Eleos on a range of microbenchmarks and two real server applications, achieving notable system performance gains. memcached and a face verification server running in-enclave with Eleos, achieves up to 2.2× and 2.3× higher throughput respectively while working on datasets up to 5× larger than the enclave's secure physical memory.",
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"title": "Eleos: ExitLess OS Services for SGX Enclaves",
"venue": "EuroSys",
"year": 2017
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"paperAbstract": "This paper provides an in-depth analysis of the software overheads in the MPI performance-critical path and exposes mandatory performance overheads that are unavoidable based on the MPI-3.1 specification. We first present a highly optimized implementation of the MPI-3.1 standard in which the communication stack---all the way from the application to the low-level network communication API---takes only a few tens of instructions. We carefully study these instructions and analyze the root cause of the overheads based on specific requirements from the MPI standard that are unavoidable under the current MPI standard. We recommend potential changes to the MPI standard that can minimize these overheads. Our experimental results on a variety of network architectures and applications demonstrate significant benefits from our proposed changes.",
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"title": "Why is MPI so slow?: analyzing the fundamental limits in implementing MPI-3.1",
"venue": "SC",
"year": 2017
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"title": "Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark",
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"title": "Determining Application-specific Peak Power and Energy Requirements for Ultra-low Power Processors",
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"paperAbstract": "We propose an interactive approach to resolve static analysis alarms. Our approach synergistically combines a sound but imprecise analysis with precise but unsound heuristics, through user interaction. In each iteration, it solves an optimization problem to find a set of questions for the user such that the expected payoff is maximized. We have implemented our approach in a tool, Ursa, that enables interactive alarm resolution for any analysis specified in the declarative logic programming language Datalog. We demonstrate the effectiveness of Ursa on a state-of-the-art static datarace analysis using a suite of 8 Java programs comprising 41-194 KLOC each. Ursa is able to eliminate 74% of the false alarms per benchmark with an average payoff of 12× per question. Moreover, Ursa prioritizes user effort effectively by posing questions that yield high payoffs earlier.",
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"paperAbstract": "Mobile apps are notorious for collecting a wealth of private information from users. Despite significant effort from the research community in developing privacy leak detection tools based on data flow tracking inside the app or through network traffic analysis, it is still unclear whether apps and ad libraries can hide the fact that they are leaking private information. In fact, all existing analysis tools have limitations: data flow tracking suffers from imprecisions that cause false positives, as well as false negatives when the data flow from a source of private information to a network sink is interrupted; on the other hand, network traffic analysis cannot handle encryption or custom encoding. We propose a new approach to privacy leak detection that is not affected by such limitations, and it is also resilient to obfuscation techniques, such as encoding, formatting, encryption, or any other kind of transformation performed on private information before it is leaked. Our work is based on blackbox differential analysis, and it works in two steps: first, it establishes a baseline of the network behavior of an app; then, it modifies sources of private information, such as the device ID and location, and detects leaks by observing deviations in the resulting network traffic. The basic concept of black-box differential analysis is not novel, but, unfortunately, it is not practical enough to precisely analyze modern mobile apps. In fact, their network traffic contains many sources of non-determinism, such as random identifiers, timestamps, and server-assigned session identifiers, which, when not handled properly, cause too much noise to correlate output changes with input changes. The main contribution of this work is to make black-box differential analysis practical when applied to modern Android apps. In particular, we show that the network-based non-determinism can often be explained and eliminated, and it is thus possible to reliably use variations in the network traffic as a strong signal to detect privacy leaks. We implemented this approach in a tool, called AGRIGENTO, and we evaluated it on more than one thousand Android apps. Our evaluation shows that our approach works well in practice and outperforms current state-of-the-art techniques. We conclude our study by discussing several case studies that show how popular apps and ad libraries currently exfiltrate data by using complex combinations of encoding and encryption mechanisms that other approaches fail to detect. Our results show that these apps and libraries seem to deliberately hide their data leaks from current approaches and clearly demonstrate the need for an obfuscation-resilient approach such as ours.",
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"title": "Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis",
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"paperAbstract": "The method of \"random Fourier features (RFF)\" has become a popular tool for approximating the \"radial basis function (RBF)\" kernel. The variance of RFF is actually large. Interestingly, the variance can be substantially reduced by a simple normalization step as we theoretically demonstrate. We name the improved scheme as the \"normalized RFF (NRFF)\", and we provide a technical proof of the asymptotic variance of NRFF, as validated by simulations.\n We also propose the \"generalized min-max (GMM)\" kernel as a measure of data similarity, where data vectors can have both positive and negative entries. GMM is positive definite as there is an associate hashing method named \"generalized consistent weighted sampling (GCWS)\" which linearizes this (nonlinear) kernel. We provide an extensive empirical evaluation of the RBF and GMM kernels on more than 50 datasets. For a majority of the datasets, the (tuning-free) GMM kernel outperforms the best-tuned RBF kernel.\n We then conduct extensive classification experiments for comparing the linearized RBF kernel using NRFF with the linearized GMM kernel using GCWS. We observe that, in order to reach a similar accuracy, GCWS typically requires substantially fewer samples than NRFF, even on datasets where the original RBF kernel outperforms the original GMM kernel. As the training, storage, and processing costs are directly proportional to the sample size, our experiments can help demonstrate that GCWS would be a more practical scheme for large-scale machine learning applications.\n The empirical success of GCWS (compared to NRFF) can also be explained theoretically, from at least two aspects. Firstly, the relative variance (normalized by the squared expectation) of GCWS is substantially smaller than that of NRFF, except for the very high similarity region (where the variances of both methods approach zero). Secondly, if we are allowed to make a general model assumption on the data, then we can show analytically that GCWS exhibits much smaller variance than NRFF for estimating the same object (e.g., the RBF kernel), except for the very high similarity region.",
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"title": "Linearized GMM Kernels and Normalized Random Fourier Features",
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"doi": "10.1145/3133871",
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"title": "Unifying typing and subtyping",
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"name": "James Caverlee"
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"name": "Xia Hu"
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"title": "SQLizer: query synthesis from natural language",
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"paperAbstract": "Encryption is often employed to protect sensitive information stored in memory and storage. It is the most powerful countermeasure against data breach, but it has performance overhead. As a low-cost alternative to encryption, an access-control memory (ACM) has been introduced, which integrates an access-control mechanism with memory. While ACM minimizes the performance overhead of encryption, it provides similar levels of security as to encryption method. ACM reveals information only when the access codes are correct. However, if an adversary attempts to access data directly from memory cells through a physical attack without going through a standard interface, the vulnerability could occur. This paper discusses feasibility and countermeasures for physical attacks, including fault injection attack, power analysis attack, chip modification, microprobing, and imaging for ACM. Moreover, as a concrete example of ACM, we compare the security aspects of SSDs when the write buffers in the SSDs employ ACM with emerging non-volatile memories such as STTRAM, PRAM, and RRAM.",
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"title": "Vulnerability Analysis of On-Chip Access-Control Memory",
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"year": 2017
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"paperAbstract": "Manycore optimizations are essential for achieving performance worthy of anticipated exascale systems. Utilization of manycore chips is inevitable to attain the desired floating point performance of these energy-austere systems. In this work, we revisit ExaFMM, the open source Fast Multiple Method (FMM) library, in light of highly tuned shared-memory parallelization and detailed performance analysis on the new highly parallel Intel manycore architecture, Knights Landing (KNL). We assess scalability and performance gain using task-based parallelism of the FMM tree traversal. We also provide an in-depth analysis of the most computationally intensive part of the traversal kernel (i.e., the particle-to-particle (P2P) kernel), by comparing its performance across KNL and Broadwell architectures. We quantify different configurations that exploit the on-chip 512-bit vector units within different taskbased threading paradigms. MPI communication-reducing and NUMAaware approaches for the FMM\u2019s global tree data exchange are examined with different cluster modes of KNL. By applying several algorithmand architecture-aware optimizations for FMM, we show that the N -Body kernel on 256 threads of KNL achieves on average 2.8\u00d7 speedup compared to the non-vectorized version, whereas on 56 threads of Broadwell, it achieves on average 2.9\u00d7 speedup. In addition, the tree traversal kernel on KNL scales monotonically up to 256 threads with task-based programming models. The MPI-based communication-reducing algorithms show expected improvements of the data locality across the KNL on-chip network.",
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"title": "Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture",
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"year": 2017
},
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"doi": "10.1145/3078468.3078481",
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"paperAbstract": "One of the main motivations for the shift to the Cloud (and the more recent shift of telco operators into NFV) is cost reduction due to high utilization of infrastructure resources. However, achieving high utilization in practical scenarios is complex since the term \"resources\" covers different orthogonal aspects, such as server CPU, storage (or disk) usage and network capacity, and the workload characterization varies over time and over different users.\n In this paper we study the placement of Virtual Machines (VMs) that implement services over the physical infrastructure, trying to understand what makes a placement scheme better than others in the overall utilization of the various resources. We show that the multidimensional case is inherently different from the single dimension case, and develop novel placement heuristics to address the specific challenges. We then show, by extensive evaluation over real data, that operators can significantly improve their resource utilization by selecting the most appropriate placement policy, according to their system specifications and the deployed services. In particular, two of our new heuristics that dynamically change the placement logic according to the amount of available (unused) resources are shown to perform very well in many practical scenarios.",
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"paperAbstract": "Complex code bases with several layers of abstractions have abundant inefficiencies that affect the execution time. Value redundancy is a kind of inefficiency where the same values are repeatedly computed, stored, or retrieved over the course of execution. Not all redundancies can be easily detected or eliminated with compiler optimization passes due to the inherent limitations of the static analysis.\n Microscopic observation of whole executions at instruction- and operand-level granularity breaks down abstractions and helps recognize redundancies that masquerade in complex programs. We have developed REDSPY---a fine-grained profiler to pinpoint and quantify redundant operations in program executions. Value redundancy may happen over time at same locations or in adjacent locations, and thus it has temporal and spatial locality. REDSPY identifies both temporal and spatial value locality. Furthermore, REDSPY is capable of identifying values that are approximately the same, enabling optimization opportunities in HPC codes that often use floating point computations. REDSPY provides intuitive optimization guidance by apportioning redundancies to their provenance---source lines and execution calling contexts. REDSPY pinpointed dramatically high volume of redundancies in programs that were optimization targets for decades, such as SPEC CPU2006 suite, Rodinia benchmark, and NWChem---a production computational chemistry code. Guided by REDSPY, we were able to eliminate redundancies that resulted in significant speedups.",
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"title": "REDSPY: Exploring Value Locality in Software",
"venue": "ASPLOS",
"year": 2017
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{
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"name": "Hyunwoo Yu"
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{
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{
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"name": "Younghoon Kim"
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"paperAbstract": "Today, search for dashcam video evidences is conducted manually and its procedure does not guarantee privacy. In this paper, we motivate, design, and implement ViewMap, an automated public service system that enables sharing of private dashcam videos under anonymity. ViewMap takes a profile-based approach where each video is represented in a compact form called a view profile (VP), and the anonymized VPs are treated as entities for search, verification, and reward instead of their owners. ViewMap exploits the line-of-sight (LOS) properties of dedicated short-range communications (DSRC) such that each vehicle makes VP links with nearby ones that share the same sight while driving. ViewMap uses such LOS-based VP links to build a map of visibility around a given incident, and identifies VPs whose videos are worth reviewing. Original videos are never transmitted unless they are verified to be taken near the incident and anonymously solicited. ViewMap offers untraceable rewards for the provision of videos whose owners remain anonymous. We demonstrate the feasibility of ViewMap via field experiments on real roads using our DSRC testbeds and trace-driven simulations.",
"pdfUrls": [
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"https://www.usenix.org/sites/default/files/conference/protected-files/nsdi17_slides_kim_0.pdf",
"https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/kim-minho",
"https://www.usenix.org/system/files/conference/nsdi17/nsdi17-kim-minho.pdf"
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"title": "ViewMap: Sharing Private In-Vehicle Dashcam Videos",
"venue": "NSDI",
"year": 2017
},
"492c2218f1cd089cbd336b96275e5afbc2a5b648": {
"authors": [
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"name": "Sebastian Lekies"
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"name": "Krzysztof Kotowicz"
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"name": "Samuel Gro\u00df"
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"name": "Eduardo A. Vela Nava"
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{
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"name": "Martin Johns"
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"doi": "10.1145/3133956.3134091",
"doiUrl": "https://doi.org/10.1145/3133956.3134091",
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"paperAbstract": "Cross-Site Scripting (XSS) is an unremitting problem for the Web. Since its initial public documentation in 2000 until now, XSS has been continuously on top of the vulnerability statistics. Even though there has been a considerable amount of research and developer education to address XSS on the source code level, the overall number of discovered XSS problems remains high. Because of this, various approaches to mitigate XSS have been proposed as a second line of defense, with HTML sanitizers, Web Application Firewalls, browser-based XSS filters, and the Content Security Policy being some prominent examples. Most of these mechanisms focus on script tags and event handlers, either by removing them from user-provided content or by preventing their script code from executing.\n In this paper, we demonstrate that this approach is no longer sufficient for modern applications: We describe a novel Web attack that can circumvent all of theses currently existing XSS mitigation techniques. In this attack, the attacker abuses so called script gadgets (legitimate JavaScript fragments within an application's legitimate code base) to execute JavaScript. In most cases, these gadgets utilize DOM selectors to interact with elements in the Web document. Through an initial injection point, the attacker can inject benign-looking HTML elements which are ignored by these mitigation techniques but match the selector of the gadget. This way, the attacker can hijack the input of a gadget and cause processing of his input, which in turn leads to code execution of attacker-controlled values. We demonstrate that these gadgets are omnipresent in almost all modern JavaScript frameworks and present an empirical study showing the prevalence of script gadgets in productive code. As a result, we assume most mitigation techniques in web applications written today can be bypassed.",
"pdfUrls": [
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"title": "Code-Reuse Attacks for the Web: Breaking Cross-Site Scripting Mitigations via Script Gadgets",
"venue": "CCS",
"year": 2017
},
"492f7339ff5744a0d2723102828e4bac50a2cc98": {
"authors": [
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"name": "XianWei Zhang"
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"name": "Youtao Zhang"
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"name": "Bruce R. Childers"
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"name": "Jun Yang"
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"doi": "10.1109/PACT.2017.34",
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"title": "Scalable Kernel Density Classification via Threshold-Based Pruning",
"venue": "SIGMOD Conference",
"year": 2017
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"name": "Shashank Gugnani"
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"name": "Xiaoyi Lu"
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"doi": "10.1109/HiPC.2017.00033",
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"title": "MPI-LiFE: Designing High-Performance Linear Fascicle Evaluation of Brain Connectome with MPI",
"venue": "2017 IEEE 24th International Conference on High Performance Computing (HiPC)",
"year": 2017
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"paperAbstract": "Large-scale information processing often relies on subset matching for data classification and routing. Examples are publish/subscribe and stream processing systems, database systems, social media, and information-centric networking. For instance, an advanced Twitter-like messaging service where users might follow specific publishers as well as specific topics encoded as tag sets must join a stream of published messages with the users and their preferred tag sets so that the user tag set is a subset of the message tags.\n Subset matching is an old but also notoriously difficult problem. We present TagMatch, a system that solves this problem by taking advantage of a hybrid CPU/GPU stream processing architecture. TagMatch targets large-scale applications with thousands of matching operations per seconds against hundreds of millions of tag sets. We evaluate TagMatch on an advanced message streaming application, with very positive results both in absolute terms and in comparison with existing systems. As a notable example, our experiments demonstrate that TagMatch running on a single, commodity machine with two GPUs can easily sustain the traffic throughput of Twitter even augmented with expressive tag-based selection.",
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"paperAbstract": "As graphs continue to grow, external memory graph processing systems serve as a promising alternative to inmemory solutions for low cost and high scalability. Unfortunately, not only does this approach require considerable efforts in programming and IO management, but its performance also lags behind, in some cases by an order of magnitude. In this work, we strive to achieve an ambitious goal of achieving ease of programming and high IO performance (as in-memory processing) while maintaining graph data on disks (as external memory processing). To this end, we have designed and developed Graphene that consists of four new techniques: an IO request centric programming model, bitmap based asynchronous IO, direct hugepage support, and data and workload balancing. The evaluation shows that Graphene can not only run several times faster than several external-memory processing systems, but also performs comparably with in-memory processing on large graphs.",
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"title": "Graphene: Fine-Grained IO Management for Graph Computing",
"venue": "FAST",
"year": 2017
},
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"name": "Shijie Jia"
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"paperAbstract": "Mobile devices today have been increasingly used to store and process sensitive information. To protect sensitive data, mobile operating systems usually incorporate a certain level of encryption to protect sensitive data. However, conventional encryption cannot defend against a coercive attacker who can capture the device owner, and force the owner to disclose keys used for decrypting sensitive information. To defend against such a coercive adversary, Plausibly Deniable Encryption (PDE) was introduced to allow the device owner to deny the very existence of sensitive data stored on his/her device. The existing PDE systems, built on flash storage devices, are problematic, since they either neglect the special nature of the underlying storage medium (which is usually NAND flash), or suffer from deniability compromises.\n In this paper, we propose DEFTL, a Deniability Enabling Flash Translation Layer for devices which use flash-based block devices as storage media. DEFTL is the first PDE design which incorporates deniability to Flash Translation Layer (FTL), a pervasively deployed \"translation layer\" which stays between NAND flash and the file system in literally all the computing devices. A salient advantage of DEFTL lies in its capability of achieving deniability while being able to accommodate the special nature of NAND flash as well as eliminate deniability compromises from it. We implement DEFTL using an open-source NAND flash controller. The experimental results show that, compared to conventional encryption which does not provide deniability, our DEFTL design only incurs a small overhead.",
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"title": "DEFTL: Implementing Plausibly Deniable Encryption in Flash Translation Layer",
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"year": 2017
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"name": "Liqun Chen"
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"name": "Manu Drijvers"
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"name": "Anja Lehmann"
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"name": "Rainer Urian"
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"paperAbstract": "The Trusted Platform Module (TPM) is an international standard for a security chip that can be used for the management of cryptographic keys and for remote attestation. The specification of the most recent TPM 2.0 interfaces for direct anonymous attestation unfortunately has a number of severe shortcomings. First of all, they do not allow for security proofs (indeed, the published proofs are incorrect). Second, they provide a Diffie-Hellman oracle w.r.t. the secret key of the TPM, weakening the security and preventing forward anonymity of attestations. Fixes to these problems have been proposed, but they create new issues: they enable a fraudulent TPM to encode information into an attestation signature, which could be used to break anonymity or to leak the secret key. Furthermore, all proposed ways to remove the Diffie-Hellman oracle either strongly limit the functionality of the TPM or would require significant changes to the TPM 2.0 interfaces. In this paper we provide a better specification of the TPM 2.0 interfaces that addresses these problems and requires only minimal changes to the current TPM 2.0 commands. We then show how to use the revised interfaces to build q-SDH-and LRSW-based anonymous attestation schemes, and prove their security. We finally discuss how to obtain other schemes addressing different use cases such as key-binding for U-Prove and e-cash.",
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"title": "One TPM to Bind Them All: Fixing TPM 2.0 for Provably Secure Anonymous Attestation",
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"year": 2017
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"name": "Travis Mayberry"
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"paperAbstract": "Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to <i>linear</i> w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.",
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"title": "Efficient Correlated Topic Modeling with Topic Embedding",
"venue": "KDD",
"year": 2017
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"paperAbstract": "While the Java runtime is installed on billions of devices and servers worldwide, it remains a primary attack vector for online criminals. As recent studies show, the majority of all exploited Java vulnerabilities comprise incorrect or insufficient implementations of access-control checks. This paper for the first time studies the problem in depth. As we find, attacks are enabled by shortcuts that short-circuit Java\u2019s general principle of stack-based access control. These shortcuts, originally introduced for ease of use and to improve performance, cause Java to elevate the privileges of code implicitly. As we show, this creates many pitfalls for software maintenance, making it all too easy for maintainers of the runtime to introduce blatant confuseddeputy vulnerabilities even by just applying normally semanticspreserving refactorings. How can this problem be solved? Can one implement Java\u2019s access control without shortcuts, and if so, does this implementation remain usable and efficient? To answer those questions, we conducted a tool-assisted adaptation of the Java Class Library (JCL), avoiding (most) shortcuts and therefore moving to a fully explicit model of privilege elevation. As we show, the proposed changes significantly harden the JCL against attacks: they effectively hinder the introduction of new confused-deputy vulnerabilities in future library versions, and successfully restrict the capabilities of attackers when exploiting certain existing vulnerabilities. We discuss usability considerations, and through a set of large-scale experiments show that with current JVM technology such a faithful implementation of stack-based access control induces no observable performance loss.",
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"title": "Hardening Java's Access Control by Abolishing Implicit Privilege Elevation",
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"paperAbstract": "The popularization of video capture devices has created strong storage demand for encoded videos. Approximate storage can ease this demand by enabling denser storage at the expense of occasional errors. Unfortunately, even minor storage errors, such as bit flips, can result in major visual damage in encoded videos. Similarly, video encryption, widely employed for privacy and digital rights management, may create long dependencies between bits that show little or no tolerance to storage errors.\n In this paper we propose VideoApp, a novel and efficient methodology to compute bit-level reliability requirements for encoded videos by tracking visual and metadata dependencies within encoded bitstreams. We further show how VideoApp can be used to trade video quality for storage density in an optimal way. We integrate our methodology into a popular H.264 encoder to partition an encoded video stream into multiple streams that can receive different levels of error correction according to their reliability needs. When applied to a dense and highly error-prone multi-level cell storage substrate, our variable error correction mechanism reduces the error correction overhead by half under the most error-intolerant encoder settings, achieving quality/density points that neither compression nor approximation can achieve alone. Finally, we define the basic invariants needed to support encrypted approximate video storage. We present an analysis of block cipher modes of operation, showing that some are fully compatible with approximation, enabling approximate and secure video storage systems.",
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"paperAbstract": "Secure multiparty computation enables a set of parties to securely carry out a joint computation of their private inputs without revealing anything but the output. In the past few years, the efficiency of secure computation protocols has increased in leaps and bounds. However, when considering the case of security in the presence of malicious adversaries (who may arbitrarily deviate from the protocol specification), we are still very far from achieving high efficiency. In this paper, we consider the specific case of three parties and an honest majority. We provide general techniques for improving efficiency of cut-and-choose protocols on multiplication triples and utilize them to significantly improve the recently published protocol of Furukawa et al. (ePrint 2016/944). We reduce the bandwidth of their protocol down from 10 bits per AND gate to 7 bits per AND gate, and show how to improve some computationally expensive parts of their protocol. Most notably, we design cache-efficient shuffling techniques for implementing cut-and-choose without randomly permuting large arrays (which is very slow due to continual cache misses). We provide a combinatorial analysis of our techniques, bounding the cheating probability of the adversary. Our implementation achieves a rate of approximately 1.15 billion AND gates per second on a cluster of three 20-core machines with a 10Gbps network. Thus, we can securely compute 212,000 AES encryptions per second (which is hundreds of times faster than previous work for this setting). Our results demonstrate that high-throughput secure computation for malicious adversaries is possible.",
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"title": "Optimized Honest-Majority MPC for Malicious Adversaries — Breaking the 1 Billion-Gate Per Second Barrier",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"name": "Yufei Tao"
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"paperAbstract": "Parallel join algorithms have received much attention in recent years, due to the rapid development of massively parallel systems such as MapReduce and Spark. In the database theory community, most efforts have been focused on studying worst-optimal algorithms. However, the worst-case optimality of these join algorithms relies on the hard instances having very large output sizes. In the case of a two-relation join, the hard instance is just a Cartesian product, with an output size that is quadratic in the input size.\n In practice, however, the output size is usually much smaller. One recent parallel join algorithm by Beame et al.[8] has achieved <i>output-optimality</i>, i.e., its cost is optimal in terms of both the input size and the output size, but their algorithm only works for a 2-relation equi-join, and has some imperfections. In this paper, we first improve their algorithm to true optimality. Then we design output-optimal algorithms for a large class of similarity joins. Finally, we present a lower bound, which essentially eliminates the possibility of having output-optimal algorithms for any join on more than two relations.",
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"https://home.cse.ust.hk/~xhuam/pods085.pdf",
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"title": "Output-optimal Parallel Algorithms for Similarity Joins",
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"year": 2017
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"name": "Amelie Chi Zhou"
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"paperAbstract": "Recently, various applications including data analytics and machine learning have been developed for geo-distributed cloud data centers. For those applications, the ways to map parallel processes to physical nodes (i.e., \"process mapping\") could significantly impact the performance of the applications because of non-uniform communication cost in such geo-distributed environments. While process mapping has been widely studied in grid/cluster environments, few of the existing studies have considered the problem in geo-distributed cloud environments. In this paper, we propose a novel model to formulate the geo-distributed process mapping problem and develop a new method to efficiently find the near optimal solution. Our algorithm considers both the network communication performance of geo-distributed data centers as well as the communication matrix of the target application. Evaluation results with real experiments on Amazon EC2 and simulations demonstrate that our proposal achieves significant performance improvement (50% on average) compared to the state-of-the-art algorithms.",
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"title": "Efficient process mapping in geo-distributed cloud data centers",
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"paperAbstract": "Triangle listing has been a long-standing problem, with many heuristics, bounds, and experimental results, but not much asymptotically accurate complexity analysis. To address this issue, we introduce a novel stochastic framework, based on Glivenko-Cantelli results for functions of order statistics, that allows modeling cost of in-memory triangle enumeration in families of random graphs. Unlike prior work that usually studies the <i>O</i>(.) notation, we derive the exact limits of CPU complexity of all vertex/edge iterators under arbitrary acyclic orientations as graph size <i>n</i> → ∞. These results are obtained in simple closed form as functions of the degree distribution. This allows us to establish optimal orientations for all studied algorithms, compare them to each other, and discover the best technique within each class.",
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"paperAbstract": "Event races are a common source of subtle errors in JavaScript web applications. Several automated tools for detecting event races have been developed, but experiments show that their accuracy is generally quite low. We present a new approach that focuses on three categories of event race errors that often appear during the initialization phase of web applications: form-input-overwritten errors, late-event-handler-registration errors, and access-before-definition errors. The approach is based on a dynamic analysis that uses a combination of adverse and approximate execution. Among the strengths of the approach are that it does not require browser modifications, expensive model checking, or static analysis. \n In an evaluation on 100 widely used websites, our tool InitRacer reports 1085 initialization races, while providing informative explanations of their causes and effects. A manual study of 218 of these reports shows that 111 of them lead to uncaught exceptions and at least 47 indicate errors that affect the functionality of the websites.",
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"title": "Micro-benchmarking MPI Neighborhood Collective Operations",
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"doi": "10.1109/HPCC-SmartCity-DSS.2017.24",
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"title": "Protecting Bare-Metal Embedded Systems with Privilege Overlays",
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"title": "Drizzle: Fast and Adaptable Stream Processing at Scale",
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"name": "Ang Chen"
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"name": "Andreas Haeberlen"
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"name": "Wenchao Zhou"
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"doi": "10.1145/3064176.3064212",
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"paperAbstract": "Today, network operators are increasingly playing the role of part-time detectives: they must routinely diagnose intricate problems and malfunctions, e.g., routing or performance issues, and they must often perform forensic investigations of past misbehavior, e.g., intrusions or cybercrimes. However, the current Internet architecture offers little direct support for them. A variety of solutions have been proposed, but each solution tends to address only one specific problem. Moreover, each solution proposes a different fix that is incompatible with the others, which complicates deployment.\n In this paper, we make the observation that most of the existing solutions share a common \"functional core\", which suggests that it may be possible to add a single primitive to the Internet architecture that can support a wide variety of diagnostic and forensic tasks. We then present one specific candidate that we call secure packet provenance (SPP). We show that SPP is easy to add to the current architecture, that it can be implemented efficiently in both software and hardware, and that it can be used to approximate (and sometimes surpass) the capabilities offered by a variety of existing diagnostic and forensic systems.",
"pdfUrls": [
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"http://www.seas.upenn.edu/~angchen/papers/eurosys-2017.pdf",
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"title": "One Primitive to Diagnose Them All: Architectural Support for Internet Diagnostics",
"venue": "EuroSys",
"year": 2017
},
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"name": "Chengxi Zang"
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"name": "Peng Cui"
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"name": "Christos Faloutsos"
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"name": "Wenwu Zhu"
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"doi": "10.1145/3097983.3098055",
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"title": "Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity",
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"paperAbstract": "We analyze how modern distributed storage systems behave in the presence of file-system faults such as data corruption and read and write errors. We characterize eight popular distributed storage systems and uncover numerous bugs related to file-system fault tolerance. We find that modern distributed systems do not consistently use redundancy to recover from file-system faults: a single file-system fault can cause catastrophic outcomes such as data loss, corruption, and unavailability. Our results have implications for the design of next generation fault-tolerant distributed and cloud storage systems.",
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"paperAbstract": "Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named <b>NRT</b> which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to \"translate'' user and item latent representations into a concise sentence. Extensive experiments on benchmark datasets from different domains show that NRT achieves significant improvements over the state-of-the-art methods. Moreover, the generated tips can vividly predict the user experience and feelings.",
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"paperAbstract": "A novel efficient inplace, multithreaded, and cachefriendly parallel 2-D wavelet transform algorithm based on the lifting transform is introduced. In order to maximize the cache utilization and consequently minimize the memory bus bandwidth use, the threads compete to work on a small memory area maximizing the chance of finding it in the cache and their synchronization is done with very low overhead without the use of any locks and relying solely on the basic compare-and-swap (CAS) atomic primitive. An implementation in the C programming language with and without the use of vector (single instruction multiple data - SIMD) instructions is provided for both single (serial) and multi (parallel) threaded single-loop DWT implementations as well as serial and parallel naive implementations using linear (row order) and strided (column order) memory access patterns for comparison. Results show a significant improvement over the single-threaded optimized implementation and a much greater improvement over both the single and multi threaded naive implementations, reaching minimum running time depending on the number of processor cores and the available memory bus bandwidth, i.e., it becomes memory bound using the minimum number of memory accesses. Given the simplicity and high speed of the lifting steps, an analysis based on the number of memory bus operations (read and write) is done for images that are larger than twice the shared cache size which establishes a lower bound for the running time of all linear memory access algorithms and also determines the maximum speed gains to be expected in relation to currently implemented parallel schemes based on the parallel execution of independent lifting steps. It also shows the optimality of the parallel algorithm presented. Finally, a comparison with currently available implementations shows the gains achieved by the proposed algorithm.",
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"title": "A Novel Minimum Time Parallel 2-D Discrete Wavelet Transform Algorithm for General Purpose Processors",
"venue": "2017 46th International Conference on Parallel Processing (ICPP)",
"year": 2017
},
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"name": "Elliott Slaughter"
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"name": "Wonchan Lee"
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{
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"name": "Sean Treichler"
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"name": "Wen Zhang"
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"name": "Galen M. Shipman"
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"name": "Patrick S. McCormick"
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{
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"name": "Alexander Aiken"
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"doi": "10.1145/3126908.3126949",
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"paperAbstract": "We present control <i>replication</i>, a technique for generating high-performance and scalable SPMD code from implicitly parallel programs. In contrast to traditional parallel programming models that require the programmer to explicitly manage threads and the communication and synchronization between them, implicitly parallel programs have sequential execution semantics and naturally avoid the pitfalls of explicitly parallel code. However, without optimizations to distribute control overhead, scalability is often poor.\n Performance on distributed-memory machines is especially sensitive to communication and synchronization in the program, and thus optimizations for these machines require an intimate understanding of a program's memory accesses. Control replication achieves particularly effective and predictable results by leveraging language support for first-class data partitioning in the source programming model. We evaluate an implementation of control replication for Regent and show that it achieves up to 99% parallel efficiency at 1024 nodes with absolute performance comparable to hand-written MPI(+X) codes.",
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"http://legion.stanford.edu/pdfs/cr2017.pdf",
"http://doi.acm.org/10.1145/3126908.3126949"
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"title": "Control replication: compiling implicit parallelism to efficient SPMD with logical regions",
"venue": "SC",
"year": 2017
},
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"authors": [
{
"ids": [
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],
"name": "Andrea E. F. Clementi"
},
{
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],
"name": "Luciano Gual\u00e0"
},
{
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],
"name": "Guido Proietti"
},
{
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],
"name": "Giacomo Scornavacca"
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],
"doi": "10.1109/IPDPS.2017.67",
"doiUrl": "https://doi.org/10.1109/IPDPS.2017.67",
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],
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"paperAbstract": "The rational fair consensus problem can be informally defined as follows. Consider a network of n (selfish) rational agents, each of them initially supporting a color chosen from a finite set Σ. The goal is to design a protocol that leads the network to a stable monochromatic configuration (i.e. a consensus) such that the probability thatthe winning color is c is equal to the fraction of the agents that initially support c, for any c Σ Σ. Furthermore, this fairness property must be guaranteed (with high probability) even in presence of any fixed coalition of rational agents that may deviate fromthe protocol in order to increase the winning probability of their supported colors. A protocol having this property, in presence of coalitions of size at most t, is said to be a whp\\,-t-strong equilibrium. We investigate, for the first time, the rational fair consensus problem in the Gossip communication model where, at everyround, every agent can actively contact at most one neighbor via a push/pull operation. We provide a randomized Gossip protocol that, starting from any initial color configuration of the complete graph, achieves rational fair consensus within O(log n) rounds using messages of O(log2 n) size, w.h.p. More in details, we prove that our protocol is a whpt-strong equilibrium for any t = o(n/log n) and, moreover, it tolerates worst-case permanent faults provided that the number of non-faulty agents is Ω(n). As far as we know, our protocol is the first solution which avoids any all-to-all communication, thus resulting in o(n2) message complexity.",
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"title": "Rational Fair Consensus in the Gossip Model",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"authors": [
{
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"name": "Srikanth Sundaresan"
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"name": "Xiaohong Deng"
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{
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"name": "Yun Feng"
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{
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"name": "Danny Lee"
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{
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"name": "Amogh Dhamdhere"
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],
"doi": "10.1145/3131365.3131382",
"doiUrl": "https://doi.org/10.1145/3131365.3131382",
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"paperAbstract": "We revisit the use of crowdsourced throughput measurements to infer and localize congestion on end-to-end paths, with particular focus on points of interconnections between ISPs. We analyze three challenges with this approach. First, accurately identifying which link on the path is congested requires fine-grained network tomography techniques not supported by existing throughput measurement platforms. Coarse-grained network tomography can perform this link identification under certain topological conditions, but we show that these conditions do not always hold on the global Internet. Second, existing measurement platforms provide limited visibility of paths to popular web content sources, and only capture a small fraction of interconnections between ISPs. Third, crowdsourcing measurements inherently risks sample bias: using measurements from volunteers across the Internet leads to uneven distribution of samples across time of day, access link speeds, and home network conditions. Finally, it is not clear how large a drop in throughput to interpret as evidence of congestion. We investigate these challenges in detail, and offer guidelines for deployment of measurement infrastructure, strategies, and technologies that can address empirical gaps in our understanding of congestion on the Internet.",
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"http://www.caida.org/publications/papers/2017/challenges_inferring_internet_congestion/challenges_inferring_internet_congestion.pdf",
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"http://doi.acm.org/10.1145/3131365.3131382"
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"title": "Challenges in inferring internet congestion using throughput measurements",
"venue": "IMC",
"year": 2017
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"authors": [
{
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"name": "Yan Shoshitaishvili"
},
{
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"name": "Michael Weissbacher"
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{
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"name": "Lukas Dresel"
},
{
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"name": "Christopher Salls"
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{
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"name": "Ruoyu Wang"
},
{
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"name": "Christopher Kr\u00fcgel"
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{
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"name": "Giovanni Vigna"
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],
"doi": "10.1145/3133956.3134105",
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"paperAbstract": "Software permeates every aspect of our world, from our homes to the infrastructure that provides mission-critical services.\n As the size and complexity of software systems increase, the number and sophistication of software security flaws increase as well. The analysis of these flaws began as a manual approach, but it soon became apparent that a manual approach alone cannot scale, and that tools were necessary to assist human experts in this task, resulting in a number of techniques and approaches that automated certain aspects of the vulnerability analysis process.\n Recently, DARPA carried out the Cyber Grand Challenge, a competition among autonomous vulnerability analysis systems designed to push the tool-assisted human-centered paradigm into the territory of complete automation, with the hope that, by removing the human factor, the analysis would be able to scale to new heights. However, when the autonomous systems were pitted against human experts it became clear that certain tasks, albeit simple, could not be carried out by an autonomous system, as they require an understanding of the logic of the application under analysis.\n Based on this observation, we propose a shift in the vulnerability analysis paradigm, from tool-assisted human-centered to human-assisted tool-centered. In this paradigm, the automated system orchestrates the vulnerability analysis process, and leverages humans (with different levels of expertise) to perform well-defined sub-tasks, whose results are integrated in the analysis. As a result, it is possible to scale the analysis to a larger number of programs, and, at the same time, optimize the use of expensive human resources.\n In this paper, we detail our design for a human-assisted automated vulnerability analysis system, describe its implementation atop an open-sourced autonomous vulnerability analysis system that participated in the Cyber Grand Challenge, and evaluate and discuss the significant improvements that non-expert human assistance can offer to automated analysis approaches.",
"pdfUrls": [
"http://www.cs.ucsb.edu/~vigna/publications/2017_CCS_HaCRS.pdf",
"https://arxiv.org/pdf/1708.02749v1.pdf",
"http://arxiv.org/abs/1708.02749",
"http://doi.acm.org/10.1145/3133956.3134105",
"http://sefcom.asu.edu/publications/rise-of-the-hacrs-ccs2017.pdf"
],
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"title": "Rise of the HaCRS: Augmenting Autonomous Cyber Reasoning Systems with Human Assistance",
"venue": "CCS",
"year": 2017
},
"4c54a8ee4b99125a52a3a3389972c7af1f3016ae": {
"authors": [
{
"ids": [
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],
"name": "Salessawi Ferede Yitbarek"
},
{
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],
"name": "Misiker Tadesse Aga"
},
{
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],
"name": "Reetuparna Das"
},
{
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"name": "Todd M. Austin"
}
],
"doi": "10.1109/HPCA.2017.10",
"doiUrl": "https://doi.org/10.1109/HPCA.2017.10",
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"Central processing unit",
"Cipher",
"Cold boot attack",
"DIMM",
"Disk encryption",
"Dynamic random-access memory",
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"Encryption",
"Non-volatile memory",
"Overhead (computing)",
"Power supply",
"Reboot (computing)",
"Replay attack",
"Salted Challenge Response Authentication Mechanism",
"Scrambler",
"Signal integrity",
"Skylake (microarchitecture)",
"Stream cipher",
"Vector (malware)"
],
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"paperAbstract": "Previous work has demonstrated that systems with unencrypted DRAM interfaces are susceptible to cold boot attacks – where the DRAM in a system is frozen to give it sufficient retention time and is then re-read after reboot, or is transferred to an attacker's machine for extracting sensitive data. This method has been shown to be an effective attack vector for extracting disk encryption keys out of locked devices. However, most modern systems incorporate some form of data scrambling into their DRAM interfaces making cold boot attacks challenging. While first added as a measure to improve signal integrity and reduce power supply noise, these scram-blers today serve the added purpose of obscuring the DRAM contents. It has previously been shown that scrambled DDR3 systems do not provide meaningful protection against cold boot attacks. In this paper, we investigate the enhancements that have been introduced in DDR4 memory scramblers in the 6th generation Intel Core (Skylake) processors. We then present an attack that demonstrates these enhanced DDR4 scramblers still do not provide sufficient protection against cold boot attacks. We detail a proof-of-concept attack that extracts memory resident AES keys, including disk encryption keys. The limitations of memory scramblers we point out in this paper motivate the need for strong yet low-overhead full-memory encryption schemes. Existing schemes such as Intel's SGX can effectively prevent such attacks, but have overheads that may not be acceptable for performance-sensitive applications. However, it is possible to deploy a memory encryption scheme that has zero performance overhead by forgoing integrity checking and replay attack protections afforded by Intel SGX. To that end, we present analyses that confirm modern stream ciphers such as ChaCha8 are sufficiently fast that it is now possible to completely overlap keystream generation with DRAM row buffer access latency, thereby enabling the creation of strongly encrypted DRAMs with zero exposed latency. Adopting such low-overhead measures in future generation of products can effectively shut down cold boot attacks in systems where the overhead of existing memory encryption schemes is unacceptable. Furthermore, the emergence of non-volatile DIMMs that fit into DDR4 buses is going to exacerbate the risk of cold boot attacks. Hence, strong full memory encryption is going to be even more crucial on such systems.",
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"title": "Towards Energy Budget Control in HPC",
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"paperAbstract": "The existing slowness of the web on mobile devices frustrates users and hurts the revenue of website providers. Prior studies have attributed high page load times to dependencies within the page load process: network latency in fetching a resource delays its processing, which in turn delays when dependent resources can be discovered and fetched.\n To securely address the impact that these dependencies have on page load times, we present Vroom, a rethink of how clients and servers interact to facilitate web page loads. Unlike existing solutions, which require clients to either trust proxy servers or discover all the resources on any page themselves, Vroom's key characteristics are that clients fetch every resource directly from the domain that hosts it but web servers aid clients in discovering resources. Input from web servers decouples a client's processing of resources from its fetching of resources, thereby enabling independent use of both the CPU and the network. As a result, Vroom reduces the median page load time by more than 5 seconds across popular News and Sports sites. To enable these benefits, our contributions lie in making web servers capable of accurately aiding clients in resource discovery and judiciously scheduling a client's receipt of resources.",
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"paperAbstract": "Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments.\n This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.",
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"title": "Topology-aware GPU scheduling for learning workloads in cloud environments",
"venue": "SC",
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"paperAbstract": "In this paper, we study the link scheduling problem considering the fluctuating fading effect in transmissions. We extend the previous deterministic physical interference model to the Rayleigh-fading model that uses the stochastic propagation to address fading effects. Based on this model, we formulate a problem called Fading-Resistant Link Scheduling (Fading-R-LS) problem, which aims to maximize the throughput of all links in a single time slot. We prove that this problem is NP-hard. Based on the geometric structure of Fading-R-LS, we then propose two centralized schemes with O(g(L)) and O(1) performance guarantee, respectively, where g(L) is the number of magnitudes of transmission link lengths. Our experimental results show that the superior performance of our proposed schemes compared to previous schemes.",
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"paperAbstract": "Continuous advances in molecular sequencing technologies now allow for inferring evolutionary trees (phylogenies) on supercomputers that comprise hundreds to thousands of species at the whole-transcriptome or whole-genome level. The phylogenetic likelihood function (PLF) consumes 90-95% of total execution time in such analyses and is therefore typically parallelized. Recently, the site repeats (SR) technique for substantially accelerating the PLF has been introduced. It identifies repeating patterns in parts of the likelihood computation and omits the respective redundant calculations to save time and space. However, the SR technique induces a parallel load imbalance. In this paper, we introduce a novel randomized data distribution algorithm to improve load balance (RDDA) for SR-based likelihood calculations. The algorithm is available as open-source code, induces minimal run-time overhead, and yields up to 25% run time improvements on empirical datasets and up to 50% for a synthetic, worst-case scenario. This improvement is substantial as current evolutionary data analyses may require tens of millions of core hours on supercomputer systems.",
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"paperAbstract": "Cloud spot markets offer virtual machines (VMs) for a dynamic price that is much lower than the fixed price of on-demand VMs. In exchange, spot VMs expose applications to multiple forms of risk, including <i>price risk</i>, or the risk that a VM's price will increase relative to others. Since spot prices vary continuously across hundreds of different types of VMs, flexible applications can mitigate price risk by moving to the VM that currently offers the lowest cost. To enable this flexibility, we present HotSpot, a resource container that \"hops\" VMs---by dynamically selecting and self-migrating to new VMs---as spot prices change. HotSpot containers define a migration policy that lowers cost by determining when to hop VMs based on the transaction costs (from vacating a VM early and briefly double paying for it) and benefits (the expected cost savings). As a side effect of migrating to minimize cost, HotSpot is also able to reduce the number of revocations without degrading performance. HotSpot is simple and transparent: since it operates at the systems-level on each host VM, users need only run an HotSpot-enabled VM image to use it. We implement a HotSpot prototype on EC2, and evaluate it using job traces from a production Google cluster. We then compare HotSpot to using on-demand VMs and spot VMs (with and without fault-tolerance) in EC2, and show that it is able to lower cost and reduce the number of revocations without degrading performance.",
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"title": "HotSpot: automated server hopping in cloud spot markets",
"venue": "SoCC",
"year": 2017
},
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"name": "Chris I. Dalton"
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"name": "Paolo Faraboschi"
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"name": "Moritz Hoffmann"
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"name": "Timothy Roscoe"
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"name": "Adrian L. Shaw"
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"paperAbstract": "It is time to reconsider memory protection. The emergence of large non-volatile main memories, scalable interconnects, and rack-scale computers running large numbers of small \"micro services\" creates significant challenges for memory protection based solely on MMU mechanisms. Central to this is a tension between protection and translation: optimizing for translation performance often comes with a cost in protection flexibility.\n We argue that a key-based memory protection scheme, complementary to but separate from regular page-level translation, is a better match for this new world. We present MaKC, a new architecture which combines two levels of capability-based protection to scale fine-grained memory protection at both user and kernel level to large numbers of protection domains without compromising efficiency at scale or ease of revocation.",
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"title": "Separating Translation from Protection in Address Spaces with Dynamic Remapping",
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"title": "Obstacles to the Adoption of Secure Communication Tools",
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"paperAbstract": "Multi Level Cell (MLC) Phase Change Memory (PCM) is an enhancement of PCM technology, which provides higher capacity by allowing multiple digital bits to be stored in a single PCM cell. However, the retention time of MLC PCM is limited by the resistance drift problem and refresh operations are required. Previous work shows that there exists a trade-off between write latency and retention—a write scheme with more SET iterations and smaller current provides a longer retention time but at the cost of a longer write latency. Otherwise, a write scheme with fewer SET iterations achieves high performance for writes but requires a greater number of refresh operations due to its significantly reduced retention time, and this hurts the lifetime of MLC PCM. In this paper, we show that only a small part of memory (i.e., hot memory regions) will be frequently accessed in a given period of time. Based on such an observation, we propose Region Retention Monitor (RRM), a novel structure that records and predicts the write frequency of memory regions. For every incoming memory write operation, RRM select a proper write latency for it. Our evaluations show that RRM helps the system improves the balance between system performance and memory lifetime. On the performance side, the system with RRM bridges 77.2% of the performance gap between systems with long writes and systems with short writes. On the lifetime side, a system with RRM achieves a lifetime of 6.4 years, while systems using only long writes and short writes achieve lifetimes of 10.6 and 0.3 years, respectively. Also, we can easily control the aggressiveness of RRM through an attribute called hot threshold. A more aggressively configured RRM can achieve the performance which is only 3.5% inferior than the system using static short writes, while still achieve a lifetime of 5.78 years.",
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"paperAbstract": "The densification power law is a concept in the realm of temporal graph evolution. The number of edges grows in a power law over the number of nodes over time, replacing the pre-2005 general assumption of a linear trend. The densification power law has been verified by several real networks over a long period of time. In this work, one such graph, the arXiv citation network is investigated to examine how the densification power law is working ten years after its publication. The network is evaluated and compared with the discussion in a previous work. It is observed that the graph densification continues over time, but instead of maintaining a constant densification power exponent, as suggested by previous work, the exponent is actually dropping over time, which suggests the densification power law is now fading away. Here, this fading effect is literature analysed, and it is suggested that node capability is the major obstacle to the continuation of the original trend. To fully compare with the previous work on graph evolution, the change of the average path length over time is also investigated on our and other's results. The results imply the decreasing of the average path length in the temporal evolution is very slow, which suggests that there exists a new universal degree of separation in social networks of around three.",
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"paperAbstract": "The near ubiquitous availability and success of mobile broadband networks has motivated verticals that range from public safety communication to intelligent transportation systems and beyond to consider choosing them as the communication mean of choice. Several of these verticals, however, expect high availability of multiple nines. This paper leverages end-to-end measurements to investigate the potential of current mobile broadband networks to support these expectations. We conduct a large-scale measurement study of network availability in four networks in Norway. This study is based on three years of measurements from hundreds of stationary measurement nodes and several months of measurements from four mobile nodes. We find that the mobile network centralized architecture and infrastructure sharing between operators are responsible for a non-trivial fraction of network failures. Most episodes of degraded availability, however, are uncorrelated. We also find that using two networks simultaneously can result in more than five nines of availability for stationary nodes and three nines of availability for mobile nodes. Our findings point to potential avenues for enhancing the availability of future mobile networks.",
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"paperAbstract": "Non-volatile memories (NVMs) have attracted significant interest recently due to their high-density, low static power, and persistence. There are, however, several challenges associated with building practical systems from NVMs, including limited write endurance and long latencies. Researchers have proposed a variety of architectural techniques which can achieve different tradeoffs between lifetime, performance and energy efficiency; however, no individual technique can satisfy requirements for all applications and different objectives. Hence, we propose <i>Memory Cocktail Therapy (MCT)</i>, a general, learning-based framework that adaptively chooses the best techniques for the current application and objectives.\n Specifically, MCT performs four procedures to adapt the techniques to various scenarios. First, MCT formulates a high-dimensional configuration space from all different combinations of techniques. Second, MCT selects primary features from the configuration space with lasso regularization. Third, MCT estimates lifetime, performance and energy consumption using lightweight online predictors (eg. quadratic regression and gradient boosting) and a small set of configurations guided by the selected features. Finally, given the estimation of all configurations, MCT selects the optimal configuration based on the user-defined objectives. As a proof of concept, we test MCT's ability to guarantee different lifetime targets and achieve 95% of maximum performance, while minimizing energy consumption. We find that MCT improves performance by 9.24% and reduces energy by 7.95% compared to the best static configuration. Moreover, the performance of MCT is 94.49% of the ideal configuration with only 5.3% more energy consumption.",
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"paperAbstract": "We present NetCache, a new key-value store architecture that leverages the power and flexibility of new-generation programmable switches to handle queries on hot items and balance the load across storage nodes. NetCache provides high aggregate throughput and low latency even under highly-skewed and rapidly-changing workloads. The core of NetCache is a packet-processing pipeline that exploits the capabilities of modern programmable switch ASICs to efficiently detect, index, cache and serve hot key-value items in the switch data plane. Additionally, our solution guarantees cache coherence with minimal overhead. We implement a NetCache prototype on Barefoot Tofino switches and commodity servers and demonstrate that a single switch can process 2+ billion queries per second for 64K items with 16-byte keys and 128-byte values, while only consuming a small portion of its hardware resources. To the best of our knowledge, this is the first time that a sophisticated application-level functionality, such as in-network caching, has been shown to run at line rate on programmable switches. Furthermore, we show that NetCache improves the throughput by 3-10x and reduces the latency of up to 40% of queries by 50%, for high-performance, in-memory key-value stores.",
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"http://www.cs.princeton.edu/~haoyuz/publications/netcache-sosp17.pdf",
"http://doi.acm.org/10.1145/3132747.3132764",
"https://www.sigops.org/sosp/sosp17/slides/netcache-sosp17-slides.pdf",
"http://www.inf.usi.ch/faculty/soule/sosp2017.pdf"
],
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"title": "NetCache: Balancing Key-Value Stores with Fast In-Network Caching",
"venue": "SOSP",
"year": 2017
},
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"authors": [
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"name": "Haogang Chen"
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"name": "Tej Chajed"
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"name": "Alex Konradi"
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"name": "Stephanie Wang"
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"name": "Atalay Ileri"
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"paperAbstract": "DFSCQ is the first file system that (1) provides a precise specification for fsync and fdatasync, which allow applications to achieve high performance and crash safety, and (2) provides a machine-checked proof that its implementation meets this specification. DFSCQ's specification captures the behavior of sophisticated optimizations, including log-bypass writes, and DFSCQ's proof rules out some of the common bugs in file-system implementations despite the complex optimizations.\n The key challenge in building DFSCQ is to write a specification for the file system and its internal implementation without exposing internal file-system details. DFSCQ introduces a metadata-prefix specification that captures the properties of fsync and fdatasync, which roughly follows the behavior of Linux ext4. This specification uses a notion of tree sequences---logical sequences of file-system tree states---for succinct description of the possible states after a crash and to describe how data writes can be reordered with respect to metadata updates. This helps application developers prove the crash safety of their own applications, avoiding application-level bugs such as forgetting to invoke fsync on both the file and the containing directory.\n An evaluation shows that DFSCQ achieves 103 MB/s on large file writes to an SSD and durably creates small files at a rate of 1,618 files per second. This is slower than Linux ext4 (which achieves 295 MB/s for large file writes and 4,977 files/s for small file creation) but much faster than two recent verified file systems, Yggdrasil and FSCQ. Evaluation results from application-level benchmarks, including TPC-C on SQLite, mirror these microbenchmarks.",
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"http://doi.acm.org/10.1145/3132747.3132776",
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"title": "Verifying a high-performance crash-safe file system using a tree specification",
"venue": "SOSP",
"year": 2017
},
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"name": "Shay Gershtein"
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"doi": "10.1145/3087801.3087821",
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"paperAbstract": "In this paper we initiate the study of property testing in simultaneous and non-simultaneous multi-party communication complexity, focusing on testing triangle-freeness in graphs. We consider the coordinator model, where we have k players receiving private inputs, and a coordinator who receives no input; the coordinator can communicate with all the players, but the players cannot communicate with each other. In this model, we ask: if an input graph is divided between the players, with each player receiving some of the edges, how many bits do the players and the coordinator need to exchange to determine if the graph is triangle-free, or far from triangle-free? For general communication protocols, we show that \u00d5(k(nd)1/4+k2) bits are sufficient to test trianglefreeness in graphs of size n with average degree d (the degree need not be known in advance). For simultaneous protocols, where there is only one communication round, we give a protocol that uses \u00d5(k \u221a n) bits when d = O( \u221a n) and \u00d5(k(nd)1/3) when d = \u03a9( \u221a n); here, again, the average degree d does not need to be known in advance. We show that for average degree d = O(1), our simultaneous protocol is asymptotically optimal up to logarithmic factors. For higher degrees, we are not able to give lower bounds on testing triangle-freeness, but we give evidence that the problem is hard by showing that finding an edge that participates in a triangle is hard, even when promised that at least a constant fraction of the edges must be removed in order to make the graph triangle-free.",
"pdfUrls": [
"http://arxiv.org/abs/1705.08438",
"https://arxiv.org/pdf/1705.08438v1.pdf",
"http://doi.acm.org/10.1145/3087801.3087821"
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"title": "On the Multiparty Communication Complexity of Testing Triangle-Freeness",
"venue": "PODC",
"year": 2017
},
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"authors": [
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"name": "Fraser Brown"
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{
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"name": "Shravan Narayan"
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{
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"name": "Riad S. Wahby"
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"name": "Dawson R. Engler"
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"name": "Ranjit Jhala"
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"doi": "10.1109/SP.2017.68",
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"paperAbstract": "JavaScript, like many high-level languages, relies on runtime systemswritten in low-level C and C++. For example, the Node.js runtime systemgives JavaScript code access to the underlying filesystem, networking, and I/O by implementing utility functions in C++. Since C++'s typesystem, memory model, and execution model differ significantly fromJavaScript's, JavaScript code must call these runtime functions viaintermediate binding layer code that translates type, state, and failure between the two languages. Unfortunately, binding code isboth hard to avoid and hard to get right. This paper describes several types of exploitable errors that bindingcode creates, and develops both a suite of easily-to-build static checkersto detect such errors and a backwards-compatible, low-overhead API toprevent them. We show that binding flaws are a serious security problem byusing our checkers to craft 81 proof-of-concept exploits forsecurity flaws in the binding layers of the Node.js and Chrome, runtimesystems that support hundreds of millions of users. As one practical measure of binding bug severity, we were awarded $6,000 in bounties for just two Chrome bug reports.",
"pdfUrls": [
"https://cseweb.ucsd.edu/~dstefan/pubs/brown:2017:finding.pdf",
"https://doi.org/10.1109/SP.2017.68",
"http://ranjitjhala.github.io/static/binding-bugs-sp2017.pdf"
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"title": "Finding and Preventing Bugs in JavaScript Bindings",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"name": "Rudolf Berrendorf"
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{
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"name": "Florian Mannu\u00df"
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],
"doi": "10.1007/978-3-319-64203-1_38",
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"title": "New Efficient General Sparse Matrix Formats for Parallel SpMV Operations",
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},
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"doi": "10.1145/3135974.3135988",
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"paperAbstract": "Rivulet is a fault-tolerant distributed platform for running smart-home applications; it can tolerate failures typical for a home environment (<i>e.g.</i>, link losses, network partitions, sensor failures, and device crashes). In contrast to existing cloud-centric solutions, which rely exclusively on a home gateway device, Rivulet leverages redundant smart consumer appliances (e.g., TVs, Refrigerators) to spread sensing and actuation across devices local to the home, and avoids making the Smart-Home Hub a single point of failure. Rivulet ensures event delivery in the presence of link loss, network partitions and other failures in the home, to enable applications with reliable sensing in the case of sensor failures, and event processing in the presence of device crashes. In this paper, we present the design and implementation of Rivulet, and evaluate its effective handling of failures in a smart home.",
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"title": "Rivulet: a fault-tolerant platform for smart-home applications",
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"paperAbstract": "Since the dawn of computing, memory capacity has been a primary limitation in system design. Forthcoming memory technology such as Intel and Micron's 3D XPoint memory and other technologies may provide far larger memory capacity than ever before. Furthermore, these new memory technologies are inherently persistent and save data across system crashes or power failures.\n We conjecture that current operating systems are ill-equipped for an environment where there is ample memory. For example, operating systems do substantial work for every page allocated, which adds unnecessary overhead when dealing with terabytes of memory.\n We suggest that now is the time for a complete rethinking of memory management for both operating systems and language runtimes considering excess memory capacity. We propose a new guiding principle: Order(1) operation, so that memory operations have low constant time independent of size. We describe a concrete proposal of this principle with the idea of file-only memory, in which most dynamic memory allocation is managed with file-system mechanisms rather than common virtual memory mechanisms.",
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"paperAbstract": "Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfy certain conditions and result in scalable solutions, however, with often only satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal adaptation decisions by using an often costly optimization step, which typically does not scale well for larger problems. We propose a rule-based and utility-driven approach that achieves the beneficial properties of each of these directions such that the adaptation decisions are optimal while the computation remains scalable since an expensive optimization step can be avoided. The approach can be used for the architecture-based self-healing of large software systems. We define the utility for large dynamic architectures of such systems based on patterns capturing issues the self-healing must address and we use patternbased adaptation rules to resolve the issues. Defining the utility as well as the adaptation rules pattern-based allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. We demonstrate the efficiency and optimality of our scheme in comparative experiments with a static rule-based scheme as a baseline and a utility-driven approach using a constraint solver.",
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"title": "Efficient Utility-Driven Self-Healing Employing Adaptation Rules for Large Dynamic Architectures",
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"paperAbstract": "Recently, mobile phone call detail records (CDR) have been used to study mobility patterns in cities. Since home and workplace are the most important places in people's lives and define the structure and activity patterns of the city, identifying home and work locations and home-work commuting patterns are of much interest. However, due to decreasing usage of voice calls and SMS, and low usage while people staying at home, identifying home and work locations are getting more difficult. In this paper, we develop a method to exploit daily-aggregated internet usage data (G-CDR) in addition to CDR for identifying work locations. We also develop a method for identifying home locations based on detecting sleeping time. This method can significantly increase home detection success rate and accuracy. From the identified home and work locations, we have explored the population distribution and commuting patterns of people in Bangkok.",
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"title": "Exploring Home and Work Locations in a City from Mobile Phone Data",
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"paperAbstract": "The atomic commit problem lies at the heart of distributed database systems. The problem consists for a set of processes (database nodes) to agree on whether to commit or abort a transaction (<i>agreement</i> property). The commit decision can only be taken if all processes are initially willing to commit the transaction, and this decision must be taken if all processes are willing to commit <i>and</i> there is no failure (<i>validity</i> property). An atomic commit protocol is said to be <i>non-blocking</i> if every correct process (a database node that does not fail) eventually reaches a decision (commit or abort) even if there are failures elsewhere in the distributed database system (termination property).\n Surprisingly, despite the importance of the atomic commit problem, little is known about its complexity. In this paper, we present, for the first time, a systematic study on the time and message complexity of the problem. We measure complexity in the executions that are considered the most frequent in practice, i.e., failure-free, with all processes willing to commit. In other words, we measure <i>how fast a transaction can commit</i>. Through our systematic study, we close many open questions like the complexity of synchronous non-blocking atomic commit. We also present optimal protocols which may be of independent interest. In particular, we present an effective protocol which solves what we call <i>indulgent atomic commit</i> that tolerates practical distributed database systems which are synchronous ``most of the time''.",
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"title": "How Fast can a Distributed Transaction Commit?",
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"year": 2017
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"paperAbstract": "Memory-corruption vulnerabilities pose a serious threat to modern computer security. Attackers exploit these vulnerabilities to manipulate code and data of vulnerable applications to generate malicious behavior by means of code-injection and code-reuse attacks. Researchers already demonstrated the power of data-only attacks by disclosing secret data such as cryptographic keys in the past. A large body of literature has investigated defenses against code-injection, code-reuse, and data-only attacks. Unfortunately, most of these defenses are tailored towards statically generated code and their adaption to dynamic code comes with the price of security or performance penalties. However, many common applications, like browsers and document viewers, embed just-in-time compilers to generate dynamic code. The contribution of this paper is twofold: first, we propose a generic data-only attack against JIT compilers, dubbed DOJITA. In contrast to previous data-only attacks that aimed at disclosing secret data, DOJITA enables arbitrary code-execution. Second, we propose JITGuard, a novel defense to mitigate code-injection, code-reuse, and data-only attacks against just-in-time compilers (including DOJITA). JITGuard utilizes Intel's Software Guard Extensions (SGX) to provide a secure environment for emitting the dynamic code to a secret region, which is only known to the JIT compiler, and hence, inaccessible to the attacker. Our proposal is the first solution leveraging SGX to protect the security critical JIT compiler operations, and tackles a number of difficult challenges. As proof of concept we implemented JITGuard for Firefox's JIT compiler SpiderMonkey. Our evaluation shows reasonable overhead of 9.8% for common benchmarks.",
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"paperAbstract": "Researchers are actively exploring techniques to enforce control-flow integrity (CFI), which restricts program execution to a predefined set of targets for each indirect control transfer to prevent code-reuse attacks. While hardware-assisted CFI enforcement may have the potential for advantages in performance and flexibility over software instrumentation, current hardware-assisted defenses are either incomplete (i.e., do not enforce all control transfers) or less efficient in comparison. We find that the recent introduction of hardware features to log complete control-flow traces, such as Intel Processor Trace (PT), provides an opportunity to explore how efficient and flexible a hardware-assisted CFI enforcement system may become. While Intel PT was designed to aid in offline debugging and failure diagnosis, we explore its effectiveness for online CFI enforcement over unmodified binaries by designing a parallelized method for enforcing various types of CFI policies. We have implemented a prototype called GRIFFIN in the Linux 4.2 kernel that enables complete CFI enforcement over a variety of software, including the Firefox browser and its jitted code. Our experiments show that GRIFFIN can enforce fine-grained CFI policies with shadow stack as recommended by researchers at a performance that is comparable to software-only instrumentation techniques. In addition, we find that alternative logging approaches yield significant performance improvements for trace processing, identifying opportunities for further hardware assistance.",
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"http://www.cse.psu.edu/~trj1/papers/asplos17.pdf"
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"title": "GRIFFIN: Guarding Control Flows Using Intel Processor Trace",
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"year": 2017
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"paperAbstract": "Data movement is increasingly becoming the bottleneck of both performance and energy efficiency in modern computation. Until recently, it was the case that there is limited freedom for communication optimization on GPUs, as conventional GPUs only provide two types of methods for inter-thread communication: using shared memory or global memory. However, a new warp shuffle instruction has been introduced since the Kepler architecture on Nvidia GPUs, which enables threads within the same warp to directly exchange data in registers. This brought new performance optimization opportunities for algorithms with intensive inter-thread communication. In this work, we deploy register shuffle in the application domain of sequence alignment (or similarly, string matching), and conduct a quantitative analysis of the opportunities and limitations of using register shuffle. We select two sequence alignment algorithms, Smith-Waterman (SW) and Pairwise-Hidden-Markov-Model (PairHMM), from the widely used Genome Analysis Toolkit (GATK) as case studies. Compared to implementations using shared memory, we obtain a significant speed-up of 1.2× and 2.1× by using shuffle instructions for SW and PairHMM. Furthermore, we develop a performance model for analyzing the kernel performance based on the measured shuffle latency from a suite of microbenchmarks. Our model provides valuable insights for CUDA programmers into how to best use shuffle instructions for performance optimization.",
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"paperAbstract": "While much of the research on transaction processing has focused on improving overall performance in terms of throughput and mean latency, surprisingly less attention has been given to performance predictability: how often individual transactions exhibit execution latency far from the mean. Performance predictability is increasingly important when transactions lie on the critical path of latency-sensitive applications, enterprise software, or interactive web services.\n In this paper, we focus on understanding and mitigating the sources of performance unpredictability in today's transactional databases. We conduct the first <i>quantitative study</i> of major sources of variance in MySQL, Postgres (two of the largest and most popular open-source products on the market), and VoltDB (a non-conventional database). We carry out our study with a tool called TProfiler that, given the source code of a database system and programmer annotations indicating the start and end of a transaction, is able to identify the dominant sources of variance in transaction latency. Based on our findings, we investigate alternative algorithms, implementations, and tuning strategies to reduce latency variance without compromising mean latency or throughput. Most notably, we propose a new lock scheduling algorithm, called Variance-Aware Transaction Scheduling (VATS), and a lazy buffer pool replacement policy. In particular, our modified MySQL exhibits significantly lower variance and 99th percentile latencies by up to 5.6× and 6.3×, respectively. Our proposal has been welcomed by the open-source community, and our VATS algorithm has already been adopted as of MySQL's 5.7.17 release (and been made the default scheduling policy in MariaDB).",
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"title": "A Top-Down Approach to Achieving Performance Predictability in Database Systems",
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"year": 2017
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"paperAbstract": "Emerging non-volatile memory (NVM) offers non-volatility, byte-addressability and fast access at the same time. To make the best use of these properties, it has been shown by empirical evidence that programs should access NVM directly through CPU load and store instructions, so that the overhead of a traditional file system or database can be avoided. Thus, durable transactions become a common choice of applications for accessing persistent memory data in a crash consistent manner. However, existing durable transaction systems employ either <i>undo logging</i>, which requires a fence for every memory write, or <i>redo logging</i>, which requires intercepting all memory reads within transactions.\n This paper presents DUDETM, a crash-consistent durable transaction system that avoids the drawbacks of both undo logging and redo logging. DUDETM uses shadow DRAM to <i>decouple</i> the execution of a durable transaction into three fully asynchronous steps. The advantage is that only minimal fences and no memory read instrumentation are required. This design also enables an out-of-the-box transactional memory (TM) to be used as an independent component in our system. The evaluation results show that DUDETM adds durability to a TM system with only 7.4 ~ 24.6% throughput degradation. Compared to the existing durable transaction systems, DUDETM provides 1.7times to 4.4times higher throughput. Moreover, DUDETM can be implemented with existing hardware TMs with minor hardware modifications, leading to a further 1.7times speedup.",
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"title": "DudeTM: Building Durable Transactions with Decoupling for Persistent Memory",
"venue": "ASPLOS",
"year": 2017
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"paperAbstract": "Wearable devices such as smartwatches offer exciting new opportunities for users to interact with their applications. However, the current wearable programming model requires the developer to write a custom companion app for each wearable form factor; the companion app extends the smartphone display onto the wearable, relays user interactions from the wearable to the phone, and updates the wearable display as needed. The development effort required to write a companion app is significant and will not scale to an increasing diversity of form factors. This paper argues for a different programming model for wearable devices. The developer writes an application for the smartphone, but only specifies a UI design for the wearable. Our UIWear system abstracts a logical model of the smartphone GUI, re-tailors the GUI for the wearable device based on the specified UI design, and compiles it into a companion app that we call the UICompanion app. We implemented UIWear on Android smartphones, AndroidWear smartwatches, and Sony SmartEyeGlasses. We evaluate 20 developer-written companion apps from the AndroidWear category on Google Play against the UIWear-created UICompanion apps. The lines-of-code required for the developer to specify the UI design in UIWear is an order-of-magnitude smaller compared to the companion app lines-of-code. Further, in most cases, the UICompanion app performed comparably or better than the corresponding companion app both in terms of qualitative metrics, including latency and energy, and quantitative metrics, including look-and-feel.",
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"paperAbstract": "Given a graph <i>G</i>, a source node <i>s</i> and a target node <i>t</i>, the <i>personalized PageRank</i> (<i>PPR</i>) of <i>t</i> with respect to <i>s</i> is the probability that a random walk starting from <i>s</i> terminates at <i>t</i>. A <i>single-source PPR</i> (<i>SSPPR</i>) query enumerates all nodes in <i>G</i>, and returns the top-<i>k</i> nodes with the highest PPR values with respect to a given source node <i>s</i>. SSPPR has important applications in web search and social networks, e.g., in Twitter's Who-To-Follow recommendation service. However, SSPPR computation is immensely expensive, and at the same time resistant to indexing and materialization. So far, existing solutions either use heuristics, which do not guarantee result quality, or rely on the strong computing power of modern data centers, which is costly.\n Motivated by this, we propose FORA, a simple and effective index-based solution for approximate SSPPR processing, with rigorous guarantees on result quality. The basic idea of FORA is to combine two existing methods Forward Push (which is fast but does not guarantee quality) and Monte Carlo Random Walk (accurate but slow) in a simple and yet non-trivial way, leading to an algorithm that is both fast and accurate. Further, FORA includes a simple and effective indexing scheme, as well as a module for top-<i>k</i> selection with high pruning power. Extensive experiments demonstrate that FORA is orders of magnitude more efficient than its main competitors. Notably, on a billion-edge Twitter dataset, FORA answers a top-500 approximate SSPPR query within 5 seconds, using a single commodity server.",
"pdfUrls": [
"http://shichuan.org/hin/topic/Ranking/2017.%20KDD2017%20FORA%20Simple%20and%20Effective%20Approximate%20Single-Source%20Personalized%20pagerank.pdf",
"http://doi.acm.org/10.1145/3097983.3098072"
],
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"title": "FORA: Simple and Effective Approximate Single-Source Personalized PageRank",
"venue": "KDD",
"year": 2017
},
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"authors": [
{
"ids": [
"1837948"
],
"name": "Ugljesa Milic"
},
{
"ids": [
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],
"name": "Oreste Villa"
},
{
"ids": [
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],
"name": "Evgeny Bolotin"
},
{
"ids": [
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"name": "Akhil Arunkumar"
},
{
"ids": [
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],
"name": "Eiman Ebrahimi"
},
{
"ids": [
"1684691"
],
"name": "Aamer Jaleel"
},
{
"ids": [
"3094183"
],
"name": "Alex Ram\u00edrez"
},
{
"ids": [
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],
"name": "David W. Nellans"
}
],
"doi": "10.1145/3123939.3124534",
"doiUrl": "https://doi.org/10.1145/3123939.3124534",
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"paperAbstract": "GPUs achieve high throughput and power efficiency by employing many small single instruction multiple thread (SIMT) cores. To minimize scheduling logic and performance variance they utilize a uniform memory system and leverage strong data parallelism exposed via the programming model. With Moore's law slowing, for GPUs to continue scaling performance (which largely depends on SIMT core count) they are likely to embrace multi-socket designs where transistors are more readily available. However when moving to such designs, maintaining the illusion of a uniform memory system is increasingly difficult. In this work we investigate multi-socket non-uniform memory access (NUMA) GPU designs and show that significant changes are needed to both the GPU interconnect and cache architectures to achieve performance scalability. We show that application phase effects can be exploited allowing GPU sockets to dynamically optimize their individual interconnect and cache policies, minimizing the impact of NUMA effects. Our NUMA-aware GPU outperforms a single GPU by 1.5×, 2.3×, and 3.2× while achieving 89%, 84%, and 76% of theoretical application scalability in 2, 4, and 8 sockets designs respectively. Implementable today, NUMA-aware multi-socket GPUs may be a promising candidate for scaling GPU performance beyond a single socket.",
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"http://doi.acm.org/10.1145/3123939.3124534"
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"title": "Beyond the socket: NUMA-aware GPUs",
"venue": "MICRO",
"year": 2017
},
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"name": "Cao Gao"
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"name": "Trevor N. Mudge"
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{
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"name": "Jason Mars"
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{
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"name": "Lingjia Tang"
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"doi": "10.1145/3037697.3037698",
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"paperAbstract": "The computation for today's intelligent personal assistants such as Apple Siri, Google Now, and Microsoft Cortana, is performed in the cloud. This cloud-only approach requires significant amounts of data to be sent to the cloud over the wireless network and puts significant computational pressure on the datacenter. However, as the computational resources in mobile devices become more powerful and energy efficient, questions arise as to whether this cloud-only processing is desirable moving forward, and what are the implications of pushing some or all of this compute to the mobile devices on the edge.\n In this paper, we examine the status quo approach of cloud-only processing and investigate computation partitioning strategies that effectively leverage both the cycles in the cloud and on the mobile device to achieve low latency, low energy consumption, and high datacenter throughput for this class of intelligent applications. Our study uses 8 intelligent applications spanning computer vision, speech, and natural language domains, all employing state-of-the-art Deep Neural Networks (DNNs) as the core machine learning technique. We find that given the characteristics of DNN algorithms, a fine-grained, layer-level computation partitioning strategy based on the data and computation variations of each layer within a DNN has significant latency and energy advantages over the status quo approach.\n Using this insight, we design Neurosurgeon, a lightweight scheduler to automatically partition DNN computation between mobile devices and datacenters at the granularity of neural network layers. Neurosurgeon does not require per-application profiling. It adapts to various DNN architectures, hardware platforms, wireless networks, and server load levels, intelligently partitioning computation for best latency or best mobile energy. We evaluate Neurosurgeon on a state-of-the-art mobile development platform and show that it improves end-to-end latency by 3.1X on average and up to 40.7X, reduces mobile energy consumption by 59.5% on average and up to 94.7%, and improves datacenter throughput by 1.5X on average and up to 6.7X.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3037697.3037698",
"http://web.eecs.umich.edu/~jahausw/publications/kang2017neurosurgeon.pdf",
"http://web.eecs.umich.edu/~ypkang/downloads/kang17neurosurgeon.pdf"
],
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"sources": [
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],
"title": "Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge",
"venue": "ASPLOS",
"year": 2017
},
"51ac7123db308803413c9c408a377dcd4bc19bee": {
"authors": [
{
"ids": [
"2607427"
],
"name": "Ivan Walulya"
},
{
"ids": [
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],
"name": "Philippas Tsigas"
}
],
"doi": "10.1109/IPDPS.2017.73",
"doiUrl": "https://doi.org/10.1109/IPDPS.2017.73",
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"Time complexity"
],
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"paperAbstract": "Dynamic vectors are among the most commonly used data structures in programming. They provide constant time random access and resizable data storage. Additionally, they provide constant time insertion (pushback) and deletion (popback) at the end of the sequence. However, in a multithreaded system, concurrent pushback and popback operations attempt to update the same shared object, creating a synchronization bottleneck. In this paper, we present a lock-free vector design that efficiently addresses the synchronization bottlenecks by utilizing a combining technique on pushback operations. Typical combining techniques come with the price of blocking. Our design introduces combining without sacrificing lock-freedom. We evaluate the performance of our design on a dual socket NUMA Intel server. The results show that our design performs comparably at low loads, and out-performs prior concurrent blocking and non-blocking vector implementations at high contention, by as much as 2.7x.",
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"title": "Scalable Lock-Free Vector with Combining",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"authors": [
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],
"name": "Ridwan Rashid Noel"
},
{
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],
"name": "Palden Lama"
}
],
"doi": "10.1109/CLOUD.2017.15",
"doiUrl": "https://doi.org/10.1109/CLOUD.2017.15",
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"paperAbstract": "Cloud storage services are associated with high latency variance, and degraded throughput which is problematic when users are fetching and storing content for interactive applications. This can be attributed to performance hotspots created by slow nodes in a storage cluster, and performance interference caused by multi-tenancy, and background tasks such as data scrubbing, backfilling, recovery, etc. In this paper, we present DLR, a system that improves the performance of cloud storage services in the presence of hardware heterogeneity, and performance interference through a dynamic load redistribution technique. We designed DLR to dynamically adjust the load serving ratio of storage servers based on the system-level performance measurements from the storage cluster. We implemented DLR using Ceph, a popular distributed object storage system, and evaluated its performance on NSFCloud's Chameleon testbed using Ceph's Rados benchmark. Experimental results show that DLR improves the average throughput and latency of Ceph storage by up to 65%, and 41% respectively compared to the default case. Compared to Ceph's in-built load balancing technique, DLR improves the throughput by up to 98%, and latency by 96%.",
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"title": "Taming Performance Hotspots in Cloud Storage with Dynamic Load Redistribution",
"venue": "2017 IEEE 10th International Conference on Cloud Computing (CLOUD)",
"year": 2017
},
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"authors": [
{
"ids": [
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],
"name": "Thang Hoang"
},
{
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"name": "Ceyhun D. Ozkaptan"
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"name": "Attila A. Yavuz"
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"name": "Jorge Guajardo"
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{
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"name": "Tam Nguyen"
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"doi": "10.1145/3133956.3134090",
"doiUrl": "https://doi.org/10.1145/3133956.3134090",
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"Client\u2013server model",
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"paperAbstract": "Oblivious Random Access Machine (ORAM) enables a client to access her data without leaking her access patterns. Existing client-efficient ORAMs either achieve O(log N) client-server communication blowup without heavy computation, or O(1) blowup but with expensive homomorphic encryptions. It has been shown that O(log N) bandwidth blowup might not be practical for certain applications, while schemes with O(1) communication blowup incur even more delay due to costly homomorphic operations.\n In this paper, we propose a new distributed ORAM scheme referred to as Shamir Secret Sharing ORAM (S3ORAM), which achieves O(1) client-server bandwidth blowup and O(1) blocks of client storage without relying on costly partial homomorphic encryptions. S3ORAM harnesses Shamir Secret Sharing, tree-based ORAM structure and a secure multi-party multiplication protocol to eliminate costly homomorphic operations and, therefore, achieves O(1) client-server bandwidth blowup with a high computational efficiency. We conducted comprehensive experiments to assess the performance of S3ORAM and its counterparts on actual cloud environments, and showed that S3ORAM achieves three orders of magnitude lower end-to-end delay compared to alternatives with O(1) client communication blowup (Onion-ORAM), while it is one order of magnitude faster than Path-ORAM for a network with a moderate bandwidth quality. We have released the implementation of S3ORAM for further improvement and adaptation.",
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"http://eprint.iacr.org/2017/819",
"http://web.engr.oregonstate.edu/~yavuza/Hoang_SS3ORAM_CCS_762.pdf",
"http://doi.acm.org/10.1145/3133956.3134090"
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"title": "S3ORAM: A Computation-Efficient and Constant Client Bandwidth Blowup ORAM with Shamir Secret Sharing",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
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"authors": [
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"name": "Noam Shalev"
},
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"name": "Idit Keidar"
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{
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"name": "Yaron Weinsberg"
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{
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"name": "Yosef Moatti"
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"paperAbstract": "System administrators have unlimited access to system resources. As the Snowden case highlighted, these permissions can be exploited to steal valuable personal, classified, or commercial data. This problem is exacerbated when a third party administers the system. For example, a bank outsourcing its IT would not want to allow administrators access to the actual data. We propose WatchIT: a strategy that constrains IT personnel's view of the system and monitors their actions. To this end, we introduce the abstraction of perforated containers -- while regular Linux containers are too restrictive to be used by system administrators, by \"punching holes\" in them, we strike a balance between information security and required administrative needs. Following the principle of least privilege, our system predicts which system resources should be accessible for handling each IT issue, creates a perforated container with the corresponding isolation, and deploys it as needed for fixing the problem.\n Under this approach, the system administrator retains superuser privileges, however only within the perforated container limits. We further provide means for the administrator to bypass the isolation, but such operations are monitored and logged for later analysis and anomaly detection.\n We provide a proof-of-concept implementation of our strategy, which includes software for deploying perforated containers, monitoring mechanisms, and changes to the Linux kernel. Finally, we present a case study conducted on the IT database of IBM Research in Israel, showing that our approach is feasible.",
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"http://webee.technion.ac.il/~idish/ftp/WatchIT-SOSP2017.pdf",
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"http://doi.acm.org/10.1145/3132747.3132752"
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"title": "WatchIT: Who Watches Your IT Guy?",
"venue": "SOSP",
"year": 2016
},
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"name": "Zijun Zhang"
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{
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"name": "Zongpeng Li"
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{
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"name": "Chuan Wu"
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],
"doi": "10.1145/3084460",
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"paperAbstract": "We study online resource allocation in a cloud computing platform through posted pricing: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios in social welfare. In the basic case of a single-type, non-recycled resource (allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that it achieves the smallest competitive ratio among all possible pricing functions. Insights obtained from the basic case are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned to the cloud resource pool.",
"pdfUrls": [
"https://arxiv.org/pdf/1704.05511v1.pdf",
"http://doi.acm.org/10.1145/3078505.3078529",
"http://arxiv.org/abs/1704.05511",
"http://doi.acm.org/10.1145/3084460"
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"title": "Optimal Posted Prices for Online Cloud Resource Allocation",
"venue": "SIGMETRICS",
"year": 2017
},
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"authors": [
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"name": "Zvika Guz"
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{
"ids": [
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"name": "Harry Li"
},
{
"ids": [
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"name": "Anahita Shayesteh"
},
{
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"name": "Vijay Balakrishnan"
}
],
"doi": "10.1145/3078468.3078483",
"doiUrl": "https://doi.org/10.1145/3078468.3078483",
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"paperAbstract": "Storage disaggregation separates compute and storage to different nodes in order to allow for independent resource scaling and thus, better hardware resource utilization. While disaggregation of hard-drives storage is a common practice, NVMe-SSD (i.e., PCIe-based SSD) disaggregation is considered more challenging. This is because SSDs are significantly faster than hard drives, so the latency overheads (due to both network and CPU processing) as well as the extra compute cycles needed for the offloading stack become much more pronounced.\n In this work we characterize the overheads of NVMe-SSD disaggregation. We show that NVMe-over-Fabrics (NVMf) - a recently-released remote storage protocol specification - reduces the overheads of remote access to a bare minimum, thus greatly increasing the cost-efficiency of Flash disaggregation. Specifically, while recent work showed that SSD storage disaggregation via iSCSI degrades application-level throughput by 20%, we report on negligible performance degradation with NVMf - both when using stress-tests as well as with a more-realistic KV-store workload.",
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"http://doi.acm.org/10.1145/3078468.3078483",
"https://www.systor.org/2017/slides/NVMe-over-Fabrics_Performance_Characterization.pdf",
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"title": "NVMe-over-fabrics performance characterization and the path to low-overhead flash disaggregation",
"venue": "SYSTOR",
"year": 2017
},
"525cd0a3f46f3d7427a2282aec5c97e92d2f0308": {
"authors": [
{
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],
"name": "Lianghong Xu"
},
{
"ids": [
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"name": "Andrew Pavlo"
},
{
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"name": "Sudipta Sengupta"
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{
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"name": "Gregory R. Ganger"
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],
"doi": "10.1145/3035918.3035938",
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"paperAbstract": "dbDedup is a similarity-based deduplication scheme for on-line database management systems (<b>DBMSs</b>). Beyond block-level compression of individual database pages or operation log (oplog) messages, as used in today's <b>DBMSs</b>, dbDedup uses byte-level delta encoding of individual records within the database to achieve greater savings. dbDedup's single-pass encoding method can be integrated into the storage and logging components of a <b>DBMS</b> to provide two benefits: (1) reduced size of data stored on disk beyond what traditional compression schemes provide, and (2) reduced amount of data transmitted over the network for replication services. To evaluate our work, we implemented dbDedup in a distributed <b>NoSQL DBMS</b> and analyzed its properties using four real datasets. Our results show that dbDedup achieves up to 37x reduction in the storage size and replication traffic of the database on its own and up to 61x reduction when paired with the <b>DBMS's</b> block-level compression. dbDedup provides both benefits with negligible effect on <b>DBMS</b> throughput or client latency (average and tail).",
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"http://db.cs.cmu.edu/papers/2017/p1355-xu.pdf"
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"title": "Online Deduplication for Databases",
"venue": "SIGMOD Conference",
"year": 2017
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"name": "Hyeontaek Lim"
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"paperAbstract": "Multi-core in-memory databases promise high-speed online transaction processing. However, the performance of individual designs suffers when the workload characteristics miss their small sweet spot of a desired contention level, read-write ratio, record size, processing rate, and so forth.\n Cicada is a single-node multi-core in-memory transactional database with serializability. To provide high performance under diverse workloads, Cicada reduces overhead and contention at several levels of the system by leveraging optimistic and multi-version concurrency control schemes and multiple loosely synchronized clocks while mitigating their drawbacks. On the TPC-C and YCSB benchmarks, Cicada outperforms Silo, TicToc, FOEDUS, MOCC, two-phase locking, Hekaton, and ERMIA in most scenarios, achieving up to 3X higher throughput than the next fastest design. It handles up to 2.07 M TPC-C transactions per second and 56.5 M YCSB transactions per second, and scans up to 356 M records per second on a single 28-core machine.",
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"title": "Cicada: Dependably Fast Multi-Core In-Memory Transactions",
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"paperAbstract": "The typical enterprise data architecture consists of several actively updated data sources (e.g., NoSQL systems, data warehouses), and a central data lake such as HDFS, in which all the data is periodically loaded through ETL processes. To simplify query processing, state-of-the-art data analysis approaches solely operate on top of the local, historical data in the data lake, and ignore the fresh tail end of data that resides in the original remote sources. However, as many business operations depend on real-time analytics, this approach is no longer viable. The alternative is hand-crafting the analysis task to explicitly consider the characteristics of the various data sources and identify optimization opportunities, rendering the overall analysis non-declarative and convoluted.\n Based on our experiences operating in data lake environments, we design <i>System-PV</i>, a real-time analytics system that masks the complexity of dealing with multiple data sources while offering minimal response times. System-PV extends Spark with a sophisticated data virtualization module that supports multiple applications - from SQL queries to machine learning. The module features a <i>location-aware compiler</i> that considers source complexity, and a <i>two-phase optimizer</i> that produces and refines the query plans, not only for SQL queries but for all other types of analysis as well. The experiments show that System-PV is often faster than Spark by more than an order of magnitude. In addition, the experiments show that the approach of accessing both the historical and the remote fresh data is viable, as it performs comparably to solely operating on top of the local, historical data.",
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"title": "No data left behind: real-time insights from a complex data ecosystem",
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"paperAbstract": "Object bounds overflow errors are a common source of security vulnerabilities. In principle, bounds check instrumentation eliminates the problem, but this introduces high overheads and is further hampered by limited compatibility against un-instrumented code. On 64-bit systems, low-fat pointers are a recent scheme for implementing efficient and compatible bounds checking by transparently encoding meta information within the native pointer representation itself. However, low-fat pointers are traditionally used for heap objects only, where the allocator has sufficient control over object location necessary for the encoding. This is a problem for stack allocation, where there exist strong constraints regarding the location of stack objects that is apparently incompatible with the low-fat pointer approach. To address this problem, we present an extension of low-fat pointers to stack objects by using a collection of techniques, such as pointer mirroring and memory aliasing, thereby allowing stack objects to enjoy bounds error protection from instrumented code. Our extension is compatible with common special uses of the stack, such as alloca, setjmp and longjmp, exceptions, and multi-threading, which rely on direct manipulation of the stack pointer. Our experiments show that we successfully extend the advantages of the low-fat pointer encoding to stack objects. The end result is a competitive bounds checking instrumentation for the stack and heap with low memory and runtime overheads, and high compatibility with un-instrumented legacy code.",
"pdfUrls": [
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"paperAbstract": "Modern big data processing platforms employ huge in-memory key-value (KV) maps. Their applications simultaneously drive high-rate data ingestion and large-scale analytics. These two scenarios expect KV-map implementations that scale well with both real-time updates and large atomic scans triggered by range queries.\n We present KiWi, the first atomic KV-map to efficiently support simultaneous large scans and real-time access. The key to achieving this is treating scans as first class citizens,and organizing the data structure around them. KiWi provides wait-free scans, whereas its put operations are lightweight and lock-free. It optimizes memory management jointly with data structure access.We implement KiWi and compare it to state-of-the-art solutions. Compared to other KV-maps providing atomic scans, KiWi performs either long scans or concurrent puts an order of magnitude faster. Its scans are twice as fast as non-atomic ones implemented via iterators in the Java skiplist.",
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"https://www.cs.tau.ac.il/~moshesulamy/papers/ppopp092-basinA.pdf",
"http://dl.acm.org/citation.cfm?id=3018761"
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"title": "KiWi: A Key-Value Map for Scalable Real-Time Analytics",
"venue": "PPOPP",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Li-Xuan Chuo"
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{
"ids": [
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"name": "Zhihong Luo"
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{
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"name": "Dennis Sylvester"
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{
"ids": [
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"name": "David Blaauw"
},
{
"ids": [
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],
"name": "Hun-Seok Kim"
}
],
"doi": "10.1145/3117811.3117840",
"doiUrl": "https://doi.org/10.1145/3117811.3117840",
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"paperAbstract": "Long-range low-power localization is a key technology that enables a host of new applications of wireless sensor nodes. We present RF-Echo, a new low-power RF localization solution that achieves decimeter accuracy in long range indoor non-line-of-sight (NLOS) scenarios. RF-Echo introduces a custom-designed active RF reflector ASIC (application specific integrated circuit) fabricated in a 180nm CMOS process which echoes a frequency-shifted orthogonal frequency division multiplexing (OFDM) signal originally generated from an anchor. The proposed technique is based on time-of-flight (ToF) estimation in the frequency domain that effectively eliminates inter-carrier and inter-symbol interference in multipath-rich indoor NLOS channels. RF-Echo uses a relatively narrow bandwidth of $\\leq$80 MHz which does not require an expensive very high sampling rate analog-to-digital converter (ADC). Unlike ultra-wideband (UWB) systems, the active reflection scheme is designed to operate at a relatively low carrier frequency that can penetrate building walls and other blocking objects for challenging NLOS scenarios. Since the bandwidth at lower frequencies (2.4 GHz and sub-1 GHz) is severely limited, we propose novel signal processing algorithms as well as machine learning techniques to significantly enhance the localization resolution given the bandwidth constraint of the proposed system. The newly fabricated tag IC consumes 62.8 mW active power. The software defined radio (SDR) based anchor prototype is rapidly deployable without the need for accurate synchronization among anchors and tags. Field trials conducted in a university building confirm up to 85 m operation with decimeter accuracy for robust 2D localization.",
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"title": "RF-Echo: A Non-Line-of-Sight Indoor Localization System Using a Low-Power Active RF Reflector ASIC Tag",
"venue": "MobiCom",
"year": 2017
},
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"authors": [
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"name": "Alan Quach"
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"name": "Zhongjie Wang"
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"name": "Zhiyun Qian"
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"doi": "10.1145/3084441",
"doiUrl": "https://doi.org/10.1145/3084441",
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"paperAbstract": "To combat blind in-window attacks against TCP, changes proposed in RFC 5961 have been implemented by Linux since late 2012. While successfully eliminating the old vulnerabilities, the new TCP implementation was reported in August 2016 to have introduced a subtle yet serious security flaw. Assigned CVE-2016-5696, the flaw exploits the challenge ACK rate limiting feature that could allow an off-path attacker to infer the presence/absence of a TCP connection between two arbitrary hosts, terminate such a connection, and even inject payload into an unsecured TCP connection.\n In this work, we perform a comprehensive measurement of the impact of the new vulnerability. This includes (1) tracking the vulnerable Internet servers, (2) monitoring the patch behavior over time, (3) picturing the overall security status of TCP stacks at scale. Towards this goal, we design a scalable measurement methodology to scan the Alexa top 1 million websites for almost 6 months. We also present how notifications impact the patching behavior, and compare the result with the Heartbleed and the Debian PRNG vulnerability. The measurement represents a valuable data point in understanding how Internet servers react to serious security flaws in the operating system kernel.",
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"title": "Investigation of the 2016 Linux TCP Stack Vulnerability at Scale",
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},
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"authors": [
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"name": "Chun Cao"
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{
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"name": "Weiyi Wang"
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{
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"name": "Ying Zhang"
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{
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"name": "Xiaoxing Ma"
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"doi": "10.1109/CLOUD.2017.22",
"doiUrl": "https://doi.org/10.1109/CLOUD.2017.22",
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"paperAbstract": "Apache HBase is a widely used non-relational database in the Hadoop ecosystem. However, it will be inefficient if users perform multidimensional queries. Some of existing approaches incur extra costs in write performance or consistency maintenance, others are limited to specific applications. In this paper, we propose a novel data model called CFIDM, short for Column Family Indexed Data Model. In CFIDM, we convert the queried column into multiple column families. Values in the specific column are partitioned. Each partition is manifested by a column family, turning column family into an index with no additional cost. Then we provide guides to build this data model. Finally, we evaluate the effectiveness and versatility of CFIDM on the Bixi data set and the TPC-DS benchmark. Results show that CFIDM can save 6.6% disk space for Bixi and 35% for TPC-DS, maximally speeding up the queries by 5X and 5.5X respectively.",
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"title": "Leveraging Column Family to Improve Multidimensional Query Performance in HBase",
"venue": "2017 IEEE 10th International Conference on Cloud Computing (CLOUD)",
"year": 2017
},
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{
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"name": "Cho-Jui Hsieh"
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{
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"name": "Si Si"
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{
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"name": "Inderjit S. Dhillon"
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"doi": "10.1145/3097983.3098080",
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"paperAbstract": "Nonlinear kernel machines often yield superior predictive performance on various tasks; however, they suffer from severe computational challenges. In this paper, we show how to overcome the important challenge of speeding up kernel machines using multiple computers. In particular, we develop a parallel block minimization framework, and demonstrate its good scalability in solving nonlinear kernel SVM and logistic regression. Our framework proceeds by dividing the problem into smaller subproblems by forming a block-diagonal approximation of the Hessian matrix. The subproblems are then solved approximately in parallel. After that, a communication efficient line search procedure is developed to ensure sufficient reduction of the objective function value by exploiting the problem structure of kernel machines. We prove global linear convergence rate of the proposed method with a wide class of subproblem solvers, and our analysis covers strongly convex and some non-strongly convex functions. We apply our algorithm to solve large-scale kernel SVM problems on distributed systems, and show a significant improvement over existing parallel solvers. As an example, on the covtype dataset with half-a-million samples, our algorithm can obtain an approximate solution with 96% accuracy in 20 seconds using 32 machines, while all the other parallel kernel SVM solvers require more than 2000 seconds to achieve a solution with 95% accuracy. Moreover, our algorithm is the first distributed kernel SVM solver that can scale to massive data sets. On the KDDB dataset (20 million samples and 30 million features), our parallel solver can compute the kernel SVM solution within half an hour using 32 machines with 640 cores in total, while existing solvers can not scale to this dataset.",
"pdfUrls": [
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"http://www.cs.utexas.edu/~inderjit/public_papers/parallel_kernel_kdd17.pdf",
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"title": "Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines",
"venue": "KDD",
"year": 2017
},
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"authors": [
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"name": "Wang-Cheng Kang"
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{
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"name": "Chen Fang"
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{
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"name": "Zhaowen Wang"
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{
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"doi": "10.1109/ICDM.2017.30",
"doiUrl": "https://doi.org/10.1109/ICDM.2017.30",
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"paperAbstract": "Building effective recommender systems for domains like fashion is challenging due to the high level of subjectivity and the semantic complexity of the features involved (i.e., fashion styles). Recent work has shown that approaches to 'visual' recommendation (e.g. clothing, art, etc.) can be made more accurate by incorporating visual signals directly into the recommendation objective, using 'off-the-shelf' feature representations derived from deep networks. Here, we seek to extend this contribution by showing that recommendation performance can be significantly improved by learning 'fashion aware' image representations directly, i.e., by training the image representation (from the pixel level) and the recommender system jointly; this contribution is related to recent work using Siamese CNNs, though we are able to show improvements over state-of-the-art recommendation techniques such as BPR and variants that make use of pretrained visual features. Furthermore, we show that our model can be used generatively, i.e., given a user and a product category, we can generate new images (i.e., clothing items) that are most consistent with their personal taste. This represents a first step towards building systems that go beyond recommending existing items from a product corpus, but which can be used to suggest styles and aid the design of new products.",
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"doi": "10.1109/IGCC.2017.8323582",
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"title": "An energy-constrained makespan optimization framework in fine-to coarse-grain partitioned multicore systems",
"venue": "2017 Eighth International Green and Sustainable Computing Conference (IGSC)",
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"paperAbstract": "We present a new data-driven approach to achieve highly cost-effective context-sensitive points-to analysis for Java. While context-sensitivity has greater impact on the analysis precision and performance than any other precision-improving techniques, it is difficult to accurately identify the methods that would benefit the most from context-sensitivity and decide how much context-sensitivity should be used for them. Manually designing such rules is a nontrivial and laborious task that often delivers suboptimal results in practice. To overcome these challenges, we propose an automated and data-driven approach that learns to effectively apply context-sensitivity from codebases. In our approach, points-to analysis is equipped with a parameterized and heuristic rules, in disjunctive form of properties on program elements, that decide when and how much to apply context-sensitivity. We present a greedy algorithm that efficiently learns the parameter of the heuristic rules. We implemented our approach in the Doop framework and evaluated using three types of context-sensitive analyses: conventional object-sensitivity, selective hybrid object-sensitivity, and type-sensitivity. In all cases, experimental results show that our approach significantly outperforms existing techniques.",
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"title": "Data-driven context-sensitivity for points-to analysis",
"venue": "PACMPL",
"year": 2017
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"name": "Fisnik Kastrati"
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"paperAbstract": "Optimization of disjunctive predicates is a very challenging task which has been vastly neglected by the research community and commercial databases. In this work, we focus on the complex problem of optimizing disjunctive predicates by means of the bypass processing technique. In <i>bypass</i> processing, selection operators split the input tuple stream into two disjoint output streams: the <i>true-stream</i> with tuples that satisfy the selection predicate and the <i>false-stream</i> with tuples that do not. Bypass processing is crucial in avoiding expensive predicates whenever the outcome of the query predicate can be determined by evaluating the less expensive ones.\n In main memory databases, CPU architectural characteristics, such as the branch misprediction penalty, become a prominent cost factor which cannot be ignored. Our algorithm takes into account the branch misprediction penalty, and, in addition, it eliminates common subexpressions.\n The current literature relies on two assumptions: (1) predicate costs are assumed to be constant, (2) predicate selectivities are assumed to be independent. Since both assumptions do not hold in practice, our approach is not based on any of them.",
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"title": "Optimization of Disjunctive Predicates for Main Memory Column Stores",
"venue": "SIGMOD Conference",
"year": 2017
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"paperAbstract": "We introduce the design and implementation of FreeRider, the first system that enables backscatter communication with multiple commodity radios, such as 802.11g/n WiFi, ZigBee, and Bluetooth, while these radios are simultaneously used for productive data communication. Furthermore, we are, to our knowledge, the first to implement and evaluate a multi-tag system. The key technique used by FreeRider is codeword translation, where a tag can transform a codeword present in the original excitation signal into another valid codeword from the same codebook during backscattering. In other words, the backscattered signal is still a valid WiFi, ZigBee, or Bluetooth signal. Therefore, commodity radios decode the backscattered signal and extract the tag's embedded information. More importantly, FreeRider does codeword translation regardless of the data transmitted by these radios. Therefore, these radios can still do productive data communication. FreeRider accomplishes codeword translation by modifying one or more of the three dimensions of a wireless signal --- amplitude, phase and frequency. A tag ensures that the modified signal is still comprised of valid codewords that come the same codebook as the original excitation signal. We built a hardware prototype of FreeRider, and our empirical evaluations show a data rate of ~60kbps in single tag mode, 15kbps in multi-tag mode, and a backscatter communication distance up to 42m when operating on 802.11g/n WiFi.",
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"title": "FreeRider: Backscatter Communication Using Commodity Radios",
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"name": "Apan Qasem"
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"paperAbstract": "Heterogeneous compute nodes have become an integral component of today's HPC systems. Recent research has established the importance of data layout and placement on such systems. This paper explores the power and energy aspects of data layout and placement on heterogeneous systems. We present results of an experimental study that evaluates the impact of data layout and placement on candidate HPC node architectures for kernels that exhibit a wide variety of performance characteristics. The results of the study show that data layout and placement can have a significant impact on the energy efficiency of heterogeneous applications. On some platforms, selecting the appropriate layout can yield up to an order-of-magnitude improvement in energy efficiency. The study shows that the conventional approach of using a structure-of-arrays for device-mapped data structures is not always profitable and that in addition to memory divergence, data layout choices are impacted by a variety of factors including arithmetic intensity and task granularity. The results of the study are used to establish a set of energy imperatives to guide data layout and placement across different architectures.",
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"title": "Evaluating the impact of data layout and placement on the energy efficiency of heterogeneous applications",
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"paperAbstract": "We propose translation-enabled memory prefetching optimizations or TEMPO, a low-overhead hardware mechanism to boost memory performance by exploiting the operating system's (OS) virtual memory subsystem. We are the first to make the following observations: (1) a substantial fraction (20-40%) of DRAM references in modern big- data workloads are devoted to accessing page tables; and (2) when memory references require page table lookups in DRAM, the vast majority of them (98%+) also look up DRAM for the subsequent data access. TEMPO exploits these observations to enable DRAM row-buffer and on-chip cache prefetching of the data that page tables point to. TEMPO requires trivial changes to the memory controller (under 3% additional area), no OS or application changes, and improves performance by 10-30% and energy by 1-14%.",
"pdfUrls": [
"https://www.cs.rutgers.edu/~abhib/abhib-asplos17.pdf",
"http://doi.acm.org/10.1145/3037697.3037705"
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"title": "Translation-Triggered Prefetching",
"venue": "ASPLOS",
"year": 2017
},
"5390117ecd33478a412e889a061871bef0affe8e": {
"authors": [
{
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"name": "Rahul Chatterjee"
},
{
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"name": "Joanne Woodage"
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{
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"name": "Yuval Pnueli"
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{
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"name": "Anusha Chowdhury"
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{
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"name": "Thomas Ristenpart"
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],
"doi": "10.1145/3133956.3134000",
"doiUrl": "https://doi.org/10.1145/3133956.3134000",
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"Authentication",
"Dictionary",
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"Linux",
"Login",
"Operating system",
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"id": "5390117ecd33478a412e889a061871bef0affe8e",
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"paperAbstract": "Password checking systems traditionally allow login only if the correct password is submitted. Recent work on typo-tolerant password checking suggests that usability can be improved, with negligible security loss, by allowing a small number of typographical errors. Existing systems, however, can only correct a handful of errors, such as accidentally leaving caps lock on or incorrect capitalization of the first letter in a password. This leaves out numerous kinds of typos made by users, such as transposition errors, substitutions, or capitalization errors elsewhere in a password. Some users therefore receive no benefit from existing typo-tolerance mechanisms.\n We introduce personalized typo-tolerant password checking. In our approach, the authentication system learns over time the typos made by a specific user. In experiments using Mechanical Turk, we show that 45% of users would benefit from personalization. Therefore, we design a system, called TypTop, that securely implements personalized typo-tolerance. Underlying TypTop is a new stateful password-based encryption scheme that can be used to store recent failed login attempts. Our formal analysis shows that security in the face of an attacker that obtains the state of the system reduces to the difficulty of a brute-force dictionary attack against the real password. We implement TypTop for Linux and Mac OS login and report on a proof-of-concept deployment.",
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"http://doi.acm.org/10.1145/3133956.3134000",
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"http://eprint.iacr.org/2017/810"
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"title": "The TypTop System: Personalized Typo-Tolerant Password Checking",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
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"name": "Laxman Dhulipala"
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"name": "Guy E. Blelloch"
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"name": "Julian Shun"
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],
"doi": "10.1145/3087556.3087580",
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"paperAbstract": "Existing graph-processing frameworks let users develop efficient implementations for many graph problems, but none of them support efficiently bucketing vertices, which is needed for <i>bucketing-based</i> graph algorithms such as \\Delta-stepping and approximate set-cover. Motivated by the lack of simple, scalable, and efficient implementations of bucketing-based algorithms, we develop the Julienne framework, which extends a recent shared-memory graph processing framework called Ligra with an interface for maintaining a collection of buckets under vertex insertions and bucket deletions.\n We provide a theoretically efficient parallel implementation of our bucketing interface and study several bucketing-based algorithms that make use of it (either bucketing by remaining degree or by distance) to improve performance: the peeling algorithm for <i>k</i>-core (coreness), \\Delta-stepping, weighted breadth-first search, and approximate set cover. The implementations are all simple and concise (under 100 lines of code). Using our interface, we develop the first work-efficient parallel algorithm for <i>k</i>-core in the literature with nontrivial parallelism.\n We experimentally show that our bucketing implementation scales well and achieves high throughput on both synthetic and real-world workloads. Furthermore, the bucketing-based algorithms written in Julienne achieve up to 43x speedup on 72 cores with hyper-threading over well-tuned sequential baselines, significantly outperform existing work-inefficient implementations in Ligra, and either outperform or are competitive with existing special-purpose parallel codes for the same problem. We experimentally study our implementations on the largest publicly available graphs and show that they scale well in practice, processing real-world graphs with billions of edges in seconds, and hundreds of billions of edges in a few minutes. As far as we know, this is the first time that graphs at this scale have been analyzed in the main memory of a single multicore machine.",
"pdfUrls": [
"https://people.csail.mit.edu/jshun/bucketing.pdf",
"http://doi.acm.org/10.1145/3087556.3087580"
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],
"title": "Julienne: A Framework for Parallel Graph Algorithms using Work-efficient Bucketing",
"venue": "SPAA",
"year": 2017
},
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"authors": [
{
"ids": [
"1830497"
],
"name": "Yaqing Wang"
},
{
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],
"name": "Fenglong Ma"
},
{
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"name": "Lu Su"
},
{
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"name": "Jing Gao"
}
],
"doi": "10.1109/ICDM.2017.60",
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"paperAbstract": "In the big data era, the information about the same object collected from multiple sources is inevitably conflicting. The task of identifying true information (i.e., the truths) among conflicting data is referred to as truth discovery, which incorporates the estimation of source reliability degrees into the aggregation of multi-source data. However, in many real-world applications, large-scale data are distributed across multiple servers. Traditional truth discovery approaches cannot handle this scenario due to the constraints of communication overhead and privacy concern. Another limitation of most existing work is that they ignore the differences among objects, i.e., they treat all the objects equally. This limitation would be exacerbated in distributed environments where significant differences exist among the objects. To tackle the aforementioned issues, in this paper, we propose a novel distributed truth discovery framework (DTD), which can effectively and efficiently aggregate conflicting data stored across distributed servers, with the differences among the objects as well as the importance level of each server being considered. The proposed framework consists of two steps: the local truth computation step conducted by each local server and the central truth estimation step taking place in the central server. Specifically, we introduce the uncertainty values to model the differences among objects, and propose a new uncertainty-based truth discovery method (UbTD) for calculating the true information of objects in each local server. The outputs of the local truth computation step include the estimated local truths and the variances of objects, which are the input information of the central truth estimation step. To infer the final true information in the central server, we propose a new algorithm to aggregate the outputs of all the local servers with the quality of different local servers taken into account. The proposed distributed truth discovery framework can infer object truths without delivering any raw data to the central server, and thus can reduce communication overhead as well as preserve data privacy. Experimental results on three real world datasets show that the proposed DTD framework can efficiently estimate object truths with accuracy guarantee, and the proposed UbTD algorithm significantly outperforms the state-of-the-art batch truth discovery approaches.",
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"paperAbstract": "Markov Chain Monte Carlo methods provide a tool for tackling high dimensional problems. With many-core systems readily available today, it is no surprise that leveraging parallelism in these samplers has been a subject of recent research. The focus has been on solutions for shared-memory architectures, however these perform poorly in a distributed-memory environment. This paper introduces a fully decentralized version of an affine invariant sampler. By observing that a pseudorandom number generator makes stochastic algorithms deterministic, communication is both minimized and hidden by computation. Two cases at opposite ends of the communication-to-computation ratio spectrum are used during evaluation against the currently available master-slave solution, where a more than tenfold reduction in execution time is measured.",
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"paperAbstract": "Dependent Object Types (DOT) is intended to be a core calculus for modelling Scala. Its distinguishing feature is abstract type members, fields in objects that hold types rather than values. Proving soundness of DOT has been surprisingly challenging, and existing proofs are complicated, and reason about multiple concepts at the same time (e.g. types, values, evaluation). To serve as a core calculus for Scala, DOT should be easy to experiment with and extend, and therefore its soundness proof needs to be easy to modify. \n This paper presents a simple and modular proof strategy for reasoning in DOT. The strategy separates reasoning about types from other concerns. It is centred around a theorem that connects the full DOT type system to a restricted variant in which the challenges and paradoxes caused by abstract type members are eliminated. Almost all reasoning in the proof is done in the intuitive world of this restricted type system. Once we have the necessary results about types, we observe that the other aspects of DOT are mostly standard and can be incorporated into a soundness proof using familiar techniques known from other calculi.",
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"paperAbstract": "Elastic scaling of event stream processing systems has gained significant attention recently due to the prevalence of cloud computing technologies. We investigate on the complexities associated with elastic scaling of an event processing system in a private/public cloud scenario. We develop an Elastic Switching Mechanism (<b>ESM</b>) which reduces the overall average latency of event processing jobs by significant amount considering the cost of operating the system. <b>ESM</b> is augmented with adaptive compressing of upstream data. The <b>ESM</b> conducts one of the two types of switching where either part of the data is sent to the public cloud (data switching) or a selected query is sent to the public cloud (query switching) based on the characteristics of the query. We model the operation of the <b>ESM</b> as the function of two binary switching functions. We show that our elastic switching mechanism with compression is capable of handling out-of-order events more efficiently compared to techniques which does not involve compression. We used two application benchmarks called EmailProcessor and a Social Networking Benchmark (<b>SNB2016</b>) to conduct multiple experiments to evaluate the effectiveness of our approach. In a single query deployment with EmailProcessor benchmark we observed that our elastic switching mechanism provides 1.24 seconds average latency improvement per processed event which is 16.70% improvement compared to private cloud only deployment. When presented the option of scaling EmailProcessor with four public cloud <b>VMs ESM</b> further reduced the average latency by 37.55% compared to the single public cloud <b>VM</b>. In a multi-query deployment with both EmailProcessor and SNB2016 we obtained a reduction of average latency of both the queries by 39.61 seconds which is a decrease of 7% of overall latency. These performance figures indicate that our elastic switching mechanism with compressed data streams can effectively reduce the average elapsed time of stream processing happening in private/public clouds.",
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"title": "Latency Aware Elastic Switching-based Stream Processing Over Compressed Data Streams",
"venue": "ICPE",
"year": 2017
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"paperAbstract": "HTTP Adaptive Streaming (HAS) has emerged as the predominant technique for transmitting video over cellular for most content providers today. While mobile video streaming is extremely popular, delivering good streaming experience over cellular networks is technically very challenging, and involves complex interacting factors. We conduct a detailed measurement study of a wide cross-section of popular streaming video-on-demand (VOD) services to develop a holistic understanding of these services' design and performance. We identify performance issues and develop effective practical best practice solutions to mitigate these challenges. By extending the understanding of how different, potentially interacting components of service design impact performance, our findings can help developers build streaming services with better performance.",
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"title": "Dissecting VOD services for cellular: performance, root causes and best practices",
"venue": "IMC",
"year": 2017
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"paperAbstract": "During the past decade, network and storage devices have undergone rapid performance improvements, delivering ultra-low latency and several Gbps of bandwidth. Nevertheless, current network and storage stacks fail to deliver this hardware performance to the applications, often due to the loss of IO efficiency from stalled CPU performance. While many efforts attempt to address this issue solely on either the network or the storage stack, achieving high-performance for networked-storage applications requires a holistic approach that considers both.\n In this paper, we present FlashNet, a software IO stack that unifies high-performance network properties with flash storage access and management. FlashNet builds on RDMA principles and abstractions to provide a direct, asynchronous, end-to-end data path between a client and remote flash storage. The key insight behind FlashNet is to co-design the stack's components (an RDMA controller, a flash controller, and a file system) to enable cross-stack optimizations and maximize IO efficiency. In micro-benchmarks, FlashNet improves 4kB network IOPS by 38.6% to 1.22M, decreases access latency by 43.5% to 50.4 <i>µ</i>secs, and prolongs the flash lifetime by 1.6--5.9× for writes. We illustrate the capabilities of FlashNet by building a Key-Value store, and porting a distributed data store that uses RDMA on it. The use of FlashNet's RDMA API improves the performance of KV store by 2×, and requires minimum changes for the ported data store to access remote flash devices.",
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"paperAbstract": "GPUs continue to increase the number of compute resources with each new generation. Many data-parallel applications have been re-engineered to leverage the thousands of cores on the GPU. But not every kernel can fully utilize all the resources available. Many applications contain multiple kernels that could potentially be run concurrently. To better utilize the massive resources on the GPU, device vendors have started to support Concurrent Kernel Execution (CKE). However, the application throughput provided by CKE is subject to a number of factors, including the kernel configuration attributes, the dynamic behavior of each kernel (e.g., compute-intentive vs. memory-intensive), the kernel launch order and inter-kernel dependencies. Minor changes in any of theses factors can have a large impact on the effectiveness of CKE. In this paper, we present Moka, an empirical model for tuning concurrent kernel performance. Moka allows us to accurately predict the resulting performance and scalability of multi-kernel applications when using CKE. We consider both static and dynamic workload characteristics that impact the utility of CKE, and leverage these metrics to drive kernel scheduling decisions on NVIDIA GPUs. The underlying data transfer pattern and GPU resource contention are analyzed in detail. Our model is able to accurately predict the performance ceiling of concurrent kernel execution. We validate our model using several real-world applications that have multiple kernels that can run concurrently, and evaluate CKE performance on a NVIDIA Maxwell GPU. Our model is able to predict the performance of CKE applications accurately, providing estimates that differ by less than 12% as compared to actual runtime performance. Using our estimates, we can quickly find the best CKE strategy for our applications to achieve improved application throughput. We believe we have developed a useful tool to aid application programmers to accelerate their applications using CKE.",
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"title": "Moka: Model-based concurrent kernel analysis",
"venue": "2017 IEEE International Symposium on Workload Characterization (IISWC)",
"year": 2017
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"paperAbstract": "In upcoming architectures that stack processor and DRAM dies, temperatures are higher because of the increased transistor density and the high inter-layer thermal resistance. However, past research has underestimated the extent of the thermal bottleneck. Recent experimental work shows that the Die-to-Die (D2D) layers hinder effective heat transfer, likely leading to the capping of core frequencies.\n To address this problem, in this paper, we first show how to create pillars of high thermal conduction from the processor die to the heat sink. We do this by aligning and shorting dummy D2D μbumps with thermal TSVs (TTSVs). This lowers processor temperatures substantially. We then improve application performance by boosting the processor frequency until we consume the available thermal headroom. Finally, these aligned and shorted dummy <i>μ</i>bump-TTSV sites create die regions of higher vertical thermal conduction. Hence, we propose to leverage them with three new architectural techniques: <i>conductivity-aware</i> thread placement, frequency boosting, and thread migration. We evaluate our scheme, called <i>Xylem</i>, using simulations of an 8-core processor at 2.4 GHz and 8 DRAM dies on top. <i>μ</i>Bump-TTSV alignment and shorting in a generic and in a customized Xylem design enable an average increase in processor frequency of 400 MHz and 720 MHz, respectively, at an area overhead of 0.63% and 0.81%, and without exceeding acceptable temperatures. This improves average application performance by 11% and 18%, respectively. Moreover, applying Xylem's conductivity-aware techniques enables further gains.",
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"title": "Xylem: enhancing vertical thermal conduction in 3D processor-memory stacks",
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"year": 2017
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"name": "Han Chen"
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"name": "Jilong Xue"
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"name": "Yafei Dai"
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"paperAbstract": "Recent advances in storage (e.g., DDR4, SSD, NVM) and accelerators (e.g., GPU, Xeon-Phi, FPGA) provide the opportunity to efficiently process large-scale graphs on a single machine. In this paper, we present Garaph, a GPU-accelerated graph processing system on a single machine with secondary storage as memory extension. Garaph is novel in three ways. First, Garaph proposes a vertex replication degree customization scheme that maximizes the GPU utilization given vertices\u2019 degrees and space constraints. Second, Garaph adopts a balanced edge-based partition ensuring work balance over CPU threads, and also a hybrid of notify-pull and pull computation models optimized for fast graph processing on the CPU. Third, Garaph uses a dynamic workload assignment scheme which takes into account both characteristics of processing elements and graph algorithms. Our evaluation with six widely used graph applications on seven real-world graphs shows that Garaph significantly outperforms existing state-of-art CPU-based and GPUbased graph processing systems, getting up to 5.36x speedup over the fastest among them.",
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"paperAbstract": "In recent years, the popularity of graph databases has grown rapidly. This paper focuses on single-graph as an effective model to represent information and its related graph mining techniques. In frequent pattern mining in a single-graph setting, there are two main problems: support measure and search scheme. In this paper, we propose a novel framework for constructing support measures that brings together existing minimum-image-based and overlap-graph-based support measures. Our framework is built on the concept of occurrence / instance hypergraphs. Based on that, we present two new support measures: minimum instance (MI) measure and minimum vertex cover (MVC) measure, that combine the advantages of existing measures. In particular, we show that the existing minimum-image-based support measure is an upper bound of the MI measure, which is also linear-time computable and results in counts that are close to number of instances of a pattern. Although the MVC measure is NP-hard, it can be approximated to a constant factor in polynomial time. We also provide polynomial-time relaxations for both measures and bounding theorems for all presented support measures in the hypergraph setting. We further show that the hypergraph-based framework can unify all support measures studied in this paper. This framework is also flexible in that more variants of support measures can be defined and profiled in it.",
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"title": "Flexible and Feasible Support Measures for Mining Frequent Patterns in Large Labeled Graphs",
"venue": "SIGMOD Conference",
"year": 2017
},
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"name": "Youshan Miao"
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{
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"name": "Zhen Li"
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{
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"name": "Cheng Chen"
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"paperAbstract": "TUX2 is a new distributed graph engine that bridges graph computation and distributed machine learning. TUX2 inherits the benefits of an elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs; we extend and optimize it for distributed machine learning to support heterogeneity, a Stale Synchronous Parallel model, and a new MEGA (Mini-batch, Exchange, GlobalSync, and Apply) model. We have developed a set of representative distributed machine learning algorithms in TUX2, covering both supervised and unsupervised learning. Compared to implementations on distributed machine learning platforms, writing these algorithms in TUX2 takes only about 25% of the code: Our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. Our extensive evaluation of TUX2, using large data sets with up to 64 billion edges, shows that TUX2 outperforms state-of-the-art distributed graph engines PowerGraph and PowerLyra by an order of magnitude, while beating two state-of-the-art distributed machine learning systems by at least 48%.",
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"paperAbstract": "In recent years there has been a growing interest in developing \"streaming algorithms\" for efficient processing and querying of continuous data streams. These algorithms seek to provide accurate results while minimizing the required storage and the processing time, at the price of a small inaccuracy in their output. A fundamental query of interest is the intersection size of two big data streams. This problem arises in many different application areas, such as network monitoring, database systems, data integration and information retrieval. In this paper we develop a new algorithm for this problem, based on the Maximum Likelihood (ML) method. We show that this algorithm outperforms all known schemes in terms of the estimation's quality (lower variance) and that it asymptotically achieves the optimal variance.",
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"title": "A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection",
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"doi": "10.1145/3133956.3133977",
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"paperAbstract": "Despite security advice in the official documentation and an extensive body of security research about vulnerabilities and exploits, many developers still fail to write secure Android applications. Frequently, Android developers fail to adhere to security best practices, leaving applications vulnerable to a multitude of attacks. We point out the advantage of a low-time-cost tool both to teach better secure coding and to improve app security. Using the FixDroid IDE plug-in, we show that professional and hobby app developers can work with and learn from an in-environment tool without it impacting their normal work; and by performing studies with both students and professional developers, we identify key UI requirements and demonstrate that code delivered with such a tool by developers previously inexperienced in security contains significantly less security problems. Perfecting and adding such tools to the Android development environment is an essential step in getting both security and privacy for the next generation of apps.",
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"title": "A Stitch in Time: Supporting Android Developers in WritingSecure Code",
"venue": "CCS",
"year": 2017
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"paperAbstract": "High accuracy scientific simulations on high performance computing (HPC) platforms generate large amounts of data. To allow data to be efficiently analyzed, simulation outputs need to be refactored, compressed, and properly mapped onto storage tiers. This paper presents Canopus, a progressive data management framework for storing and analyzing big scientific data. Canopus allows simulation results to be refactored into a much smaller dataset along with a series of deltas with fairly low overhead. Then, the refactored data are compressed, mapped, and written onto storage tiers. For data analytics, refactored data are selectively retrieved to restore data at a specific level of accuracy that satisfies analysis requirements. Canopus enables end users to make trade-offs between analysis speed and accuracy onthe-fly. Canopus is demonstrated and thoroughly evaluated using blob detection on fusion simulation data.",
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"title": "Canopus: Enabling Extreme-Scale Data Analytics on Big HPC Storage via Progressive Refactoring",
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"year": 2017
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"doi": "10.1145/3030207.3030228",
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"year": 2017
},
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"authors": [
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"name": "Xi Wu"
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"name": "Fengan Li"
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"name": "Jeffrey F. Naughton"
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"doi": "10.1145/3035918.3064047",
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"paperAbstract": "While significant progress has been made separately on analytics systems for scalable stochastic gradient descent (SGD) and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD. There are two inter-related issues for this disconnect between research and practice: (1) low model accuracy due to added noise to guarantee privacy, and (2) high development and runtime overhead of the private algorithms. This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address <i>both</i> issues in an integrated manner. In contrast to the white-box approach adopted by previous work, we revisit and use the classical technique of <i>output perturbation</i> to devise a novel ``bolt-on'' approach to private SGD. While our approach trivially addresses (2), it makes (1) even more challenging. We address this challenge by providing a novel analysis of the <i>L</i><sub>2</sub>-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data. We integrate our algorithm, as well as other state-of-the-art differentially private SGD, into Bismarck, a popular scalable SGD-based analytics system on top of an RDBMS. Extensive experiments show that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms on many real datasets.",
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"http://arxiv.org/pdf/1606.04722v1.pdf",
"https://arxiv.org/pdf/1606.04722v1.pdf",
"https://arxiv.org/pdf/1606.04722v2.pdf",
"http://doi.acm.org/10.1145/3035918.3064047",
"https://arxiv.org/pdf/1606.04722v3.pdf",
"http://andrewxiwu.github.io/public/papers/2017/WLKCJN17-bolt-on-differential-privacy-for-scalable-stochastic-gradient-descent-based-analytics.pdf",
"http://andrewxiwu.github.io/public/talks/2017/sgd-dp-sigmod17.pdf"
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"title": "Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics",
"venue": "SIGMOD Conference",
"year": 2017
},
"55d970958b4df64b7ecda9dc7ba93eeeed677767": {
"authors": [
{
"ids": [
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"name": "Yangyang Gao"
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"name": "Haitao Zhang"
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"name": "Yanpei Zhu"
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"name": "Bingchang Tang"
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{
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"name": "Huadong Ma"
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"doi": "10.1109/HPCC-SmartCity-DSS.2017.73",
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"journalName": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"paperAbstract": "The large-scale surveillance video processing workloads are gradually migrated to cloud computing platforms. Meanwhile, the hybrid storage architecture, integrating both HDD and SSD storage devices, is increasingly used in the current cloud platforms. However, the computing and storage capabilities of the nodes are constantly changing, and this requires the delicate design of the data layout strategy for avoiding the serious load skew in the distributed computing nodes with the hybrid storage architecture. In this paper, we propose a Load-Aware Data Migration (LADM) scheme for distributed surveillance video processing with hybrid storage architecture. Specifically, according to the proposed the load estimation model and the storage capacity constraint, the Node-Level Data Migration (NLDM) strategy is used to periodically migrate the appropriate video chunks from the local HDD to the local SSD for improving the node processing performance, and the Cluster-Level Data Migration (CLDM) strategy is used to periodically migrate the appropriate video chunks from the high load nodes to the low load nodes for achieving the overall load balance of cluster. We conduct the extensive experiments based on the distributed surveillance video processing platform we developed, and the experimental results show that the proposed LADM scheme outperforms the current methods.",
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"https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.73"
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"title": "A Load-Aware Data Migration Scheme for Distributed Surveillance Video Processing with Hybrid Storage Architecture",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
"year": 2017
},
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"authors": [
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"name": "Yohei Ueda"
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{
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"paperAbstract": "Recent approximation algorithms (e.g., CounterStacks, SHARDS and AET) make lightweight, continuouslyupdated miss ratio curves (MRCs) practical for online modeling and control of LRU caches. For more complex cache-replacement policies, scaled-down simulation, introduced with SHARDS, offers a general method for emulating a given cache size by using a miniature cache processing a small spatially-hashed sample of requests. We present the first detailed study evaluating the effectiveness of this approach for modeling non-LRU algorithms, including ARC, LIRS and OPT. Experiments with over a hundred real-world traces demonstrate that scaled-down MRCs are extremely accurate while requiring dramatically less space and time than full simulation. We propose an efficient, generic framework for dynamic optimization using multiple scaled-down simulations to explore candidate cache configurations simultaneously. Experiments demonstrate significant improvements from automatic adaptation of parameters including the stack size limit in LIRS, and queue sizes in 2Q. Finally, we introduce SLIDE, a new approach inspired by Talus that uses scaled-down MRCs to remove performance cliffs automatically. SLIDE performs shadow partitioning transparently within a single unified cache, avoiding the problem of migrating state between distinct caches when partition boundaries change. Experiments demonstrate that SLIDE improves miss ratios for many cache policies, with large gains in the presence of cliffs.",
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"title": "Cache Modeling and Optimization using Miniature Simulations",
"venue": "USENIX Annual Technical Conference",
"year": 2017
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"name": "Han Dong"
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"name": "Tao Li"
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"name": "Jiabing Leng"
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"name": "Lingyan Kong"
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"name": "Gang Bai"
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"doi": "10.1109/ICPP.2017.13",
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"paperAbstract": "Hyperspectral image classification has been proved significant in remote sensing field. Traditional classification methods have meet bottlenecks due to the lack of remote sensing background knowledge or high dimensionality. Deep learning based methods, such as deep convolutional neural network (CNN), can effectively extract high level features from raw data. But the training of deep CNN is rather time-consuming. The general purpose graphic processing units (GPUs) have been considered as one of the most common co-processors that can help accelerate deep learning applications. In this paper we propose a GPU-based Cube CNN (GCN) framework for hyperspectral image classification. First, a Parallel Neighbor Pixels Extraction (PNPE) algorithm is designed to enable the framework directly loading raw hyperspectral image into GPU's global memory, and extracting samples into data cube. Then, based on the peculiarity of hyperspectral image and cube convolution, we propose a novel Cube CNN-to-GPU mapping mechanism that transfers the training of Cube CNN to GPU effectively. Finally, the mini-batch gradient descent(MBGD) algorithm is improved with Computing United Device Architecture(CUDA) multi-streaming technique, which further speeds up network training in GCN framework. Experiments on KSC dataset, PU dataset and SA dataset show that, compared with state-of-art framework Caffe, we achieve up to 83% and 67% reduction in network training time without losing accuracy, when using SGD (Stochastic Gradient Descent) and MBGD algorithm respectively. Experiments across different GPUs show the same performance trend, which demonstrates the good extendibility of GCN framework.",
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"title": "GCN: GPU-Based Cube CNN Framework for Hyperspectral Image Classification",
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"paperAbstract": "Ubiquitous mobile platforms such as Android rely on managed language run-time environments, also known as language virtual machines (VMs), to run a diverse range of user applications (apps). Each app runs in its own private VM instance, and each VM makes its own private local decisions in managing its use of processor and memory resources. Moreover, the operating system and the hardware do not communicate their low-level decisions regarding power management with the high-level app environment. This lack of coordination across layers and across apps restricts more effective global use of resources on the device.\n We address this problem by devising and implementing a global memory manager service for Android that optimizes memory usage, run-time performance, and power consumption globally across all apps running on the device. The service focuses on the impact of garbage collection (GC) along these dimensions, since GC poses a significant overhead within managed run-time environments. Our prototype collects system-wide statistics from all running VMs, makes centralized decisions about memory management across apps and across software layers, and also collects garbage centrally. Furthermore, the global memory manager coordinates with the power manager to tune collector scheduling. In our evaluation, we illustrate the impact of such a central memory management service in reducing total energy consumption (up to 18%) and increasing throughput (up to 12%), and improving memory utilization and adaptability to user activities.",
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"title": "One Process to Reap Them All: Garbage Collection as-a-Service",
"venue": "VEE",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Pedro Y\u00e9benes"
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"name": "Jes\u00fas Escudero-Sahuquillo"
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"name": "Pedro Javier Garc\u00eda"
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"name": "Francisco J. Quiles"
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"name": "Torsten Hoefler"
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],
"doi": "10.1109/HOTI.2017.11",
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"paperAbstract": "Interconnection networks must meet the communication demands of current High-Performance Computing systems. In order to interconnect efficiently the end nodes of these systems with a good performance-to-cost ratio, new network topologies have been proposed in the last years that leverage high-radix switches, such as Slim Fly. Adversarial traffic patterns, however, may reduce severely the performance of Slim Fly networks when using only minimal-path routing. In order to mitigate the performance degradation in these scenarios, Slim Fly networks should configure an oblivious or adaptive non-minimal routing. The non-minimal routing algorithms proposed for Slim Fly usually rely on Valiant's algorithm to select the paths, at the cost of doubling the average path-length, as well as the number of Virtual Channels (VCs) required to prevent deadlocks. Moreover, Valiant may introduce additional inefficiencies when applied to Slim Fly networks, such as the "turn-around problem" that we analyze in this work. With the aim of overcoming these drawbacks, we propose in this paper two variants of the Valiant's algorithm that improve the non-minimal path selection in Slim Fly networks. They are designed to be combined with adaptive routing algorithms that rely on Valiant to select non-minimalpaths, such as UGAL or PAR, which we have adapted to the Slim Fly topology. Through the results from simulation experiments, we show that our proposals improve the network performance and/or reduce the number of required VCs to prevent deadlocks, even in scenarios with adversarial traffic.",
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"title": "Improving Non-minimal and Adaptive Routing Algorithms in Slim Fly Networks",
"venue": "2017 IEEE 25th Annual Symposium on High-Performance Interconnects (HOTI)",
"year": 2017
},
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"name": "Devavret Makkar"
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"name": "David A. Bader"
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{
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"name": "Oded Green"
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],
"doi": "10.1109/HiPC.2017.00011",
"doiUrl": "https://doi.org/10.1109/HiPC.2017.00011",
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"paperAbstract": "Triangle counting is an important building block for finding key players in a graph. It is an integral part of the popular clustering coefficient analytic and can be used for pattern matching in social networks. A triangle, which is also a 3-clique, represents a strong connection between three players that are all connected. While counting triangles is not overly expensive from a computational standpoint, especially in comparison to centrality metrics (such as betweenness centrality and closeness centrality), it can still prove to be prohibitive for large scale networks, especially for those with a power-law distribution. This problem only deepens for dynamic graphs where the network is constantly changing, requiring constant updating of the graph and the analytic. In this paper, we present a new dynamic graph algorithm for counting triangles that is based on an inclusion-exclusion formulation. While our algorithm is independent of the computing platform, we show performance results on an NVIDIA GPU. Our approach handles 32 million updates per second, or up to 11 million updates per second if the graph data structure is also updated. In past approaches, when a vertex was affected due to an edge insertion or deletion, it was necessary to find the triangles from scratch for that given vertex. Our new formulation does not need this and only requires considering the affected edges. As such our algorithm is typically several hundred times faster than the past approach - in some cases up to 819X faster.",
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"http://doi.ieeecomputersociety.org/10.1109/HiPC.2017.00011"
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"title": "Exact and Parallel Triangle Counting in Dynamic Graphs",
"venue": "2017 IEEE 24th International Conference on High Performance Computing (HiPC)",
"year": 2017
},
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"name": "Christian Inzinger"
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"paperAbstract": "Trustworthy software requires strong privacy and security guarantees from a secure trust base in hardware. While chipmakers provide hardware support for basic security and privacy primitives such as enclaves and memory encryption. these primitives do not address hiding of the memory access pattern, information about which may enable attacks on the system or reveal characteristics of sensitive user data. State-of-the-art approaches to protecting the access pattern are largely based on Oblivious RAM (ORAM). Unfortunately, current ORAM implementations suffer from very significant practicality and overhead concerns, including roughly an order of magnitude slowdown, more than 100% memory capacity overheads, and the potential for system deadlock.\n Memory technology trends are moving towards 3D and 2.5D integration, enabling significant logic capabilities and sophisticated memory interfaces. Leveraging the trends, we propose a new approach to access pattern obfuscation, called ObfusMem. ObfusMem adds the memory to the trusted computing base and incorporates cryptographic engines within the memory. ObfusMem encrypts commands and addresses on the memory bus, hence the access pattern is cryptographically obfuscated from external observers. Our evaluation shows that ObfusMem incurs an overhead of 10.9% on average, which is about an order of magnitude faster than ORAM implementations. Furthermore, ObfusMem does not incur capacity overheads and does not amplify writes. We analyze and compare the security protections provided by ObfusMem and ORAM, and highlight their differences.",
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"title": "ObfusMem: A low-overhead access obfuscation for trusted memories",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
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"authors": [
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"name": "Jianyu Huang"
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"paperAbstract": "Matrix multiplication (GEMM) is a core operation to numerous scientific applications. Traditional implementations of Strassen-like fast matrix multiplication (FMM) algorithms often do not perform well except for very large matrix sizes, due to the increased cost of memory movement, which is particularly noticeable for non-square matrices. Such implementations also require considerable workspace and modifications to the standard BLAS interface. We propose a code generator framework to automatically implement a large family of FMM algorithms suitable for multiplications of arbitrary matrix sizes and shapes. By representing FMM with a triple of matrices [U, V, W] that capture the linear combinations of submatrices that are formed, we can use the Kronecker product to define a multi-level representation of Strassen-like algorithms. Incorporating the matrix additions that must be performed for Strassen-like algorithms into the inherent packing and micro-kernel operations inside GEMM avoids extra workspace and reduces the cost of memory movement. Adopting the same loop structures as high-performance GEMM implementations allows parallelization of all FMM algorithms with simple but efficient data parallelism without the overhead of task parallelism. We present a simple performance model for general FMM algorithms and compare actual performance of 20+ FMM algorithms to modeled predictions. Our implementations demonstrate a performance benefit over conventional GEMM on single core and multi-core systems. This study shows that Strassen-like fast matrix multiplication can be incorporated into libraries for practical use.",
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"title": "Generating Families of Practical Fast Matrix Multiplication Algorithms",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
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"paperAbstract": "Despite years of innovative research and development, gigabit-speed 60 GHz wireless networks are still not mainstream. The main concern for network operators and vendors is the unfavorable propagation characteristics due to short wavelength and high directionality, which renders the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this paper, we design and implement MUST, an IEEE 802.11-compliant system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. Our experiments with off-the-shelf 802.11 hardware show that MUST can achieve 25-60% throughput gain over state-of-the-art solutions, while bringing almost 2 orders of magnitude cross-band switching latency improvement.",
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"paperAbstract": "Timing attacks have been a continuous threat to users' privacy in modern browsers. To mitigate such attacks, existing approaches, such as Tor Browser and Fermata, add jitters to the browser clock so that an attacker cannot accurately measure an event. However, such defenses only raise the bar for an attacker but do not fundamentally mitigate timing attacks, i.e., it just takes longer than previous to launch a timing attack. In this paper, we propose a novel approach, called deterministic browser, which can provably prevent timing attacks in modern browsers. Borrowing from Physics, we introduce several concepts, such as an observer and a reference frame. Specifically, a snippet of JavaScript, i.e., an observer in JavaScript reference frame, will always obtain the same, fixed timing information so that timing attacks are prevented; at contrast, a user, i.e., an oracle observer, will perceive the JavaScript differently and do not experience the performance slowdown. We have implemented a prototype called DeterFox and our evaluation shows that the prototype can defend against browser-related timing attacks.",
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"paperAbstract": "Non-volatile memory technologies such as PCM, ReRAM and STT-RAM allow data to be saved to persistent storage significantly faster than hard drives or SSDs. Many of the use cases for non-volatile memory requires persistent logging since it enables a set of operations to execute in an atomic manner. However, a logging protocol must handle reordering, which causes a write to reach the non-volatile memory before a previous write operation. \n In this paper, we show that reordering results from two parts of the system: the out-of-order execution in the CPU and the cache coherence protocol. By carefully considering the properties of these reorderings, we present a logging protocol that requires only one round trip to non-volatile memory while avoiding expensive computations, thus increasing performance. We also show how the logging protocol can be extended to building a durable set (hash map) that also requires a single round trip to non-volatile memory for inserting, updating, or deleting operations.",
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"title": "Efficient logging in non-volatile memory by exploiting coherency protocols",
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"year": 2017
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"paperAbstract": "Modern cryptocurrency systems, such as Ethereum, permit complex financial transactions through scripts called smart contracts. These smart contracts are executed many, many times, always without real concurrency. First, all smart contracts are serially executed by miners before appending them to the blockchain. Later, those contracts are serially re-executed by validators to verify that the smart contracts were executed correctly by miners. Serial execution limits system throughput and fails to exploit today\u2019s concurrent multicore and cluster architectures. Nevertheless, serial execution appears to be required: contracts share state, and contract programming languages have a serial semantics. This paper presents a novel way to permit miners and validators to execute smart contracts in parallel, based on techniques adapted from software transactional memory. Miners execute smart contracts speculatively in parallel, allowing non-conflicting contracts to proceed concurrently, and \u201cdiscovering\u201d a serializable concurrent schedule for a block\u2019s transactions, This schedule is captured and encoded as a deterministic fork-join program used by validators to re-execute the miner\u2019s parallel schedule deterministically but concurrently. Smart contract benchmarks run on a JVM with ScalaSTM show that a speedup of 1.33x can be obtained for miners and 1.69x for validators with just three concurrent threads.",
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"paperAbstract": "Randomized binary exponential backoff (BEB) is a popular algorithm for coordinating access to a shared channel. With an operational history exceeding four decades, BEB is currently an important component of several wireless standards. Despite this track record, prior theoretical results indicate that under bursty traffic (1) BEB yields poor makespan and (2) superior algorithms are possible. To date, the degree to which these findings manifest in practice has not been resolved. To address this issue, we examine one of the strongest cases against BEB: n packets that simultaneously begin contending for the wireless channel. Using Network Simulator 3, we compare against more recent algorithms that are inspired by BEB, but whose makespan guarantees are superior. Surprisingly, we discover that these newer algorithms significantly underperform. Through further investigation, we identify as the culprit a flawed but common abstraction regarding the cost of collisions. Our experimental results are complemented by analytical arguments that the number of collisions \u2013 and not solely makespan \u2013 is an important metric to optimize. We believe that these findings have implications for the design of contention-resolution algorithms.",
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"paperAbstract": "Owing to great potential in smart home and human-computer interactive applications, WiFi indoor localization has attracted extensive attentions in the past several years. The state-of-the-art systems have successfully achieved decimeter-level accuracies. However, the high accuracy is acquired at the cost of dense access point (AP) deployment, employing large size of frequency bandwidths or special-purpose radar signals which are not compatible with existing WiFi protocol, limiting their practical deployments. This paper presents the design and implementation of AWL, an accurate indoor localization system that enables a single WiFi AP to achieve decimeter-level accuracy with only one channel hopping. The key enabler of the system is we novelly employ channel hopping to create virtual antennas, without the need of adding more antennas or physically move the antennas' positions for a larger antenna array. We successfully utilize the widely known \"bad\" spatial aliasing to improve the AoA estimation accuracy. A novel multipath suppression scheme is also proposed to combat the severe multipath issue indoors. We build a prototype of AWL on WARP software-defined radio platform. Comprehensive experiments manifest that AWL achieves a median localization accuracy of 38 cm in a rich multipath indoor environment with only a single AP equipped with 6 antennas. In a small scale area, AWL is able to accurately track a moving device's trajectory, enabling applications such as writing/drawing in the air.",
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"paperAbstract": "Dynamic task graph schedulers automatically balance work across processor cores by scheduling tasks among available threads while preserving dependences. In this paper, we design NABBITC, a provably efficient dynamic task graph scheduler that accounts for data locality on NUMA systems. NABBITC allows users to assign a color to each task representing the location (e.g., a processor core) that has the most efficient access to data needed during that node's execution. NABBITC then automatically adjusts the scheduling so as to preferentially execute each node at the location that matches its color—leading to better locality because the node is likely to make local rather than remote accesses. At the same time, NABBITC tries to optimize load balance and not add too much overhead compared to the vanilla NABBIT scheduler that does not consider locality. We provide a theoretical analysis that shows that NABBITC does not asymptotically impact the scalability of NABBIT.We evaluated the performance of NABBITC on a suite of benchmarks, including both memory and compute intensive applications. Our experiments indicate that adding locality awareness has a considerable performance advantage compared to the vanilla NABBIT scheduler. Furthermore, we compared NABBITC to both OpenMP tasks and OpenMP loops. For regular applications, OpenMP loops can achieve perfect locality and perfect load balance statically. For these benchmarks, NABBITC has a small performance penalty compared to OpenMP due to its dynamic scheduling strategy. Similarly, for compute intensive applications with course-grained tasks, OpenMP task's centralized scheduler provides the best performance. However, we find that NABBITC provides a good trade-off between data locality and load balance; on memory intensive jobs, it consistently outperforms OpenMP tasks while for irregular jobs where load balancing is important, it outperforms OpenMP loops. Therefore, NABBITC combines the benefits of locality-aware scheduling for regular, memory intensive, applications (the forte of static schedulers such as those in OpenMP) and dynamically adapting to load imbalance in irregular applications (the forte of dynamic schedulers such as Cilk Plus, TBB, and Nabbit).",
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"title": "Locality-Aware Dynamic Task Graph Scheduling",
"venue": "2017 46th International Conference on Parallel Processing (ICPP)",
"year": 2017
},
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"doi": "10.1109/ICPP.2017.27",
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"paperAbstract": "This paper investigates how to improve the memory locality of graph-structured analytics on large-scale shared memory systems. We demonstrate that a graph partitioning where all in-edges for a vertex are placed in the same partition improves memory locality. However, realising performance improvement through such graph partitioning poses several challenges and requires rethinking the classification of graph algorithms and preferred data structures. We introduce the notion of medium dense frontiers, a type of frontier that is sufficiently dense for a bitmap representation, yet benefits from an indexed graph layout. Using three types of frontiers, and three graph layout schemes optimized to each frontier type, we design an edge traversal algorithm that autonomously decides which type to use. The distinction of forward vs. backward graph traversal folds into this decision and need no longer be specified by the programmer.We have implemented our techniques in a NUMA-aware graph analytics framework derived from Ligra and demonstrate a speedup of up to 4.34× over Ligra and up to 2.93× over Polymer.",
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"title": "Accelerating Graph Analytics by Utilising the Memory Locality of Graph Partitioning",
"venue": "2017 46th International Conference on Parallel Processing (ICPP)",
"year": 2017
},
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"authors": [
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"name": "Bo Fang"
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"name": "Qiang Guan"
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"name": "Nathan DeBardeleben"
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}
],
"doi": "10.1145/3078597.3078609",
"doiUrl": "https://doi.org/10.1145/3078597.3078609",
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"paperAbstract": "Requirements for reliability, low power consumption, and performance place complex and conflicting demands on the design of high-performance computing (HPC) systems. Fault-tolerance techniques such as checkpoint/restart (C/R) protect HPC applications against hardware faults. These techniques, however, have non negligible overheads particularly when the fault rate exposed by the hardware is high: it is estimated that in future HPC systems, up to 60% of the computational cycles/power will be used for fault tolerance.\n To mitigate the overall overhead of fault-tolerance techniques, we propose LetGo, an approach that attempts to continue the execution of a HPC application when crashes would otherwise occur. Our hypothesis is that a class of HPC applications have good enough intrinsic fault tolerance so that its possible to re-purpose the default mechanism that terminates an application once a crash-causing error is signalled, and instead attempt to repair the corrupted application state, and continue the application execution. This paper explores this hypothesis, and quantifies the impact of using this observation in the context of checkpoint/restart (C/R) mechanisms.\n Our fault-injection experiments using a suite of five HPC applications show that, on average, LetGo is able to elide 62% of the crashes encountered by applications, of which 80% result in correct output, while incurring a negligible performance overhead. As a result, when LetGo is used in conjunction with a C/R scheme, it enables significantly higher efficiency thereby leading to faster time to solution.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3078597.3078609",
"http://blogs.ubc.ca/karthik/files/2017/04/2017_HPDC.pdf"
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"title": "LetGo: A Lightweight Continuous Framework for HPC Applications Under Failures",
"venue": "HPDC",
"year": 2017
},
"5846f40cf1cb0042e3627cb2905101d992ab6bb6": {
"authors": [
{
"ids": [
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"name": "Timoth\u00e9e Ewart"
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{
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"name": "Judit Planas"
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{
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"name": "Francesco Cremonesi"
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"name": "Kai Langen"
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"name": "Felix Sch\u00fcrmann"
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"name": "Fabien Delalondre"
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],
"doi": "10.1007/978-3-319-58667-0_10",
"doiUrl": "https://doi.org/10.1007/978-3-319-58667-0_10",
"entities": [
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"id": "5846f40cf1cb0042e3627cb2905101d992ab6bb6",
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"paperAbstract": "The increasing complexity and heterogeneity of extreme scale systems makes the optimization of large scale scientific applications particularly challenging. Efficiently leveraging these complex systems requires a great deal of technical expertise and a considerable amount of man-hours. The computational neuroscience community relies on an handful of those frameworks to model the electrical activity of brain tissue at different scales. As the members of the Blue Brain Project actively contribute to a large part of those frameworks, it becomes mandatory to implement a strategy to reduce the overall development cost. Therefore, we present Neuromapp, a computational neuroscience mini-application framework. Neuromapp consists of a number of mini-apps (small standalone applications) that represent a single functionality in one of the large scientific frameworks. The collection of several mini-apps forms a skeleton which is able to reproduce the original workflow of the scientific application. Thus, it becomes easy to investigate both single component and workflow optimizations, new software and hardware systems or future system design. New solutions can then be integrated into the large scientific applications if proved to be successful, reducing the overall development and optimization effort.",
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"title": "Neuromapp: A Mini-application Framework to Improve Neural Simulators",
"venue": "ISC",
"year": 2017
},
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"name": "Jiguang Wan"
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"name": "Wei Wu"
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{
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"name": "Ling Zhan"
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{
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"name": "Qing Yang"
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{
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"name": "Xiaoyang Qu"
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{
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"name": "Changsheng Xie"
}
],
"doi": "10.1109/IPDPS.2017.54",
"doiUrl": "https://doi.org/10.1109/IPDPS.2017.54",
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"paperAbstract": "This paper proposes a new SSD cache architecture, DEFT-cache, Delayed Erasing and Fast Taping, that maximizes I/O performance and reliability of RAID storage. First of all, DEFT-Cache exploits the inherent physical properties of flash memory SSD by making use of old data that have been overwritten but still in existence in SSD to minimize small write penalty of RAID5/6. As data pages being overwritten in SSD, old data pages are invalidated and become candidates for erasure and garbage collections. Our idea is to selectively delay the erasure of the pages and let these otherwise useless old data in SSD contribute to I/O performance for parity computations upon write I/Os. Secondly, DEFT-Cache provides inexpensive redundancy to the SSD cache by having one physical SSD and one virtual SSD as a mirror cache. The virtual SSD is implemented on HDD but using log-structured data layout, i.e. write data are quickly logged to HDD using sequential write. The dual and redundant caches provide a cost-effective and highly reliable write-back SSD cache. We have implemented DEFT-Cache on Linux system. Extensive experiments have been carried out to evaluate the potential benefits of our new techniques. Experimental results on SPC and Microsoft traces have shown that DEFT-Cache improves I/O performance by 26.81% to 56.26% in terms of average user response time. The virtual SSD mirror cache can absorb write I/Os as fast as physical SSD providing the same reliability as two physical SSD caches without noticeable performance loss.",
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"title": "DEFT-Cache: A Cost-Effective and Highly Reliable SSD Cache for RAID Storage",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
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"name": "Yunpeng Song"
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"paperAbstract": "This paper describes EM-Based Detection of Deviations in Program Execution (EDDIE), a new method for detecting anomalies in program execution, such as malware and other code injections, without introducing any overheads, adding any hardware support, changing any software, or using any resources on the monitored system itself. Monitoring with EDDIE involves receiving electromagnetic (EM) emanations that are emitted as a side effect of execution on the monitored system, and it relies on spikes in the EM spectrum that are produced as a result of periodic (e.g. loop) activity in the monitored execution. During training, EDDIE characterizes normal execution behavior in terms of peaks in the EM spectrum that are observed at various points in the program execution, but it does not need any characterization of the malware or other code that might later be injected. During monitoring, EDDIE identifies peaks in the observed EM spectrum, and compares these peaks to those learned during training. Since EDDIE requires no resources on the monitored machine and no changes to the monitored software, it is especially well suited for security monitoring of embedded and IoT devices. We evaluate EDDIE on a real IoT system and in a cycle-accurate simulator, and find that even relatively brief injected bursts of activity (a few milliseconds) are detected by EDDIE with high accuracy, and that it also accurately detects when even a few instructions are injected into an existing loop within the application.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3079856.3080223",
"http://alenka.ece.gatech.edu/wp-content/uploads/sites/463/2017/06/ISCA17.pdf"
],
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"title": "EDDIE: EM-based detection of deviations in program execution",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
"594065ed6261717f4dfc76ba6de4d8f78ff1c2a9": {
"authors": [
{
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],
"name": "Peter Rindal"
},
{
"ids": [
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],
"name": "Mike Rosulek"
}
],
"doi": "10.1145/3133956.3134044",
"doiUrl": "https://doi.org/10.1145/3133956.3134044",
"entities": [
"Eurocrypt",
"Franklin Electronic Publishers",
"PKC (conference)",
"Random oracle",
"Symmetric-key algorithm"
],
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"journalName": "IACR Cryptology ePrint Archive",
"journalPages": "769",
"journalVolume": "2017",
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"paperAbstract": "Private set intersection (PSI) allows two parties, who each hold a set of items, to compute the intersection of those sets without revealing anything about other items. Recent advances in PSI have significantly improved its performance for the case of semi-honest security, making semi-honest PSI a practical alternative to insecure methods for computing intersections. However, the semi-honest security model is not always a good fit for real-world problems.\n In this work we introduce a new PSI protocol that is secure in the presence of malicious adversaries. Our protocol is based entirely on fast symmetric-key primitives and inherits important techniques from state-of-the-art protocols in the semi-honest setting. Our novel technique to strengthen the protocol for malicious adversaries is inspired by the dual execution technique of Mohassel & Franklin (PKC 2006). Our protocol is optimized for the random-oracle model, but can also be realized (with a performance penalty) in the standard model.\n We demonstrate our protocol's practicality with a prototype implementation. To securely compute the intersection of two sets of size 2<sup>20</sup> requires only 13 seconds with our protocol, which is ~12x faster than the previous best malicious-secure protocol (Rindal & Rosulek, Eurocrypt 2017), and only 3x slower than the best semi-honest protocol (Kolesnikov et al., CCS 2016).",
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"https://eprint.iacr.org/2017/769.pdf",
"http://eprint.iacr.org/2017/769",
"http://doi.acm.org/10.1145/3133956.3134044"
],
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"sources": [
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],
"title": "Malicious-Secure Private Set Intersection via Dual Execution",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"ids": [
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],
"name": "Weilong Cui"
},
{
"ids": [
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],
"name": "Timothy Sherwood"
}
],
"doi": "10.1145/3123939.3124541",
"doiUrl": "https://doi.org/10.1145/3123939.3124541",
"entities": [
"Architectural decision",
"Best, worst and average case",
"Glossary of computer graphics",
"Risk management",
"Software architecture"
],
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"paperAbstract": "Designing a system in an era of rapidly evolving application behaviors and significant technology shifts involves taking on risk that a design will fail to meet its performance goals. While risk assessment and management are expected in both business and investment, these aspects are typically treated as independent to questions of performance and efficiency in architecture analysis. As hardware and software characteristics become uncertain (i.e. samples from a distribution), we demonstrate that the resulting performance distributions quickly grow beyond our ability to reason about with intuition alone. We further show that knowledge of the performance distribution can be used to significantly improve both the average case performance and minimize the risk of under-performance (which we term architectural risk). Our automated framework can be used to quantify the areas where trade-offs between expected performance and the \"tail\" of performance are most acute and provide new insights supporting architectural decision making (such as core selection) under uncertainty. Importantly it can do this even without a priori knowledge of an analytic model governing that uncertainty.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3123939.3124541",
"http://cs.ucsb.edu/~sherwood/pubs/MICRO-17-archrisk.pdf"
],
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"s2Url": "https://semanticscholar.org/paper/5942a6955880a7e575427683c7278c371ec2c5d4",
"sources": [
"DBLP"
],
"title": "Estimating and understanding architectural risk",
"venue": "MICRO",
"year": 2017
},
"5947017313f56af69cea1796b6501f60fcaf8e74": {
"authors": [
{
"ids": [
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],
"name": "Shahbaz Khan"
}
],
"doi": "10.1145/3087556.3087576",
"doiUrl": "https://doi.org/10.1145/3087556.3087576",
"entities": [
"Algorithm",
"Central processing unit",
"Data structure",
"Depth-first search",
"Deterministic algorithm",
"Distributed algorithm",
"Fault tolerance",
"Graph (discrete mathematics)",
"Graph theory",
"Parallel algorithm",
"Parallel random-access machine"
],
"id": "5947017313f56af69cea1796b6501f60fcaf8e74",
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"paperAbstract": "Depth first search (DFS) tree is a fundamental data structure for solving various graph problems. The classical algorithm [SIAMCOMP74] for building a DFS tree requires O(m+n) time for a given undirected graph G having n vertices and m edges. Recently, Baswana et al. [SODA16] presented a simple algorithm for updating the DFS tree of an undirected graph after an edge/vertex update in O (n) time. However, their algorithm is strictly sequential. We present an algorithm achieving similar bounds, that can be adopted easily to the parallel environment. In the parallel environment, a DFS tree can be computed from scratch using O(m) processors in expected O (1) time [SICOMP90] on an EREW PRAM, whereas the best deterministic algorithm takes O (√n) time [SIAMCOMP90,JAL93] on a CRCW PRAM. Our algorithm can be used to develop optimal (upto polylog n factors) deterministic algorithms for maintaining fully dynamic DFS and fault tolerant DFS, of an undirected graph.\n 1- Parallel Fully Dynamic DFS - Given any arbitrary online sequence of vertex or edge updates, we can maintain a DFS tree of an undirected graph in O (1) time per update using m processors on an EREW PRAM.\n 2- Parallel Fault tolerant DFS - An undirected graph can be preprocessed to build a data structure of size O(m) such that for a set of k updates (where k is constant) in the graph, a DFS tree of the updated graph can be computed in O (1) time using n processors on an EREW PRAM. For constant k, this is also work optimal (upto polylog n factors)\n Moreover, our fully dynamic DFS algorithm provides, in a seamless manner, nearly optimal (upto polylog n factors) algorithms for maintaining a DFS tree in the semi-streaming environment and a restricted distributed model. These are the first parallel, semi-streaming and distributed algorithms for maintaining a DFS tree in the dynamic setting.",
"pdfUrls": [
"http://arxiv.org/abs/1705.03637",
"https://export.arxiv.org/pdf/1705.03637",
"http://doi.acm.org/10.1145/3087556.3087576",
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"journalName": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"journalPages": "1-12",
"journalVolume": "",
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"paperAbstract": "Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU) --- deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X -- 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X -- 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.",
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"title": "Learning to Diversify Search Results via Subtopic Attention",
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"paperAbstract": "We evaluate the use of data obtained by illicit means against a broad set of ethical and legal issues. Our analysis covers both the direct collection, and secondary uses of, data obtained via illicit means such as exploiting a vulnerability, or unauthorized disclosure. We extract ethical principles from existing advice and guidance and analyse how they have been applied within more than 20 recent peer reviewed papers that deal with illicitly obtained datasets. We find that existing advice and guidance does not address all of the problems that researchers have faced and explain how the papers tackle ethical issues inconsistently, and sometimes not at all. Our analysis reveals not only a lack of application of safeguards but also that legitimate ethical justifications for research are being overlooked. In many cases positive benefits, as well as potential harms, remain entirely unidentified. Few papers record explicit Research Ethics Board (REB) approval for the activity that is described and the justifications given for exemption suggest deficiencies in the REB process.",
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"paperAbstract": "Since the first whole-genome sequencing, the biomedical research community has made significant steps towards a more precise, predictive and personalized medicine. Genomic data is nowadays widely considered privacy-sensitive and consequently protected by strict regulations and released only after careful consideration. Various additional types of biomedical data, however, are not shielded by any dedicated legal means and consequently disseminated much less thoughtfully. This in particular holds true for DNA methylation data as one of the most important and well-understood epigenetic element influencing human health. In this paper, we show that, in contrast to the aforementioned belief, releasing one's DNA methylation data causes privacy issues akin to releasing one's actual genome. We show that already a small subset of methylation regions influenced by genomic variants are sufficient to infer parts of someone's genome, and to further map this DNA methylation profile to the corresponding genome. Notably, we show that such re-identification is possible with 97.5% accuracy, relying on a dataset of more than 2500 genomes, and that we can reject all wrongly matched genomes using an appropriate statistical test. We provide means for countering this threat by proposing a novel cryptographic scheme for privately classifying tumors that enables a privacy-respecting medical diagnosis in a common clinical setting. The scheme relies on a combination of random forests and homomorphic encryption, and it is proven secure in the honest-but-curious model. We evaluate this scheme on real DNA methylation data, and show that we can keep the computational overhead to acceptable values for our application scenario.",
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"paperAbstract": "With millions of apps available to users, the mobile app market is rapidly becoming very crowded. Given the intense competition, the time to market is a critical factor for the success and profitability of an app. In order to shorten the development cycle, developers often focus their efforts on the unique features and workflows of their apps and rely on third-party Open Source Software (OSS) for the common features. Unfortunately, despite their benefits, careless use of OSS can introduce significant legal and security risks, which if ignored can not only jeopardize security and privacy of end users, but can also cause app developers high financial loss. However, tracking OSS components, their versions, and interdependencies can be very tedious and error-prone, particularly if an OSS is imported with little to no knowledge of its provenance.\n We therefore propose OSSPolice, a scalable and fully-automated tool for mobile app developers to quickly analyze their apps and identify free software license violations as well as usage of known vulnerable versions of OSS. OSSPolice introduces a novel hierarchical indexing scheme to achieve both high scalability and accuracy, and is capable of efficiently comparing similarities of app binaries against a database of hundreds of thousands of OSS sources (billions of lines of code). We populated OSSPolice with 60K C/C++ and 77K Java OSS sources and analyzed 1.6M free Google Play Store apps. Our results show that 1) over 40K apps potentially violate GPL/AGPL licensing terms, and 2) over 100K of apps use known vulnerable versions of OSS. Further analysis shows that developers violate GPL/AGPL licensing terms due to lack of alternatives, and use vulnerable versions of OSS despite efforts from companies like Google to improve app security. OSSPolice is available on GitHub.",
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"http://iisp.gatech.edu/sites/default/files/images/identifying_open-source_license_violation_and_1-day_security_risk_at_large_scale.pdf",
"http://doi.acm.org/10.1145/3133956.3134048",
"https://taesoo.kim/pubs/2017/duan:osspolice.pdf"
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"title": "Identifying Open-Source License Violation and 1-day Security Risk at Large Scale",
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"year": 2017
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"name": "Vu Nguyen"
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"paperAbstract": "One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under geometric and optimization views, hence termed geometric-based online GP (GoGP). We developed theory to guarantee that with a good convergence rate our proposed algorithm always produces a (sparse) solution which is close to the true optima to any arbitrary level of approximation accuracy specified a priori. Furthermore, our method is proven to scale seamlessly not only with large-scale datasets, but also to adapt accurately with streaming data. We extensively evaluated our proposed model against state-of-the-art baselines using several large-scale datasets for online regression task. The experimental results show that our GoGP delivered comparable, or slightly better, predictive performance while achieving a magnitude of computational speedup compared with its rivals under online setting. More importantly, its convergence behavior is guaranteed through our theoretical analysis, which is rapid and stable while achieving lower errors.",
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"title": "GoGP: Fast Online Regression with Gaussian Processes",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
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"doi": "10.1145/3133956.3134027",
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"paperAbstract": "We introduce the key reinstallation attack. This attack abuses design or implementation flaws in cryptographic protocols to reinstall an already-in-use key. This resets the key's associated parameters such as transmit nonces and receive replay counters. Several types of cryptographic Wi-Fi handshakes are affected by the attack. All protected Wi-Fi networks use the 4-way handshake to generate a fresh session key. So far, this 14-year-old handshake has remained free from attacks, and is even proven secure. However, we show that the 4-way handshake is vulnerable to a key reinstallation attack. Here, the adversary tricks a victim into reinstalling an already-in-use key. This is achieved by manipulating and replaying handshake messages. When reinstalling the key, associated parameters such as the incremental transmit packet number (nonce) and receive packet number (replay counter) are reset to their initial value. Our key reinstallation attack also breaks the PeerKey, group key, and Fast BSS Transition (FT) handshake. The impact depends on the handshake being attacked, and the data-confidentiality protocol in use. Simplified, against AES-CCMP an adversary can replay and decrypt (but not forge) packets. This makes it possible to hijack TCP streams and inject malicious data into them. Against WPA-TKIP and GCMP the impact is catastrophic: packets can be replayed, decrypted, and forged. Because GCMP uses the same authentication key in both communication directions, it is especially affected. Finally, we confirmed our findings in practice, and found that every Wi-Fi device is vulnerable to some variant of our attacks. Notably, our attack is exceptionally devastating against Android 6.0: it forces the client into using a predictable all-zero encryption key.",
"pdfUrls": [
"https://papers.mathyvanhoef.com/ccs2017.pdf",
"http://doi.acm.org/10.1145/3133956.3134027",
"https://acmccs.github.io/papers/p1313-vanhoefA.pdf"
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"title": "Key Reinstallation Attacks: Forcing Nonce Reuse in WPA2",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"name": "Akshay Jajoo"
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"name": "Rohan Gandhi"
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{
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"name": "Y. Charlie Hu"
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{
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"name": "Cheng-Kok Koh"
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"doi": "10.1145/3143361.3143364",
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"paperAbstract": "CoFlow scheduling improves data-intensive application performance by improving their networking performance. State-of-the-art CoFlow schedulers in essence approximate the classic online Shortest-Job-First (SJF) scheduling, designed for a single CPU, in a distributed setting, with no coordination among how the flows of a CoFlow at individual ports are scheduled, and as a result suffer two performance drawbacks: (1) The flows of a CoFlow may suffer the out-of-sync problem -- they may be scheduled at different times and become drifting apart, negatively affecting the CoFlow completion time (CCT); (2) FIFO scheduling of flows at each port bears no notion of SJF, leading to suboptimal CCT.\n We propose Saath, an online CoFlow scheduler that overcomes the above drawbacks by explicitly exploiting the spatial dimension of CoFlows. In Saath, the global scheduler schedules the flows of a CoFlow using an all-or-none policy which mitigates the out-of-sync problem. To order the CoFlows within each queue, Saath resorts to a Least-Contention-First (LCoF) policy which we show extends the gist of SJF to the spatial dimension, complemented with starvation freedom. Our evaluation using an Azure testbed and simulations of two production cluster traces show that compared to Aalo, Saath reduces the CCT in median (P90) cases by 1.53x (4.5x) and 1.42x (37x), respectively.",
"pdfUrls": [
"https://www.cs.purdue.edu/homes/ajajoo/papers/saath.pdf",
"https://www.cs.purdue.edu/homes/ajajoo/presentations/saath.pdf",
"http://doi.acm.org/10.1145/3143361.3143364"
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"title": "Saath: Speeding up CoFlows by Exploiting the Spatial Dimension",
"venue": "CoNEXT",
"year": 2017
},
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"name": "Georgios B. Giannakis"
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],
"doi": "10.1109/ICDM.2017.22",
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"paperAbstract": "The task of community detection over complex networks is of paramount importance in a multitude of applications. The present work puts forward a top-to-bottom community identification approach, termed DC-EgoTen, in which an egonet-tensor (EgoTen) based algorithm is developed in a divide-and-conquer (DC) fashion for breaking the network into smaller subgraphs, out of which the underlying communities progressively emerge. In particular, each step of DC-EgoTen forms a multi-dimensional egonet-based representation of the graph, whose induced structure enables casting the task of overlapping community identification as a constrained PARAFAC decomposition. Thanks to the higher representational capacity of tensors, the novel egonet-based representation improves the quality of detected communities by capturing multi-hop connectivity patterns of the network. In addition, the top-to-bottom approach ensures successive refinement of identified communities, so that the desired resolution is achieved. Synthetic as well as real-world tests corroborate the effectiveness of DC-EgoTen.",
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"title": "Overlapping Community Detection via Constrained PARAFAC: A Divide and Conquer Approach",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
"year": 2017
},
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"name": "Juhyun Kim"
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{
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"name": "Juwon Kang"
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"paperAbstract": "Network performance monitoring today is restricted by existing switch support for measurement, forcing operators to rely heavily on endpoints with poor visibility into the network core. Switch vendors have added progressively more monitoring features to switches, but the current trajectory of adding specific features is unsustainable given the ever-changing demands of network operators. Instead, we ask what switch hardware primitives are required to support an expressive language of network performance questions. We believe that the resulting switch hardware design could address a wide variety of current and future performance monitoring needs.\n We present a performance query language, Marple, modeled on familiar functional constructs like map, filter, groupby, and zip. Marple is backed by a new programmable key-value store primitive on switch hardware. The key-value store performs flexible aggregations at line rate (e.g., a moving average of queueing latencies per flow), and scales to millions of keys. We present a Marple compiler that targets a P4-programmable software switch and a simulator for high-speed programmable switches. Marple can express switch queries that could previously run only on end hosts, while Marple queries only occupy a modest fraction of a switch's hardware resources.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3098822.3098829",
"http://cs.nyu.edu/~anirudh/marple_sigcomm.pdf",
"http://nms.csail.mit.edu/papers/marple.pdf",
"http://web.mit.edu/~alephtwo/www/papers/marple-sigcomm17.pdf"
],
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"title": "Language-Directed Hardware Design for Network Performance Monitoring",
"venue": "SIGCOMM",
"year": 2017
},
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"ids": [
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"name": "Ashiwan Sivakumar"
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"name": "Chuan Jiang"
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{
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"name": "Yun Seong Nam"
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"name": "Shankaranarayanan Puzhavakath Narayanan"
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"name": "Vijay Gopalakrishnan"
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"name": "Sanjay G. Rao"
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"name": "Mithuna Thottethodi"
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"name": "T. N. Vijaykumar"
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"doi": "10.1145/3117811.3117827",
"doiUrl": "https://doi.org/10.1145/3117811.3117827",
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"paperAbstract": "Despite much recent progress, Web page latencies over cellular networks remain much higher than those over wired networks. Proxies that execute Web page JavaScript (JS) and push objects needed by the client can reduce latency. However, a key concern is the scalability of the proxy which must execute JS for many concurrent users. In this paper, we propose to scale the proxies, focusing on a design where the proxy's execution is solely to push the needed objects and the client completely executes the page as normal. Such redundant execution is a simple, yet effective approach to cutting network latencies, which dominate page load delays in cellular settings. We develop whittling, a technique to identify and execute in the proxy only the JS code necessary to identify and push the objects required for the client page load, while skipping other code. Whittling is closely related to program slicing, but with the important distinction that it is acceptable to approximate the program slice in the proxy given the client's complete execution. Experiments with top Alexa Web pages show NutShell can sustain, on average, 27\\% more user requests per second than a proxy performing fully redundant execution, while preserving, and sometimes enhancing, the latency benefits.",
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"title": "NutShell: Scalable Whittled Proxy Execution for Low-Latency Web over Cellular Networks",
"venue": "MobiCom",
"year": 2017
},
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"name": "Lee Sael"
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"paperAbstract": "How can we measure similarity between nodes quickly and accurately on large graphs? Random walk with restart (RWR) provides a good measure, and has been used in various data mining applications including ranking, recommendation, link prediction and community detection. However, existing methods for computing RWR do not scale to large graphs containing billions of edges; iterative methods are slow in query time, and preprocessing methods require too much memory.\n In this paper, we propose BePI, a fast, memory-efficient, and scalable method for computing RWR on billion-scale graphs. BePI exploits the best properties from both preprocessing methods and iterative methods. BePI uses a block elimination approach, which is a preprocessing method, to enable fast query time. Also, BePI uses a preconditioned iterative method to decrease memory requirement. The performance of BePI is further improved by decreasing non-zeros of the matrix for the iterative method. Through extensive experiments, we show that BePI processes 100 times larger graphs, and requires up to 130 times less memory space than other preprocessing methods. In the query phase, BePI computes RWR scores up to 9 times faster than existing methods.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3035918.3035950",
"http://www3.cs.stonybrook.edu/~sael/paper/Jung%20et%20al.%20-%202017%20-%20BePI.pdf"
],
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"s2Url": "https://semanticscholar.org/paper/5bf2414cb01317cd1664c896f1f1da735443a0f5",
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"title": "BePI: Fast and Memory-Efficient Method for Billion-Scale Random Walk with Restart",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
{
"ids": [
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],
"name": "Rafa\u00ebl del Pino"
},
{
"ids": [
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],
"name": "Vadim Lyubashevsky"
},
{
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],
"name": "Gregory Neven"
},
{
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],
"name": "Gregor Seiler"
}
],
"doi": "10.1145/3133956.3134101",
"doiUrl": "https://doi.org/10.1145/3133956.3134101",
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"paperAbstract": "We propose a lattice-based electronic voting scheme, EVOLVE (Electronic Voting from Lattices with Verification), which is conjectured to resist attacks by quantum computers. Our protocol involves a number of voting authorities so that vote privacy is maintained as long as at least one of the authorities is honest, while the integrity of the result is guaranteed even when all authorities collude. Furthermore, the result of the vote can be independently computed by any observer. At the core of the protocol is the utilization of a homomorphic commitment scheme with strategically orchestrated zero-knowledge proofs: voters use approximate but efficient \"Fiat-Shamir with Aborts\" proofs to show the validity of their vote, while the authorities use amortized exact proofs to show that the commitments are well-formed. We also present a novel efficient zero-knowledge proof that one of two lattice-based statements is true (so-called OR proof) and a new mechanism to control the size of the randomness when applying the homomorphism to commitments. We give concrete parameter choices to securely instantiate and evaluate the efficiency of our scheme. Our prototype implementation shows that the voters require $8$ milliseconds to submit a vote of size about $20$KB to each authority and it takes each authority $0.15$ seconds per voter to create a proof that his vote was valid. The size of the vote share that each authority produces is approximately $15$KB per voter, which we believe is well within the practical bounds for a large-scale election.",
"pdfUrls": [
"https://eprint.iacr.org/2017/1235.pdf",
"http://doi.acm.org/10.1145/3133956.3134101",
"http://eprint.iacr.org/2017/1235"
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"title": "Practical Quantum-Safe Voting from Lattices",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
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"authors": [
{
"ids": [
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],
"name": "Tayyar Rzayev"
},
{
"ids": [
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],
"name": "David H. Albonesi"
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{
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"name": "Fran\u00e7ois Guimbreti\u00e8re"
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{
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"name": "Rajit Manohar"
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{
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],
"doi": "10.1109/ISPASS.2017.7975289",
"doiUrl": "https://doi.org/10.1109/ISPASS.2017.7975289",
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"paperAbstract": "The advent of high speed input sensor and display technologies and the drive for faster interactive response suggests that human-computer interaction (HCI) task processing deadlines of a few milliseconds or less may be required in future handheld devices. At the same time, users will expect the same, if not better, battery life than today's devices under these more stringent response requirements. In this paper, we present a toolbox for exploring the design space of HCI event processors. We first describe the simulation platform for interactive environments that runs mobile user interface code with inputs recorded from human users. We validate it against a hardware platform from prior work. Given system-level constraints on latency, we demonstrate how this toolbox can be used to design a custom heterogeneous event processor that maximizes battery life. We show that our toolbox can pick design points that are 1.5–2.5x more energy-efficient than general-purpose big. LITTLE architectures.",
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"title": "Toolbox for exploration of energy-efficient event processors for human-computer interaction",
"venue": "2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)",
"year": 2017
},
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{
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"name": "George Argyros"
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{
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"name": "Kexin Pei"
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{
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],
"doi": "10.1109/SP.2017.46",
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"paperAbstract": "SSL/TLS is the most commonly deployed family of protocols for securing network communications. The security guarantees of SSL/TLS are critically dependent on the correct validation of the X.509 server certificates presented during the handshake stage of the SSL/TLS protocol. Hostname verification is a critical component of the certificate validation process that verifies the remote server's identity by checking if the hostname of the server matches any of the names present in the X.509 certificate. Hostname verification is a highly complex process due to the presence of numerous features and corner cases such as wildcards, IP addresses, international domain names, and so forth. Therefore, testing hostname verification implementations present a challenging task. In this paper, we present HVLearn, a novel black-box testing framework for analyzing SSL/TLS hostname verification implementations, which is based on automata learning algorithms. HVLearn utilizes a number of certificate templates, i.e., certificates with a common name (CN) set to a specific pattern, in order to test different rules from the corresponding specification. For each certificate template, HVLearn uses automata learning algorithms to infer a Deterministic Finite Automaton (DFA) that describes the set of all hostnames that match the CN of a given certificate. Once a model is inferred for a certificate template, HVLearn checks the model for bugs by finding discrepancies with the inferred models from other implementations or by checking against regular-expression-based rules derived from the specification. The key insight behind our approach is that the acceptable hostnames for a given certificate template form a regular language. Therefore, we can leverage automata learning techniques to efficiently infer DFA models that accept the corresponding regular language. We use HVLearn to analyze the hostname verification implementations in a number of popular SSL/TLS libraries and applications written in a diverse set of languages like C, Python, and Java. We demonstrate that HVLearn can achieve on average 11.21% higher code coverage than existing black/gray-box fuzzing techniques. By comparing the DFA models inferred by HVLearn, we found 8 unique violations of the RFC specifications in the tested hostname verification implementations. Several of these violations are critical and can render the affected implementations vulnerable to active man-in-the-middle attacks.",
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"paperAbstract": "<i>K</i>-plexes are a formal yet flexible way of defining communities in networks. They generalize the notion of cliques and are more appropriate in most real cases: while a node of a clique <i>C</i> is connected to all other nodes of <i>C</i>, a node of a <i>k</i>-plex may miss up to <i>k</i> connections. Unfortunately, computing all maximal <i>k</i>-plexes is a gruesome task and state-of-the-art algorithms can only process small-size networks. In this paper we propose a new approach for enumerating large <i>k</i>-plexes in networks that speeds up the search by several orders of magnitude, leveraging on (i) methods for strongly reducing the search space and (ii) efficient techniques for the computation of maximal cliques. Several experiments show that our strategy is effective and is able to increase the size of the networks for which the computation of large <i>k</i>-plexes is feasible from a few hundred to several hundred thousand nodes.",
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"title": "Fast Enumeration of Large k-Plexes",
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"paperAbstract": "Improving single thread performance is a key challenge in modern microprocessors especially because the traditional approach of increasing clock frequency and deep pipelining cannot be pushed further due to power constraints. Therefore, researchers have been looking at unconventional architectures to boost single thread performance without running into the power wall. HW/SW co-designed processors like Nvidia Denver, are emerging as a promising alternative. However, HW/SW co-designed processors need to address some key challenges such as startup delay, providing high performance with simple hardware, translation/optimization overhead, etc. before they can become mainstream. A fundamental requirement for evaluating different design choices and trade-offs to meet these challenges is to have a simulation infrastructure. Unfortunately, there is no such infrastructure available today. Building the aforementioned infrastructure itself poses significant challenges as it encompasses the complexities of not only an architectural framework but also of a compilation one. This paper identifies the key challenges that HW/SW codesigned processors face and the basic requirements for a simulation infrastructure targeting these architectures. Furthermore, the paper presents DARCO, a simulation infrastructure to enable research in this domain.",
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"http://www.research.ed.ac.uk/portal/files/33061455/DARCO_ISPASS17_1.pdf",
"http://homepages.inf.ed.ac.uk/jcanore/pub/2017_arm-summit_poster.pdf",
"http://homepages.inf.ed.ac.uk/jcanore/pub/2017_ispass.pdf",
"https://doi.org/10.1109/ISPASS.2017.7975290",
"http://www.ispass.org/ispass2017/slides/cano_codesign.pdf"
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"title": "HW/SW co-designed processors: Challenges, design choices and a simulation infrastructure for evaluation",
"venue": "2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)",
"year": 2017
},
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"authors": [
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"name": "Maliheh Shirvanian"
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"name": "Nitesh Saxena"
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"doi": "10.1145/3133956.3134013",
"doiUrl": "https://doi.org/10.1145/3133956.3134013",
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"paperAbstract": "Crypto Phones aim to establish end-to-end secure voice (and text) communications based on human-centric (usually) short checksum validation. They require end users to perform: (1) <i>checksum comparison</i> to detect traditional data-based man-in-the-middle (data MITM) attacks, and, optionally, (2) <i>speaker verification</i> to detect sophisticated voice-based man-in-the-middle (voice MITM) attacks. However, research shows that both tasks are prone to human errors making Crypto Phones highly vulnerable to MITM attacks, especially to data MITM given the prominence of these attacks. Further, human errors under benign settings undermine usability since legitimate calls would often need to be rejected.\n We introduce <i>Closed Captioning Crypto Phones</i> (CCCP), that remove the human user from the loop of checksum comparison by utilizing <i>speech transcription</i>. CCCP simply requires the user to announce the checksum to the other party--the system automatically transcribes the spoken checksum and performs the comparison. Automating checksum comparisons offers many key advantages over traditional designs: (1) the chances of data MITM due to human errors and <i>\"click-through\"</i> could be highly reduced (even eliminated); (2) longer checksums can be utilized, which increases the protocol security against data MITM; (3) users' cognitive burden is reduced due to the need to perform only a <i>single task</i>, thereby lowering the potential of human errors.\n As a main component of CCCP, we first design and implement an automated checksum comparison tool based on standard <i>Speech to Text</i> engines. To evaluate the security and usability benefits of CCCP, we then design and conduct an online user study that mimics a realistic VoIP scenario, and collect and transcribe a comprehensive data set spoken by a wide variety of speakers in real-life conditions. Our study results demonstrate that, by using our automated checksum comparison, CCCP can <i>completely resist</i> data MITM, while significantly reducing human errors in the benign case compared to the traditional approach. They also show that CCCP may help reduce the likelihood of voice MITM. Finally, we discuss how CCCP can be improved by designing specialized transcribers and carefully selected checksum dictionaries, and how it can be integrated with existing Crypto Phones to bolster their security and usability.",
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"http://doi.acm.org/10.1145/3133956.3134013",
"https://info.cs.uab.edu/saxena/docs/ss-ccs17.pdf",
"https://info.cs.uab.edu/saxena/docs/ss-ccs17-PP.pdf"
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"title": "CCCP: Closed Caption Crypto Phones to Resist MITM Attacks, Human Errors and Click-Through",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"name": "Nimrod Raifer"
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"name": "Fiana Raiber"
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"name": "Moshe Tennenholtz"
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"paperAbstract": "In competitive search settings as the Web, there is an ongoing <i>ranking competition</i> between document authors (publishers) for certain queries. The goal is to have documents highly ranked, and the means is document manipulation applied in response to rankings. Existing retrieval models, and their theoretical underpinnings (e.g., the probability ranking principle), do not account for post-ranking corpus dynamics driven by this strategic behavior of publishers. However, the dynamics has major effect on retrieval effectiveness since it affects content availability in the corpus. Furthermore, while manipulation strategies observed over the Web were reported in past literature, they were not analyzed as ongoing, and changing, post-ranking response strategies, nor were they connected to the foundations of classical ad hoc retrieval models (e.g., content-based document-query surface level similarities and document relevance priors). We present a novel theoretical and empirical analysis of the strategic behavior of publishers using these foundations. Empirical analysis of controlled ranking competitions that we organized reveals a key strategy of publishers: making their documents (gradually) become similar to documents ranked the highest in previous rankings. Our theoretical analysis of the ranking competition as a <i>repeated game</i>, and its <i>minmax regret equilibrium</i>, yields a result that supports the merits of this publishing strategy. We further show that it can be predicted with high accuracy, and without explicit knowledge of the ranking function, whether documents will be promoted to the highest rank in our competitions. The prediction utilizes very few features which quantify changes of documents, specifically with respect to those previously ranked the highest.",
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"title": "Information Retrieval Meets Game Theory: The Ranking Competition Between Documents? Authors",
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"year": 2017
},
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"year": 2017
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"paperAbstract": "In this paper, we design and develop RIO, a novel battery-free touch sensing user interface (UI) primitive for future IoT and smart spaces. RIO enables UIs to be constructed using off-the-shelf RFID readers and tags, and provides a unique approach to designing smart IoT spaces. With RIO, any surface can be turned into a touch-aware surface by simply attaching RFID tags to them. RIO also supports custom-designed RFID tags, and thus allows specially customized UIs to be easily deployed into a real-world environment. RIO is built using the technique of impedance tracking: when a human finger touches the surface of an RFID tag, the impedance of the antenna changes. This change manifests as a change in the phase of the RFID backscattered signal, and is used by RIO to track fine-grained touch movement over both off-the shelf and custom built tags. We study this impedance behavior in-depth and show how RIO is a reliable UI primitive that is robust even within a multi-tag environment. We leverage this primitive to build a prototype of RIO that can continuously locate a finger during a swipe movement to within 3 mm of its actual position. We also show how custom-design RFID tags can be built and used with RIO, and provide two example applications that demonstrate its real-world use.",
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"title": "RIO: A Pervasive RFID-based Touch Gesture Interface",
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"year": 2017
},
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"paperAbstract": "Crowdsourcing mobile user\u2019s network performance has become an effective way of understanding and improving mobile network performance and user qualityof-experience. However, the current measurement method is still based on the landline measurement paradigm in which a measurement app measures the path to fixed (measurement or web) servers. In this work, we introduce a new paradigm of measuring per-app mobile network performance. We design and implement MopEye, an Android app to measure network round-trip delay for each app whenever there is app traffic. This opportunistic measurement can be conducted automatically without user intervention. Therefore, it can facilitate a large-scale and long-term crowdsourcing of mobile network performance. In the course of implementing MopEye, we have overcome a suite of challenges to make the continuous latency monitoring lightweight and accurate. We have deployed MopEye to Google Play for an IRB-approved crowdsourcing study in a period of ten months, which obtains over five million measurements from 6,266 Android apps on 2,351 smartphones. The analysis reveals a number of new findings on the per-app network performance and mobile DNS performance.",
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"https://www.usenix.org/system/files/conference/atc17/atc17-wu.pdf",
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"title": "MopEye: Opportunistic Monitoring of Per-app Mobile Network Performance",
"venue": "USENIX Annual Technical Conference",
"year": 2017
},
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"name": "Kim Laine"
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"paperAbstract": "Private Set Intersection (PSI) is a cryptographic technique that allows two parties to compute the intersection of their sets without revealing anything except the intersection. We use fully homomorphic encryption to construct a fast PSI protocol with a small communication overhead that works particularly well when one of the two sets is much smaller than the other, and is secure against semi-honest adversaries.\n The most computationally efficient PSI protocols have been constructed using tools such as hash functions and oblivious transfer, but a potential limitation with these approaches is the communication complexity, which scales linearly with the size of the larger set. This is of particular concern when performing PSI between a constrained device (cellphone) holding a small set, and a large service provider (e.g. <i>WhatsApp</i>), such as in the Private Contact Discovery application.\n Our protocol has communication complexity linear in the size of the smaller set, and logarithmic in the larger set. More precisely, if the set sizes are <i>N<sub>y</sub></i> < <i>N<sub>x</sub></i>, we achieve a communication overhead of <i>O</i>(<i>N<sub>y</sub></i> log <i>N<sub>x</sub></i>). Our running-time-optimized benchmarks show that it takes 36 seconds of online-computation, 71 seconds of non-interactive (receiver-independent) pre-processing, and only 12.5MB of round trip communication to intersect five thousand 32-bit strings with 16 million 32-bit strings. Compared to prior works, this is roughly a 38--115x reduction in communication with minimal difference in computational overhead.",
"pdfUrls": [
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"http://eprint.iacr.org/2017/299",
"https://eprint.iacr.org/2017/299.pdf",
"http://doi.acm.org/10.1145/3133956.3134061"
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"title": "Fast Private Set Intersection from Homomorphic Encryption",
"venue": "CCS",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Matthew K. Mukerjee"
},
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"ids": [
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"name": "Ilker Nadi Bozkurt"
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{
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"name": "Devdeep Ray"
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{
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"name": "Bruce M. Maggs"
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{
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"name": "Srinivasan Seshan"
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{
"ids": [
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"name": "Hui Zhang"
}
],
"doi": "10.1145/3143361.3143366",
"doiUrl": "https://doi.org/10.1145/3143361.3143366",
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"paperAbstract": "Various trends are reshaping Internet video delivery: exponential growth in video traffic, rising expectations of high video quality of experience (QoE), and the proliferation of varied content delivery network (CDN) deployments (e.g., cloud computing-based, content provider-owned datacenters, and ISP-owned CDNs). More fundamentally though, content providers are shifting delivery from a single CDN to multiple CDNs, through the use of a content broker. Brokers have been shown to invalidate many traditional delivery assumptions (e.g., shifting traffic invalidates short- and long-term traffic prediction) by not communicating their decisions with CDNs. In this work, we analyze these problems using data from a CDN and a broker. We examine the design space of potential solutions, finding that a marketplace design (inspired by advertising exchanges) potentially provides interesting tradeoffs. A marketplace allows all CDNs to profit on video delivery through fine-grained pricing and optimization, where CDNs learn risk-adverse bidding strategies to aid in traffic prediction. We implement a marketplace-based system (which we dub Video Delivery eXchange or VDX) in CDN and broker data-driven simulation, finding significant improvements in cost and data-path distance.",
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"https://users.cs.duke.edu/~ilker/papers/conference/conext-17.pdf",
"https://users.cs.duke.edu/~bmm/assets/pubs/MukerjeeBRMSZ17.pdf",
"http://doi.acm.org/10.1145/3143361.3143366"
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"title": "Redesigning CDN-Broker Interactions for Improved Content Delivery",
"venue": "CoNEXT",
"year": 2017
},
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"name": "Robert Birke"
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"name": "Mathias Bj\u00f6rkqvist"
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],
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"doiUrl": "https://doi.org/10.1109/ICAC.2017.37",
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"paperAbstract": "With the rapid growth of social media and Internet-of-Things, real-time processing of big data has become a core operation in various business areas. It is of paramount importance that big-data analyses are executed timely with specified accuracy guarantees. However, workloads in the wild are highly bursty with skewed contents and often present the conundrum of meeting latency and accuracy requirements simultaneously. In this paper we propose AccStream, which selectively samples and processes data tuples and blocks on emerging batch streaming platforms with a special focus on analysis of aggregation, e.g., counts, and top-k. AccStream dynamically learns the latency model of analysis jobs via on-line probing technique and employs sample theory to determine the lower limit of data so as to fulfill given accuracy targets. A unique feature of AccStream ensuring strong latency-accuracy fulfillment even under conflicts is the hybrid windowing that trades off data freshness via a combination of tumbling and rolling windows. We evaluate the prototype of AccStream on Spark Streaming, analyzing Twitter data. Our extensive results confirm that AccStream is able to achieve the latency and accuracy target against a wide range of conditions, i.e., slow and fast dynamic load intensities and content skewnesses, even when facing conflicting latency and accuracy targets. All in all, the effectiveness of AccStream in delivering timely, accurate, and (partial) fresh streaming analytics lies in shedding the adequate amount of input data at the right time and place.",
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"title": "AccStream: Accuracy-Aware Overload Management for Stream Processing Systems",
"venue": "2017 IEEE International Conference on Autonomic Computing (ICAC)",
"year": 2017
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"paperAbstract": "Compositional recurrence analysis (CRA) is a static-analysis method based on a combination of symbolic analysis and abstract interpretation. This paper addresses the problem of creating a context-sensitive interprocedural version of CRA that handles recursive procedures. The problem is non-trivial because there is an â\u0080\u009cimpedance mismatchâ\u0080\u009d between CRA, which relies on analysis techniques based on <em>regular languages</em> (i.e., Tarjanâ\u0080\u0099s path-expression method), and the <em>context-free-language underpinnings</em> of context-sensitive analysis. \nWe show how to address this impedance mismatch by augmenting the CRA abstract domain with additional operations. We call the resulting algorithm Interprocedural CRA (ICRA). Our experiments with ICRA show that it has broad overall strength compared with several state-of-the-art software model checkers.",
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"http://research.cs.wisc.edu/wpis/papers/tr1840.pdf",
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"title": "Compositional recurrence analysis revisited",
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"year": 2017
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"name": "Hyunyoon Cho"
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{
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"name": "Jung Ho Ahn"
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"doi": "10.1109/HPCA.2017.31",
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"paperAbstract": "Memory access latency has a significant impact on application performance. Unfortunately, the random access latency of DRAM has been scaling relatively slowly, and often directly affects the critical path of execution, especially for applications with insufficient locality or memory-level parallelism. The existing low-latency DRAM organizations either incur significant area overhead or burden the software stack with non-uniform access latency. This paper proposes two microarchitectural techniques to provide uniformly low access time over the entire DRAM chip. The first technique is SALAD, a new DRAM device architecture that provides symmetric access latency with asymmetric DRAM bank organizations. Because local regions have lower data transfer time due to their proximity to the I/O pads, SALAD applies high aspect-ratio (i.e., low-latency) mats only to remote regions to offset the difference in data transfer time, resulting in symmetrically low latency across regions. The second technique is SOUP (skewed organization of µ banks with pipelined accesses), which leverages asymmetry in column access latency within a region due to non-uniform distance to the column decoders. By starting I/O transfers as soon as data from near cells arrive, instead of waiting for the entire column data, SOUP further saves two memory clock cycles for column accesses for all regions. The resulting design, called SOUP-N-SALAD, improves IPC and EDP by 9.6% (11.2%) and 18.2% (21.8%) over the baseline DDR4 device, respectively, for memory-intensive SPEC CPU2006 workloads without any software modifications, while incurring only 3% (6%) area overhead.",
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"title": "SOUP-N-SALAD: Allocation-Oblivious Access Latency Reduction with Asymmetric DRAM Microarchitectures",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
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"name": "Shangqing Zhao"
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"name": "Zhengping Luo"
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{
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"name": "Zhuo Lu"
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"name": "Xiang Lu"
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{
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"name": "Yao Liu"
}
],
"doi": "10.1145/3117811.3117846",
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"title": "Needle: Leveraging Program Analysis to Analyze and Extract Accelerators from Whole Programs",
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"paperAbstract": "We revisit the question of delegation vs. synchronized access to shared memory, and show through analysis and demonstration that delegation can be much faster than locking under a range of common circumstances. Starting from first principles, we propose fast, fly-weight delegation (ffwd). The highly optimized design of ffwd allows it to significantly outperform prior work on delegation, while retaining the scalability advantage.\n In experiments with 6 benchmark applications, and 6 shared data structures, running on four different multi-socket systems with up to 128 hardware threads, we compare ffwd to a selection of lock, combining, lock-free, software transactional memory and delegation designs. Overall, we find that ffwd often offers a simple and highly competitive alternative to existing work. By definition, the performance of a fully delegated data structure is limited by the single-thread throughput of said data structure. However, due to cache effects, many data structures offer their best performance when confined to a single thread. With an efficient delegation mechanism, we approach this single-threaded performance in a multi-threaded setting. In application-level benchmarks, we see improvements up to 100% over the next best solution tested (RCL), and multiple micro-benchmarks show improvements in the 5-10x range.",
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"title": "Ffwd: Delegation Is (much) Faster than You Think",
"venue": "SOSP",
"year": 2017
},
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"doi": "10.1145/3037697.3037743",
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"paperAbstract": "Software optimization is constantly a serious concern for developing high-performance systems. To accelerate the workflow execution of a specific functionality, software developers usually define and implement a fast path to speed up the critical and commonly executed functions in the workflow. However, producing a bug-free fast path is nontrivial. Our study on the Linux kernel discloses that a committed fast path can have up to 19 follow-up patches for bug fixing, and most of them are deep semantic bugs, which are difficult to be pinpointed by existing bug-finding tools.\n In this paper, we present such a new category of software bugs based on our fast-path bug study across various system software including virtual memory manager, file systems, network, and device drivers. We investigate their root causes and identify five error-prone aspects in a fast path: path state, trigger condition, path output, fault handling, and assistant data structure. We find that many of the deep bugs can be prevented by applying static analysis incorporating simple semantic information. We extract a set of rules based on our findings and build a toolkit PALLAS to check fast-path bugs. The evaluation results show that PALLAS can effectively reveal fast-path bugs in a variety of systems including Linux kernel, mobile operating system, software-defined networking system, and web browser.",
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"name": "Fan Jing Meng"
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{
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"name": "Xiao Zhang"
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"name": "Pengfei Chen"
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"name": "Jing Min Xu"
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"doi": "10.1109/CLOUD.2017.37",
"doiUrl": "https://doi.org/10.1109/CLOUD.2017.37",
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"Cloud computing",
"FUJITSU Cloud IaaS Trusted Public S5",
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"paperAbstract": "Detecting anomalous behaviors of cloud platforms is one of critical tasks for cloud providers. Every anomalous behavior potentially causes incidents, especially some unaware and/or unknown issues, which severely harm their SLA (Service Level Agreement). Existing solutions generally monitor cloud platform at different layers and then detect anomalies based on rules or learning algorithms on monitoring metrics. However, complexity of nowadays cloud platforms, high dynamics of cloud workloads and thousands of various types of metrics make anomalous behavior detection more challenging to be applied in production, especially in large scale cloud production environments. In this paper, we present a practical cloud anomalous behavior detection system called DriftInsight. It firstly analyzes multi-denominational metrics of each component and identifies a set of representative steady components based on the convergences of their states. Then it generates a state model and a state transit model for each steady cloud component. Finally, it detects behavior anomalies of these steady components in near real-time and meanwhile evolve behavior models on the fly. The evaluation results of this approach in a commercial large-scale PaaS (Platform-as-a-Service) cloud are demonstrated its capability and efficiency.",
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"title": "DriftInsight: Detecting Anomalous Behaviors in Large-Scale Cloud Platform",
"venue": "2017 IEEE 10th International Conference on Cloud Computing (CLOUD)",
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"5e75d9d75536f8b126c47ae4ce91b47d51c7cc69": {
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"name": "Vladimir Kolesnikov"
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"name": "Naor Matania"
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"name": "Benny Pinkas"
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"name": "Mike Rosulek"
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{
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"name": "Ni Trieu"
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"doi": "10.1145/3133956.3134065",
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"paperAbstract": "We present a new paradigm for multi-party private set intersection (PSI) that allows $n$ parties to compute the intersection of their datasets without revealing any additional information. We explore a variety of instantiations of this paradigm. Our protocols avoid computationally expensive public-key operations and are secure in the presence of any number of semi-honest participants (i.e., without an honest majority).\n We demonstrate the practicality of our protocols with an implementation. To the best of our knowledge, this is the first implementation of a multi-party PSI protocol. For 5 parties with data-sets of 2<sup>20</sup> items each, our protocol requires only 72 seconds. In an optimization achieving a slightly weaker variant of security (augmented semi-honest model), the same task requires only 22 seconds.\n The technical core of our protocol is oblivious evaluation of a <i>programmable</i> pseudorandom function (OPPRF), which we instantiate in three different ways. We believe our new OPPRF abstraction and constructions may be of independent interest.",
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"title": "Practical Multi-party Private Set Intersection from Symmetric-Key Techniques",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
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"authors": [
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"name": "Yuanfeng Peng"
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"name": "Benjamin P. Wood"
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"name": "Joseph Devietti"
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"doi": "10.1145/3123939.3123946",
"doiUrl": "https://doi.org/10.1145/3123939.3123946",
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"paperAbstract": "Data race detection is a useful dynamic analysis for multithreaded programs that is a key building block in record-and-replay, enforcing strong consistency models, and detecting concurrency bugs. Existing software race detectors are precise but slow, and hardware support for precise data race detection relies on assumptions like type safety that many programs violate in practice.\n We propose P<scp>arsnip</scp>, a fully precise hardware-supported data race detector. P<scp>arsnip</scp> exploits new insights into the redundancy of race detection metadata to reduce storage overheads. P<scp>arsnip</scp> also adopts new race detection metadata encodings that accelerate the common case while preserving soundness and completeness. When bounded hardware resources are exhausted, P<scp>arsnip</scp> falls back to a software race detector to preserve correctness. P<scp>arsnip</scp> does not assume that target programs are type safe, and is thus suitable for race detection on arbitrary code.\n Our evaluation of P<scp>arsnip</scp> on several PARSEC benchmarks shows that performance overheads range from negligible to 2.6x, with an average overhead of just 1.5x. Moreover, P<scp>arsnip</scp> outperforms the state-of-the-art Radish hardware race detector by 4.6x.",
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"title": "PARSNIP: performant architecture for race safety with no impact on precision",
"venue": "MICRO",
"year": 2017
},
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"authors": [
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"name": "Shilin Zhu"
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{
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"name": "Chi Zhang"
},
{
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"name": "Xinyu Zhang"
}
],
"doi": "10.1145/3117811.3117820",
"doiUrl": "https://doi.org/10.1145/3117811.3117820",
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"paperAbstract": "The ubiquity of mobile camera devices has been triggering an outcry of privacy concerns, whereas privacy protection still relies on the cooperation of the photographer or camera hardware, which can hardly be guaranteed in practice. In this paper, we introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in specialized waveforms. We use a model-driven approach to optimize the waveform, so as to ensure protection against the (uncontrollable) cameras and potential image-processing based attacks. We have also designed mechanisms to unblock authorized cameras and enable graceful degradation under strong ambient light interference. Our prototype implementation and experiments show that LiShield can effectively destroy unauthorized capturing while maintaining robustness against potential attacks.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3117811.3117820",
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"title": "Automating Visual Privacy Protection Using a Smart LED",
"venue": "MobiCom",
"year": 2017
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"name": "Maxime Schmitt"
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"doi": "10.1109/HiPC.2017.00028",
"doiUrl": "https://doi.org/10.1109/HiPC.2017.00028",
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"title": "Adaptive Code Refinement: A Compiler Technique and Extensions to Generate Self-Tuning Applications",
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"paperAbstract": "For years, Internet topology research has been conducted through active measurement. For instance, C<scp>aida</scp> builds router level topologies on top of IP level traces obtained with traceroute. The resulting graphs contain a significant amount of nodes with a very large degree, often exceeding the actual number of interfaces of a router. Although this property may result from inaccurate alias resolution, we believe that opaque MPLS clouds made of invisible tunnels are the main cause. Using Layer-2 technologies such as MPLS, routers can be configured to hide internal IP hops from traceroute. Consequently, an entry point of an MPLS network appears as the neighbor of all exit points and the whole Layer-3 network turns into a dense mesh of high degree nodes.\n This paper tackles three problems: the revelation of IP hops hidden by MPLS tunnels, the MPLS deployment underestimation, and the overestimation of high degree nodes. We develop new measurement techniques able to reveal the presence and content of invisible MPLS tunnels. We assess them through emulation and cross-validation and perform a large-scale measurement campaign targeting suspicious networks on which we apply statistical analysis. Finally, based on our dataset, we look at basic graph properties impacted by invisible tunnels.",
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"title": "Through the wormhole: tracking invisible MPLS tunnels",
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"title": "knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library",
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"year": 2017
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"paperAbstract": "A search engine that can return the ideal results for a person's information need, independent of the specific query that is used to express that need, would be preferable to one that is overly swayed by the individual terms used; search engines should be <i>consistent</i> in the presence of syntactic query variations responding to the same information need. In this paper we examine the retrieval consistency of a set of five systems responding to syntactic query variations over one hundred topics, working with the UQV100 test collection, and using Rank-Biased Overlap (RBO) relative to a centroid ranking over the query variations per topic as a measure of consistency. We also introduce a new data fusion algorithm, Rank-Biased Centroid (RBC), for constructing a centroid ranking over a set of rankings from query variations for a topic. RBC is compared with alternative data fusion algorithms.\n Our results indicate that consistency is positively correlated to a moderate degree with \"deep'' relevance measures. However, it is only weakly correlated with \"shallow'' relevance measures, as well as measures of topic complexity and variety in query expression. These findings support the notion that consistency is an independent property of a search engine's retrieval effectiveness.",
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"title": "Retrieval Consistency in the Presence of Query Variations",
"venue": "SIGIR",
"year": 2017
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"paperAbstract": "Applications have commonly been oblivious to their cloud runtimes. This is primarily because they started their journey in IaaS clouds, running on a guestOS inside VMs. Then to increase performance, many guestOSes have been paravirtualized making them virtualization aware, so that they can bypass some of the virtualization layers, as in virtio. This approach still kept applications unmodified. Recently, we are witnessing a rapid adoption of containers due to their packaging benefits, high density, fast start-up and low overhead. Applications are increasingly being on-boarded to PaaS clouds in the form of application containers or appc, where they are run directly on a cloud substrate like Kubernetes or Docker Swarm. This shift in deployment practices present an opportunity to make applications aware of their cloud. In this paper, we present Paracloud framework for application containers and discuss the Paracloud interface (PaCI) for three cloud operations namely migration, auto-scaling and load-balancing.",
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"title": "Paracloud: Bringing Application Insight into Cloud Operations",
"venue": "HotCloud",
"year": 2017
},
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"paperAbstract": "Tuple-independent probabilistic databases (TI-PDBs) handle uncertainty by annotating each tuple with a probability parameter; when the user submits a query, the database derives the marginal probabilities of each output-tuple, assuming input-tuples are statistically independent. While query processing in TI-PDBs has been studied extensively, limited research has been dedicated to the problems of <i>updating or deriving the parameters from observations of query results</i>. Addressing this problem is the main focus of this paper. We introduce <i>Beta Probabilistic Databases</i> (B-PDBs), a generalization of TI-PDBs designed to support both (i) <i>belief updating</i> and (ii) <i>parameter learning</i> in a principled and scalable way. The key idea of B-PDBs is to treat each parameter as a latent, Beta-distributed random variable. We show how this simple expedient enables both belief updating and parameter learning in a principled way, without imposing any burden on regular query processing. We use this model to provide the following key contributions: (i) we show how to scalably compute the posterior densities of the parameters given new evidence; (ii) we study the complexity of performing Bayesian belief updates, devising efficient algorithms for tractable classes of queries; (iii) we propose a soft-EM algorithm for computing maximum-likelihood estimates of the parameters; (iv) we show how to embed the proposed algorithms into a standard relational engine; (v) we support our conclusions with extensive experimental results.",
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"title": "Beta Probabilistic Databases: A Scalable Approach to Belief Updating and Parameter Learning",
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"year": 2017
},
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"authors": [
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"name": "Juncheng Yang"
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"paperAbstract": "The growing pressure on cloud application scalability has accentuated storage performance as a critical bottleneck. Although cache replacement algorithms have been extensively studied, cache prefetching - reducing latency by retrieving items before they are actually requested - remains an underexplored area. Existing approaches to history-based prefetching, in particular, provide too few benefits for real systems for the resources they cost.\n We propose Mithril, a prefetching layer that efficiently exploits historical patterns in cache request associations. Mithril is inspired by sporadic association rule mining and only relies on the timestamps of requests. Through evaluation of 135 block-storage traces, we show that Mithril is effective, giving an average of a 55% hit ratio increase over LRU and Probability Graph, and a 36% hit ratio gain over Amp at reasonable cost. Finally, we demonstrate the improvement comes from Mithril being able to capture mid-frequency blocks.",
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"paperAbstract": "As the size of Deep Neural Networks (DNNs) continues to grow to increase accuracy and solve more complex problems, their energy footprint also scales. Weight pruning reduces DNN model size and the computation by removing redundant weights. However, we implemented weight pruning for several popular networks on a variety of hardware platforms and observed surprising results. For many networks, the network sparsity caused by weight pruning will actually hurt the overall performance despite large reductions in the model size and required multiply-accumulate operations. Also, encoding the sparse format of pruned networks incurs additional storage space overhead. To overcome these challenges, we propose Scalpel that customizes DNN pruning to the underlying hardware by matching the pruned network structure to the data-parallel hardware organization. Scalpel consists of two techniques: SIMD-aware weight pruning and node pruning. For low-parallelism hardware (e.g., microcontroller), SIMD-aware weight pruning maintains weights in aligned fixed-size groups to fully utilize the SIMD units. For high-parallelism hardware (e.g., GPU), node pruning removes redundant nodes, not redundant weights, thereby reducing computation without sacrificing the dense matrix format. For hardware with moderate parallelism (e.g., desktop CPU), SIMD-aware weight pruning and node pruning are synergistically applied together. Across the microcontroller, CPU and GPU, Scalpel achieves mean speedups of 3.54x, 2.61x, and 1.25x while reducing the model sizes by 88%, 82%, and 53%. In comparison, traditional weight pruning achieves mean speedups of 1.90x, 1.06x, 0.41x across the three platforms.",
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"title": "Scalpel: Customizing DNN pruning to the underlying hardware parallelism",
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},
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"name": "Tien Tuan Anh Dinh"
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"name": "Ji Wang"
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"name": "Gang Chen"
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"name": "Beng Chin Ooi"
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"doi": "10.1145/3035918.3064033",
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"title": "BLOCKBENCH: A Framework for Analyzing Private Blockchains",
"venue": "SIGMOD Conference",
"year": 2017
},
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"paperAbstract": "Science applications are producing an ever-increasing volume of multi-dimensional data that are mainly processed with distributed array databases. These raw arrays are ``cooked'' into derived data products using complex pipelines that are time-consuming. As a result, derived data products are released infrequently and become stale soon thereafter. In this paper, we introduce materialized array views as a database construct for scientific data products. We model the ``cooking'' process as incremental view maintenance with batch updates and give a three-stage heuristic that finds effective update plans. Moreover, the heuristic repartitions the array and the view continuously based on a window of past updates as a side-effect of view maintenance without overhead. We design an analytical cost model for integrating materialized array views in queries. A thorough experimental evaluation confirms that the proposed techniques are able to incrementally maintain a real astronomical data product in a production environment.",
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"title": "Incremental View Maintenance over Array Data",
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"name": "Zain Shamsi"
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"name": "Daren B. H. Cline"
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"doi": "10.1145/3133956.3133963",
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"paperAbstract": "Recent work in OS fingerprinting has focused on overcoming random distortion in network and user features during Internet-scale SYN scans. These classification techniques work under an assumption that all parameters of the profiled network are known a-priori -- the likelihood of packet loss, the popularity of each OS, the distribution of network delay, and the probability of user modification to each default TCP/IP header value. However, it is currently unclear how to obtain realistic versions of these parameters for the public Internet and/or customize them to a particular network being analyzed. To address this issue, we derive a non-parametric Expectation-Maximization (EM) estimator, which we call <i>Faulds</i>, for the unknown distributions involved in single-probe OS fingerprinting and demonstrate its significantly higher robustness to noise compared to methods in prior work. We apply Faulds to a new scan of 67M webservers and discuss its findings.",
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"title": "Faulds: A Non-Parametric Iterative Classifier for Internet-Wide OS Fingerprinting",
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"year": 2017
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"paperAbstract": "Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete count data such as text and images. Applications require LDA to handle both large datasets and a large number of topics. Though distributed CPU systems have been used, GPU-based systems have emerged as a promising alternative because of the high computational power and memory bandwidth of GPUs. However, existing GPU-based LDA systems cannot support a large number of topics because they use algorithms on dense data structures whose time and space complexity is linear to the number of topics.\n In this paper, we propose SaberLDA, a GPU-based LDA system that implements a sparsity-aware algorithm to achieve sublinear time complexity and scales well to learn a large number of topics. To address the challenges introduced by sparsity, we propose a novel data layout, a new warp-based sampling kernel, and an efficient sparse count matrix updating algorithm that improves locality, makes efficient utilization of GPU warps, and reduces memory consumption. Experiments show that SaberLDA can learn from billions-token-scale data with up to 10,000 topics, which is almost two orders of magnitude larger than that of the previous GPU-based systems. With a single GPU card, SaberLDA is able to learn 10,000 topics from a dataset of billions of tokens in a few hours, which is only achievable with clusters with tens of machines before.",
"pdfUrls": [
"https://arxiv.org/pdf/1610.02496v1.pdf",
"http://doi.acm.org/10.1145/3037697.3037740",
"http://arxiv.org/abs/1610.02496",
"https://arxiv.org/pdf/1610.02496v2.pdf"
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"title": "SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs",
"venue": "ASPLOS",
"year": 2017
},
"600a6810334f46d9f44bec0d0a9927154ded60dd": {
"authors": [
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"name": "Shai Bergman"
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{
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"name": "Tanya Brokhman"
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{
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"name": "Tzachi Cohen"
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{
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"name": "Mark Silberstein"
}
],
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"Central processing unit",
"Direct memory access",
"Disk buffer",
"End-to-end principle",
"Experiment",
"Graphics processing unit",
"High- and low-level",
"Operating system",
"POSIX",
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"Peer-to-peer file sharing",
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"RAID",
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"paperAbstract": "Recent GPUs enable Peer-to-Peer Direct Memory Access (P2P) from fast peripheral devices like NVMe SSDs to exclude the CPU from the data path between them for efficiency. Unfortunately, using P2P to access files is challenging because of the subtleties of low-level nonstandard interfaces, which bypass the OS file I/O layers and may hurt system performance. SPIN integrates P2P into the standard OS file I/O stack, dynamically activating P2P where appropriate, transparently to the user. It combines P2P with page cache accesses, re-enables read-ahead for sequential reads, all while maintaining standard POSIX FS consistency, portability across GPUs and SSDs, and compatibility with virtual block devices such as software RAID. We evaluate SPIN on NVIDIA and AMD GPUs using standard file I/O benchmarks, application traces and end-to-end experiments. SPIN achieves significant performance speedups across a wide range of workloads, exceeding P2P throughput by up to an order of magnitude. It also boosts the performance of an aerial imagery rendering application by 2.6\u00d7 by dynamically adapting to its input-dependent file access pattern, and enables 3.3\u00d7 higher throughput for a GPU-accelerated log server.",
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"https://www.usenix.org/conference/atc17/technical-sessions/presentation/bergman",
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"title": "SPIN: Seamless Operating System Integration of Peer-to-Peer DMA Between SSDs and GPUs",
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"name": "Yuhua Guo"
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"name": "Qing Liu"
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"name": "Weijun Xiao"
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{
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"name": "Ping Huang"
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"name": "Norbert Podhorszki"
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"name": "Scott Klasky"
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"name": "Xubin He"
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"doi": "10.1109/MASCOTS.2017.23",
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"paperAbstract": "Die-stacked DRAM (a.k.a., on-chip DRAM) provides much higher bandwidth and lower latency than off-chip DRAM. It is a promising technology to break the "memory wall". Die-stacked DRAM can be used either as a cache (i.e., DRAM cache) or as a part of memory (PoM). A DRAM cache design would suffer from more page faults than a PoM design as the DRAM cache cannot contribute towards capacity of main memory. At the same time, obtaining high performance requires PoM systems to swap requested data to the die-stacked DRAM. Existing PoM designs fall into two categories &#x2013; line-based and page-based. The former ensures low off-chip bandwidth utilization but suffers from a low hit ratio of on-chip memory due to limited temporal locality. In contrast, page-based designs achieve a high hit ratio of on-chip memory albeit at the cost of moving large amounts of data between on-chip and off-chip memories, leading to increased off-chip bandwidth utilization and significant system performance degradation.To achieve a similar high hit ratio of on-chip memory as page-based designs, and eliminate excessive off-chip traffic involved, we propose SELF, a high performance and bandwidth efficient approach. The key idea is to SElectively swap Lines in a requested page that are likely to be accessed according to page Footprint, instead of blindly swapping an entire page. In doing so, SELF allows incoming requests to be serviced from the on-chip memory as much as possible, while avoiding swapping unused lines to reduce memory bandwidth consumption. We evaluate a memory system which consists of 4GB on-chip DRAM and 12GB off-chip DRAM. Compared to a baseline system that has the same total capacity of 16GB off-chip DRAM, SELF improves the performance in terms of instructions per cycle by 26.9%, and reduces the energy consumption per memory access by 47.9% on average. In contrast, state-of-the-art line-based and page-based PoM designs can only improve the performance by 9.5% and 9.9%, respectively, against the same baseline system.",
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"title": "SELF: A High Performance and Bandwidth Efficient Approach to Exploiting Die-Stacked DRAM as Part of Memory",
"venue": "2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)",
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"doi": "10.1145/3131365.3131367",
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"paperAbstract": "IP traffic with forged source addresses (i.e., spoofed traffic) enables a series of threats ranging from the impersonation of remote hosts to massive denial-of-service attacks. Consequently, IP address spoofing received considerable attention with efforts to either suppress spoofing, to mitigate its consequences, or to actively measure the ability to spoof in individual networks. However, as of today, we still lack a comprehensive understanding both of the prevalence and the characteristics of spoofed traffic \"in the wild\" as well as of the networks that inject spoofed traffic into the Internet.\n In this paper, we propose and evaluate a method to passively detect spoofed packets in traffic exchanged between networks in the inter-domain Internet. Our detection mechanism identifies both source IP addresses that should never be visible in the inter-domain Internet (i.e., unrouted and bogon sources) as well as source addresses that should not be sourced by individual networks, as inferred from BGP routing information. We apply our method to classify the traffic exchanged between more than 700 networks at a large European IXP. We find that the majority of connected networks do not, or not consistently, filter their outgoing traffic. Filtering strategies and contributions of spoofed traffic vary heavily across networks of different types and sizes. Finally, we study qualitative characteristics of spoofed traffic, regarding both application popularity as well as structural properties of addresses. Combining our observations, we identify and study dominant attack patterns.",
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"title": "Detection, classification, and analysis of inter-domain traffic with spoofed source IP addresses",
"venue": "IMC",
"year": 2017
},
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"paperAbstract": "We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely <i>metapath2vec</i> and <i>metapath2vec++</i>. The <i>metapath2vec</i> model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The <i>metapath2vec++</i> model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and <i>metapath2vec++</i> are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.",
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"http://shichuan.org/hin/topic/Embedding/2017.%20KDD%20metapath2vec%20Scalable%20Representation%20Learning%20for%20Heterogeneous%20Networks.pdf",
"https://ericdongyx.github.io/papers/KDD17-dong-chawla-swami-metapath2vec-poster.pdf",
"https://www3.nd.edu/~dial/publications/dong2017metapath2vec.pdf",
"http://hanj.cs.illinois.edu/cs512/survey_slides/4-5-metapath2vec-KDD17.pdf"
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"title": "metapath2vec: Scalable Representation Learning for Heterogeneous Networks",
"venue": "KDD",
"year": 2017
},
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"name": "Hong Zhang"
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{
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"name": "Junxue Zhang"
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"paperAbstract": "Production datacenters operate under various uncertainties such as traffic dynamics, topology asymmetry, and failures. Therefore, datacenter load balancing schemes must be resilient to these uncertainties; i.e., they should accurately sense path conditions and timely react to mitigate the fallouts. Despite significant efforts, prior solutions have important drawbacks. On the one hand, solutions such as Presto and DRB are oblivious to path conditions and blindly reroute at fixed granularity. On the other hand, solutions such as CONGA and CLOVE can sense congestion, but they can only reroute when flowlets emerge; thus, they cannot always react timely to uncertainties. To make things worse, these solutions fail to detect/handle failures such as blackholes and random packet drops, which greatly degrades their performance.\n In this paper, we introduce Hermes, a datacenter load balancer that is resilient to the aforementioned uncertainties. At its heart, Hermes leverages comprehensive sensing to detect path conditions including failures unattended before, and it reacts using timely yet cautious rerouting. Hermes is a practical edge-based solution with no switch modification. We have implemented Hermes with commodity switches and evaluated it through both testbed experiments and large-scale simulations. Our results show that Hermes achieves comparable performance to CONGA and Presto in normal cases, and well handles uncertainties: under asymmetries, Hermes achieves up to 10% and 20% better flow completion time (FCT) than CONGA and CLOVE; under switch failures, it outperforms all other schemes by over 32%.",
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"http://doi.acm.org/10.1145/3098822.3098841"
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"title": "Resilient Datacenter Load Balancing in the Wild",
"venue": "SIGCOMM",
"year": 2017
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"name": "Jeongkeun Lee"
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"name": "Minlan Yu"
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"doi": "10.1145/3098822.3098824",
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"paperAbstract": "In this paper, we show that up to hundreds of software load balancer (SLB) servers can be replaced by a single modern switching ASIC, potentially reducing the cost of load balancing by over two orders of magnitude. Today, large data centers typically employ hundreds or thousands of servers to load-balance incoming traffic over application servers. These software load balancers (SLBs) map packets destined to a service (with a virtual IP address, or VIP), to a pool of servers tasked with providing the service (with multiple direct IP addresses, or DIPs). An SLB is stateful, it must always map a connection to the same server, even if the pool of servers changes and/or if the load is spread differently across the pool. This property is called per-connection consistency or PCC. The challenge is that the load balancer must keep track of millions of connections simultaneously.\n Until recently, it was not possible to implement a load balancer with PCC in a merchant switching ASIC, because high-performance switching ASICs typically can not maintain per-connection states with PCC. Newer switching ASICs provide resources and primitives to enable PCC at a large scale. In this paper, we explore how to use switching ASICs to build much faster load balancers than have been built before. Our system, called SilkRoad, is defined in a 400 line P4 program and when compiled to a state-of-the-art switching ASIC, we show it can load-balance ten million connections simultaneously at line rate.",
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"title": "SilkRoad: Making Stateful Layer-4 Load Balancing Fast and Cheap Using Switching ASICs",
"venue": "SIGCOMM",
"year": 2017
},
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"name": "Changchang Yin"
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"name": "Qinghua Zheng"
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"name": "Ian Davidson"
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"title": "Knowledge Guided Short-Text Classification for Healthcare Applications",
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"paperAbstract": "Existing wired optical interconnects face a challenge of supporting wide-spread communications in production clusters. Initial proposals are constrained, as they only support connections among a small number of racks (e.g., 2 or 4) at a time, with switching time of milliseconds. Recent efforts on reducing optical circuit reconfiguration time to microseconds partially mitigate this problem by rapidly time-sharing optical circuits across more nodes, but are still limited by the total number of parallel circuits available simultaneously. In this paper, we seek an optical interconnect that can enable unconstrained communications within a computing cluster of thousands of servers. We present MegaSwitch, a multi-fiber ring optical fabric that exploits space division multiplexing across multiple fibers to deliver rearrangeably non-blocking communications to 30+ racks and 6000+ servers. We have implemented a 5-rack 40-server MegaSwitch prototype with commercial optical devices, and used testbed experiments as well as large-scale simulations to explore MegaSwitch\u2019s architectural benefits and tradeoffs.",
"pdfUrls": [
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"https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/chen",
"http://www.usenix.org./system/files/conference/nsdi17/nsdi17-chen.pdf",
"http://www.cse.ust.hk/~kaichen/papers/megaswitch-nsdi17.pdf",
"https://www.usenix.org/system/files/conference/nsdi17/nsdi17-chen.pdf",
"http://cseweb.ucsd.edu/~gmporter/papers/nsdi17-megaswitch.pdf",
"http://www.usenix.org./sites/default/files/conference/protected-files/nsdi17_slides_chen_li.pdf"
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"title": "Enabling Wide-Spread Communications on Optical Fabric with MegaSwitch",
"venue": "NSDI",
"year": 2017
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{
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{
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{
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"paperAbstract": "A memory consistency model specifies which writes to shared memory a given read may see. Ambiguities or errors in these specifications can lead to bugs in both compilers and applications. Yet architectures usually define their memory models with prose and litmus tests—small concurrent programs that demonstrate allowed and forbidden outcomes. Recent work has formalized the memory models of common architectures through substantial manual effort, but as new architectures emerge, there is a growing need for tools to aid these efforts. \n This paper presents MemSynth, a synthesis-aided system for reasoning about axiomatic specifications of memory models. MemSynth takes as input a set of litmus tests and a framework sketch that defines a class of memory models. The sketch comprises a set of axioms with missing expressions (or holes). Given these inputs, MemSynth synthesizes a completion of the axioms—i.e., a memory model—that gives the desired outcome on all tests. The MemSynth engine employs a novel embedding of bounded relational logic in a solver-aided programming language, which enables it to tackle complex synthesis queries intractable to existing relational solvers. This design also enables it to solve new kinds of queries, such as checking if a set of litmus tests unambiguously defines a memory model within a framework sketch. \n We show that MemSynth can synthesize specifications for x86 in under two seconds, and for PowerPC in 12 seconds from 768 litmus tests. Our ambiguity check identifies missing tests from both the Intel x86 documentation and the validation suite of a previous PowerPC formalization. We also used MemSynth to reproduce, debug, and automatically repair a paper on comparing memory models in just two days.",
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"http://homes.cs.washington.edu/~emina/pubs/memsynth.pldi17.pdf",
"https://homes.cs.washington.edu/~emina/doc/memsynth.pldi17.pdf",
"https://homes.cs.washington.edu/~bornholt/papers/memsynth-pldi17.pdf",
"https://homes.cs.washington.edu/~bornholt/papers/memsynth-pldi17.slides.pdf",
"http://doi.acm.org/10.1145/3062341.3062353"
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"title": "Synthesizing memory models from framework sketches and Litmus tests",
"venue": "PLDI",
"year": 2017
},
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"name": "Rong Ge"
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"name": "Pengfei Zou"
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"ids": [
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"name": "Xizhou Feng"
}
],
"doi": "10.1109/ICPP.2017.68",
"doiUrl": "https://doi.org/10.1109/ICPP.2017.68",
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"paperAbstract": "Power is a critical factor that limits the performance and scalability of modern high performance computer systems. Considering power as a first-order constraint and a scarce system resource, power-bounded computing represents a new perspective to address the power challenge in HPC.In this work we present an application-aware, multi-dimensional power allocation framework to support power-bounded parallel computing on NUMA-enabled multicore systems. This framework utilizes multiple complementary software and hardware power management mechanisms to manage power distribution among sockets, cores, and NUMA memory nodes under a total power budget. More importantly, this framework implements a hierarchical power coordination method that leverages applications' performance and power scalability to efficiently identify an ideal power distribution.We describe the design of the framework and evaluate its performance on a NUMA-enabled multicore system with 24 cores. Experimental results show that the proposed framework performs close to the oracle solution for parallel programs with various power budgets.",
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"title": "Application-Aware Power Coordination on Power Bounded NUMA Multicore Systems",
"venue": "2017 46th International Conference on Parallel Processing (ICPP)",
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},
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"doi": "10.1109/SP.2017.28",
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"paperAbstract": "We provide the first machine-checked proof of privacy-related properties (including ballot privacy) for an electronic voting protocol in the computational model. We target the popular Helios family of voting protocols, for which we identify appropriate levels of abstractions to allow the simplification and convenient reuse of proof steps across many variations of the voting scheme. The resulting framework enables machine-checked security proofs for several hundred variants of Helios and should serve as a stepping stone for the analysis of further variations of the scheme. In addition, we highlight some of the lessons learned regarding the gap between pen-and-paper and machine-checked proofs, and report on the experience with formalizing the security of protocols at this scale.",
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"https://doi.org/10.1109/SP.2017.28"
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"title": "Machine-Checked Proofs of Privacy for Electronic Voting Protocols",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
"612cf39494d5ea3db60616ef50836746dd289674": {
"authors": [
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"name": "Shuo Yang"
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{
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"name": "Kai Wu"
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{
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"name": "Yifan Qiao"
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"name": "Dong Li"
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{
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"name": "Jidong Zhai"
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"doi": "10.1109/CLUSTER.2017.61",
"doiUrl": "https://doi.org/10.1109/CLUSTER.2017.61",
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"paperAbstract": "Fault tolerance is one of the major design goals for HPC. The emergence of non-volatile memories (NVM) provides a solution to build fault tolerant HPC. Data in NVM-based main memory are not lost when the system crashes because of the non-volatility nature of NVM. However, because of volatile caches, data must be logged and explicitly flushed from caches into NVM to ensure consistence and correctness before crashes, which can cause large runtime overhead.In this paper, we introduce an algorithm-based method to establish crash consistence in NVM for HPC applications. We slightly extend application data structures or sparsely flush cache blocks, which introduce ignorable runtime overhead. Such extension or cache flushing allows us to use algorithm knowledge to reason data consistence or correct inconsistent data when the application crashes. We demonstrate the effectiveness of our method for three algorithms, including an iterative solver, dense matrix multiplication, and Monte-Carlo simulation. Based on comprehensive performance evaluation on a variety of test environments, we demonstrate that our approach has very small runtime overhead (at most 8.2% and less than 3% in most cases), much smaller than that of traditional checkpoint, while having the same or less recomputation cost.",
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"paperAbstract": "Infrastructure-as-a-Service clouds are rapidly evolving into market-like environments that offer a wide range of server contracts. Amazon EC2\u2019s spot market is the clearest example of this trend: it operates over 5000 markets globally where users can rent servers for a variable price. To exploit spot instances, while mitigating the risk of price spikes and revocations, many researchers and startups have developed techniques for modeling and predicting prices to optimize spot server selection. However, prior approaches focus largely on predicting individual server prices, which is akin to predicting the price of a single stock. We argue that researchers should instead focus on \u201cindex-based\u201d modeling and prediction that aggregates prices from many markets in each region and availability zone. We show that, for applications flexible enough to select and \u201ctrade\u201d servers globally, making decisions based on broader indices lowers costs and improves availability compared to index-agnostic policies.",
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"paperAbstract": "With the increasing size of HPC systems, the system mean time to interrupt will decrease. This requires checkpoints to be stored in a smaller time when using checkpoint/restart (C/R) for mitigation. Multilevel checkpointing improves C/R efficiency by saving most checkpoints to fast compute-node local storage. But it incurs a high cost for writing a few checkpoints to slow global-I/O. We show that leveraging NDP to offload writing of checkpoints to global-I/O improves C/R efficiency. We explore additional opportunities using NDP to further reduce C/R overhead and evaluate checkpoint compression using NDP as a starting point.\n We evaluate the performance of our novel application of NDP for C/R and compare it to existing C/R optimizations. Our evaluation for a projected exascale system using multilevel checkpointing shows that with NDP, the host processor is able to increase its efficiency on an average from 51% to 78% (i.e., a >50% speedup in performance).",
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"title": "Leveraging near data processing for high-performance checkpoint/restart",
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"title": "Enabling Lightweight Transactions with Precision Time",
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"year": 2017
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"paperAbstract": "Big data analytic applications give rise to large-scale extract-transform-load (ETL) as a fundamental step to transform new data into a native representation. ETL workloads pose significant performance challenges on conventional architectures, so we propose the design of the unstructured data processor (UDP), a software programmable accelerator that includes multi-way dispatch, variable-size symbol support, Flexible-source dispatch (stream buffer and scalar registers), and memory addressing to accelerate ETL kernels both for current and novel future encoding and compression. Specifically, UDP excels at branch-intensive and symbol and pattern-oriented workloads, and can offload them from CPUs.\n To evaluate UDP, we use a broad set of data processing workloads inspired by ETL, but broad enough to also apply to query execution, stream processing, and intrusion detection/monitoring. A single UDP accelerates these data processing tasks 20-fold (geometric mean, largest increase from 0.4 GB/s to 40 GB/s) and performance per watt by a geomean of 1,900-fold. UDP ASIC implementation in 28nm CMOS shows UDP logic area of 3.82<i>mm</i><sup>2</sup> (8.69<i>mm</i><sup>2</sup> with 1MB local memory), and logic power of 0.149<i>W</i> (0.864<i>W</i> with 1MB local memory); both much smaller than a single core.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3123939.3123983"
],
"pmid": "",
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"s2Url": "https://semanticscholar.org/paper/620eac63b314969fa88a0e3be1d3d682e5266f3d",
"sources": [
"DBLP"
],
"title": "UDP: a programmable accelerator for extract-transform-load workloads and more",
"venue": "MICRO",
"year": 2017
},
"622f4d1463868b59b547363d13d96f953527ae14": {
"authors": [
{
"ids": [
"1808863"
],
"name": "Vladimir Braverman"
},
{
"ids": [
"2225550"
],
"name": "Stephen R. Chestnut"
},
{
"ids": [
"2696609"
],
"name": "Nikita Ivkin"
},
{
"ids": [
"1757306"
],
"name": "Jelani Nelson"
},
{
"ids": [
"2574148"
],
"name": "Zhengyu Wang"
},
{
"ids": [
"1727403"
],
"name": "David P. Woodruff"
}
],
"doi": "10.1145/3034786.3034798",
"doiUrl": "https://doi.org/10.1145/3034786.3034798",
"entities": [
"Algorithm",
"Euler\u2013Bernoulli beam theory",
"Insertion sort",
"PSPACE",
"Polynomial"
],
"id": "622f4d1463868b59b547363d13d96f953527ae14",
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"paperAbstract": "The task of finding heavy hitters is one of the best known and well studied problems in the area of data streams. One is given a list <i>i</i><sub>1</sub>,<i>i</i><sub>2</sub>,...,<i>i<sub>m</sub></i>∈[<i>n</i>] and the goal is to identify the items among [<i>n</i>] that appear frequently in the list. In sub-polynomial space, the strongest guarantee available is the <i>l</i><sub>2</sub> guarantee, which requires finding all items that occur at least ε||ƒ||<sub>2</sub> times in the stream, where the vector ƒ∈<b>R</b><i><sup>n</sup></i> is the count histogram of the stream with <i>i</i>th coordinate equal to the number of times <i>i</i> appears ƒ<sub>i</sub>:=#{<i>j</i>ε[<i>m</i>]:<i>i<sub>j</sub></i>=<i>i</i>. The first algorithm to achieve the <i>l</i><sub>2</sub> guarantee was the <b>CountSketch</b> of [11], which requires <i>O</i>(ε<sup>-2</sup>log <i>n</i>) words of memory and <i>O</i>(log <i>n</i>) update time and is known to be space-optimal if the stream allows for deletions. The recent work of [7] gave an improved algorithm for insertion-only streams, using only <i>O</i>(ε<sup>-2</sup>logε<sup>-1</sup>log log <i>n</i>) words of memory. In this work, we give an algorithm <b>BPTree</b> for <i>l</i><sub>2</sub> heavy hitters in insertion-only streams that achieves <i>O</i>(ε<sup>-2</sup>logε<sup>-1</sup>) words of memory and <i>O</i>(logε<sup>-1</sup>) update time, which is the optimal dependence on <i>n</i> and <i>m</i>. In addition, we describe an algorithm for tracking ||ƒ||<sub>2</sub> at all times with <i>O</i>(ε<sup>-2</sup>) memory and update time. Our analyses rely on bounding the expected supremum of a Bernoulli process involving Rademachers with limited independence, which we accomplish via a Dudley-like chaining argument that may have applications elsewhere.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3034786.3034798",
"http://people.seas.harvard.edu/~minilek/papers/bptreev2.pdf",
"https://dash.harvard.edu/bitstream/handle/1/34744122/99166508.pdf?sequence=1",
"http://arxiv.org/abs/1603.00759"
],
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"s2Url": "https://semanticscholar.org/paper/622f4d1463868b59b547363d13d96f953527ae14",
"sources": [
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],
"title": "BPTree: An \u21132 Heavy Hitters Algorithm Using Constant Memory",
"venue": "PODS",
"year": 2017
},
"625922f62451f1e3b556e6aceca588946880427a": {
"authors": [
{
"ids": [
"1757205"
],
"name": "Vasileios Giotsas"
},
{
"ids": [
"1891855"
],
"name": "Philipp Richter"
},
{
"ids": [
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],
"name": "Georgios Smaragdakis"
},
{
"ids": [
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],
"name": "Anja Feldmann"
},
{
"ids": [
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],
"name": "Christoph Dietzel"
},
{
"ids": [
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],
"name": "Arthur W. Berger"
}
],
"doi": "10.1145/3131365.3131379",
"doiUrl": "https://doi.org/10.1145/3131365.3131379",
"entities": [
"Black hole (networking)",
"Border Gateway Protocol",
"DDoS mitigation",
"Denial-of-service attack",
"Forwarding plane",
"Internet",
"Internet access",
"Reachability"
],
"id": "625922f62451f1e3b556e6aceca588946880427a",
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"paperAbstract": "The Border Gateway Protocol (BGP) has been used for decades as the de facto protocol to exchange reachability information among networks in the Internet. However, little is known about how this protocol is used to <i>restrict</i> reachability to selected destinations, e.g., that are under attack. While such a feature, BGP blackholing, has been available for some time, we lack a systematic study of its Internet-wide adoption, practices, and network efficacy, as well as the profile of blackholed destinations.\n In this paper, we develop and evaluate a methodology to automatically detect BGP blackholing activity in the wild. We apply our method to both public and private BGP datasets. We find that hundreds of networks, including large transit providers, as well as about 50 Internet exchange points (IXPs) offer blackholing service to their customers, peers, and members. Between 2014--2017, the number of blackholed prefixes increased by a factor of 6, peaking at 5K concurrently blackholed prefixes by up to 400 Autonomous Systems. We assess the effect of blackholing on the data plane using both targeted active measurements as well as passive datasets, finding that blackholing is indeed highly effective in dropping traffic before it reaches its destination, though it also discards legitimate traffic. We augment our findings with an analysis of the target IP addresses of blackholing. Our tools and insights are relevant for operators considering offering or using BGP blackholing services as well as for researchers studying DDoS mitigation in the Internet.",
"pdfUrls": [
"https://conferences.sigcomm.org/imc/2017/papers/imc17-final90.pdf",
"http://people.csail.mit.edu/gsmaragd/publications/IMC2017/IMC2017.pdf",
"http://doi.acm.org/10.1145/3131365.3131379",
"http://people.csail.mit.edu/awberger/papers/Inferring_BGP_Blackholing_Activity_in_the_Internet.pdf"
],
"pmid": "",
"s2PdfUrl": "",
"s2Url": "https://semanticscholar.org/paper/625922f62451f1e3b556e6aceca588946880427a",
"sources": [
"DBLP"
],
"title": "Inferring BGP blackholing activity in the internet",
"venue": "IMC",
"year": 2017
},
"625b567202a6bef1d15c15d95c4d1cf743fc34c5": {
"authors": [
{
"ids": [
"2300090"
],
"name": "Amrita Mathuriya"
},
{
"ids": [
"37082461"
],
"name": "Ye Luo"
},
{
"ids": [
"30761222"
],
"name": "Raymond C. Clay"
},
{
"ids": [
"3444715"
],
"name": "Anouar Benali"
},
{
"ids": [
"2692314"
],
"name": "Luke Shulenburger"
},
{
"ids": [
"2718090"
],
"name": "Jeongnim Kim"
}
],
"doi": "10.1145/3126908.3126952",
"doiUrl": "https://doi.org/10.1145/3126908.3126952",
"entities": [
"Automatic vectorization",
"Blue Gene",
"Central processing unit",
"Compiler",
"Monte Carlo",
"Monte Carlo method",
"On the fly",
"Quantum Monte Carlo",
"Simulation",
"Single-precision floating-point format",
"Speedup",
"Supercomputer"
],
"id": "625b567202a6bef1d15c15d95c4d1cf743fc34c5",
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"journalName": "",
"journalPages": "38:1-38:12",
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"paperAbstract": "QMCPACK has enabled cutting-edge materials research on supercomputers for over a decade. It scales nearly ideally but has low single-node efficiency due to the physics-based abstractions using array-of-structures objects, causing inefficient vectorization. We present a systematic approach to transform QMCPACK to better exploit the new hardware features of modern CPUs in portable and maintainable ways. We develop miniapps for fast prototyping and optimizations. We implement new containers in structure-of-arrays data layout to facilitate vectorizations by the compilers. Further speedup and smaller memory-footprints are obtained by computing data on the fly with the vectorized routines and expanding single-precision use. All these are seamlessly incorporated in production QMCPACK. We demonstrate upto 4.5x speedups on recent Intel<sup>®</sup> processors and IBM Blue Gene/Q for representative workloads. Energy consumption is reduced significantly commensurate to the speedup factor. Memory-footprints are reduced by up-to 3.8x, opening the possibility to solve much larger problems of future.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3126908.3126952",
"http://arxiv.org/abs/1708.02645",
"https://arxiv.org/pdf/1708.02645v1.pdf"
],
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"sources": [
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"title": "Embracing a new era of highly efficient and productive quantum Monte Carlo simulations",
"venue": "SC",
"year": 2017
},
"628bd6edede6a6604ea3152317c548d6bb5f7a4f": {
"authors": [
{
"ids": [
"8075036"
],
"name": "Riad Akram"
},
{
"ids": [
"2581019"
],
"name": "Abdullah Muzahid"
}
],
"doi": "10.1109/IISWC.2017.8167765",
"doiUrl": "https://doi.org/10.1109/IISWC.2017.8167765",
"entities": [
"Approximate computing",
"Approximation algorithm",
"Cognitive dimensions of notations",
"Error-tolerant design",
"Experiment",
"Monte Carlo",
"Monte Carlo method",
"Programmer",
"Scalability",
"Traction TeamPage"
],
"id": "628bd6edede6a6604ea3152317c548d6bb5f7a4f",
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"journalName": "2017 IEEE International Symposium on Workload Characterization (IISWC)",
"journalPages": "116-117",
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"paperAbstract": "Approximate computing is getting a lot of traction especially for its potential in improving power, performance, and scalability of a computing system. However, prior work heavily relies upon a programmer to identify code sections where various approximation techniques can be applied. Such an approach is error prone and cannot scale well beyond small applications. In this paper, we contribute with a tool, called Approximeter, to automatically identify and quantify code sections where approximation can be used and to what extant. The tool works by first identifying potential approximable functions and then, injecting errors at appropriate locations. The tool runs Monte Carlo experiments to quantify statistical relation between injected error and corresponding output accuracy. The tool also provides a rough estimate of potential performance gain from approximating a certain function. Finally, it ranks the approximable functions based on their error tolerance and performance gain.",
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"http://doi.ieeecomputersociety.org/10.1109/IISWC.2017.8167765"
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"title": "Approximeter: Automatically finding and quantifying code sections for approximation",
"venue": "2017 IEEE International Symposium on Workload Characterization (IISWC)",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Hao Wang"
},
{
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],
"name": "Jing Zhang"
},
{
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"paperAbstract": "Energy efficiency is one of the most important design considerations in running modern datacenters. Datacenter operating systems rely on software techniques such as execution migration to achieve energy efficiency across pools of machines. Execution migration is possible in datacenters today because they consist mainly of homogeneous-ISA machines. However, recent market trends indicate that alternate ISAs such as ARM and PowerPC are pushing into the datacenter, meaning current execution migration techniques are no longer applicable. How can execution migration be applied in future heterogeneous-ISA datacenters?\n In this work we present a compiler, runtime, and an operating system extension for enabling execution migration between heterogeneous-ISA servers. We present a new multi-ISA binary architecture and heterogeneous-OS containers for facilitating efficient migration of natively-compiled applications. We build and evaluate a prototype of our design and demonstrate energy savings of up to 66% for a workload running on an ARM and an x86 server interconnected by a high-speed network.",
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"title": "Breaking the Boundaries in Heterogeneous-ISA Datacenters",
"venue": "ASPLOS",
"year": 2017
},
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],
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"paperAbstract": "A number of security mechanisms have been proposed to harden programs written in unsafe languages, each of which mitigates a specific type of memory error. Intuitively, enforcing multiple security mechanisms on a target program will improve its overall security. However, this is not yet a viable approach in practice because the execution slowdown caused by various security mechanisms is often non-linearly accumulated, making the combined protection prohibitively expensive; further, most security mechanisms are designed for independent or isolated uses and thus are often in conflict with each other, making it impossible to fuse them in a straightforward way. In this paper, we present BUNSHIN, an N-versionbased system that enables different and even conflicting security mechanisms to be combined to secure a program while at the same time reducing the execution slowdown. In particular, we propose an automated mechanism to distribute runtime security checks in multiple program variants in such a way that conflicts between security checks are inherently eliminated and execution slowdown is minimized with parallel execution. We also present an N-version execution engine to seamlessly synchronize these variants so that all distributed security checks work together to guarantee the security of a target program.",
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"paperAbstract": "Triangular Inequality (TI) has been used in many manual algorithm designs to achieve good efficiency in solving some distance calculation-based problems. This paper presents our generalization of the idea into a compiler optimization technique, named TI-based strength reduction. The generalization consists of three parts. The first is the establishment of the theoretic foundation of this new optimization via the development of a new form of TI named Angular Triangular Inequality, along with several fundamental theorems. The second is the revealing of the properties of the new forms of TI and the proposal of guided TI adaptation, a systematic method to address the difficulties in effective deployments of TI optimizations. The third is an integration of the new optimization technique in an open-source compiler. Experiments on a set of data mining and machine learning algorithms show that the new technique can speed up the standard implementations by as much as 134X and 46X on average for distance-related problems, outperforming previous TI-based optimizations by 2.35X on average. It also extends the applicability of TI-based optimizations to vector related problems, producing tens of times of speedup.",
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"title": "Generalizations of the theory and deployment of triangular inequality for compiler-based strength reduction",
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"name": "George Prekas"
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"name": "Marios Kogias"
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"name": "Edouard Bugnion"
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"doi": "10.1145/3132747.3132780",
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"title": "ZygOS: Achieving Low Tail Latency for Microsecond-scale Networked Tasks",
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"year": 2017
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"paperAbstract": "In future performance improvement of the basic building block of supercomputers has to come through increased integration enabled by 3D (vertical) and 2.5D (horizontal) die-stacking. But to take advantage of this integration we need an interconnection network between the memory and compute die that not only can provide an order of magnitude higher bandwidth but also consume an order of magnitude less power than today's state of the art electronic interconnects. Weshow how Arrayed Waveguide Grating Router-based photonic interconnects implemented on the silicon interposer can be used to realize a 16 × 16 photonic Network-on-Chip (NoC) with a bisection bandwidth of 16 Tb/s. We propose a baseline network, which consumes 2.57 pJ/bit assuming 100% utilization. We show that the power is dominated by the electro-optical interface of the transmitter, which can be reduced by a more aggressive design that improves the energy per bit to 0.454 pJ/bit at 100% utilization. Compared to recently proposed interposer-based electrical NoC's we show an average performance improvement of 25% on the PARSEC benchmark suite on a 64-core system using the Gem5 simulation framework.",
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"title": "Design and Evaluation of AWGR-Based Photonic NoC Architectures for 2.5D Integrated High Performance Computing Systems",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
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"name": "Cheng-Chieh Huang"
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"paperAbstract": "Contemporary server workloads feature massive instruction footprints stemming from deep, layered software stacks. The active instruction working set of the entire stack can easily reach into megabytes, resulting in frequent front-end stalls due to instruction cache misses and pipeline flushes due to branch target buffer (BTB) misses. While a number of techniques have been proposed to address these problems, every one of them requires dedicated metadata structures, translating into significant storage and complexity costs. In this paper, we ask the question whether it is possible to achieve high-performance control flow delivery without the metadata costs of prior techniques. We revisit a previously proposed approach of branch-predictor-directed prefetching, which leverages just the branch predictor and BTB to discover and prefetch the missing instruction cache blocks by exploring the program control flow ahead of the core front-end. Contrary to conventional wisdom, we find that this approach can be effective in covering instruction cache misses in modern CMPs with long LLC access latencies and multi-MB server binaries. Our first contribution lies in explaining the reasons for the efficacy of branch-predictor-directed prefetching. Our second contribution is in Boomerang, a metadata-free architecture for control flow delivery. Boomerang leverages a branch-predictor-directed prefetcher to discover and prefill not only the instruction cache blocks, but also the missing BTB entries. Crucially, we demonstrate that the additional hardware cost required to identify and fill BTB misses is negligible. Our experimental evaluation shows that Boomerang matches the performance of the state-of-the-art control flow delivery scheme without the latter's high metadata and complexity overheads.",
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"paperAbstract": "Time-based one-time password (TOTP) systems in use today require storing secrets on both the client and the server. As a result, an attack on the server can expose all second factors for all users in the system. We present T/Key, a time-based one-time password system that requires no secrets on the server. Our work modernizes the classic S/Key system and addresses the challenges in making such a system secure and practical. At the heart of our construction is a new lower bound analyzing the hardness of inverting hash chains composed of independent random functions, which formalizes the security of this widely used primitive. Additionally, we develop a near-optimal algorithm for quickly generating the required elements in a hash chain with little memory on the client. We report on our implementation of T/Key as an Android application. T/Key can be used as a replacement for current TOTP systems, and it remains secure in the event of a server-side compromise. The cost, as with S/Key, is that one-time passwords are longer than the standard six characters used in TOTP.",
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"paperAbstract": "Visible light is gaining significant attention as a medium to achieve accurate relative localization. Most of the studies in the area focus on indoor positioning and rely on two important assumptions: (i) lights are static, and (ii) the receiver has line-of-sight with multiple lights. These requirements limit the application of localization methods in scenarios where nodes have a single light and are mobile, such as motorbikes or swarms of robots. In general, this particular type of scenarios (single lights moving on a plane) leads to under-determined localization systems where no unique solution can be found. We follow a holistic approach that includes theory, simulations, and experiments to overcome some of the limitations present in such type of scenarios. Our theoretical and simulation results show that if nodes are enhanced with sensors providing relative directions (such as compasses), we can derive dependencies in the system to obtain unique solutions. Our proof-of-concept implementation validates our model by showing that single lights can provide relative localization with high accuracy: an average error below 5 cm.",
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"paperAbstract": "High performance computing systems will need to operate with certain power budgets while maximizing performance in the exascale era. Such systems are built with power aware components, whose collective peak power may exceed the specified power budget. Cluster level power bounded computing addresses this power challenge by coordinating power among components within compute nodes and further adjusting the number of participating nodes. It offers more space to increase system performance by utilizing the available power budget more efficiently within and across the nodes.In this paper, we present the design of a hierarchical multi-dimensional power aware allocation framework, CLIP, for power bounded parallel computing on multicore-based computer clusters. The framework satisfies the specified power bound by managing the power distribution among nodes at the cluster level, and among sockets, cores and NUMA memory modules at the node level. The power allocation is enforced with multiple complementary power management techniques, including memory power level setting, thread concurrency throttling, and core-thread affinity. We present an application characterization method based on applications' scalability and an associated performance model, which can accurately determine the optimal number of participating compute nodes and components, and their power distribution for given applications. Experimental results on a Haswell-based computer cluster show that the proposed scheduler outperforms compared methods by over 20% on average for various power budgets.",
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"paperAbstract": "In this paper, we present a fine-grained multi-stage metricbased triangular remeshing algorithm on manycore and NUMA architectures. It is motivated by the dynamically evolving data dependencies and workload of such irregular algorithms, often resulting in poor performance and data locality at high number of cores. In this context, we devise a multi-stage algorithm in which a task graph is built for each kernel. Parallelism is then extracted through fine-grained independent set, maximal cardinality matching and graph coloring heuristics. In addition to index ranges precalculation, a dual-step atomic-based synchronization scheme is used for nodal data updates. Despite its intractable latencyboundness, a good overall scalability is achieved on a NUMA dual-socket Intel Haswell and a dual-memory Intel KNL computing nodes (64 cores). The relevance of our synchronization scheme is highlighted through a comparison with the state-of-the-art.",
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"title": "Scalable Fine-Grained Metric-Based Remeshing Algorithm for Manycore/NUMA Architectures",
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"paperAbstract": "As we move towards an era of hundreds of cores, the research community has witnessed the emergence of optoelectronic network on-chip designs based on nanophotonics, in order to achieve higher network throughput, lower latencies, and lower dynamic power. However, traditional nanophotonics options face limitations such as large device footprints compared with electronics, higher static power due to continuous laser operation, and an upper limit on achievable data rates due to large device capacitances. Nanoplasmonics is an emerging technology that has the potential for providing transformative gains on multiple metrics due to its potential to increase the light-matter interaction. In this paper, we propose and analyze a hybrid opto-electric NoC that incorporates Hybrid Plasmonics Photonics Interconnect (HyPPI), an optical interconnect that combines photonics with plasmonics. We explore various opto-electronic network hybridization options by augmenting a mesh network with HyPPI links, and compare them with the equivalent options afforded by conventional nanophotonics as well as pure electronics. Our design space exploration indicates that augmenting an electronic NoC with HyPPI gives a performance to cost ratio improvement of up to 1.8×. To further validate our estimates, we conduct trace based simulations using the NAS Parallel Benchmark suite. These benchmarks show latency improvements up to 1.64×, with negligible energy increase. We then further carry out performance and cost projections for fully optical NoCs, using HyPPI as well as conventional nanophotonics. These futuristic projections indicate that all-HyPPI NoCs would be two orders more energy efficient than electronics, and two orders more area efficient than all-photonic NoCs.",
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"title": "HyPPI NoC: Bringing Hybrid Plasmonics to an Opto-Electronic Network-on-Chip",
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"paperAbstract": "Emerging fast, persistent memories will enable systems that combine conventional DRAM with large amounts of non-volatile main memory (NVMM) and provide huge increases in storage performance. Fully realizing this potential requires fundamental changes in how system software manages, protects, and provides access to data that resides in NVMM. We address these needs by describing an NVMM-optimized file system called NOVA-Fortis that is both fast and resilient in the face of corruption due to media errors and software bugs. We identify and propose solutions for the unique challenges in adding fault tolerance to an NVMM file system, adapt state-of-the-art reliability techniques to an NVMM file system, and quantify the performance and storage overheads of these techniques. We find that NOVA-Fortis' reliability features consume 14.8% of the storage for redundancy and reduce application-level performance by between 2% and 38% compared to the same file system with the features removed. NOVA-Fortis outperforms DAX-aware file systems without reliability features by 1.5x on average. It outperforms reliable, block-based file systems running on NVMM by 3x on average.",
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"title": "NOVA-Fortis: A Fault-Tolerant Non-Volatile Main Memory File System",
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"doi": "10.1145/3133956.3134009",
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"paperAbstract": "Monitoring network behaviors of mobile applications, controlling their resource access and detecting potentially harmful apps are becoming increasingly important for the security protection within today's organizational, ISP and carriers. For this purpose, apps need to be identified from their communication, based upon their individual traffic signatures (called <i>imprints</i> in our research). Creating imprints for a large number of apps is nontrivial, due to the challenges in comprehensively analyzing their network activities at a large scale, for millions of apps on today's rapidly-growing app marketplaces. Prior research relies on automatic exploration of an app's user interfaces (UIs) to trigger its network activities, which is less likely to scale given the cost of the operation (at least 5 minutes per app) and its effectiveness (limited coverage of an app's behaviors).\n In this paper, we present <i>Tiger</i> (Traffic Imprint Generator), a novel technique that makes comprehensive app imprint generation possible in a massive scale. At the center of Tiger is a unique <i>instantiated slicing</i> technique, which aggressively prunes the program slice extracted from the app's network-related code by evaluating each variable's impact on possible network invariants, and removing those unlikely to contribute through assigning them concrete values. In this way, Tiger avoids exploring a large number of program paths unrelated to the app's identifiable traffic, thereby reducing the cost of the code analysis by more than one order of magnitude, in comparison with the conventional slicing and execution approach. Our experiments show that Tiger is capable of recovering an app's full network activities within 18 seconds, achieving over 98% coverage of its identifiable packets and 0.742% false detection rate on app identification. Further running the technique on over 200,000 real-world Android apps (including 78.23% potentially harmful apps) leads to the discovery of surprising new types of traffic invariants, including fake device information, hardcoded time values, session IDs and credentials, as well as complicated trigger conditions for an app's network activities, such as human involvement, Intent trigger and server-side instructions. Our findings demonstrate that many network activities cannot easily be invoked through automatic UI exploration and code-analysis based approaches present a promising alternative.",
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"title": "Mass Discovery of Android Traffic Imprints through Instantiated Partial Execution",
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"year": 2017
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"paperAbstract": "Traditional machine learning algorithms often require computations on centralized data, but modern datasets are collected and stored in a distributed way. In addition to the cost of moving data to centralized locations, increasing concerns about privacy and security warrant distributed approaches. We propose keybin, a distributed key-based binning clustering algorithm for high-dimensional spaces. keybin locally generates a spatial key for each data point across all dimensions without needing knowledge of other data. Then, it performs a conceptual Map- Reduce procedure in the index space to form a global clustering assignment. We present an implementation and a case study on the capabilities and limitations of this approach, showing that this algorithm can learn a global clustering structure with limited communication and can scale with the dimensionality and size of data sets.",
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"title": "keybin: Key-Based Binning for Distributed Clustering",
"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
"year": 2017
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"name": "Xiaorui Pan"
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"name": "Xueqiang Wang"
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"name": "Yue Duan"
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"name": "XiaoFeng Wang"
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"paperAbstract": "Hidden sensitive operations (HSO) such as stealing privacy user data upon receiving an SMS message are increasingly utilized by mobile malware and other potentially-harmful apps (PHAs) to evade detection. Identification of such behaviors is hard, due to the challenge in triggering them during an app\u2019s runtime. Current static approaches rely on the trigger conditions or hidden behaviors known beforehand and therefore cannot capture previously unknown HSO activities. Also these techniques tend to be computationally intensive and therefore less suitable for analyzing a large number of apps. As a result, our understanding of real-world HSO today is still limited, not to mention effective means to mitigate this threat. In this paper, we present HSOMINER, an innovative machinelearning based program analysis technique that enables a largescale discovery of unknown HSO activities. Our approach leverages a set of program features that characterize an HSO branch and can be relatively easy to extract from an app. These features summarize a set of unique observations about an HSO condition, its paths and the relations between them, and are designed to be general for finding hidden suspicious behaviors. Particularly, we found that a trigger condition is less likely to relate to the path of its branch through data flows or shared resources, compared with a legitimate branch. Also, the behaviors exhibited by the two paths of an HSO branch tend to be conspicuously different (innocent on one side and sinister on the other). Most importantly, even though these individual features are not sufficiently accurate for capturing HSO on their own, collectively they are shown to be highly effective in identifying such behaviors. This differentiating power is harnessed by HSOMINER to classify Android apps, which achieves a high precision (>98%) and coverage (>94%), and is also efficient as discovered in our experiments. The new tool was further used in a measurement study involving 338,354 realworld apps, the largest one ever conducted on suspicious hidden operations. Our research brought to light the pervasiveness of HSO activities, which are present in 18.7% of the apps we analyzed, surprising trigger conditions (e.g., click on a certain region of a view) and behaviors (e.g., hiding operations in a dynamically generated receiver), which help better understand 1A branch, unless otherwise specified, refers to a branching structure, which contains a condition and multiple paths. the problem and contribute to more effective defense against this new threat to the mobile platform.",
"pdfUrls": [
"http://www.cs.ucr.edu/~heng/pubs/ndss2017.pdf",
"https://www.ndss-symposium.org/ndss2017/ndss-2017-programme/dark-hazard-learning-based-large-scale-discovery-hidden-sensitive-operations-android-apps/",
"https://www.informatics.indiana.edu/xw7/papers/ndss2017-05a_1-pan_slides.pdf"
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"title": "Dark Hazard: Learning-based, Large-Scale Discovery of Hidden Sensitive Operations in Android Apps",
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"year": 2017
},
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"name": "Liang Deng"
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"name": "Peng Liu"
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"name": "Jun Xu"
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"name": "Ping Chen"
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"name": "Qingkai Zeng"
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"doi": "10.1145/3050748.3050750",
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"paperAbstract": "In domains such as the Web, sensor networks and social media, sources often provide conflicting information for the same data item. Several data fusion techniques have been proposed recently to resolve conflicts and identify correct data. The performance of these fusion systems, while quite accurate, is far from perfect. In this paper, we propose to leverage user feedback for validating data conflicts and rapidly improving the performance of fusion. To present the most <i>beneficial</i> data items for the user to validate, we take advantage of the level of consensus among sources, and the output of fusion to generate an effective ordering of items. We first evaluate data items individually, and then define a novel decision-theoretic framework based on the concept of value of perfect information (VPI) to order items by their ability to boost the performance of fusion. We further derive approximate formulae to scale up the decision-theoretic framework to large-scale data. We empirically evaluate our algorithms on three real-world datasets with different characteristics, and show that the accuracy of fusion can be significantly improved even while requesting feedback on a few data items. We also show that the performance of the proposed methods depends on the characteristics of data, and assess the trade-off between the amount of feedback acquired, and the effectiveness and efficiency of the methods.",
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"title": "Staging User Feedback toward Rapid Conflict Resolution in Data Fusion",
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"year": 2017
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"title": "Swift Birth and Quick Death: Enabling Fast Parallel Guest Boot and Destruction in the Xen Hypervisor",
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"year": 2017
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"name": "Le Shi"
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{
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"name": "Yuming Wu"
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{
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"name": "Yubin Xia"
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{
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"name": "Nathan Dautenhahn"
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{
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"name": "Haibo Chen"
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{
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"name": "Binyu Zang"
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{
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"name": "Jinming Li"
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"paperAbstract": "Hypervisors have quickly become essential but are vulnerable to attack. Unfortunately, efficiently hardening hypervisors is challenging because they lack a privileged security monitor and decomposition strategies. In this work we systematically analyze the 191 Xen hypervisor vulnerabilities from Xen Security Advisories, revealing that the majority (144) are in the core hypervisor not Dom0. We then use the analysis to provide a novel deconstruction of Xen, called Nexen, into a security monitor, a shared service domain, and per-VM Xen slices that are isolated by a least-privileged sandboxing framework. We implement Nexen using the Nested Kernel architecture, efficiently nesting itself within the Xen address space, and extend the Nested Kernel design by adding services for arbitrarily many protection domains along with dynamic allocators, data isolation, and cross-domain control-flow integrity. The effect is that Nexen confines VM-based hypervisor compromises to single Xen VM instances, thwarts 74% (107/144) of known Xen vulnerabilities, and enforces Xen code integrity (defending against all code injection compromises) while observing negligible overhead (1.2% on average). Overall, we believe that Nexen is uniquely positioned to provide a fundamental need for hypervisor hardening at minimal performance and implementation costs.",
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"title": "Deconstructing Xen",
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"paperAbstract": "Using dynamic Searchable Symmetric Encryption, a user with limited storage resources can securely outsource a database to an untrusted server, in such a way that the database can still be searched and updated efficiently. For these schemes, it would be desirable that updates do not reveal any information <i>a priori</i> about the modifications they carry out, and that deleted results remain inaccessible to the server <i>a posteriori</i>. If the first property, called <i>forward privacy</i>, has been the main motivation of recent works, the second one, <i>backward privacy</i>, has been overlooked.\n In this paper, we study for the first time the notion of backward privacy for searchable encryption. After giving formal definitions for different flavors of backward privacy, we present several schemes achieving both forward and backward privacy, with various efficiency trade-offs.\n Our constructions crucially rely on primitives such as constrained pseudo-random functions and puncturable encryption schemes. Using these advanced cryptographic primitives allows for a fine-grained control of the power of the adversary, preventing her from evaluating functions on selected inputs, or decrypting specific ciphertexts. In turn, this high degree of control allows our SSE constructions to achieve the stronger forms of privacy outlined above. As an example, we present a framework to construct forward-private schemes from range-constrained pseudo-random functions.\n Finally, we provide experimental results for implementations of our schemes, and study their practical efficiency.",
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"title": "Forward and Backward Private Searchable Encryption from Constrained Cryptographic Primitives",
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"title": "Towards Production-Run Heisenbugs Reproduction on Commercial Hardware",
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"paperAbstract": "In this paper we present the design and implementation of Flow, a fast and precise type checker for JavaScript that is used by thousands of developers on millions of lines of code at Facebook every day. Flow uses sophisticated type inference to understand common JavaScript idioms precisely. This helps it find non-trivial bugs in code and provide code intelligence to editors without requiring significant rewriting or annotations from the developer. We formalize an important fragment of Flow's analysis and prove its soundness. Furthermore, Flow uses aggressive parallelization and incrementalization to deliver near-instantaneous response times. This helps it avoid introducing any latency in the usual edit-refresh cycle of rapid JavaScript development. We describe the algorithms and systems infrastructure that we built to scale Flow's analysis.",
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"title": "Fast and precise type checking for JavaScript",
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"paperAbstract": "This paper presents <b>GRAPE</b>, a parallel system for graph computations. <b>GRAPE</b> differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole. Underlying GRAPE are a simple programming model and a principled approach, based on partial evaluation and incremental computation. We show that sequential graph algorithms can be \"plugged into\" <b>GRAPE</b> with minor changes, and get parallelized. As long as the sequential algorithms are correct, their <b>GRAPE</b> parallelization guarantees to terminate with correct answers under a monotonic condition. Moreover, we show that algorithms in MapReduce, BSP and PRAM can be optimally simulated on <b>GRAPE</b>. In addition to the ease of programming, we experimentally verify that <b>GRAPE</b> achieves comparable performance to the state-of-the-art graph systems, using real-life and synthetic graphs.",
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"https://people.csail.mit.edu/jshun/6886-s18/papers/GRAPE.pdf",
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"title": "Parallelizing Sequential Graph Computations",
"venue": "SIGMOD Conference",
"year": 2017
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"title": "A scalable distributed spatial index for the internet-of-things",
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"paperAbstract": "A common approach for designing scalable algorithms for massive data sets is to distribute the computation across, say <i>k</i>, machines and process the data using limited communication between them. A particularly appealing framework here is the simultaneous communication model whereby each machine constructs a small representative summary of its own data and one obtains an approximate/exact solution from the union of the representative summaries. If the representative summaries needed for a problem are small, then this results in a <i>communication-efficient</i> and \\emph{round-optimal} (requiring essentially no interaction between the machines) protocol. Some well-known examples of techniques for creating summaries include sampling, linear sketching, and composable coresets. These techniques have been successfully used to design communication efficient solutions for many fundamental graph problems. However, two prominent problems are notably absent from the list of successes, namely, the <i>maximum matching</i> problem and the <i>minimum vertex cover</i> problem. Indeed, it was shown recently that for both these problems, even achieving a modest approximation factor of \\polylog{(n)} requires using representative summaries of size \\widetilde{\\Omega}(n^2) i.e. essentially no better summary exists than each machine simply sending its entire input graph.\n The main insight of our work is that the intractability of matching and vertex cover in the simultaneous communication model is inherently connected to an <i>adversarial</i> partitioning of the underlying graph across machines. We show that when the underlying graph is randomly partitioned across machines, both these problems admit \\emph{randomized composable coresets} of size \\widetilde{O}(n) that yield an \\widetilde{O}(1)-approximate solution\\footnote{Here and throughout the paper, we use \\Ot(\\cdot) notation to suppress \\polylog{(n)} factors, where <i>n</i> is the number of vertices in the graph. In other words, a small <i>subgraph</i> of the input graph at each machine can be identified as its representative summary and the final answer then is obtained by simply running any maximum matching or minimum vertex cover algorithm on these combined subgraphs. This results in an Õ(1)-approximation <i>simultaneous</i> protocol for these problems with Õ(nk) total communication when the input is randomly partitioned across <i>k</i> machines. We also prove our results are optimal in a very strong sense: we not only rule out existence of smaller randomized composable coresets for these problems but in fact show that our \\Ot(nk) bound for total communication is optimal for <i>em any</i> simultaneous communication protocol (i.e. not only for randomized coresets) for these two problems. Finally, by a standard application of composable coresets, our results also imply MapReduce algorithms with the same approximation guarantee in one or two rounds of communication, improving the previous best known round complexity for these problems.",
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"name": "Gang Qu"
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"name": "Liquan Xiao"
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"name": "Dezun Dong"
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"doi": "10.1109/IPDPS.2017.15",
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"paperAbstract": "This paper explores how fork-join parallelism, as supported by concurrency platforms such as Cilk and OpenMP, can be embedded into a compiler's intermediate representation (IR). Mainstream compilers typically treat parallel linguistic constructs as syntactic sugar for function calls into a parallel runtime. These calls prevent the compiler from performing optimizations across parallel control constructs. Remedying this situation is generally thought to require an extensive reworking of compiler analyses and code transformations to handle parallel semantics.\n Tapir is a compiler IR that represents logically parallel tasks asymmetrically in the program's control flow graph. Tapir allows the compiler to optimize across parallel control constructs with only minor changes to its existing analyses and code transformations. To prototype Tapir in the LLVM compiler, for example, we added or modified about 6000 lines of LLVM's 4-million-line codebase. Tapir enables LLVM's existing compiler optimizations for serial code -- including loop-invariant-code motion, common-subexpression elimination, and tail-recursion elimination -- to work with parallel control constructs such as spawning and parallel loops. Tapir also supports parallel optimizations such as loop scheduling.",
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"title": "Tapir: Embedding Fork-Join Parallelism into LLVM's Intermediate Representation",
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"year": 2017
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"name": "Niladrish Chatterjee"
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"name": "Stephen W. Keckler"
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"paperAbstract": "Future GPUs and other high-performance throughput processors will require multiple TB/s of bandwidth to DRAM. Satisfying this bandwidth demand within an acceptable energy budget is a challenge in these extreme bandwidth memory systems. We propose a new high-bandwidth DRAM architecture, Fine-Grained DRAM (FGDRAM), which improves bandwidth by 4× and improves the energy efficiency of DRAM by 2× relative to the highest-bandwidth, most energy-efficient contemporary DRAM, High Bandwidth Memory (HBM2). These benefits are in large measure achieved by partitioning the DRAM die into many independent units, called grains, each of which has a local, adjacent I/O. This approach unlocks the bandwidth of all the banks in the DRAM to be used simultaneously, eliminating shared buses interconnecting various banks. Furthermore, the on-DRAM data movement energy is significantly reduced due to the much shorter wiring distance between the cell array and the local I/O. This FGDRAM architecture readily lends itself to leveraging existing techniques to reducing the effective DRAM row size in an area efficient manner, reducing wasteful row activate energy in applications with low locality. In addition, when FGDRAM is paired with a memory controller optimized to exploit the additional concurrency provided by the independent grains, it improves GPU system performance by 19% over an iso-bandwidth and iso-capacity future HBM baseline. Thus, this energy-efficient, high-bandwidth FGDRAM architecture addresses the needs of future extreme-bandwidth memory systems.",
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"http://www.cs.utah.edu/~nil/pubs/micro17.pdf"
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"title": "Fine-grained DRAM: energy-efficient DRAM for extreme bandwidth systems",
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"year": 2017
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"paperAbstract": "Live migration is one of the key technologies to improve data center utilization, power efficiency, and maintenance. Various live migration algorithms have been proposed; each exhibiting distinct characteristics in terms of completion time, amount of data transferred, virtual machine (VM) downtime, and VM performance degradation. To make matters worse, not only the migration algorithm but also the applications running inside the migrated VM affect the different performance metrics. With service-level agreements and operational constraints in place, choosing the optimal live migration technique has so far been an open question. In this work, we propose an adaptive machine learning-based model that is able to predict with high accuracy the key characteristics of live migration in dependence of the migration algorithm and the workload running inside the VM. We discuss the important input parameters for accurately modeling the target metrics, and describe how to profile them with little overhead. Compared to existing work, we are not only able to model all commonly used migration algorithms but also predict important metrics that have not been considered so far such as the performance degradation of the VM. In a comparison with the state-of-the-art, we show that the proposed model outperforms existing work by a factor 2 to 5.",
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"title": "A machine learning approach to live migration modeling",
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"paperAbstract": "As in most information retrieval (IR) studies, evaluation plays an essential part in Web search research. Both offline and online evaluation metrics are adopted in measuring the performance of search engines. Offline metrics are usually based on relevance judgments of query-document pairs from assessors while online metrics exploit the user behavior data, such as clicks, collected from search engines to compare search algorithms. Although both types of IR evaluation metrics have achieved success, to what extent can they predict user satisfaction still remains under-investigated. To shed light on this research question, we meta-evaluate a series of existing online and offline metrics to study how well they infer actual search user satisfaction in different search scenarios. We find that both types of evaluation metrics significantly correlate with user satisfaction while they reflect satisfaction from different perspectives for different search tasks. Offline metrics better align with user satisfaction in homogeneous search (i.e. ten blue links) whereas online metrics outperform when vertical results are federated. Finally, we also propose to incorporate mouse hover information into existing online evaluation metrics, and empirically show that they better align with search user satisfaction than click-based online metrics.",
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"title": "Meta-evaluation of Online and Offline Web Search Evaluation Metrics",
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"paperAbstract": "An interesting class of irregular algorithms is tree traversal algorithms, which repeatedly traverse various trees to perform efficient computations. Tree traversal algorithms form the algorithmic kernels in an important set of applications in scientific computing, computer graphics, bioinformatics, and data mining, etc. There has been increasing interest in understanding tree traversal algorithms, optimizing them, and applying them in a wide variety of settings. Crucially, while there are many possible optimizations for tree traversal algorithms, which optimizations apply to which algorithms is dependent on algorithmic characteristics. In this work, we present a suite of tree traversal kernels, drawn from diverse domains, called Treelogy, to explore the connection between tree traversal algorithms and state-of-the-art optimizations. We characterize these algorithms by developing an ontology based on their structural properties. The attributes extracted through our ontology, for a given traversal kernel, can aid in quick analysis of the suitability of platform- and application-specific as well as independent optimizations. We provide reference implementations of these kernels for three platforms: shared memory multicores, distributed memory systems, and GPUs, and evaluate their scalability.",
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"title": "Treelogy: A benchmark suite for tree traversals",
"venue": "2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)",
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"name": "Kazutomo Yoshii"
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"doi": "10.1109/IPDPS.2017.25",
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"paperAbstract": "Exascale systems are expected to feature hundreds of thousands of compute nodes with hundreds of hardware threads and complex memory hierarchies with a mix of on-package and persistent memory modules. In this context, the Argo project is developing a new operating system for exascale machines. Targeting production workloads using workflows or coupled codes, we improve the Linux kernel on several fronts. We extendthe memory management of Linux to be able to subdivide NUMA memory nodes, allowing better resource partitioning among processes running on the same node. We also add support for memory-mapped access tonode-local, PCIe-attached NVRAM devices and introduce a new scheduling class targeted at parallel runtimes supporting user-level load balancing. These features are unified into compute containers, a containerization approach focused on providing modern HPC applications with dynamic control over a wide range of kernel interfaces. To keep our approach compatible with industrial containerization products, we also identifycontentions points for the adoption of containers in HPC settings. Each NodeOS feature is evaluated by using a set of parallel benchmarks, miniapps, and coupled applications consisting of simulation and data analysis components, running on a modern NUMA platform. We observe out-of-the-box performance improvements easily matching, and often exceeding, those observed with expert-optimized configurations on standard OS kernels. Our lightweight approach to resource management retains the many benefits of a full OS kernel that application programmers have learned to depend on, at the same time providing a set of extensions that can be freely mixed and matched to best benefit particular application components.",
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"title": "Argo NodeOS: Toward Unified Resource Management for Exascale",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"authors": [
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"doi": "10.1109/HPCC-SmartCity-DSS.2017.27",
"doiUrl": "https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.27",
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"paperAbstract": "The MPI two-sided communication model has been widely used in scientific applications for decades. The nonblocking version of the two-sided routines allows the application to potentially improve performance on many systems by overlapping communication and computation. In practice, unfortunately, the overlap is hard to achieve because of the limitations of the MPI internal progress engine and the underlying network. The traditional approach to resolving this issue is to implement an asynchronous progress engine based on either additional threads or hardware interrupts; however, such approaches may result in reduced computing power or expensive overheads. In this paper, we present a portable process-based asynchronous progress approach for two-sided communication in the PMPI-based Casper framework. It allows the user to specify an arbitrary number of cores on a multicore or many-core architecture and offload the point-to-point communication to these cores, thus ensuring asynchronous progress with low overhead. Unlike our previous work that supports asynchronous progress for the MPI one-sided model, a completely new design is needed for the message-matching-based two-sided model in order to ensure comprehensive semantics correctness as defined in the MPI standard. We present a detailed design of this two-sided model and compare it with the traditional thread-based approach on both a multicore Intel Xeon cluster and a many-core Knights Landing cluster.",
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"title": "Process-Based Asynchronous Progress Model for MPI Point-to-Point Communication",
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"name": "Robert Patro"
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"doi": "10.1145/3035918.3035963",
"doiUrl": "https://doi.org/10.1145/3035918.3035963",
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"paperAbstract": "Approximate Membership Query (AMQ) data structures, such as the Bloom filter, quotient filter, and cuckoo filter, have found numerous applications in databases, storage systems, networks, computational biology, and other domains. However, many applications must work around limitations in the capabilities or performance of current AMQs, making these applications more complex and less performant. For example, many current AMQs cannot delete or count the number of occurrences of each input item, take up large amounts of space, are slow, cannot be resized or merged, or have poor locality of reference and hence perform poorly when stored on SSD or disk. This paper proposes a new general-purpose AMQ, the counting quotient filter (CQF). The CQF supports approximate membership testing and counting the occurrences of items in a data set. This general-purpose AMQ is small and fast, has good locality of reference, scales out of RAM to SSD, and supports deletions, counting (even on skewed data sets), resizing, merging, and highly concurrent access. The paper reports on the structure's performance on both manufactured and application-generated data sets.\n In our experiments, the CQF performs in-memory inserts and queries up to an order-of magnitude faster than the original quotient filter, several times faster than a Bloom filter, and similarly to the cuckoo filter, even though none of these other data structures support counting. On SSD, the CQF outperforms all structures by a factor of at least 2 because the CQF has good data locality.\n The CQF achieves these performance gains by restructuring the metadata bits of the quotient filter to obtain fast lookups at high load factors (i.e., even when the data structure is almost full). As a result, the CQF offers good lookup performance even up to a load factor of 95%. Counting is essentially free in the CQF in the sense that the structure is comparable or more space efficient even than non-counting data structures (e.g., Bloom, quotient, and cuckoo filters).\n The paper also shows how to speed up CQF operations by using new x86 bit-manipulation instructions introduced in Intel's Haswell line of processors. The restructured metadata transforms many quotient filter metadata operations into rank-and-select bit-vector operations. Thus, our efficient implementations of rank and select may be useful for other rank-and-select-based data structures.",
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"http://doi.acm.org/10.1145/3035918.3035963",
"http://www3.cs.stonybrook.edu/~ppandey/files/SIGMOD17_Talk_CQF_long.pdf"
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"title": "A General-Purpose Counting Filter: Making Every Bit Count",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
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"name": "Francois Tessier"
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{
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"name": "Venkatram Vishwanath"
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{
"ids": [
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"name": "Emmanuel Jeannot"
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],
"doi": "10.1109/CLUSTER.2017.80",
"doiUrl": "https://doi.org/10.1109/CLUSTER.2017.80",
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"paperAbstract": "Reading and writing data efficiently from storage system is necessary for most scientific simulations to achieve good performance at scale. Many software solutions have been developed to decrease the I/O bottleneck. One well-known strategy, in the context of collective I/O operations, is the two-phase I/O scheme. This strategy consists of selecting a subset of processes to aggregate contiguous pieces of data before performing reads/writes. In this paper, we present TAPIOCA, an MPI-based library implementing an efficient topology-aware two-phase I/O algorithm. We show how TAPIOCA can take advantage of double-buffering and one-sided communication to reduce as much as possible the idle time during data aggregation. We also introduce our cost model leading to a topology-aware aggregator placement optimizing the movements of data. We validate our approach at large scale on two leadership-class supercomputers: Mira (IBM BG/Q) and Theta (Cray XC40). We present the results obtained with TAPIOCA on a micro-benchmark and the I/O kernel of a large-scale simulation. On both architectures, we show a substantial improvement of I/O performance compared with the default MPI I/O implementation. On BG/Q+GPFS, for instance, our algorithm leads to a performance improvement by a factor of twelve while on the Cray XC40 system associated with a Lustre filesystem, we achieve an improvement of four.",
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"title": "TAPIOCA: An I/O Library for Optimized Topology-Aware Data Aggregation on Large-Scale Supercomputers",
"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
"year": 2017
},
"6c94924012ceaa0e85d707f7bd4d1506fea576e6": {
"authors": [
{
"ids": [
"1703441"
],
"name": "Mihir Bellare"
},
{
"ids": [
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],
"name": "Wei Dai"
}
],
"doi": "10.1145/3133956.3133965",
"doiUrl": "https://doi.org/10.1145/3133956.3133965",
"entities": [
"Block size (cryptography)",
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"Encryption",
"Public-key cryptography",
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"Time complexity"
],
"id": "6c94924012ceaa0e85d707f7bd4d1506fea576e6",
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"journalName": "IACR Cryptology ePrint Archive",
"journalPages": "217",
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"paperAbstract": "Towards advancing the use of big keys as a practical defense against key exfiltration, this paper provides efficiency improvements for cryptographic schemes in the bounded retrieval model (BRM). We identify probe complexity (the number of scheme accesses to the slow storage medium storing the big key) as the dominant cost. Our main technical contribution is what we call the large-alphabet subkey prediction lemma. It gives good bounds on the predictability under leakage of a random sequence of blocks of the big key, as a function of the block size. We use it to significantly reduce the probe complexity required to attain a given level of security. Together with other techniques, this yields security-preserving performance improvements for BRM symmetric encryption schemes and BRM public-key identification schemes.",
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"http://doi.acm.org/10.1145/3133956.3133965"
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"title": "Defending Against Key Exfiltration: Efficiency Improvements for Big-Key Cryptography via Large-Alphabet Subkey Prediction",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
"6c977b4cfc2963165c4ccfaa0fc20e5ad75e2c4b": {
"authors": [
{
"ids": [
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],
"name": "Shaikha Saleh Mohamed"
},
{
"ids": [
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],
"name": "Nedaa Baker Al Barghuthi"
},
{
"ids": [
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],
"name": "Huwida E. Said"
}
],
"doi": "10.1109/HPCC-SmartCity-DSS.2017.26",
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"paperAbstract": "Microsoft Azure Storage is a global cloud storage system with a footprint in 38 geographic regions. To protect customer data against catastrophic data center failures, it optionally replicates data to secondary DCs hundreds of miles away. Using Microsoft OneDrive as an example, this paper illustrates the characteristics of typical cloud storage workloads and the opportunity to lower storage cost for geo-redundancy with erasure coding. The paper presents the design, implementation and evaluation of Giza \u2013 a strongly consistent, versioned object store that applies erasure coding across global data centers. The key technical challenge Giza addresses is to achieve single cross-DC round trip latency for the common contention-free workload, while also maintaining strong consistency when there are conflicting access. Giza addresses the challenge with a novel implementation of well-known distributed consensus algorithms tailored for restricted cloud storage APIs. Giza is deployed to 11 DCs across 3 continents and experimental results demonstrate that it achieves our design goals.",
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"title": "Giza: Erasure Coding Objects across Global Data Centers",
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"title": "Alive-Infer: data-driven precondition inference for peephole optimizations in LLVM",
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"paperAbstract": "With Tor being a popular anonymity network, many attacks have been proposed to break its anonymity or leak information of a private communication on Tor. However, guaranteeing complete privacy in the face of an adversary on Tor is especially difficult because Tor relays are under complete control of world-wide volunteers. Currently, one can gain private information, such as circuit identifiers and hidden service identifiers, by running Tor relays and can even modify their behaviors with malicious intent. This paper presents a practical approach to effectively enhancing the security and privacy of Tor by utilizing Intel SGX, a commodity trusted execution environment. We present a design and implementation of Tor, called SGX-Tor, that prevents code modification and limits the information exposed to untrusted parties. We demonstrate that our approach is practical and effectively reduces the power of an adversary to a traditional network-level adversary. Finally, SGX-Tor incurs moderate performance overhead; the end-to-end latency and throughput overheads for HTTP connections are 3.9% and 11.9%, respectively.",
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"title": "Enhancing Security and Privacy of Tor's Ecosystem by Using Trusted Execution Environments",
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"paperAbstract": "Increasingly, smart Network Interface Cards (sNICs) are being used in data centers to offload networking functions (NFs) from host processors thereby making these processors available for tenant applications. Modern sNICs have fully programmable, energy-efficient multi-core processors on which many packet processing functions, including a full-blown programmable switch, can run. However, having multiple switch instances deployed across the host hypervisor and the attached sNICs makes controlling them difficult and data plane operations more complex.\n This paper proposes a generalized SDN-controlled NF offload architecture called UNO. It can transparently offload dynamically selected host processors' packet processing functions to sNICs by using multiple switches in the host while keeping the data centerwide network control and management planes unmodified. UNO exposes a single virtual control plane to the SDN controller and hides dynamic NF offload behind a unified virtual management plane. This enables UNO to make optimal use of host's and sNIC's combined packet processing capabilities with local optimization based on locally observed traffic patterns and resource consumption, and without central controller involvement. Experimental results based on a real UNO prototype in realistic scenarios show promising results: it can save processing worth up to 8 CPU cores, reduce power usage by up to 2x, and reduce the control plane overhead by more than 50%.",
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"title": "UNO: uniflying host and smart NIC offload for flexible packet processing",
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},
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"paperAbstract": "In cloud datacenters, energy-efficient Virtual Machine Placement (VMP) mechanism is needed to maximize energy efficiency. Existing virtual machine (VM) allocation strategies based on whether the VMs' resource demands are assumed to be static or dynamic. Apparently, the former fails to fully utilize resources while the latter, which is implemented on shorter timescales, is either complicated or inefficient. Moreover, most prior VMP algorithms place VMs one by one, which lacks an optimization space. To handle these problems, we predict Gaussian distribution patterns of VM demands and propose an ant-colony-system VM placement algorithm (GACO-VMP) which synchronously coordinates the VMs with complementary resource requirements on the same server. The Gaussian distribution pattern is derived from the VMs running the same job. This mechanism minimizes energy consumption, while guaranteeing high resource utilization and also balancing resource utilization across multiple resources. In addition, we design two new metrics, called cumulative utilization ratio(CUR) and resource balance distance (RBD), in order to measure the overall resource utilization level and the equilibrium of multi-dimensional resource utilization, respectively. Simulations based on Google Cluster real trace indicate that GACO-VMP can achieve remarkable performance gains over two existing strategies in energy efficiency, VM migrations, resource utilization and resource balance.",
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"title": "Energy-Aware VM Placement with Periodical Dynamic Demands in Cloud Datacenters",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"name": "Hongyu Liu"
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"paperAbstract": "In spite of years of improvements to software security, heap-related attacks still remain a severe threat. One reason is that many existing memory allocators fall short in a variety of aspects. For instance, performance-oriented allocators are designed with very limited countermeasures against attacks, but secure allocators generally suffer from significant performance overhead, e.g., running up to 10x slower. This paper, therefore, introduces FreeGuard, a secure memory allocator that prevents or reduces a wide range of heap-related security attacks, such as heap overflows, heap over-reads, use-after-frees, as well as double and invalid frees. FreeGuard has similar performance to the default Linux allocator, with less than 2% overhead on average, but provides significant improvement to security guarantees.",
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"http://www.utdallas.edu/~zxl111930/file/CCS17c.pdf",
"http://doi.acm.org/10.1145/3133956.3133957",
"http://arxiv.org/abs/1709.02746",
"https://arxiv.org/pdf/1709.02746v2.pdf",
"https://arxiv.org/pdf/1709.02746v1.pdf"
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"title": "FreeGuard: A Faster Secure Heap Allocator",
"venue": "CCS",
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"paperAbstract": "Searchable encryption (SE) allows a client to outsource a dataset to an untrusted server while enabling the server to answer keyword queries in a private manner. SE can be used as a building block to support more expressive private queries such as range/point and boolean queries, while providing formal security guarantees. To scale SE to big data using external memory, new schemes with small <i>locality</i> have been proposed, where locality is defined as the number of non-continuous reads that the server makes for each query. Previous space-efficient SE schemes achieve optimal locality by increasing the <i>read efficiency</i>-the number of additional memory locations (false positives) that the server reads per result item. This can hurt practical performance.\n In this work, we design, formally prove secure, and evaluate the first SE scheme with tunable locality and linear space. Our first scheme has optimal locality and outperforms existing approaches (that have a slightly different leakage profile) by up to 2.5 orders of magnitude in terms of read efficiency, for all practical database sizes. Another version of our construction with the same leakage as previous works can be tuned to have bounded locality, optimal read efficiency and up to 60x more efficient end-to-end search time. We demonstrate that our schemes work fast in in-memory as well, leading to search time savings of up to 1 order of magnitude when compared to the most practical in-memory SE schemes. Finally, our construction can be tuned to achieve trade-offs between space, read efficiency, locality, parallelism and communication overhead.",
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"title": "Fast Searchable Encryption With Tunable Locality",
"venue": "SIGMOD Conference",
"year": 2017
},
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"paperAbstract": "We present Catena, an efficiently-verifiable Bitcoinwitnessing scheme. Catena enables any number of thin clients, such as mobile phones, to efficiently agree on a log of application-specific statements managed by an adversarial server. Catenaimplements a log as an OP_RETURN transaction chain andprevents forks in the log by leveraging Bitcoin's security againstdouble spends. Specifically, if a log server wants to equivocate ithas to double spend a Bitcoin transaction output. Thus, Catenalogs are as hard to fork as the Bitcoin blockchain: an adversarywithout a large fraction of the network's computational powercannot fork Bitcoin and thus cannot fork a Catena log either. However, different from previous Bitcoin-based work, Catenadecreases the bandwidth requirements of log auditors from 90GB to only tens of megabytes. More precisely, our clients onlyneed to download all Bitcoin block headers (currently less than35 MB) and a small, 600-byte proof for each statement in a block. We implement Catena in Java using the bitcoinj library and use itto extend CONIKS, a recent key transparency scheme, to witnessits public-key directory in the Bitcoin blockchain where it can beefficiently verified by auditors. We show that Catena can securemany systems today, such as public-key directories, Tor directoryservers and software transparency schemes.",
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"title": "Catena: Efficient Non-equivocation via Bitcoin",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"title": "Scaling Energy Adaptive Applications for Sustainable Profitability",
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"paperAbstract": "Estimating frequencies of items over data streams is a common building block in streaming data measurement and analysis. Misra and Gries introduced their seminal algorithm for the problem in 1982, and the problem has since been revisited many times due its practicality and applicability. We describe a highly optimized version of Misra and Gries' algorithm that is suitable for deployment in industrial settings. Our code is made public via an open source library called Data Sketches that is already used by several companies and production systems.\n Our algorithm improves on two theoretical and practical aspects of prior work. First, it handles <i>weighted</i> updates in amortized constant time, a common requirement in practice. Second, it uses a simple and fast method for merging summaries that asymptotically improves on prior work even for unweighted streams. We describe experiments confirming that our algorithms are more efficient than prior proposals.",
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"paperAbstract": "Parallel computing architectures like GPUs have traditionally been used to accelerate applications with dense and highly-structured workloads; however, many important applications in science and engineering are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Numerical simulation of charged particle beam dynamics is one such application where the distribution of work and data in the accurate computation of collective effects at each time step is irregular and exhibits control-flow and memory access patterns that are not readily amenable to GPU's architecture. Algorithms with these properties tend to present both significant branch and memory divergence on GPUs which leads to severe performance bottlenecks.We present a novel cache-aware algorithm that uses machine learning to address this problem. The algorithm presented here uses supervised learning to adaptively model and track irregular access patterns in the computation of collective effects at each time step of the simulation to anticipate the future control-flow and data access patterns. Access pattern forecast are then used to formulate runtime decisions that minimize branch and memory divergence on GPUs, thereby improving the performance of collective effects computation at a future time step based on the observations from earlier time steps. Experimental results on NVIDIA Tesla K40 GPU shows that our approach is effective in maximizing data reuse, ensuring workload balance among parallel threads, and in minimizing both branch and memory divergence. Further, the parallel implementation delivers up to 485 Gflops of double precision performance, which translates to a speedup of up to 2.5X compared to the fastest known GPU implementation.",
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"paperAbstract": "Domain squatting is a common adversarial practice where attackers register domain names that are purposefully similar to popular domains. In this work, we study a specific type of domain squatting called \"combosquatting,\" in which attackers register domains that combine a popular trademark with one or more phrases (e.g., betterfacebook[.]com, youtube-live[.]com). We perform the first large-scale, empirical study of combosquatting by analyzing more than 468 billion DNS records - collected from passive and active DNS data sources over almost six years. We find that almost 60% of abusive combosquatting domains live for more than 1,000 days, and even worse, we observe increased activity associated with combosquatting year over year. Moreover, we show that combosquatting is used to perform a spectrum of different types of abuse including phishing, social engineering, affiliate abuse, trademark abuse, and even advanced persistent threats. Our results suggest that combosquatting is a real problem that requires increased scrutiny by the security community.",
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"title": "Off-Road Performance Modeling - How to Deal with Segmented Data",
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"doi": "10.1145/3098822.3098833",
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"paperAbstract": "We present a Network Address Translator (NAT) written in C and proven to be semantically correct according to RFC 3022, as well as crash-free and memory-safe. There exists a lot of recent work on network verification, but it mostly assumes models of network functions and proves properties specific to network configuration, such as reachability and absence of loops. Our proof applies directly to the C code of a network function, and it demonstrates the absence of implementation bugs. Prior work argued that this is not feasible (i.e., that verifying a real, stateful network function written in C does not scale) but we demonstrate otherwise: NAT is one of the most popular network functions and maintains per-flow state that needs to be properly updated and expired, which is a typical source of verification challenges. We tackle the scalability challenge with a new combination of symbolic execution and proof checking using separation logic; this combination matches well the typical structure of a network function. We then demonstrate that formally proven correctness in this case does not come at the cost of performance. The NAT code, proof toolchain, and proofs are available at [58].",
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"http://doi.acm.org/10.1145/3098822.3098833",
"https://vignat.github.io/vignat-paper.pdf"
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"title": "A Formally Verified NAT",
"venue": "SIGCOMM",
"year": 2017
},
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"paperAbstract": "Thread schedulers are designed to dynamically map parallel programs to processors to optimize performance metrics including memory footprint, number of cache misses at each cache level, and load balance, so as to minimize the total running time of the program. Programs with dynamic memory allocation pose particular challenges for thread schedulers, and indeed prior schedulers that are provably cache- and time-efficient on multi-level cache hierarchies require static memory allocation. Not only do many thread schedulers fail to reuse memory effectively, but there is often an inherent trade-off between parallelism and memory use in algorithms. In this paper, we present the first runtime thread scheduler for multi-level cache hierarchies, called the space-bounded recursive-PDF scheduler, that is provably space-, cache-, and time-efficient for parallel programs that dynamically allocate memory. Our bounds hold for nested parallel programs with good regularity as measured by the effective cache complexity \u2014 a program-centric metric. The cache and time bounds are asymptotically optimal, while the space bound is asymptotically optimal for highly parallel and regular programs.",
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"title": "Provably Efficient Scheduling of Dynamically Allocating Programs on Parallel Cache Hierarchies",
"venue": "2017 IEEE 24th International Conference on High Performance Computing (HiPC)",
"year": 2017
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"doi": "10.1145/3035918.3035958",
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"paperAbstract": "Under update intensive workloads (TPC, LinkBench) small updates dominate the write behavior, e.g. 70% of all updates change less than 10 bytes across all TPC OLTP workloads. These are typically performed as in-place updates and result in random writes in page-granularity, causing major write-overhead on Flash storage, a write amplification of several hundred times and lower device longevity.\n In this paper we propose an approach that transforms those small in-place updates into small update deltas that are appended to the original page. We utilize the commonly ignored fact that modern Flash memories (SLC, MLC, 3D NAND) can handle appends to already programmed physical pages by using various low-level techniques such as ISPP to avoid expensive erases and page migrations. Furthermore, we extend the traditional NSM page-layout with a delta-record area that can absorb those small updates. We propose a scheme to control the write behavior as well as the space allocation and sizing of database pages.\n The proposed approach has been implemented under Shore- MT and evaluated on real Flash hardware (OpenSSD) and a Flash emulator. Compared to In-Page Logging [21] it performs up to 62% less reads and writes and up to 74% less erases on a range of workloads. The experimental evaluation indicates: (i) significant reduction of erase operations resulting in twice the longevity of Flash devices under update-intensive workloads; (ii) 15%-60% lower read/write I/O latencies; (iii) up to 45% higher transactional throughput; (iv) 2x to 3x reduction in overall write amplification.",
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"title": "From In-Place Updates to In-Place Appends: Revisiting Out-of-Place Updates on Flash",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
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"name": "Luke N. Olson"
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"paperAbstract": "With the rate of errors that can silently effect an application's state/output expected to increase on future HPC machines, numerous application-level detection and recovery schemes have been proposed. Recovery is more efficient when errors are contained and affect only part of the computation's state. Containment is usually achieved by verifying all information leaking out of a statically defined containment domain, which is an expensive procedure. Alternatively, error propagation can be analyzed to bound the domain that is affected by a detected error. This paper investigates how silent data corruption (SDC) due to soft errors propagates through three HPC applications: HPCCG, Jacobi, and CoMD. To allow for more detailed view of error propagation, the paper tracks propagation at the instruction and application variable level. The impact of detection latency on error propagation is shown along with an application's ability to recover. Finally, the impact of compiler optimizations are explored along with the impact of local problem size on error propagation.",
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"title": "Towards a More Complete Understanding of SDC Propagation",
"venue": "HPDC",
"year": 2017
},
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"name": "Luis Eduardo de Souza Amorim"
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"paperAbstract": "Context-free grammars are suitable for formalizing the syntax of programming languages concisely and declaratively. Thus, such grammars are often found in reference manuals of programming languages, and used in language workbenches for language prototyping. However, the natural and concise way of writing a context-free grammar is often ambiguous. \n Safe and complete declarative disambiguation of operator precedence and associativity conflicts guarantees that all ambiguities arising from combining the operators of the language are resolved. Ambiguities can occur due to <i>shallow conflicts</i>, which can be captured by one-level tree patterns, and <i>deep conflicts</i>, which require more elaborate techniques. Approaches to solve deep priority conflicts include grammar transformations, which may result in large unambiguous grammars, or may require adapted parser technologies to include data-dependency tracking at parse time. \n In this paper we study deep priority conflicts <i>\"in the wild\"</i>. We investigate the efficiency of grammar transformations to solve deep priority conflicts by using a lazy parse table generation technique. On top of lazily-generated parse tables, we define metrics, aiming to answer how often deep priority conflicts occur in real-world programs and to what extent programmers explicitly disambiguate programs themselves. By applying our metrics to a small corpus of popular open-source repositories we found that in OCaml, up to 17% of the source files contain deep priority conflicts.",
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"title": "Deep priority conflicts in the wild: a pilot study",
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"paperAbstract": "General Purpose Graphics Processing Units (GPGPUs) are present in most modern computing platforms. They are also increasingly integrated as a computational resource on clusters, data centers, and cloud infrastructure, making them possible targets for attacks. We present a first study of covert channel attacks on GPGPUs. GPGPU attacks offer a number of attractive properties relative to CPU covert channels. These channels also have characteristics different from their counterparts on CPUs. To enable the attack, we first reverse engineer the hardware block scheduler as well as the warp to warp scheduler to characterize how co-location is established. We exploit this information to manipulate the scheduling algorithms to create co-residency between the trojan and the spy. We study contention on different resources including caches, functional units and memory, and construct operational covert channels on all these resources. We also investigate approaches to increase the bandwidth of the channel including: (1) using synchronization to reduce the communication cycle and increase robustness of the channel; (2) exploiting the available parallelism on the GPU to increase the bandwidth; and (3) exploiting the scheduling algorithms to create exclusive co-location to prevent interference from other possible applications. We demonstrate operational versions of all channels on three different Nvidia GPGPUs, obtaining error-free bandwidth of over 4 Mbps, making it the fastest known microarchitectural covert channel under realistic conditions.",
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"paperAbstract": "This paper addresses energy efficient VNF placement and chaining over NFV enabled infrastructures. VNF placement and chaining are formulated as a decision tree search to overcome this NP-Hard problem complexity. The proposed approach is an extension of the Monte Carlo Tree Search(MCTS) method to achieve energy savings using physical resourceconsolidation and sharing VNFs between multiple tenants. A realcloud testbed and extensive simulations are used to assessperformance and ability to scale with problem size. Evaluationresults show significant reduction in energy consumption of theproposed placement solution compared to related work. The polynomialcomplexity of our proposal is highlighted by the simulation results.",
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},
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"paperAbstract": "Computing in virtualized environments has become a common practice for many businesses. Typically, hosting companies aim for lower operational costs by targeting high utilization of host machines maintaining just <i>enough</i> machines to meet the demand. In this scenario, frequent virtual machine context switches are common, resulting in increased TLB miss rates (often, by over 5<i>X</i> when contexts are doubled) and subsequent expensive page walks. Since each TLB miss in a virtual environment initiates a 2D page walk, the data caches get filled with a large fraction of page table entries (often, in excess of 50%) thereby evicting potentially more useful data contents.\n In this work, we propose <i>CSALT</i> - a Context-Switch Aware Large TLB, to address the problem of increased TLB miss rates and their adverse impact on data caches. First, we demonstrate that the <i>CSALT</i> architecture can effectively cope with the demands of increased context switches by its capacity to store a very large number of TLB entries. Next, we show that <i>CSALT</i> mitigates data cache contention caused by conflicts between data and translation entries by employing a novel <i>TLB-Aware Cache Partitioning</i> scheme. On 8-core systems that switch between two virtual machine contexts executing multi-threaded workloads, <i>CSALT</i> achieves an average performance improvement of 85% over a baseline with conventional L1-L2 TLBs and 25% over a baseline which has a large L3 TLB.",
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"http://doi.acm.org/10.1145/3123939.3124549",
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"title": "CSALT: context switch aware large TLB",
"venue": "MICRO",
"year": 2017
},
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"authors": [
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"name": "Huang Fang"
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"name": "Minhao Cheng"
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"name": "Cho-Jui Hsieh"
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"paperAbstract": "We consider the semi-supervised dimension reduction problem: given a high dimensional dataset with a small number of labeled data and huge number of unlabeled data, the goal is to find the low-dimensional embedding that yields good classification results. Most of the previous algorithms for this task are linkage-based algorithms. They try to enforce the must-link and cannot-link constraints in dimension reduction, leading to a nearest neighbor classifier in low dimensional space. In this paper, we propose a new hyperplane-based semi-supervised dimension reduction method—the main objective is to learn the low-dimensional features that can both approximate the original data and form a good separating hyperplane. We formulate this as a non-convex optimization problem and propose an efficient algorithm to solve it. The algorithm can scale to problems with millions of features and can easily incorporate non-negative constraints in order to learn interpretable non-negative features. Experiments on real world datasets demonstrate that our hyperplane-based dimension reduction method outperforms state-of-art linkage-based methods when very few labels are available.",
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"title": "A Hyperplane-Based Algorithm for Semi-Supervised Dimension Reduction",
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"paperAbstract": "Kernel vulnerabilities are critical in security because they naturally allow attackers to gain unprivileged root access. Although there has been much research on finding kernel vulnerabilities from source code, there are relatively few research on kernel fuzzing, which is a practical bug finding technique that does not require any source code. Existing kernel fuzzing techniques involve feeding in random input values to kernel API functions. However, such a simple approach does not reveal latent bugs deep in the kernel code, because many API functions are dependent on each other, and they can quickly reject arbitrary parameter values based on their calling context. In this paper, we propose a novel fuzzing technique for commodity OS kernels that leverages inferred dependence model between API function calls to discover deep kernel bugs. We implement our technique on a fuzzing system, called IMF. IMF has already found 32 previously unknown kernel vulnerabilities on the latest macOS version 10.12.3 (16D32) at the time of this writing.",
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"title": "IMF: Inferred Model-based Fuzzer",
"venue": "CCS",
"year": 2017
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"doi": "10.1145/3123939.3123972",
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"paperAbstract": "In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used as one of the most common techniques to achieve both energy-and resource-utilization efficiency. Live migrations of VMs are used for dynamic consolidation but due to dynamic resource demand variation of VMs may lead to frequent and non-optimal migrations. Assuming deterministic workload of the VMs may ensure the most energy/resource-efficient VM allocations but eventually may lead to significant resource contention or under-utilization if the workload varies significantly over time. On the other hand, adopting a conservative approach by allocating VMs depending on their peak demand may lead to low utilization, if the peak occurs infrequently or for a short period of time. Therefore, in this work we design a robust VM migration scheme that strikes a balance between protection for resource contention and additional energy costs due to powering on more servers while considering uncertainties on VMs resource demands. We use the theory of Gamma robustness and derive a robust Mixed Integer Linear programming (MILP) formulation. Due to the complexity, the problem is hard to solve for online optimization and we propose a novel heuristic based on Tabu Search. Using several scenarios, we show that that the proposed heuristic can achieve near optimal solution qualities in a short time and scales well with the instance sizes. Moreover, we quantitatively analyze the trade-off between energy cost versus protection level and robustness.",
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"paperAbstract": "Motivated by emerging applications to the edge computing paradigm, we introduce a two-layer erasure-coded fault-tolerant distributed storage system offering atomic access for read and write operations. In edge computing, clients interact with an edge-layer of servers that is geographically near; the edge-layer in turn interacts with a back-end layer of servers. The edge-layer provides low latency access and temporary storage for client operations, and uses the back-end layer for persistent storage. Our algorithm, termed Layered Data Storage (LDS) algorithm, offers several features suitable for edge-computing systems, works under asynchronous message-passing environments, supports multiple readers and writers, and can tolerate f1 < n1/2 and f2 < n2/3 crash failures in the two layers having n1 and n2 servers, respectively. We use a class of erasure codes known as regenerating codes for storage of data in the back-end layer. The choice of regenerating codes, instead of popular choices like Reed-Solomon codes, not only optimizes the cost of back-end storage, but also helps in optimizing communication cost of read operations, when the value needs to be recreated all the way from the back-end. The two-layer architecture permits a modular implementation of atomicity and erasure-code protocols; the implementation of erasurecodes is mostly limited to interaction between the two layers. We prove liveness and atomicity of LDS, and also compute performance costs associated with read and write operations. In a system with n1 = \u0398(n2), f1 = \u0398(n1), f2 = \u0398(n2), the write and read costs are respectively given by \u0398(n1) and \u0398(1) + n1I(\u03b4 > 0). Here \u03b4 is a parameter closely related to the number of write operations that are concurrent with the read operation, and I(\u03b4 > 0) is 1 if \u03b4 > 0, and 0 if \u03b4 = 0. The cost of persistent storage in the back-end layer is \u0398(1). The impact of temporary storage is minimally felt in a multiobject system running N independent instances of LDS, where only a small fraction of the objects undergo concurrent accesses at any point during the execution. For the multi-object system, we identify a condition on the rate of concurrent writes in the system such that Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. PODC \u201917, July 25-27, 2017, Washington, DC, USA \u00a9 2017 Association for Computing Machinery. ACM ISBN 978-1-4503-4992-5/17/07. . . $15.00 https://doi.org/10.1145/3087801.3087832 the overall storage cost is dominated by that of persistent storage in the back-end layer, and is given by \u0398(N ).",
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"title": "A Layered Architecture for Erasure-Coded Consistent Distributed Storage",
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"paperAbstract": "Content based page sharing techniques improve memory efficiency in virtualized systems by identifying and merging identical pages. Kernel Same-page Merging (KSM), a Linux kernel utility for page sharing, sequentially scans memory pages of virtual machines to deduplicate pages. Sequential scanning of pages has several undesirable side effects---wasted CPU cycles when no sharing opportunities exist, and rate of discovery of sharing being dependent on the scanning rate and corresponding CPU availability. In this work, we exploit presence of GPUs on modern systems to enable rapid memory sharing through targeted scanning of pages. Our solution, Catalyst, works in two phases, the first where pages of virtual machines are processed by the GPU to identify likely pages for sharing and a second phase that performs page-level similarity checks on a targeted set of shareable pages. Opportunistic usage of the GPU to produce sharing hints enables rapid and low-overhead duplicate detection, and sharing of memory pages in virtualization environments. We evaluate Catalyst against various benchmarks and workloads to demonstrate that Catalyst can achieve higher memory sharing in lesser time compared to different scan rate configurations of KSM, at lower or comparable compute costs.",
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"title": "Catalyst: GPU-assisted rapid memory deduplication in virtualization environments",
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"paperAbstract": "Previous work has shown that benchmark and application performance in public cloud computing environments can be highly variable. Utilizing Amazon EC2 traces that include measurements affected by CPU, memory, disk, and network performance, we study commonly used methodologies for comparing performance measurements in cloud computing environments. The results show considerable flaws in these methodologies that may lead to incorrect conclusions. For instance, these methodologies falsely report that the performance of two identical systems differ by 38% using a confidence level of 95%. We then study the efficacy of the Randomized Multiple Interleaved Trials (RMIT) methodology using the same traces. We demonstrate that RMIT could be used to conduct repeatable experiments that enable fair comparisons in this cloud computing environment despite the fact that changing conditions beyond the user's control make comparing competing alternatives highly challenging.",
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"paperAbstract": "Next-generation non-volatile memories (<i>NVMs</i>) will provide byte addressability, persistence, high density, and DRAM-like performance. They have the potential to benefit many datacenter applications. However, most previous research on NVMs has focused on using them in a single machine environment. It is still unclear how to best utilize them in distributed, datacenter environments.\n We introduce <i>Distributed Shared Persistent Memory (DSPM)</i>, a new framework for using persistent memories in distributed data-center environments. DSPM provides a new abstraction that allows applications to both perform traditional memory load and store instructions and to name, share, and persist their data.\n We built <i>Hotpot</i>, a kernel-level DSPM system that provides low-latency, transparent memory accesses, data persistence, data reliability, and high availability. The key ideas of Hotpot are to integrate distributed memory caching and data replication techniques and to exploit application hints. We implemented Hotpot in the Linux kernel and demonstrated its benefits by building a distributed graph engine on Hotpot and porting a NoSQL database to Hotpot. Our evaluation shows that Hotpot outperforms a recent distributed shared memory system by 1.3× to 3.2× and a recent distributed PM-based file system by 1.5× to 3.0×.",
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"title": "Distributed shared persistent memory",
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{
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"name": "Matei Ripeanu"
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],
"doi": "10.1007/978-3-319-64203-1_7",
"doiUrl": "https://doi.org/10.1007/978-3-319-64203-1_7",
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"paperAbstract": "This study characterizes the NVIDIA Jetson TK1 and TX1 Platforms, both built on a NVIDIA Tegra System on Chip and combining a quad-core ARM CPU and an NVIDIA GPU. Their heterogeneous nature, as well as their wide operating frequency range, make it hard for application developers to reason about performance and determine which optimizations are worth pursuing. This paper attempts to inform developers\u2019 choices by characterizing the platforms\u2019 performance using Roofline models obtained through an empirical measurement-based approach as well as through a case study of a heterogeneous application (matrix multiplication). Our results highlight a difference of more than an order of magnitude in compute performance between the CPU and GPU on both platforms. Given that the CPU and GPU share the same memory bus, their Roofline models\u2019 balance points are also more than an order of magnitude apart. We also explore the impact of frequency scaling: build CPU and GPU Roofline profiles and characterize both platforms\u2019 balance point variation, power consumption, and performance per watt as frequency is scaled. The characterization we provide can be used in two main ways. First, given an application, it can inform the choice and number of processing elements to use (i.e., CPU/GPU and number of cores) as well as the optimizations likely to lead to high performance gains. Secondly, this characterization indicates that developers can use frequency scaling to tune the Jetson Platform to suit the requirements of their applications. Third, given a required power/performance budget, application developers can identify the appropriate parameters to use to tune the Jetson platforms to their specific workload requirements. We expect that this optimization approach can lead to overall gains in performance and/or power efficiency without requiring application changes.",
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"http://www.ece.ubc.ca/~matei/papers/europar17.pdf"
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"title": "NVIDIA Jetson Platform Characterization",
"venue": "Euro-Par",
"year": 2017
},
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"authors": [
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"name": "Zafar Ayyub Qazi"
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"name": "Melvin Walls"
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"name": "Aurojit Panda"
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"name": "Vyas Sekar"
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"name": "Sylvia Ratnasamy"
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"name": "Scott Shenker"
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],
"doi": "10.1145/3098822.3098848",
"doiUrl": "https://doi.org/10.1145/3098822.3098848",
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"paperAbstract": "Cellular traffic continues to grow rapidly making the scalability of the cellular infrastructure a critical issue. However, there is mounting evidence that the current Evolved Packet Core (EPC) is ill-suited to meet these scaling demands: EPC solutions based on specialized appliances are expensive to scale and recent software EPCs perform poorly, particularly with increasing numbers of devices or signaling traffic.\n In this paper, we design and evaluate a new system architecture for a software EPC that achieves high and scalable performance. We postulate that the poor scaling of existing EPC systems stems from the manner in which the system is decomposed which leads to device state being duplicated across multiple components which in turn results in frequent interactions between the different components. We propose an alternate approach in which state for a single device is consolidated in one location and EPC functions are (re)organized for efficient access to this consolidated state. In effect, our design \"slices\" the EPC by user.\n We prototype and evaluate PEPC, a software EPC that implements the key components of our design. We show that PEPC achieves 3-7x higher throughput than comparable software EPCs that have been implemented in industry and over 10x higher throughput than a popular open-source implementation (OpenAirInterface). Compared to the industrial EPC implementations, PEPC sustains high data throughput for 10-100x more users devices per core, and a 10x higher ratio of signaling-to-data traffic. In addition to high performance, PEPC's by-user organization enables efficient state migration and customization of processing pipelines. We implement user migration in PEPC and show that state can be migrated with little disruption, e.g., migration adds only up to 4μs of latency to median per packet latencies.",
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"http://doi.acm.org/10.1145/3098822.3098848",
"https://people.eecs.berkeley.edu/~zaqazi/sigcomm17.pdf"
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"title": "A High Performance Packet Core for Next Generation Cellular Networks",
"venue": "SIGCOMM",
"year": 2017
},
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"name": "Daniel J. Sorin"
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"doi": "10.1145/3123939.3123971",
"doiUrl": "https://doi.org/10.1145/3123939.3123971",
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"paperAbstract": "Recent work in formal verification theory and verification-aware design has sought to bridge the divide between the class of protocols architects want to design and the class of protocols that are verifiable with state of the art tools. Particularly the recent Neo work in formal verification theory, for the first time, formalizes how to compose flat subprotocols with an arbitrary number of nodes into a hierarchy while maintaining correct behavior. However, it is unclear if this theory scales to realistic systems. Moreover, there is a diversity of systems architects would be interested in, to which it is not clear if the theory applies.\n In this paper, we show how the abstract Neo theory can be leveraged to design a realistic hierarchical coherence protocol. As such, we present the first realistic hierarchical coherence protocol verified with fully-automated (push-button) verification tools for all scales and tree configurations. We explore the practical limitations posed by both the theory and the verification tools in designing this verifiable hierarchical protocol. We experimentally evaluate our protocol, comparing it to more complex protocols that have optimizations prohibited by the theory and verification tool. Finally, we discuss how a variety of system configurations and protocols architects might be interested in can be adapted to the Neo theory, which we hope opens up the theory to future work in verification-aware protocol design.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3123939.3123971",
"http://web.eecs.umich.edu/~luwa/papers/MICRO17/Neo_MICRO17.pdf"
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"title": "Architecting hierarchical coherence protocols for push-button parametric verification",
"venue": "MICRO",
"year": 2017
},
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"name": "Arvinder Kaur"
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"doi": "10.1145/3030207.3044533",
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"paperAbstract": "The detection of early-warning signals of performance deterioration can help technical support teams in taking swift remedial actions, thus ensuring rigor in production support operations of large scale software systems. Performance anomalies or deterioration, if left unattended, often result in system slowness and unavailability. In this paper, we presents a simple, intuitive and low-overhead technique for recognizing the early warning signs in near real time before they impact the system The technique is based on the inverse relationship which exists between throughput and average response time in a closed system. Because of this relationship, a significant increase in the average system response time causes an abrupt fall in system throughput. To identify such occurrences automatically, Individuals and Moving Range (XmR) control charts are used. We also provide a case study from a real-world production system, in which the technique has been successfully used. The use of this technique has reduced the occurrence of performance related incidents significantly in our daily operations. The technique is tool agnostic and can also be easily implemented in popular system monitoring tools by building custom extensions.",
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"title": "Technique for Detecting Early-Warning Signals of Performance Deterioration in Large Scale Software Systems",
"venue": "ICPE",
"year": 2017
},
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"name": "Shruti Tople"
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"paperAbstract": "Intel SGX, a new security capability in emerging CPUs, allows user-level application code to execute in hardwareisolated enclaves. Enclave memory is isolated from all other software on the system, even from the privileged OS or hypervisor. While being a promising hardware-rooted building block, enclaves have severely limited capabilities, such as no native access to system calls and standard OS abstractions. These OS abstractions are used ubiquitously in real-world applications. In this paper, we present a new system called PANOPLY which bridges the gap between the SGX-native abstractions and the standard OS abstractions which feature-rich, commodity Linux applications require. PANOPLY provides a new abstraction called a micro-container (or a \u201cmicron\u201d), which is a unit of code and data isolated in SGX enclaves. Microns expose the standard POSIX abstractions to application logic, including access to filesystems, network, multi-threading, multi-processing and thread synchronization primitives. Further, PANOPLY enforces a strong integrity property for the inter-enclave interactions, ensuring that the execution of the application follows the legitimate control and data-flow even if the OS misbehaves. Thus, commodity Linux applications can enhance security by splitting their application logic in one or more microns, or by importing micron-libraries, with little effort. In contrast to previous systems that enable comparable richness, PANOPLY offers two orders of magnitude lower TCB (about 20 KLOC in total), more than half of which is boiler-plate and can be automatically verified in the future. We demonstrate how PANOPLY enables much stronger security in 4 real-world applications \u2014 including Tor, OpenSSL, and web services \u2014 which can base security on hardware-root of trust.",
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"title": "Panoply: Low-TCB Linux Applications With SGX Enclaves",
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"paperAbstract": "This paper addresses the shared resource contention problem associated with the auto-parallelization of running queries in distributed stream processing engines. In such platforms, analyzing a large amount of data often requires to execute user-defined queries over continues raw-inputs in a parallel fashion at each single host. However, previous studies showed that the collocated applications can fiercely compete for shared resources, resulting in a severe performance degradation among applications. This paper presents an advanced resource allocation strategy for handling scenarios in which the target applications have different quality of service (QoS) requirements while shared-resource interference is considered as a key performance-limiting parameter.To properly allocate the best possible resource to each query, the proposed controller predicts the performance degradation of the running pane-level as well as the window-level queries when co-running with other queries. This is addressed as an optimization problem where a set of cost functions is defined to achieve the following goals: a) reduce the sum of QoS violation incidents over all machines; b) keep the CPU utilization level within an accepted range; and c) avoid fierce shared resource interference among collocated applications. Particle swarm optimization is used to find an acceptable solution at each round of the controlling period. The performance of the proposed solution is benchmarked with Round-Robin and best-effort strategies, and the experimental results clearly demonstrate that the proposed controller has the following advantages over its opponents: it increases the overall resource utilization by 15% on average while can reduce the average tuple latencies by 14%. It also achieves an average 123% improvement in preventing QoS violation incidents",
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"title": "QoS- and Contention- Aware Resource Provisioning in a Stream Processing Engine",
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"paperAbstract": "We present improved bounds on the cover time of the <i>coalescing-branching random walk</i> process COBRA. The COBRA process, introduced in [Dutta et al., SPAA 2013], can be viewed as spreading a single item of information throughout an undirected graph in synchronised rounds. In each round, each vertex which has received the information in the previous round (possibly simultaneously from more than one neighbour and possibly not for the first time), 'pushes' the information to <i>b</i> randomly selected neighbours. The COBRA process is typically studied for integer branching rates b \\ge 2 (with the case b=1 corresponding to a random walk). The aim of the process is to propagate the information quickly, but with a limited number of transmissions per vertex per round.\n The cover time of COBRA is defined as the expected number of rounds until each vertex has received the information at least once. Our main results are a bound of O(m + (d_{\\max})^2\\log n) = O(n^2\\log n) on the COBRA cover time for an arbitrary connected graph with <i>n</i> vertices, <i>m</i> edges and the maximum vertex degree d_{\\max}, and a bound of O((r^2 + r/(1-\\lambda)) \\log n) for <i>r</i>-regular connected graphs with the second eigenvalue \\lambda. Our bounds improve the O(n^{11/4}\\log n) and ((r^4/\\phi^2)\\log^2 n) bounds shown in [Mitzenmacher et al., SPAA 2016], where \\phi is the conductance of the graph, and complement the O((1/(1-\\l))^3 \\log n) bound shown in [Cooper et al., PODC 2016]. We obtain our bounds by analysing the process called <i>Biased Infection with Persistent Source</i> (BIPS), which was introduced in [Cooper et al., PODC 2016] as a dual process for COBRA.",
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"title": "Improved Cover Time Bounds for the Coalescing-Branching Random Walk on Graphs",
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"paperAbstract": "Open experimentation with operational Mobile Broadband (MBB) networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need of innovative solutions for mobile communications. Even more, there is a strong need for objective data about stability and performance of MBB (e.g., 3G/4G) networks, and for tools that rigorously and scientifically assess their status. In this paper, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks. The MONROE platform enables accurate, realistic and meaningful assessment of the performance and reliability of 11 MBB networks in Europe. We report on our experience designing, implementing and testing the solution we propose for the platform. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. We describe and exemplify the capabilities of the platform and the wide variety of experiments that external users already perform using the system.",
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"paperAbstract": "In data-intensive applications, such as databases and keyvalue stores, reducing the request handling latency is important for providing better data services. In such applications, I/O-intensive background tasks, such as checkpointing, are the major culprit in worsening the latency due to the contention in shared I/O stack and storage. To minimize the contention, properly prioritizing I/Os is crucial but the effectiveness of existing approaches is limited for two reasons. First, statically deciding the priority of an I/O is insufficient since high-priority tasks can wait for low-priority I/Os due to I/O priority inversion. Second, multiple independent layers in modern storage stacks are not holistically considered by existing approacheswhich thereby fail to effectively prioritize I/Os throughout the I/O path. In this paper, we propose a request-centric I/O prioritization that dynamically detects and prioritizes I/Os delaying request handling at all layers in the I/O path. The proposed scheme is implemented on Linux and is evaluated with three applications, PostgreSQL, MongoDB, and Redis. The evaluation results show that our scheme achieves up to 53% better request throughput and 42\u00d7 better 99 percentile request latency (84 ms vs. 3581 ms), compared to the default configuration in Linux.",
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"paperAbstract": "We study two fundamental communication primitives: broadcasting and leader election in the classical model of multi-hop radio networks with unknown topology and without collision detection mechanisms. It has been known for almost 20 years that in undirected networks with n nodes and diameterD, randomized broadcasting requires\u03a9(D log n D +log n) rounds in expectation, assuming that uninformed nodes are not allowed to communicate (until they are informed). Only very recently, Haeupler and Wajc (PODC\u20192016) showed that this bound can be slightly improved for the model with spontaneous transmissions, providing an O(D log n log logn logD + log O(1) n)-time broadcasting algorithm. In this paper, we give a new and faster algorithm that completes broadcasting in O(D logn logD + log O(1) n) time, with high probability. This yields the first optimal O(D)-time broadcasting algorithm whenever D is polynomial in n. Furthermore, our approach can be applied to design a new leader election algorithm that matches the performance of our broadcasting algorithm. Previously, all fast randomized leader election algorithms have been using broadcasting as their subroutine and their complexity have been asymptotically strictly bigger than the complexity of broadcasting. In particular, the fastest previously known randomized leader election algorithm of Ghaffari and Haeupler (SODA\u20192013) requiresO(D log n D min{log logn, log n D }+ log n)-time with high probability. Our new algorithm requiresO(D logn logD+log O(1) n) time with high probability, and it achieves the optimalO(D) time wheneverD is polynomial in n. Research partially supported by the Centre for Discrete Mathematics and its Applications (DIMAP). Contact information: {A.Czumaj, P.W.Davies}@warwick.ac.uk. Phone: +44 24 7657 3796.",
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"paperAbstract": "We propose Bohr, a similarity aware geo-distributed data analytics system that minimizes query completion time. The key idea is to exploit similarity between data in different data centers (DCs), and transfer similar data from the bottleneck DC to other sites with more WAN bandwidth. Though these sites have more input data to process, these data are more similar and can be more efficiently aggregated by the combiner to reduce the intermediate data that needs to be shuffled across the WAN. Thus our similarity aware approach reduces the shuffle time and in turn the query completion time (QCT). We design and implement Bohr based on OLAP data cubes to perform efficient similarity checking among datasets in different sites. Evaluation across ten sites of AWS EC2 shows that Bohr decreases the QCT by 30% compared to state-of-the-art solutions.",
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"paperAbstract": "As the extension of Distributed Denial-of-Service (DDoS) attacks to application layer in recent years, researchers pay much interest in these new variants due to a low-volume and intermittent pattern with a higher level of stealthiness, invaliding the state-of-the-art DDoS detection/defense mechanisms. We describe a new type of low-volume application layer DDoS attack--Tail Attacks on Web Applications. Such attack exploits a newly identified system vulnerability of n-tier web applications (millibottlenecks with sub-second duration and resource contention with strong dependencies among distributed nodes) with the goal of causing the long-tail latency problem of the target web application (e.g., 95th percentile response time > 1 second) and damaging the long-term business of the service provider, while all the system resources are far from saturation, making it difficult to trace the cause of performance degradation.\n We present a modified queueing network model to analyze the impact of our attacks in n-tier architecture systems, and numerically solve the optimal attack parameters. We adopt a feedback control-theoretic (e.g., Kalman filter) framework that allows attackers to fit the dynamics of background requests or system state by dynamically adjusting attack parameters. To evaluate the practicality of such attacks, we conduct extensive validation through not only analytical, numerical, and simulation results but also real cloud production setting experiments via a representative benchmark website equipped with state-of-the-art DDoS defense tools. We further proposed a solution to detect and defense the proposed attacks, involving three stages: fine-grained monitoring, identifying bursts, and blocking bots.",
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"paperAbstract": "Packrat parsing is a popular technique for implementing top-down, unlimited-lookahead parsers that operate in guaranteed linear time. In this paper, we describe a method for turning a standard packrat parser into an <em>incremental parser</em> through a simple modification to its memoization strategy. By â\u0080\u009cincrementalâ\u0080\u009d, we mean that the parser can perform syntax analysis without completely reparsing the input after each edit operation. This makes packrat parsing suitable for interactive use in code editors and IDEs â\u0080\u0094 even with large inputs. Our experiments show that with our technique, an incremental packrat parser for JavaScript can outperform even a hand-optimized, non-incremental parser.",
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"paperAbstract": "In this paper we study network architecture for unlicensed cellular networking for outdoor coverage in TV white spaces. The main technology proposed for TV white spaces is 802.11af, a Wi-Fi variant adapted for TV frequencies. However, 802.11af is originally designed for improved indoor propagation. We show that long links, typical for outdoor use, exacerbate known Wi-Fi issues, such as hidden and exposed terminal, and significantly reduce its efficiency.\n Instead, we propose CellFi, an alternative architecture based on LTE. LTE is designed for long-range coverage and throughput efficiency, but it is also designed to operate in tightly controlled and centrally managed networks. CellFi overcomes these problems by designing an LTE-compatible spectrum database component, mandatory for TV white space networking, and introducing an interference management component for distributed coordination. CellFi interference management is compatible with existing LTE mechanisms, requires no explicit communication between base stations, and is more efficient than CSMA for long links.\n We evaluate our design through extensive real world evaluation on off-the-shelf LTE equipment and simulations. We show that, compared to 802.11af, it increases coverage by 40% and reduces median flow completion times by 2.3x.",
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"title": "Towards unlicensed cellular networks in TV white spaces",
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"paperAbstract": "Existing probabilistic privacy enforcement approaches permit the execution of a program that processes sensitive data only if the information it leaks is within the bounds specified by a given policy. Thus, to extract any information, users must manually design a program that satisfies the policy. In this work, we present a novel synthesis approach that automatically transforms a program into one that complies with a given policy. Our approach consists of two ingredients. First, we phrase the problem of determining the amount of leaked information as Bayesian inference, which enables us to leverage existing probabilistic programming engines. Second, we present two synthesis procedures that add uncertainty to the program's outputs as a way of reducing the amount of leaked information: an optimal one based on SMT solving and a greedy one with quadratic running time. We implemented and evaluated our approach on 10 representative programs from multiple application domains. We show that our system can successfully synthesize a permissive enforcement mechanism for all examples.",
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"title": "Synthesis of Probabilistic Privacy Enforcement",
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"paperAbstract": "Mobile edge cloud has been increasingly concerned by researchers due to its closer distance to mobile users than the traditional cloud on Internet. Offloading computations from mobile devices to the nearby edge cloud is an effective technique to accelerate the applications and/or save energy on the mobile devices. However, the mobile edge cloud usually has limited computation resources and constrained access bandwidth shared by multiple users in its proximity. Thus, allocation of resources and bandwidth among the users is significant to the overall application performance. In this paper, we study network aware multi-user computation partitioning problem in mobile edge clouds, i.e., to decide for each user which parts of the application should be offload onto the edge cloud, and which others should be executed locally, and meanwhile to allocate the access bandwidth among the users, such that the average application performance of the users is maximized.This problem is novel in that we consider the competition among users for both computing resources and bandwidth, and jointly optimizes the partitioning decisions with the allocation of resources and bandwidths among users, while most existing works either focus on the single user computation partitioning or study the multiple user computation partitioning without regard of the constrained network bandwidth. We first formulate the problem, and then transform it into the classic Multi-class Multi-dimensional Knapsack Problem and develop an effective algorithm, namely Performance Function Matrix based Heuristic (PFM-H), to solve it. Comprehensive simulations show that our proposed algorithm outperforms the benchmark algorithms significantly in the average application performance.",
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"paperAbstract": "Drive-Managed SMR (Shingled Magnetic Recording) disks offer a plug-compatible higher-capacity replacement for conventional disks. For non-sequential workloads, these disks show bimodal behavior: After a short period of high throughput they enter a continuous period of low throughput. We introduce ext4-lazy1, a small change to the Linux ext4 file system that significantly improves the throughput in both modes. We present benchmarks on four different drive-managed SMR disks from two vendors, showing that ext4-lazy achieves 1.7-5.4\u00d7 improvement over ext4 on a metadata-light file server benchmark. On metadata-heavy benchmarks it achieves 2-13\u00d7 improvement over ext4 on drive-managed SMR disks as well as on conventional disks.",
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"paperAbstract": "Non-Volatile Memories (NVMs) can significantly improve the performance of data-intensive applications. A popular form of NVM is Battery-backed DRAM, which is available and in use today with DRAMs latency and without the endurance problems of emerging NVM technologies. Modern servers can be provisioned with up-to 4 TB of DRAM, and provisioning battery backup to write out such large memories is hard because of the large battery sizes and the added hardware and cooling costs. We present Viyojit, a system that exploits the skew in write working sets of applications to provision substantially smaller batteries while still ensuring durability for the entire DRAM capacity. Viyojit achieves this by bounding the number of dirty pages in DRAM based on the provisioned battery capacity and proactively writing out infrequently written pages to an SSD. Even for write-heavy workloads with less skew than we observe in analysis of real data center traces, Viyojit reduces the required battery capacity to 11% of the original size, with a performance overhead of 7-25%. Thus, Viyojit frees battery-backed DRAM from stunted growth of battery capacities and enables servers with terabytes of battery-backed DRAM.",
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"paperAbstract": "Virtual Guard (Vguard) is a track-based static mapping translation layer for shingled magnetic recording (SMR) drives. Data is written in-place by caching data from the next track in the shingling direction, allowing direct overwrite of sectors in the target track. This enables Vguard to take advantage of track-level locality, nearly eliminating cleaning for many workloads. We compare performance of Vguard to an available drive-managed SMR drive analyzed and modeled in previous research. Vguard reduces the 99.9% latency by 15\u00d7 for real-world traces, and maximum latency by 32% for synthetic random write workloads.",
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"paperAbstract": "Position prediction of moving object has become a reality utilizing the vast amount of location data acquired by positioning devices embedded in mobile phones and cars. In this paper, we proposed a position prediction system which focuses on the time regularity of object moving. Historical location data of the object is used to extract personal trajectory patterns to obtain candidate next positions. Each of the candidate positions is scored by the proposed Time Mode-based Prediction (TMP) algorithm according to the proximity between the time component of patterns and current time. The position with the highest score is regarded as predicted next position. Furthermore, a hybrid B/S and C/S architecture is employed to perform the real-time prediction and results display. An evaluation based on a public trajectory data set of 12 objects demonstrates that the proposed TMP algorithm can realize position prediction with high accuracy. Moreover, the average accuracy rate of our prediction algorithm is about 85.5%, which is 33.7% greater than the Markov-based algorithm with one known position.",
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"paperAbstract": "Epilepsy is a chronic brain disorder characterized by the occurrence of spontaneous seizures of which about 30 percent of patients remain medically intractable and may undergo surgical intervention; despite the latter, some may still fail to attain a seizure-free outcome. Functional changes may precede structural ones in the epileptic brain and may be detectable using existing noninvasive modalities. Functional connectivity analysis through electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI), complemented by diffusion tensor imaging (DTI), has provided such meaningful input in cases of temporal lobe epilepsy (TLE). Recently, the emergence of edge computing has provided competent solutions enabling context-aware and real-time response services for users. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for the monitoring, evaluation and regulation of the epileptic brain, with responsive neurostimulation (RNS; Neuropace). First, an autonomic edge computing framework is proposed for processing of big data as part of a decision support system for surgical candidacy. Second, an optimized model for estimation of the epileptogenic network using independently acquired EEG and rs-fMRI is presented. Third, an unsupervised feature extraction model is developed based on a convolutional deep learning structure for distinguishing interictal epileptic discharge (IED) periods from nonIED periods using electrographic signals from electrocorticography (ECoG). Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods.",
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"title": "Deep Learning with Edge Computing for Localization of Epileptogenicity Using Multimodal rs-fMRI and EEG Big Data",
"venue": "2017 IEEE International Conference on Autonomic Computing (ICAC)",
"year": 2017
},
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"name": "Calvin C. Newport"
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"doi": "10.1145/3087801.3087813",
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"paperAbstract": "In this paper, we study the fundamental problem of gossip in the mobile telephone model: a recently introduced variation of the classical telephone model modified to better describe the local peer-to-peer communication services implemented in many popular smartphone operating systems. In more detail, the mobile telephone model differs from the classical telephone model in three ways: (1) each device can participate in at most one connection per round; (2) the network topology can undergo a parameterized rate of change; and (3) devices can advertise a parameterized number of bits about their state to their neighbors in each round before connection attempts are initiated. We begin by describing and analyzing new randomized gossip algorithms in this model under the harsh assumption of a network topology that can change completely in every round. We prove a significant time complexity gap between the case where nodes can advertise 0 bits to their neighbors in each round, and the case where nodes can advertise 1 bit. For the latter assumption, we present two solutions: the first depends on a shared randomness source, while the second eliminates this assumption using a pseudorandomness generator we prove to exist with a novel generalization of a classical result from the study of two-party communication complexity. We then turn our attention to the easier case where the topology graph is stable, and describe and analyze a new gossip algorithm that provides a substantial performance improvement for many parameters. We conclude by studying a relaxed version of gossip in which it is only necessary for nodes to each learn a specified fraction of the messages in the system. We prove that our existing algorithms for dynamic network topologies and a single advertising bit solve this relaxed version up to a polynomial factor faster (in network size) for many parameters. These are the first known gossip results for the mobile telephone model, and they significantly expand our understanding of how to communicate and coordinate in this increasingly relevant setting.",
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"title": "Gossip in a Smartphone Peer-to-Peer Network",
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"year": 2017
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"paperAbstract": "Transactional memory (TM) has been the focus of numerous studies, and it is supported in processors such as the IBM Blue Gene/Q and Intel Haswell. Many studies have used the STAMP benchmark suite to evaluate their designs. However, the speedups obtained for the STAMP benchmarks on all TM systems we know of are quite limited; for example, with 64 threads on the IBM Blue Gene/Q, we observe a median speedup of 1.4X using the Blue Gene/Q hardware transactional memory (HTM), and a median speedup of 4.1X using a software transactional memory (STM).\n What limits the performance of these benchmarks on TMs? In this paper, we argue that the problem lies with the programming model and data structures used to write them. To make this point, we articulate two principles that we believe must be embodied in any scalable program and argue that STAMP programs violate both of them. By modifying the STAMP programs to satisfy both principles, we produce a new set of programs that we call the Stampede suite. Its median speedup on the Blue Gene/Q is 8.0X when using an STM. The two principles also permit us to simplify the TM design. Using this new STM with the Stampede benchmarks, we obtain a median speedup of 17.7X with 64 threads on the Blue Gene/Q and 13.2X with 32 threads on an Intel Westmere system.\n These results suggest that HTM and STM designs will benefit if more attention is paid to the division of labor between application programs, systems software, and hardware.",
"pdfUrls": [
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"title": "What Scalable Programs Need from Transactional Memory",
"venue": "ASPLOS",
"year": 2017
},
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"name": "Himchan Park"
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"name": "Min-Soo Kim"
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"doi": "10.1145/3035918.3064014",
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"paperAbstract": "As many applications encounter exponential growth in graph sizes, a fast and scalable graph generator has become more important than ever before due to lack of large-scale realistic graphs for evaluating the performance of graph processing methods. Although there have been proposed a number of methods to generate synthetic graphs, they are not very efficient in terms of space and time complexities, and so, cannot generate even trillion-scale graphs using a moderate size cluster of commodity machines. Here, we propose an efficient and scalable disk-based graph generator, <i>TrillionG</i> that can generate massive graphs in a short time only using a small amount of memory. It can generate a graph of a trillion edges following the RMAT or Kronecker models within two hours only using 10 PCs. We first generalize existing graph generation models to the <i>scope-based generation</i> model, where RMAT and Kronecker correspond to two extremes. Then, we propose a new graph generation model called the <i>recursive vector</i> model, which compromises two extremes, and so, solves the space and time complexity problems existing in RMAT and Kronecker. We also extend the recursive vector model so as to generate a semantically richer graph database. Through extensive experiments, we have demonstrated that TrillionG outperforms the state-of-the-art graph generators by up to orders of magnitude.",
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"title": "TrillionG: A Trillion-scale Synthetic Graph Generator using a Recursive Vector Model",
"venue": "SIGMOD Conference",
"year": 2017
},
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{
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"name": "Tushar Krishna"
}
],
"doi": "10.1109/HPCA.2017.44",
"doiUrl": "https://doi.org/10.1109/HPCA.2017.44",
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"paperAbstract": "Future SoCs are expected to have irregular on-chip topologies, either at design time due to heterogeneity in the size of core/accelerator tiles, or at runtime due to link/node failures or power-gating of network elements such as routers/router datapaths. A key challenge with irregular topologies is that of routing deadlocks (cyclic dependence between buffers), since conventional XY or turn-model based approaches are no longer applicable. Most prior works in heterogeneous SoC design, resiliency, and power-gating, have addressed the deadlock problem by constructing spanning trees over the physical topology, messages are routed via the root removing cyclic dependencies. However, this comes at a cost of tree construction at runtime, and increased latency and energy for certain flows as they are forced to use non-minimal routes. In this work, we sweep the design space of possible topologies as the number of disconnected components (links/routers) increase, and demonstrate that while most of the resulting topologies are deadlock prone (i.e., have cycles), the injection rates at which they deadlock are often much higher than the injection rates of real applications, making the current solutions highly conservative. We propose a novel framework for deadlock-freedom called Static Bubble, that can be applied at design time to the underlying mesh topology, and guarantees deadlock-freedom for any runtime topology derived from this mesh due to power-gating or failure of router/link. We present an algorithm to augment a subset of routers in any n × m mesh (21 routers in a 64-core mesh) with an additional buffer called static bubble, such that any dependence chain has at least one static bubble. We also present the microarchitecture of a low-cost (less than 1% overhead) FSM at every router to activate one static bubble for deadlock recovery. Static Bubble enhances existing solutions for NoC resiliency and power-gating by providing up to 30% less network latency, 4x more throughput and 50% less EDP.",
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"http://synergy.ece.gatech.edu/wp-content/uploads/sites/332/2016/09/staticbubble_hpca2017.pdf"
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"title": "Static Bubble: A Framework for Deadlock-Free Irregular On-chip Topologies",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
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{
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"name": "Srinivas Aiyar"
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"name": "Varun Arora"
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"name": "Manosiz Bhattacharyya"
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{
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"name": "Chern Cheah"
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{
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"name": "Brent N. Chun"
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{
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"name": "Karan Gupta"
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"name": "Vinayak Khot"
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"name": "Arvind Krishnamurthy"
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"paperAbstract": "Modern cluster storage systems perform a variety of background tasks to improve the performance, availability, durability, and cost-efficiency of stored data. For example, cleaners compact fragmented data to generate long sequential runs, tiering services automatically migrate data between solid-state and hard disk drives based on usage, recovery mechanisms replicate data to improve availability and durability in the face of failures, cost saving techniques perform data transformations to reduce the storage costs, and so on. In this work, we present Curator, a background MapReduce-style execution framework for cluster management tasks, in the context of a distributed storage system used in enterprise clusters. We describe Curator\u2019s design and implementation, and evaluate its performance using a handful of relevant metrics. We further report experiences and lessons learned from its five-year construction period, as well as thousands of customer deployments. Finally, we propose a machine learning-based model to identify an efficient execution policy for Curator\u2019s management tasks that can adapt to varying workload characteristics.",
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"title": "Unleashing Use-Before-Initialization Vulnerabilities in the Linux Kernel Using Targeted Stack Spraying",
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"paperAbstract": "We present the Hippo system to enable the diagnosis of distributed machine learning (ML) pipelines by leveraging fine-grained data lineage. Hippo exposes a concise yet powerful API, derived from primitive lineage types, to capture fine-grained data lineage for each data transformation. It records the input datasets, the output datasets and the cell-level mapping between them. It also collects sufficient information that is needed to reproduce the computation. Hippo efficiently enables common ML diagnosis operations such as code debugging, result analysis, data anomaly removal, and computation replay. By exploiting the metadata separation and high-order function encoding strategies, we observe an O(10^3)x total improvement in lineage storage efficiency vs. the baseline of cell-wise mapping recording while maintaining the lineage integrity. Hippo can answer the real use case lineage queries within a few seconds, which is low enough to enable interactive diagnosis of ML pipelines.",
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"year": 2017
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"paperAbstract": "The trend of unsustainable power consumption and large memory bandwidth demands in massively parallel multicore systems, with the advent of the big data era, has brought upon the onset of alternate computation paradigms utilizing heterogeneity, specialization, processor-in-memory and approximation. Approximate Computing is being touted as a viable solution for high performance computation by relaxing the accuracy constraints of applications. This trend has been accentuated by emerging data intensive applications in domains like image/video processing, machine learning and big data analytics that allow inaccurate outputs within an acceptable variance. Leveraging relaxed accuracy for high throughput in Networks-on-Chip (NoCs), which have rapidly become the accepted method for connecting a large number of on-chip components, has not yet been explored. We propose APPROX-NoC, a hardware data approximation framework with an online data error control mechanism for high performance NoCs. APPROX-NoC facilitates approximate matching of data patterns, within a controllable value range, to compress them thereby reducing the volume of data movement across the chip.\n Our evaluation shows that APPROX-NoC achieves on average up to 9% latency reduction and 60% throughput improvement compared with state-of-the-art NoC data compression mechanisms, while maintaining low application error. Additionally, with a data intensive graph processing application we achieve a 36.7% latency reduction compared to state-of-the-art compression mechanisms.",
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"paperAbstract": "While flash-based SSDs have much higher access speed than hard disks, they have an Achilles heel, which is the service of write requests. Not only is writing slower than reading, but also it can incur expensive garbage collection operations and reduce SSDs' lifetime. The deduplication technique can help to avoid writing data objects whose contents have been on the disk. A typical object is the disk block, for which a block-level deduplication scheme can help identify duplicate ones and avoid their writing. For the technique to be effective, data written to the disk must not only be the same as those currently on the disk but also be block-aligned.\n In this work, we will show that many deduplication opportunities are lost due to block misalignment, leading to a substantially large number of unnecessary writes. As case studies, we develop a scheme to retain alignments of the data that are read from the disk in the file modifications by using small additional spaces for two important applications, a log-based key-value store (e.g., FAWN) and an LSM-tree based key-value store (e.g., LevelDB). Our experiments show that the proposed scheme can achieve up to 4.5X and 26% of throughput improvement for FAWN and LevelDB systems, respectively, with a less than 5% space overhead.",
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"paperAbstract": "The power consumed by memory system in GPUs is a significant fraction of the total chip power. As thread level parallelism increases, GPUs are likely to stress cache and memory bandwidth even more, thereby exacerbating power consumption. We observe that neighboring concurrent thread arrays (CTAs) within GPU applications share considerable amount of data. However, the default GPU scheduling policy spreads these CTAs to different streaming multiprocessor cores (SM) in a round-robin fashion. Since each SM has a private L1 cache, the shared data among CTAs are replicated across L1 caches of different SMs. Data replication reduces the effective L1 cache size which in turn increases the data movement and power consumption. The goal of this paper is to reduce data movement and increase effective cache space in GPUs. We propose a sharing-aware CTA scheduler that attempts to assign CTAs with data sharing to the same SM to reduce redundant storage of data in private L1 caches across SMs. We further enhance the scheduler with a sharing-aware cache allocation and replacement policy. The sharing-aware cache management approach dynamically classifies private and shared data. Private blocks are given higher priority to stay longer in L1 cache, and shared blocks are given higher priority to stay longer in L2 cache. Essentially, this approach increases the lifetime of shared blocks and private blocks in different cache levels. The experimental results show that the proposed scheme reduces the off-chip traffic by 19\\% which translates to an average DRAM power reduction of 10% and performance improvement of 7%.",
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"paperAbstract": "The Internet-of-Things (IoT) has quickly evolved to a new appified era where third-party developers can write apps for IoT platforms using programming frameworks. Like other appified platforms, e.g., the smartphone platform, the permission system plays an important role in platform security. However, design flaws in current IoT platform permission models have been reported recently, exposing users to significant harm such as break-ins and theft. To solve these problems, a new access control model is needed for both current and future IoT platforms. In this paper, we propose ContexIoT, a context-based permission system for appified IoT platforms that provides contextual integrity by supporting fine-grained context identification for sensitive actions, and runtime prompts with rich context information to help users perform effective access control. Context definition in ContexIoT is at the inter-procedure control and data flow levels, that we show to be more comprehensive than previous context-based permission systems for the smartphone platform. ContexIoT is designed to be backward compatible and thus can be directly adopted by current IoT platforms. We prototype ContexIoT on the Samsung SmartThings platform, with an automatic app patching mechanism developed to support unmodified commodity SmartThings apps. To evaluate the system\u2019s effectiveness, we perform the first extensive study of possible attacks on appified IoT platforms by reproducing reported IoT attacks and constructing new IoT attacks based on smartphone malware classes. We categorize these attacks based on lifecycle and adversary techniques, and build the first taxonomized IoT attack app dataset. Evaluating ContexIoT on this dataset, we find that it can effectively distinguish the attack context for all the tested apps. The performance evaluation on 283 commodity IoT apps shows that the app patching adds nearly negligible delay to the event triggering latency, and the permission request frequency is far below the threshold that is considered to risk user habituation or annoyance.",
"pdfUrls": [
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"title": "ContexloT: Towards Providing Contextual Integrity to Appified IoT Platforms",
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"year": 2017
},
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"paperAbstract": "Driven by the rising popularity of cloud storage, the costs associated with implementing reliable storage services from a collection of fault-prone servers have recently become an actively studied question. The well-known ABD result shows that an f -tolerant register can be emulated using a collection of 2f + 1 fault-prone servers each storing a single read-modify-write object, which is known to be optimal. In this paper we generalize this bound: we investigate the inherent space complexity of emulating reliable multi-writer registers as a function of the type of the base objects exposed by the underlying servers, the number of writers to the emulated register, the number of available servers, and the failure threshold. We establish a sharp separation between registers, and both max-registers (the base object type assumed by ABD) and CAS in terms of the resources (i.e., the number of base objects of the respective types) required to support the emulation; we show that no such separation exists between max-registers and CAS. Our main technical contribution is lower and upper bounds on the resources required in case the underlying base objects are fault-prone read/write registers. We show that the number of required registers is directly proportional to the number of writers and inversely proportional to the number of servers.",
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"title": "Space Complexity of Fault-Tolerant Register Emulations",
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"paperAbstract": "In this paper, we argue that in many \u201cBig Data\u201d applications, getting data into the system correctly and at scale via traditional ETL (Extract, Transform, and Load) processes is a fundamental roadblock to being able to perform timely analytics or make real-time decisions. The best way to address this problem is to build a new architecture for ETL which takes advantage of the push-based nature of a stream processing system. We discuss the requirements for a streaming ETL engine and describe a generic architecture which satisfies those requirements. We also describe our implementation of streaming ETL using a scalable messaging system (Apache Kafka), a transactional stream processing system (S-Store), and a distributed polystore (Intel\u2019s BigDAWG), as well as propose a new time-series database optimized to handle ingestion internally.",
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"paperAbstract": "The process of performance tuning is time consuming and costly even if it is carried out automatically. It is crucial to learn from the experience of experts. Our long-term goal is to construct a database of facts extracted from specific performance tuning histories of computation-intensive applications such that we can search the database for promising optimization patterns that fit a given kernel.\n In this study, as a significant step toward our goal, we explored a thousand computation-intensive applications in terms of the distribution of kernel classes, each of which is related to expected efficiency and specific tuning patterns. To statistically estimate the distribution of the kernel classes, 100 loops were randomly sampled and then manually classified by experienced performance engineers. The result indicates that 50-70% of the kernels are memory-bound and hence difficult to run efficiently on modern scalar processors. In addition, based on the classification results, we constructed experimental classifiers for identifying loop kernels and for predicting kernel classes, which achieved cross-validated classification accuracy of 81% and 65%, respectively.",
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"title": "An Empirical Study of Computation-Intensive Loops for Identifying and Classifying Loop Kernels: Full Research Paper",
"venue": "ICPE",
"year": 2017
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"paperAbstract": "Stateful middleboxes, such as intrusion detection systems and application-level firewalls, have provided key functionalities in operating modern IP networks. However, designing an efficient middlebox is challenging due to the lack of networking stack abstraction for TCP flow processing. Thus, middlebox developers often write the complex flow management logic from scratch, which is not only prone to errors, but also wastes efforts for similar functionalities across applications. This paper presents the design and implementation of mOS, a reusable networking stack for stateful flow processing in middlebox applications. Our API allows developers to focus on the core application logic instead of dealing with low-level packet/flow processing themselves. Under the hood, it implements an efficient event system that scales to monitoring millions of concurrent flow events. Our evaluation demonstrates that mOS enables modular development of stateful middleboxes, often significantly reducing development efforts represented by the source lines of code, while introducing little performance overhead in multi-10Gbps network environments.",
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"paperAbstract": "This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score. The whole model is trained end-to-end. The ranking layer learns desired feature patterns from the pairwise ranking loss. The kernels transfer the feature patterns into soft-match targets at each similarity level and enforce them on the translation matrix. The word embeddings are tuned accordingly so that they can produce the desired soft matches. Experiments on a commercial search engine's query log demonstrate the improvements of K-NRM over prior feature-based and neural-based states-of-the-art, and explain the source of K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric tailored for matching query words to document words, and provides effective multi-level soft matches.",
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"title": "End-to-End Neural Ad-hoc Ranking with Kernel Pooling",
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"paperAbstract": "Scaling the transaction throughput of decentralized blockchain ledgers such as Bitcoin and Ethereum has been an ongoing challenge. Two-party duplex payment channels have been designed and used as building blocks to construct linked payment networks, which allow atomic and trust-free payments between parties without exhausting the resources of the blockchain.\n Once a payment channel, however, is depleted (e.g., because transactions were mostly unidirectional) the channel would need to be closed and re-funded to allow for new transactions. Users are envisioned to entertain multiple payment channels with different entities, and as such, instead of refunding a channel (which incurs costly on-chain transactions), a user should be able to leverage his existing channels to rebalance a poorly funded channel.\n To the best of our knowledge, we present the first solution that allows an arbitrary set of users in a payment channel network to securely rebalance their channels, according to the preferences of the channel owners. Except in the case of disputes (similar to conventional payment channels), our solution does not require on-chain transactions and therefore increases the scalability of existing blockchains. In our security analysis, we show that an honest participant cannot lose any of its funds while rebalancing. We finally provide a proof of concept implementation and evaluation for the Ethereum network.",
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"title": "Revive: Rebalancing Off-Blockchain Payment Networks",
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"title": "D\u00e9j\u00e0Vu: a map of code duplicates on GitHub",
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"title": "APUNet: Revitalizing GPU as Packet Processing Accelerator",
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"paperAbstract": "Early reliability assessment of hardware structures using microarchitecture level simulators can effectively guide major error protection decisions in microprocessor design. Statistical fault injection on microarchitectural structures modeled in performance simulators is an accurate method to measure their Architectural Vulnerability Factor (AVF) but requires excessively long campaigns to obtain high statistical significance.\n We propose MeRLiN1, a methodology to boost microarchitecture level injection-based reliability assessment by several orders of magnitude and keep the accuracy of the assessment unaffected even for large injection campaigns with very high statistical significance. The core of MeRLiN is the grouping of faults of an initial list in equivalent classes. All faults in the same group target equivalent vulnerable intervals of program execution ending up to the same static instruction that reads the faulty entries. Faults in the same group occur in different times and entries of a structure and it is extremely likely that they all have the same effect in program execution; thus, fault injection is performed only on a few representatives from each group.\n We evaluate MeRLiN for different sizes of the physical register file, the store queue and the first level data cache of a contemporary microarchitecture running MiBench and SPEC CPU2006 benchmarks. For all our experiments, MeRLiN is from 2 to 3 orders of magnitude faster than an extremely high statistical significant injection campaign, reporting the same reliability measurements with negligible loss of accuracy. Finally, we theoretically analyze MeRLiN's statistical behavior to further justify its accuracy.",
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"title": "MeRLiN: Exploiting dynamic instruction behavior for fast and accurate microarchitecture level reliability assessment",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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"name": "Yuanjie Li"
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"name": "Zengwen Yuan"
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"paperAbstract": "Control-plane operations are indispensable to providing data access to mobile devices in the 4G LTE networks. They provision necessary control states at the device and network nodes to enable data access. However, the current design may suffer from long data access latency even under good radio conditions. The fundamental problem is that, data-plane packet delivery cannot start or resume until all control-plane procedures are completed, and these control procedures run sequentially by design. We show both are more than necessary under popular use cases. We design DPCM, which reduces data access latency through parallel processing approaches and exploiting device-side state replica. We implement DPCM and validate its effectiveness with extensive evaluations.",
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"http://metro.cs.ucla.edu/papers/mobicom17-dpcm.pdf"
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"title": "A Control-Plane Perspective on Reducing Data Access Latency in LTE Networks",
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"paperAbstract": "In the large-scale Wireless Metropolitan Area Network (WMAN) consisting of many wireless Access Points (APs),choosing the appropriate position to place cloudlet is very important for reducing the user's access delay. For service provider, it isalways very costly to deployment cloudlets. How many cloudletsshould be placed in a WMAN and how much resource eachcloudlet should have is very important for the service provider. In this paper, we study the cloudlet placement and resourceallocation problem in a large-scale Wireless WMAN, we formulatethe problem as an novel cloudlet placement problem that givenan average access delay between mobile users and the cloudlets, place K cloudlets to some strategic locations in the WMAN withthe objective to minimize the number of use cloudlet K. Wethen propose an exact solution to the problem by formulatingit as an Integer Linear Programming (ILP). Due to the poorscalability of the ILP, we devise a clustering algorithm K-Medoids(KM) for the problem. For a special case of the problem whereall cloudlets computing capabilities have been given, we proposean efficient heuristic for it. We finally evaluate the performanceof the proposed algorithms through experimental simulations. Simulation result demonstrates that the proposed algorithms areeffective.",
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"title": "DOTA: Delay Bounded Optimal Cloudlet Deployment and User Association in WMANs",
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"paperAbstract": "Moore's Law may be slowing, but, perhaps as a result, other measures of processor complexity are only accelerating. In recent years, Intel's architects have turned to an alphabet soup of instruction set extensions such as MPX, SGX, MPK, and CET as a way to sell CPUs through new security features. Unlike prior extensions, which mostly focused on accelerating user-mode data processing, these new features exhibit complex interactions and give system designers plenty to think about.\n This calls for a rethink of how we approach the instruction set. In this paper we highlight some of the challenges arising from recent security-focused extensions, and speculate about the longer-term implications.",
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"title": "Hardware is the new Software",
"venue": "HotOS",
"year": 2017
},
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"paperAbstract": "Population protocols are required to converge to the correct answer, and are subject to a fairness condition that guarantees eventual progress, but generally have no internal mechanism for detecting when this progress has occurred. We define an extension to the standard population protocol that provides each agent with a clock signal that indicates when the agent has waited long enough. To simplify the model, we represent \u201clong enough\u201d as an infinite time interval, and treat a clocked population protocol as operating over transfinite time. This gives a clean theoretical model that we show how to translate back into finite real-world executions where the clock ticks whenever the underlying protocol is looping or stuck. Over finite time intervals, the protocol behaves as in the standard model. At nonzero limit ordinals\u03c9,\u03c9 \u00b72, etc., corresponding to clock ticks, the protocol switches to a limit of previous configurations supplemented by an signal registering in an extra component in some of the agents\u2019 states. Using transfinite timesmeans that we can represent fairness over sequences of transitions that may include clock ticks with the same definition as over smaller intervals. Using arbitrary ordinals allows using times like \u03c92 or \u03c93 to represent convergence that depends on detecting convergence repeatedly at",
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"paperAbstract": "Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of a "careful" placement of micro-clouds and services over the network. To achieve this, this paper proposes to leverage state information about the network to inform service placement decisions, and to do so through a fast heuristic algorithm, which is vital to quickly react to changing conditions. To evaluate its performance, we compare our heuristic with one based on random placement in Guifi.net, the biggest CN worldwide. Our experimental results show that our heuristic consistently outperforms random placement by 211% in terms of bandwidth gain. We quantify the benefits of our heuristic on a real live video-streaming service, and demonstrate that video chunk losses decrease significantly, attaining a 37% decrease in the loss packet rate. Further, using a popular Web 2.0 service, we demonstrate that the client response times decrease up to an order of magnitude when using our heuristic.",
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"title": "Practical Service Placement Approach for Microservices Architecture",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
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"paperAbstract": "The increasingly growing data sets processed on HPC platforms raise major challenges for the underlying storage layer. A promising alternative to POSIX-IO-compliant file systems are simpler blobs (binary large objects), or object storage systems. They offer lower overhead and better performance at the cost of largely unused features such as file hierarchies or permissions. Similarly, blobs are increasingly considered for replacing distributed file systems for big data analytics or as a base for storage abstractions like key-value stores or time-series databases. This growing interest in such object storage on HPC and big data platforms raises the question: Are blobs the right level of abstraction to enable storage-based convergence between HPC and Big Data? In this paper we take a first step towards answering the question by analyzing the applicability of blobs for both platforms.",
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"title": "Could Blobs Fuel Storage-Based Convergence Between HPC and Big Data?",
"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
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"paperAbstract": "Diagnosing concurrency bugs---the process of understanding the root causes of concurrency failures---is hard. Developers depend on reproducing concurrency bugs to diagnose them. Traditionally, systems that attempt to reproduce concurrency bugs record fine-grained thread schedules of events (e.g., shared memory accesses) that lead to failures. Recording schedules incurs high runtime performance overhead and scales poorly, making existing techniques unsuitable in production.\n In this paper, we formulate the coarse interleaving hypothesis, which states that the events leading to many concurrency bugs are coarsely interleaved. Therefore, a fine-grained and expensive recording is unnecessary for diagnosing such concurrency bugs. We test the coarse interleaving hypothesis by studying 54 bugs in 13 systems and find that it holds in all cases. In particular, the time elapsed between events leading to concurrency bugs is on average 5 orders of magnitude greater than what is used today in fine-grained recording.\n Using the coarse interleaving hypothesis, we develop Lazy Diagnosis, a hybrid dynamic-static interprocedural pointer and type analysis to diagnose the root causes of concurrency bugs. Our Lazy Diagnosis prototype, Snorlax, relies on commodity hardware to track thread interleavings at a coarse granularity. Snorlax does not require any source code changes and can diagnose complex concurrency bugs in real large-scale systems (MySQL, httpd, memcached, etc.) with full accuracy and an average runtime performance overhead of below 1%. Broadly, we believe that our findings can be used to build more efficient in-production bug detection and record/replay techniques.",
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"title": "Lazy Diagnosis of In-Production Concurrency Bugs",
"venue": "SOSP",
"year": 2017
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"name": "Ester Livshits"
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"name": "Benny Kimelfeld"
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"doi": "10.1145/3034786.3056107",
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"paperAbstract": "In the traditional sense, a subset repair of an inconsistent database refers to a consistent subset of facts (tuples) that is maximal under set containment. Preferences between pairs of facts allow to distinguish a set of preferred repairs based on relative reliability (source credibility, extraction quality, recency, etc.) of data items. Previous studies explored the problem of categoricity, where one aims to determine whether preferences suffice to repair the database unambiguously, or in other words, whether there is precisely one preferred repair. In this paper we study the ability to quantify ambiguity, by investigating two classes of problems. The first is that of counting the number of subset repairs, both preferred (under various common semantics) and traditional. We establish dichotomies in data complexity for the entire space of (sets of) functional dependencies. The second class of problems is that of enumerating (i.e., generating) the preferred repairs. We devise enumeration algorithms with efficiency guarantees on the delay between generated repairs, even for constraints represented as general conflict graphs or hypergraphs.",
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"title": "Counting and Enumerating (Preferred) Database Repairs",
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"year": 2017
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"name": "Yezhou Huang"
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"name": "Jeffrey S. Chase"
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"name": "Jong Youl Choi"
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"doi": "10.1145/3078597.3078614",
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"paperAbstract": "In this paper, we develop a predictive model useful for output performance prediction of supercomputer file systems under production load. Our target environment is Titan---the 3rd fastest supercomputer in the world---and its Lustre-based multi-stage write path. We observe from Titan that although output performance is highly variable at small time scales, the mean performance is stable and consistent over typical application run times. Moreover, we find that output performance is non-linearly related to its correlated parameters due to interference and saturation on individual stages on the path. These observations enable us to build a predictive model of expected write times of output patterns and I/O configurations, using feature transformations to capture non-linear relationships. We identify the candidate features based on the structure of the Lustre/Titan write path, and use feature transformation functions to produce a model space with 135,000 candidate models. By searching for the minimal mean square error in this space we identify a good model and show that it is effective.",
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"title": "Predicting Output Performance of a Petascale Supercomputer",
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"year": 2017
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"name": "Ugur Demiryurek"
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"paperAbstract": "Due to the recent vast availability of transportation traffic data, major research efforts have been devoted to traffic prediction, which is useful in many applications such as urban planning, traffic management and navigations systems. Current prediction methods that independently train a model per traffic sensor cannot accurately predict traffic in every situation (e.g., rush hours, constructions and accidents) because there may not exist sufficient training samples per sensor for all situations. To address this shortcoming, our core idea is to explore the commonalities of prediction tasks across multiple sensors who behave similarly in a specific traffic situation. Instead of building a model independently per sensor, we propose a Multi-Task Learning (MTL) framework that aims to first automatically identify the traffic situations and then simultaneously build one forecasting model for similar-behaving sensors per traffic situation. The key innovation here is that instead of the straightforward application of MTL where each "task" corresponds to a sensor, we relate each MTL's "task" to a traffic situation. Specifically, we first identify these traffic situations by running clustering algorithms on all sensors' data. Subsequently, to enforce the commonalities under each identified situation, we use the group Lasso regularization in MTL to select a common set of features for the prediction tasks, and we adapt efficient FISTA algorithm with guaranteed convergence rate. We evaluated our methods with a large volume of real-world traffic sensor data; our results show that by incorporating traffic situations, our proposed MTL framework performs consistently better than naively applying MTL per sensor. Moreover, our holistic approach, under different traffic situations, outperforms all the best traffic prediction approaches for a given situation by up to 18% and 30% in short and long term predictions, respectively.",
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"title": "Situation Aware Multi-task Learning for Traffic Prediction",
"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
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"paperAbstract": "The exploitation of user search queries by search engines is at the heart of their economic model. As consequence, offering private Web search functionalities is essential to the users who care about their privacy. Nowadays, there exists no satisfactory approach to enable users to access search engines in a privacy-preserving way. Existing solutions are either too costly due to the heavy use of cryptographic mechanisms (e.g., private information retrieval protocols), subject to attacks (e.g., Tor, TrackMeNot, GooPIR) or rely on weak adversarial models (e.g., PEAS). This paper introduces X-S<scp>earch</scp>, a novel private Web search mechanism building on the disruptive Software Guard Extensions (SGX) proposed by Intel. We compare X-S<scp>earch</scp> to its closest competitors, Tor and PEAS, using a dataset of real web search queries. Our evaluation shows that: (1) X-S<scp>earch</scp> offers stronger privacy guarantees than its competitors as it operates under a stronger adversarial model; (2) it better resists state-of-the-art re-identification attacks; and (3) from the performance perspective, X-S<scp>earch</scp> outperforms its competitors both in terms of latency and throughput by orders of magnitude.",
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"paperAbstract": "This paper presents ProRace, a dynamic data race detector practical for production runs. It is lightweight, but still offers high race detection capability. To track memory accesses, ProRace leverages instruction sampling using the performance monitoring unit (PMU) in commodity processors. Our PMU driver enables ProRace to sample more memory accesses at a lower cost compared to the state-of-the-art Linux driver. Moreover, ProRace uses PMU-provided execution contexts including register states and program path, and reconstructs unsampled memory accesses offline. This technique allows \\ProRace to overcome inherent limitations of sampling and improve the detection coverage by performing data race detection on the trace with not only sampled but also reconstructed memory accesses. Experiments using racy production software including <i>apache</i> and <i>mysql</i> shows that, with a reasonable offline cost, ProRace incurs only 2.6% overhead at runtime with 27.5% detection probability with a sampling period of 10,000.",
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"title": "ProRace: Practical Data Race Detection for Production Use",
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"paperAbstract": "Accurate, real-time Automatic Speech Recognition (ASR) comes at a high energy cost, so accuracy has often to be sacrificed in order to fit the strict power constraints of mobile systems. However, accuracy is extremely important for the end-user, and today's systems are still unsatisfactory for many applications. The most critical component of an ASR system is the acoustic scoring, as it has a large impact on the accuracy of the system and takes up the bulk of execution time. The vast majority of ASR systems implement the acoustic scoring by means of Gaussian Mixture Models (GMMs), where the acoustic scores are obtained by evaluating multidimensional Gaussian distributions.In this paper, we propose a hardware accelerator for GMM evaluation that reduces the energy required for acoustic scoring by three orders of magnitude compared to solutions based on CPUs and GPUs. Our accelerator implements a lazy evaluation scheme where Gaussians are computed on demand, avoiding 50% of the computations. Furthermore, it employs a novel clustering scheme to reduce the size of the acoustic model, which results in 8x memory bandwidth savings with a negligible impact on accuracy. Finally, it includes a novel memoization scheme that avoids 74.88% of floating-point operations. The end design provides a 164x speedup and 3532x energy reduction when compared with a highly-tuned implementation running on a modern mobile CPU. Compared to a state-of-the-art mobile GPU, the GMM accelerator achieves 5.89x speedup over a highly optimized CUDA implementation, while reducing energy by 241x.",
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"http://doi.ieeecomputersociety.org/10.1109/PACT.2017.11"
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"title": "An Ultra Low-Power Hardware Accelerator for Acoustic Scoring in Speech Recognition",
"venue": "2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT)",
"year": 2017
},
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"name": "David R. Choffnes"
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"name": "Cristina Nita-Rotaru"
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"doi": "10.1145/3131365.3131368",
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"paperAbstract": "Google's QUIC protocol, which implements TCP-like properties at the application layer atop a UDP transport, is now used by the vast majority of Chrome clients accessing Google properties but has no formal state machine specification, limited analysis, and ad-hoc evaluations based on snapshots of the protocol implementation in a small number of environments. Further frustrating attempts to evaluate QUIC is the fact that the protocol is under rapid development, with extensive rewriting of the protocol occurring over the scale of months, making individual studies of the protocol obsolete before publication.\n Given this unique scenario, there is a need for alternative techniques for understanding and evaluating QUIC when compared with previous transport-layer protocols. First, we develop an approach that allows us to conduct analysis across multiple versions of QUIC to understand how code changes impact protocol effectiveness. Next, we instrument the source code to infer QUIC's state machine from execution traces. With this model, we run QUIC in a large number of environments that include desktop and mobile, wired and wireless environments and use the state machine to understand differences in transport- and application-layer performance across multiple versions of QUIC and in different environments. QUIC generally outperforms TCP, but we also identified performance issues related to window sizes, re-ordered packets, and multiplexing large number of small objects; further, we identify that QUIC's performance diminishes on mobile devices and over cellular networks.",
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"year": 2017
},
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"name": "Byoungyoung Lee"
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"name": "Dongsu Han"
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"paperAbstract": "Traditional execution environments deploy Address Space Layout Randomization (ASLR) to defend against memory corruption attacks. However, Intel Software Guard Extension (SGX), a new trusted execution environment designed to serve security-critical applications on the cloud, lacks such an effective, well-studied feature. In fact, we find that applying ASLR to SGX programs raises non-trivial issues beyond simple engineering for a number of reasons: 1) SGX is designed to defeat a stronger adversary than the traditional model, which requires the address space layout to be hidden from the kernel; 2) the limited memory uses in SGX programs present a new challenge in providing a sufficient degree of entropy; 3) remote attestation conflicts with the dynamic relocation required for ASLR; and 4) the SGX specification relies on known and fixed addresses for key data structures that cannot be randomized. This paper presents SGX-Shield, a new ASLR scheme designed for SGX environments. SGX-Shield is built on a secure in-enclave loader to secretly bootstrap the memory space layout with a finer-grained randomization. To be compatible with SGX hardware (e.g., remote attestation, fixed addresses), SGX-Shield is designed with a software-based data execution protection mechanism through an LLVM-based compiler. We implement SGX-Shield and thoroughly evaluate it on real SGX hardware. It shows a high degree of randomness in memory layouts and stops memory corruption attacks with a high probability. SGX-Shield shows 7.61% performance overhead in running common microbenchmarks and 2.25% overhead in running a more realistic workload of an HTTPS server.",
"pdfUrls": [
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"https://taesoo.gtisc.gatech.edu/pubs/2017/seo:sgx-shield.pdf",
"https://www.ndss-symposium.org/ndss2017/ndss-2017-programme/sgx-shield-enabling-address-space-layout-randomization-sgx-programs/"
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"title": "SGX-Shield: Enabling Address Space Layout Randomization for SGX Programs",
"venue": "NDSS",
"year": 2017
},
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"authors": [
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"name": "Rachid Guerraoui"
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"name": "Anne-Marie Kermarrec"
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"doi": "10.1145/3077136.3080783",
"doiUrl": "https://doi.org/10.1145/3077136.3080783",
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"paperAbstract": "Recommenders are becoming one of the main ways to navigate the Internet. They recommend appropriate items to users based on their <i>clicks</i>, i.e., <i>likes, ratings, purchases</i>, etc. These clicks are key to providing relevant recommendations and, in this sense, have a significant <i>utility</i>. Since clicks reflect the preferences of users, they also raise <i>privacy</i> concerns. At first glance, there seems to be an inherent trade-off between the utility and privacy effects of a click. Nevertheless, a closer look reveals that the situation is more subtle: some clicks do improve utility without compromising privacy, whereas others decrease utility while hampering privacy.\n In this paper, for the first time, we propose a way to quantify the exact utility and privacy effects of each user click. More specically, we show how to compute the privacy effect (<i>disclosure risk</i>) of a click using an <i>information-theoretic</i> approach, as well as its <i>utility</i>, using a <i>commonality-based</i> approach. We determine precisely when utility and privacy are antagonist and when they are not. To illustrate our metrics, we apply them to recommendation traces from Movielens and Jester datasets. We show, for instance, that, considering the Movielens dataset, 5.94% of the clicks improve the recommender utility without loss of privacy, whereas 16.43% of the clicks induce a high privacy risk without any utility gain.\n An appealing application of our metrics is what we call a <i>click-advisor</i>, a visual user-aware clicking platform that helps users decide whether it is actually worth clicking on an item or not (after evaluating its potential utility and privacy effects using our techniques). Using a <i>game-theoretic</i> approach, we evaluate several user clicking strategies. We highlight in particular what we define as a <i>smart</i> strategy, leading to a Nash equilibrium, where every user reaches the maximum possible privacy while preserving the average overall recommender utility for all users (with respect to the case where user clicks are based solely on their genuine preferences, i.e., without consulting the click-advisor).",
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"title": "The Utility and Privacy Effects of a Click",
"venue": "SIGIR",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Tae Joon Jun"
},
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"name": "Myong Hwan Yoo"
},
{
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"name": "Daeyoung Kim"
},
{
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"name": "Kyu Tae Cho"
},
{
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"name": "Seung Young Lee"
},
{
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"name": "Kyuoke Yeun"
}
],
"doi": "10.1145/3030207.3044531",
"doiUrl": "https://doi.org/10.1145/3030207.3044531",
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"Application programming interface",
"Data Distribution Service",
"Desktop virtualization",
"Graphics processing unit",
"InfiniBand",
"Interlock (engineering)",
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"Quad data rate",
"Radar",
"Scalability",
"Virtual machine",
"X86 virtualization"
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"paperAbstract": "Tactical Operations Center (TOC) system in military field is an advanced computer system composed of multiple servers and desktops to interlock internal/external weapon systems processing mission-critical applications in combat situation. However, the current TOC system has several limitations such as difficulty of integrating tactical weapon systems including missile launch system and radar system into the single TOC system due to the heterogeneity of HW and SW between systems, and an inefficient computing resource management for the weapon systems.\n In this paper, we proposed a novel HPC supported mission-critical Cloud architecture as TOC for Surface-to-Air-Missile (SAM) system with OpenStack Cloud OS, Data Distribution Service (DDS), and GPU virtualization techniques. With this approach, our system provides elastic resource management over the weapon systems with virtual machines, integration of heterogeneous systems with different kinds of guest OS, real-time, reliable, and high-speed communication between the virtual machines and virtualized GPU resource over the virtual machines. Evaluation of our TOC system includes DDS performance measurement over 10Gbps Ethernet and QDR InfiniBand networks on the virtualized environment with OpenStack Cloud OS, and GPU virtualization performance evaluation with two different methods, PCI pass-through and remote-API. With the evaluation results, we conclude that our system provides reasonable performance in the combat situation compared to the previous TOC system while additionally supports scalable and elastic use of computing resource through the virtual machines.",
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"title": "HPC Supported Mission-Critical Cloud Architecture",
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"year": 2017
},
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"name": "Gang Tan"
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"name": "Trent Jaeger"
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"paperAbstract": "Partitioning a security-sensitive application into least-privileged components and putting each into a separate protection domain have long been a goal of security practitioners and researchers. However, a stumbling block to automatically partitioning C/C++ applications is the presence of pointers in these applications. Pointers make calculating data dependence, a key step in program partitioning, difficult and hard to scale; furthermore, C/C++ pointers do not carry bounds information, making it impossible to automatically marshall and unmarshall pointer data when they are sent across the boundary of partitions. In this paper, we propose a set of techniques for supporting general pointers in automatic program partitioning. Our system, called PtrSplit, constructs a Program Dependence Graph (PDG) for tracking data and control dependencies in the input program and employs a parameter-tree approach for representing data of pointer types; this approach is modular and avoids global pointer analysis. Furthermore, it performs selective pointer bounds tracking to enable automatic marshalling/unmarshalling of pointer data, even when there is circularity and arbitrary aliasing. As a result, PtrSplit can automatically generate executable partitions for C applications that contain arbitrary pointers.",
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"paperAbstract": "As GPUs become more pervasive in both scalable high-performance computing systems and safety-critical embedded systems, evaluating and analyzing their resilience to soft errors caused by high-energy particle strikes will grow increasingly important. GPU designers must develop tools and techniques to understand the effect of these soft errors on applications. This paper presents an error injection-based methodology and tool called SASSIFI to study the soft error resilience of massively parallel applications running on state-of-the-art NVIDIA GPUs. Our approach uses a low-level assembly-language instrumentation tool called SASSI to profile and inject errors. SASSI provides efficiency by allowing instrumentation code to execute entirely on the GPU and provides the ability to inject into different architecture-visible state. For example, SASSIFI can inject errors in general-purpose registers, GPU memory, condition code registers, and predicate registers. SASSIFI can also inject errors into addresses and register indices. In this paper, we describe the SASSIFI tool, its capabilities, and present experiments to illustrate some of the analyses SASSIFI can be used to perform.",
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"paperAbstract": "Entity resolution in settings with rich relational structure often introduces complex dependencies between co-references. Exploiting these dependencies is challenging - it requires seamlessly combining statistical, relational, and logical dependencies. One task of particular interest is entity resolution in familial networks. In this setting, multiple partial representations of a family tree are provided, from the perspective of different family members, and the challenge is to reconstruct a family tree from these multiple, noisy, partial views. This reconstruction is crucial for applications such as understanding genetic inheritance, tracking disease contagion, and performing census surveys. Here, we design a model that incorporates statistical signals, such as name similarity, relational information, such as sibling overlap, and logical constraints, such as transitivity and bijective matching, in a collective model. We show how to integrate these features using probabilistic soft logic, a scalable probabilistic programming framework. In experiments on real-world data, our model significantly outperforms state-of-the-art classifiers that use relational features but are incapable of collective reasoning.",
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"title": "Cryptographically Secure Information Flow Control on Key-Value Stores",
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"doi": "10.1109/CLUSTER.2017.30",
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"title": "Extending Skel to Support the Development and Optimization of Next Generation I/O Systems",
"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
"year": 2017
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"paperAbstract": "We propose a new class of <i>post-quantum</i> digital signature schemes that: (a) derive their security entirely from the security of symmetric-key primitives, believed to be quantum-secure, and (b) have extremely small keypairs, and, (c) are highly parameterizable.\n In our signature constructions, the public key is an image <i>y</i>=<i>f</i>(<i>x</i>) of a one-way function <i>f</i> and secret key <i>x</i>. A signature is a non-interactive zero-knowledge proof of <i>x</i>, that incorporates a message to be signed. For this proof, we leverage recent progress of Giacomelli et al. (USENIX'16) in constructing an efficient Σ-protocol for statements over general circuits. We improve this Σ-protocol to reduce proof sizes by a factor of two, at no additional computational cost. While this is of independent interest as it yields more compact proofs for any circuit, it also decreases our signature sizes.\n We consider two possibilities to make the proof non-interactive: the Fiat-Shamir transform and Unruh's transform (EUROCRYPT'12, '15,'16). The former has smaller signatures, while the latter has a security analysis in the quantum-accessible random oracle model. By customizing Unruh's transform to our application, the overhead is reduced to 1.6x when compared to the Fiat-Shamir transform, which does not have a rigorous post-quantum security analysis.\n We implement and benchmark both approaches and explore the possible choice of <i>f</i>, taking advantage of the recent trend to strive for practical symmetric ciphers with a particularly low number of multiplications and end up using Low MC (EUROCRYPT'15).",
"pdfUrls": [
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"http://doi.acm.org/10.1145/3133956.3133997",
"http://ramacher.at/_static/talks/cryptosymposium.pdf",
"http://eprint.iacr.org/2017/279"
],
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"title": "Post-Quantum Zero-Knowledge and Signatures from Symmetric-Key Primitives",
"venue": "CCS",
"year": 2017
},
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"name": "Gokcen Kestor"
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"name": "Sriram Krishnamoorthy"
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"name": "Wenjing Ma"
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],
"doi": "10.1109/IPDPS.2017.75",
"doiUrl": "https://doi.org/10.1109/IPDPS.2017.75",
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"paperAbstract": "Nested fork-join programs scheduled using work stealing can automatically balance load and adapt to changes in the execution environment. In this paper, we design an approach to efficiently recover from faults encountered by these programs. Specifically, we focus on localized recovery of the task space in the presence of fail-stop failures. We present an approach to efficiently track, under work stealing, the relationships between the work executed by various threads. This information is used to identify and schedule the tasks to be re-executed without interfering with normal task execution. The algorithm precisely computes the work lost, incurs minimal re-execution overhead, and can recover from an arbitrary number of failures. Experimental evaluation demonstrates low overheads in the absence of failures, recovery overheads on the same order as the lost work, and much lower recovery costs than alternative strategies.",
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"title": "Will Serverless End the Dominance of Linux in the Cloud?",
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"year": 2017
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"title": "How secure is your cache against side-channel attacks?",
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"year": 2017
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"paperAbstract": "Third-party libraries are commonly used by app developers for alleviating the development efforts and for monetizing their apps. On Android, the host app and its third-party libraries reside in the same sandbox and share all privileges awarded to the host app by the user, putting the users' privacy at risk of intrusions by third-party libraries. In this paper, we introduce a new privilege separation approach for third-party libraries on stock Android. Our solution partitions Android applications at compile-time into isolated, privilege-separated compartments for the host app and the included third-party libraries. A particular benefit of our approach is that it leverages compiler-based instrumentation available on stock Android versions and thus abstains from modification of the SDK, the app bytecode, or the device firmware. A particular challenge for separating libraries from their host apps is the reconstruction of the communication channels and the preservation of visual fidelity between the now separated app and its libraries. We solve this challenge through new IPC-based protocols to synchronize layout and lifecycle management between different sandboxes. Finally, we demonstrate the efficiency and effectiveness of our solution by applying it to real world apps from the Google Play Store that contain advertisements.",
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"title": "The ART of App Compartmentalization: Compiler-based Library Privilege Separation on Stock Android",
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"title": "BL-MNE: Emerging Heterogeneous Social Network Embedding Through Broad Learning with Aligned Autoencoder",
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"paperAbstract": "This paper promotes convertible data center network architectures, which can dynamically change the network topology to combine the benefits of multiple architectures. We propose the flat-tree prototype architecture as the first step to realize this concept. Flat-tree can be implemented as a Clos network and later be converted to approximate random graphs of different sizes, thus achieving both Clos-like implementation simplicity and random-graph-like transmission performance. We present the detailed design for the network architecture and the control system. Simulations using real data center traffic traces show that flat-tree is able to optimize various workloads with different topology options. We implement an example flat-tree network on a 20-switch 24-server testbed. The traffic reaches the maximal throughput in 2.5s after a topology change, proving the feasibility of converting topology at run time. The network core bandwidth is increased by 27.6% just by converting the topology from Clos to approximate random graph. This improvement can be translated into acceleration of applications as we observe reduced communication time in Spark and Hadoop jobs.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3098822.3098837",
"https://www.cs.rice.edu/~eugeneng/papers/SIGCOMM17.pdf"
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"title": "A Tale of Two Topologies: Exploring Convertible Data Center Network Architectures with Flat-tree",
"venue": "SIGCOMM",
"year": 2017
},
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"name": "Nico D\u00f6ttling"
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"name": "Satrajit Ghosh"
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"doi": "10.1145/3133956.3134024",
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"paperAbstract": "We introduce a new approach to actively secure two-party computation based on so-called oblivious linear function evaluation (OLE), a natural generalisation of oblivious transfer (OT) and a special case of the notion of oblivious polynomial evaluation introduced by Naor and Pinkas at STOC 1999. OLE works over a finite field F. In an OLE the sender inputs two field elements <i>a</i> ƒ F and <i>b</i> ƒ F, and the receiver inputs a field element <i>x</i> ∈ F and learns only ƒ<i>x</i>) = <i>ax</i> + <i>b</i>. Our protocol can evaluate an arithmetic circuit over a finite field F given black-box access to OLE for F. The protocol is unconditionally secure and consumes only a constant number of OLEs per multiplication gate. An OLE over a field F of size <i>O</i>(2<sup>κ</sup>) be implemented with communication <i>O</i>(κ). This gives a protocol with communication complexity <i>O</i>(<i>C</i> κ) for large enough fields, where <i>C</i> is an arithmetic circuit computing the desired function.\n This asymptotically matches the best previous protocols, but our protocol at the same time obtains significantly smaller constants hidden by the big-O notation, yielding a highly practical protocol. Conceptually our techniques lift the techniques for basing practical actively secure 2PC of Boolean circuits on OT introduced under the name TinyOT by Nielsen, Nordholt, Orlandi and Burra at Crypto 2012 to the arithmetic setting. In doing so we develop several novel techniques for generating various flavours of OLE and combining these.\n We believe that the efficiency of our protocols, both in asymptotic and practical terms, establishes OLE and its variants as an important foundation for efficient actively secure 2PC.",
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"title": "TinyOLE: Efficient Actively Secure Two-Party Computation from Oblivious Linear Function Evaluation",
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"year": 2017
},
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"paperAbstract": "Virtual Data Center (VDC) is becoming the real world tendency of IT model that offers flexible and on-demand service, most of which adopt Software-Defined Networking (SDN) as their network architecture to simplify network management and ensure network isolation. In our paper, by following our Just-in-Time Architecture (JITA) design, we further studied the composable architecture in optical domain. For this architecture to work efficiently, Terabit per second (Tbps) bandwidth and negligible latency (i.e., less than microsecond latency) are needed to support disaggregated infrastructures. Therefore, in this paper, we present an all-optical packets switch solution with proposed SDN controlling scheme. The modeling of the all-optical switch is also proposed, implemented and studied. Based on the modeling of the switch, the output Optical Signal-to-Noise Ratio (OSNR) and latency performance are studied. With the result of the simulation, we find the limits and dominated factors for designing the large scale all-optical switch in the composable data centers. An example of analyzing the maximum throughput and the latency of the all-optical switch is given at the end.",
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"title": "An Optical Interconnect Network Design for Dynamically Composable Data Centers",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"paperAbstract": "Broadcast operations (e.g. MPI_Bcast) have been widely used in deep learning applications to exchange a large amount of data among multiple graphics processing units (GPUs). Recent studies have shown that leveraging the InfiniBand hardware-based multicast (IB-MCAST) protocol can enhance scalability of GPU-based broadcast operations. However, these initial designs with IB-MCAST are not optimized for multi-source broadcast operations with large messages, which is the common communication scenario for deep learning applications. In this paper, we first model existing broadcast schemes and analyze their performance bottlenecks on GPU clusters. Then, we propose a novel broadcast design based on message streaming to better exploit IB-MCAST and NVIDIA GPUDirect RDMA (GDR) technology for efficient large message transfer operation. The proposed design can provide high overlap among multi-source broadcast operations. Experimental results show up to 68% reduction of latency compared to state-of-the-art solutions in a benchmark-level evaluation. The proposed design also shows near-constant latency for a single broadcast operation as a system grows. Furthermore, it yields up to 24% performance improvement in the popular deep learning framework, Microsoft CNTK, which uses multi-source broadcast operations; notably, the performance gains are achieved without modifications to applications. Our model validation shows that the proposed analytical model and experimental results match within a 10% range. Our model also predicts that the proposed design outperforms existing schemes for multi-source broadcast scenarios with increasing numbers of broadcast sources in large-scale GPU clusters.",
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"paperAbstract": "In this paper, we describe the GUIDE framework used to collect, federate, and analyze log data from the Oak Ridge Leadership Computing Facility (OLCF), and how we use that data to derive insights into facility operations. We collect system logs and extract monitoring data at every level of the various OLCF subsystems, and have developed a suite of pre-processing tools to make the raw data consumable. The cleansed logs are then ingested and federated into a central, scalable data warehouse, Splunk, that offers storage, indexing, querying, and visualization capabilities. We have further developed and deployed a set of tools to analyze these multiple disparate log streams in concert and derive operational insights. We describe our experience from developing and deploying the GUIDE infrastructure, and deriving valuable insights on the various subsystems, based on two years of operations in the production OLCF environment.",
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"title": "GUIDE: a scalable information directory service to collect, federate, and analyze logs for operational insights into a leadership HPC facility",
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"title": "It's Time to Think About an Operating System for Near Data Processing Architectures",
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"paperAbstract": "Recent work have used both failure logs and resource use data separately (and together) to detect system failureinducing errors and to diagnose system failures. System failure occurs as a result of error propagation and the (unsuccessful) execution of error recovery mechanisms. Knowledge of error propagation patterns and unsuccessful error recovery is important for more accurate and detailed failure diagnosis, and knowledge of recovery protocols deployment is important for improving system reliability. This paper presents the CORRMEXT framework which carries failure diagnosis another significant step forward by analyzing and reporting error propagation patterns and degrees of success and failure of error recovery protocols. CORRMEXT uses both error messages and resource use data in its analyses. Application of CORRMEXT to data from the Ranger supercomputer have produced new insights. CORRMEXT has: (i) identified correlations between resource use counters that capture recovery attempts after an error, (ii) identified correlations between error events to capture error propagation patterns within the system, (iii) identified error propagation and recovery paths during system execution to explain system behaviour, (iv) showed that the earliest times of change in system behaviour can only be identified by analyzing both the correlated resource use counters and correlated errors. CORRMEXT will be installed on the HPC clusters at the Texas Advanced Computing Center in Autumn 2017.",
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"paperAbstract": "Energy-based billing as well as energy-efficient software require accurate knowledge of energy consumption. Model-based energy accounting and external measurement hardware are the main methods to obtain energy data, but cost and the need for frequent recalibration have impeded their large-scale adoption. Running Average Power Limit (RAPL) by Intel R \u00a9 enables non-intrusive, off-the-shelf energy monitoring, but only on a per-socket level. To enable apportioning of energy to individual applications we present E-Team, a non-intrusive, scheduler-based, easy-to-use energy-accounting mechanism. By leveraging RAPL, our method can be used on any Intel system built after 2011 without the need for external infrastructure, application modification, or model calibration. E-Team allows starting and stopping measurements at arbitrary points in time while maintaining a low performance overhead. E-Team provides high accuracy, compared to external instrumentation, with an error of less than 3.5 %.",
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"paperAbstract": "The increasing popularity of adblockers has prompted online publishers to retaliate against adblock users by deploying anti-adblock scripts, which detect adblock users and bar them from accessing content unless they disable their adblocker. To circumvent anti-adblockers, adblockers rely on manually curated anti-adblock filter lists for removing anti-adblock scripts. Anti-adblock filter lists currently rely on informal crowdsourced feedback from users to add/remove filter list rules. In this paper, we present the first comprehensive study of anti-adblock filter lists to analyze their effectiveness against anti-adblockers. Specifically, we compare and contrast the evolution of two popular anti-adblock filter lists. We show that these filter lists are implemented very differently even though they currently have a comparable number of filter list rules. We then use the Internet Archive's Wayback Machine to conduct a retrospective coverage analysis of these filter lists on Alexa top-5K websites over the span of last five years. We find that the coverage of these filter lists has considerably improved since 2014 and they detect anti-adblockers on about 9% of Alexa top-5K websites. To improve filter list coverage and speedup addition of new filter rules, we also design and implement a machine learning based method to automatically detect anti-adblock scripts using static JavaScript code analysis.",
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"title": "The ad wars: retrospective measurement and analysis of anti-adblock filter lists",
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"year": 2017
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"paperAbstract": "In native Linux systems, spinlock's implementation relies on the assumption that both the lock holder thread and lock waiter threads cannot be preempted. However, in a virtualized environment, these threads are scheduled on top of virtual CPUs (vCPU) that can be preempted by the hypervisor at any time, thus forcing lock waiter threads on other vCPUs to busy wait and to waste CPU cycles. This leads to the well-known Lock Holder Preemption (LHP) and Lock Waiter Preemption (LWP) issues.\n In this paper, we propose I-Spinlock (for Informed Spinlock), a new spinlock implementation for virtualized environments. Its main principle is to only allow a thread to acquire a lock if and only if the remaining time-slice of its vCPU is sufficient to enter and leave the critical section. This is possible if the spinlock primitive is aware (informed) of its time-to-preemption (by the hypervisor).\n We implemented I-Spinlock in the Xen virtualization system. We show that our solution is compliant with both para-virtual and hardware virtualization modes. We performed extensive performance evaluations with various reference benchmarks and compared our solution to previous solutions. The evaluations demonstrate that I-Spinlock outperforms other solutions, and more significantly when the number of core increases.",
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"title": "The lock holder and the lock waiter pre-emption problems: nip them in the bud using informed spinlocks (I-Spinlock)",
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"year": 2017
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"name": "Dzung T. Phan"
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"paperAbstract": "This paper proposes a new framework for anomaly detection when collectively monitoring many complex systems. The prerequisite for condition-based monitoring in industrial applications is the capability of (1) capturing multiple operational states, (2) managing many similar but different assets, and (3) providing insights into the internal relationship of the variables. To meet these criteria, we propose a multi-task learning approach based on a sparse mixture of sparse Gaussian graphical models (GGMs). Unlike existing fused- and group-lasso-based approaches, each task is represented by a sparse mixture of sparse GGMs, and can handle multi-modalities. We develop a variational inference algorithm combined with a novel sparse mixture weight selection algorithm. To handle issues in the conventional automatic relevance determination (ARD) approach, we propose a new ℓ0-regularized formulation that has guaranteed sparsity in mixture weights. We show that our framework eliminates well-known issues of numerical instability in the iterative procedure of mixture model learning. We also show better performance in anomaly detection tasks on real-world data sets. To the best of our knowledge, this is the first proposal of multi-task GGM learning allowing multi-modal distributions.",
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"title": "Multi-task Multi-modal Models for Collective Anomaly Detection",
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"paperAbstract": "In this paper we consider the data caching problem in next generation data services in the cloud, which is characterized by using monetary cost and access trajectory information to control cache replacements, instead of exploiting capacityoriented strategies as in traditional research. In particular, given a stream of requests to a shared data item with respect to a homogeneous cost model, we first propose a fast off-line algorithm using dynamic programming techniques. The proposed algorithm can generate optimal schedule within O(mn) timespace complexity to cache, migrate as well as replicate the shared data item to serve an n-length request sequence with minimum cost in a fully connected m-node network, substantially improving the previous results. Additionally, we also study this problem in its online form, and present a 3-competitive online algorithm by leveraging a speculative caching idea. The algorithm can serve an online request in constant time, and is space efficient in O(m) as well, rendering it to be more practical in reality. Our research complements the shortage of similar research in literature on this problem.",
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"doi": "10.1109/SP.2017.33",
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"paperAbstract": "Authorization bugs, when present in online social networks, are usually caused by missing or incorrect authorization checks and can allow attackers to bypass the online social network's protections. Unfortunately, there is no practical way to fully guarantee that an authorization bug will never be introduced—even with good engineering practices—as a web application and its data model become more complex. Unlike other web application vulnerabilities such as XSS and CSRF, there is no practical general solution to prevent missing or incorrect authorization checks. In this paper we propose Invariant Detector (IVD), a defense-in-depth system that automatically learns authorization rules from normal data manipulation patterns and distills them into likely invariants. These invariants, usually learned during the testing or pre-release stages of new features, are then used to block any requests that may attempt to exploit bugs in the social network's authorization logic. IVD acts as an additional layer of defense, working behind the scenes, complementary to privacy frameworks and testing. We have designed and implemented IVD to handle the unique challenges posed by modern online social networks. IVD is currently running at Facebook, where it infers and evaluates daily more than 200,000 invariants from a sample of roughly 500 million client requests, and checks the resulting invariants every second against millions of writes made to a graph database containing trillions of entities. Thus far IVD has detected several high impact authorization bugs and has successfully blocked attempts to exploit them before code fixes were deployed.",
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"title": "IVD: Automatic Learning and Enforcement of Authorization Rules in Online Social Networks",
"venue": "2017 IEEE Symposium on Security and Privacy (SP)",
"year": 2017
},
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"doi": "10.1145/3079856.3080217",
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"paperAbstract": "To mitigate excessive TLB misses in large memory applications, techniques such as large pages, variable length segments, and HW coalescing, increase the coverage of limited hardware translation entries by exploiting the contiguous memory allocation. However, recent studies show that in non-uniform memory systems, using large pages often leads to performance degradation, or allocating large chunks of memory becomes more difficult due to memory fragmentation. Although each of the prior techniques favors its own best chunk size, diverse contiguity of memory allocation in real systems cannot always provide the optimal chunk of each technique. Under such fragmented and diverse memory allocations, this paper proposes a novel HW-SW hybrid translation architecture, which can adapt to different memory mappings efficiently. In the proposed hybrid coalescing technique, the operating system encodes memory contiguity information in a subset of page table entries, called anchor entries. During address translation through TLBs, an anchor entry provides translation for contiguous pages following the anchor entry. As a smaller number of anchor entries can cover a large portion of virtual address space, the efficiency of TLB can be significantly improved. The most important benefit of hybrid coalescing is its ability to change the coverage of the anchor entry dynamically, reflecting the current allocation contiguity status. By using the contiguity information directly set by the operating system, the technique can provide scalable translation coverage improvements with minor hardware changes, while allowing the flexibility of memory allocation. Our experimental results show that across diverse allocation scenarios with different distributions of contiguous memory chunks, the proposed scheme can effectively reap the potential translation coverage improvement from the existing contiguity.",
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"title": "Hybrid TLB coalescing: Improving TLB translation coverage under diverse fragmented memory allocations",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
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{
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"name": "Khaled Hamidouche"
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{
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"name": "Brad Benton"
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"name": "Mauricio Breternitz"
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"name": "Steven K. Reinhardt"
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"name": "Lizy Kurian John"
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"doi": "10.1145/3126908.3126950",
"doiUrl": "https://doi.org/10.1145/3126908.3126950",
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"paperAbstract": "GPUs are widespread across clusters of compute nodes due to their attractive performance for data parallel codes. However, communicating between GPUs across the cluster is cumbersome when compared to CPU networking implementations. A number of recent works have enabled GPUs to more naturally access the network, but suffer from performance problems, require hidden CPU helper threads, or restrict communications to kernel boundaries.\n In this paper, we propose GPU Triggered Networking, a novel, GPU-centric networking approach which leverages the best of CPUs and GPUs. In this model, CPUs create and stage network messages and GPUs trigger the network interface when data is ready to send. GPU Triggered Networking decouples these two operations, thereby removing the CPU from the critical path. We illustrate how this approach can provide up to 25% speedup compared to standard GPU networking across microbenchmarks, a Jacobi stencil, an important MPI collective operation, and machine-learning workloads.",
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"title": "GPU triggered networking for intra-kernel communications",
"venue": "SC",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Tarun Kathuria"
},
{
"ids": [
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],
"name": "S. Sudarshan"
}
],
"doi": "10.1145/3034786.3034792",
"doiUrl": "https://doi.org/10.1145/3034786.3034792",
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"Jumpstart Our Business Startups Act",
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"paperAbstract": "Complex queries for massive data analysis jobs have become increasingly commonplace. Many such queries contain common subexpressions, either within a single query or among multiple queries submitted as a batch. Conventional query optimizers do not exploit these subexpressions and produce sub-optimal plans. The problem of multi-query optimization (MQO) is to generate an optimal <i>combined evaluation</i> plan by computing common subexpressions once and reusing them. Exhaustive algorithms for MQO explore an <i>O</i>(<i>n<sup>n</sup></i>) search space. Thus, this problem has primarily been tackled using various heuristic algorithms, without providing any theoretical guarantees on the quality of their solution.\n In this paper, instead of the conventional cost minimization problem, we treat the problem as maximizing a linear transformation of the cost function. We propose a greedy algorithm for this transformed formulation of the problem, which under weak, intuitive assumptions, provides an approximation factor guarantee for this formulation. We go on to show that this factor is optimal, unless <b>P</b> = <b>NP</b>. An- other noteworthy point about our algorithm is that it can be easily incorporated into existing transformation-based optimizers. We finally propose optimizations which can be used to improve the efficiency of our algorithm.",
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"title": "Efficient and Provable Multi-Query Optimization",
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"title": "Wi-Fly?: Detecting Privacy Invasion Attacks by Consumer Drones",
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"paperAbstract": "Existing Greybox Fuzzers (GF) cannot be effectively directed, for instance, towards problematic changes or patches, towards critical system calls or dangerous locations, or towards functions in the stack-trace of a reported vulnerability that we wish to reproduce. In this paper, we introduce Directed Greybox Fuzzing (DGF) which generates inputs with the objective of reaching a given set of target program locations efficiently. We develop and evaluate a simulated annealing-based power schedule that gradually assigns more energy to seeds that are closer to the target locations while reducing energy for seeds that are further away. Experiments with our implementation AFLGo demonstrate that DGF outperforms both directed symbolic-execution-based whitebox fuzzing and undirected greybox fuzzing. We show applications of DGF to patch testing and crash reproduction, and discuss the integration of AFLGo into Google's continuous fuzzing platform OSS-Fuzz. Due to its <i>directedness</i>, AFLGo could find 39 bugs in several well-fuzzed, security-critical projects like LibXML2. 17 CVEs were assigned.",
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"title": "Directed Greybox Fuzzing",
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"paperAbstract": "<i>Dynamic clustering</i>---how to efficiently maintain data clusters along with updates in the underlying dataset---is a difficult topic. This is especially true for <i>density-based clustering</i>, where objects are aggregated based on transitivity of proximity, under which deciding the cluster(s) of an object may require the inspection of numerous other objects. The phenomenon is unfortunate, given the popular usage of this clustering approach in many applications demanding data updates.\n Motivated by the above, we investigate the algorithmic principles for dynamic clustering by DBSCAN, a successful representative of density-based clustering, and ρ-approximate DBSCAN, proposed to bring down the computational hardness of the former on <i>static</i> data. Surprisingly, we prove that the ρ-approximate version <i>suffers from the very same hardness when the dataset is fully dynamic</i>, namely, when both insertions and deletions are allowed. We also show that this issue goes away as soon as tiny further relaxation is applied, yet still ensuring the same quality---known as the ``sandwich guarantee''---of ρ-approximate DBSCAN. Our algorithms guarantee near-constant update processing, and outperform existing approaches by a factor over two orders of magnitude.",
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"title": "Dynamic Density Based Clustering",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
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"name": "Ryan Stutsman"
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"paperAbstract": "Large scale in-memory key-value stores like RAMCloud can perform millions of operations per second per server with a few microseconds of access latency. However, these systems often only provide simple feature sets, and the lack of extensibility is an obstacle for building higher-level services. We evaluate the possibility of using JavaScript for shipping computation to data and for extending database functionality by comparing against other possible approaches. Microbenchmarks are promising; the V8 JavaScript runtime provides near native performance with reduced isolation costs when compared with native code and hardware-based protections. We conclude with initial thoughts on how this technology can be deployed for fast procedures that operate on in-memory data, that maximize gains from JIT, and that exploit the kernelbypass DMA capabilities of modern network cards.",
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"title": "JavaScript for Extending Low-latency In-memory Key-value Stores",
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},
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"name": "Adeesha Wijayasiri"
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"name": "Tania Banerjee-Mishra"
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"name": "Sanjay Ranka"
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{
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"name": "Sartaj Sahni"
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{
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"name": "Mark S. Schmalz"
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],
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"paperAbstract": "Hybrid multicore processors (HMPs) are poised to dominate the landscape of the next generation of computing on the desktop as well as on exascale systems. HMPs consist of general purpose CPU cores along with specialized co-processors and can provide high performance for a wide spectrum of applications at significantly lower energy requirements per FLOP. In this paper, we develop parallel algorithms and software for constructing multi-resolution SAR images on HMPs. We develop several load balancing algorithms for optimizing time performance and energy on HMPs. We also present a systematic approach for deriving the energy-time performance trade-offs on HMPs in the presence of Dynamic Voltage Frequency Scaling. Pareto-optimal curves are presented on a system consisting of 24 traditional cores and a GPU.",
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"title": "Parallel Dynamic Data Driven Approaches for Synthetic Aperture Radar",
"venue": "2017 IEEE 24th International Conference on High Performance Computing (HiPC)",
"year": 2017
},
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"title": "Execution Templates: Caching Control Plane Decisions for Strong Scaling of Data Analytics",
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"title": "Watch Me, but Don't Touch Me! Contactless Control Flow Monitoring via Electromagnetic Emanations",
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"paperAbstract": "Memory accesses limit the performance and scalability of countless applications. Many design and optimization efforts will benefit from an in-depth understanding of memory access behavior, which is not offered by extant access tracing and profiling methods.\n In this paper, we adopt a holistic memory access profiling approach to enable a better understanding of program-system memory interactions. We have developed a two-pass tool adopting fast online and slow offline profiling, with which we have profiled, at the <i>variable/object level</i>, a collection of 38 representative applications spanning major domains (HPC, personal computing, data analytics, AI, graph processing, and datacenter workloads), at varying problem sizes. We have performed detailed result analysis and code examination. Our findings provide new insights into application memory behavior, including insights on per-object access patterns, adoption of data structures, and memory-access changes at different problem sizes. We find that scientific computation applications exhibit distinct behaviors compared to datacenter workloads, motivating separate memory system design/optimizations.",
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"title": "Understanding object-level memory access patterns across the spectrum",
"venue": "SC",
"year": 2017
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"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
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"paperAbstract": "Target imaging and material identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.",
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"title": "TagScan: Simultaneous Target Imaging and Material Identification with Commodity RFID Devices",
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"paperAbstract": "Modern HPC centers comprise clusters, storage, networks, power and cooling infrastructure, and more. Analyzing the efficiency of these complex facilities is a daunting task. Increasingly, facilities deploy sensors and monitoring tools, but with millions of instrumented components, analyzing collected data manually is intractable. Data from an HPC center comprises different formats, granularities, and semantics, and handwritten scripts no longer suffice to transform the data into a digestible form.\n We present ScrubJay, an intuitive, scalable framework for <i>automatic</i> analysis of disparate HPC data. ScrubJay decouples the task of <i>specifying</i> data relationships from the task of <i>analyzing</i> data. Domain experts can store reusable transformations that describe relations between domains. ScrubJay also automates performance analysis. Analysts provide a query over logical domains of interest, and ScrubJay automatically derives needed steps to transform raw measurements. ScrubJay makes large-scale analysis tractable, reproducible, and provides insights into HPC facilities.",
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"http://graphics.cs.ucdavis.edu/~hamann/GimenezGamblinBhateleWoodShogaMaratheBremerHamannSchulzSC17PaperAsSubmitted04032017.pdf",
"http://graphics.idav.ucdavis.edu/~hamann/GimenezGamblinBhateleWoodShogaMaratheBremerHamannSchulzSC17PaperFinal08092017.pdf",
"http://doi.acm.org/10.1145/3126908.3126935"
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"title": "ScrubJay: deriving knowledge from the disarray of HPC performance data",
"venue": "SC",
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"doi": "10.1145/3035918.3035934",
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"paperAbstract": "Recently, massive data management plays an increasingly important role in data analytics because data access is a major bottleneck. Data skipping is a promising technique to reduce the number of data accesses. Data skipping partitions data into pages and accesses only pages that contain data to be retrieved by a query. Therefore, effective data partitioning is required to minimize the number of page accesses. However, it is an NP-hard problem to obtain optimal data partitioning given query pattern and data distribution.\n We propose a framework that involves a multidimensional indexing technique based on a space-filling curve. A space-filling curve is a way to define which portion of data can be stored in the same page. Therefore, the problem can be interpreted as selecting a curve that distributes data to be accessed by a query to minimize the number of page accesses. To solve this problem, we analyzed how different space-filling curves affect the number of page accesses. We found that it is critical for a curve to fit a query pattern and be robust against any data distribution. We propose a cost model for measuring how well a space-filling curve fits a given query pattern and tolerates data skew. Also we propose a method for designing a query-aware and skew-tolerant curve for a given query pattern.\n We prototyped our framework using the defined query-aware and skew-tolerant curve. We conducted experiments using a skew data set, and confirmed that our framework can reduce the number of page accesses by an order of magnitude for data warehousing (DWH) and geographic information systems (GIS) applications with real-world data.",
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"doi": "10.1145/3064176.3064215",
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"paperAbstract": "Data structures for non-volatile memories have to be designed such that they can be atomically modified using transactions. Existing atomicity methods require data to be copied in the critical path which significantly increases the latency of transactions. These overheads are further amplified for transactions on byte-addressable persistent memories where often the byte ranges modified for data structure updates are significantly smaller compared to the granularity at which data can be efficiently copied and logged. We propose Kamino-Tx that provides a new way to perform transactional updates on non-volatile byte-addressable memories (NVM) without requiring any copying of data in the critical path. Kamino-Tx maintains an additional copy of data off the critical path to achieve atomicity. But in doing so Kamino-Tx has to overcome two important challenges of safety and minimizing NVM storage overhead. We propose a more dynamic approach to maintaining the additional copy of data to reduce storage overheads. To further mitigate the storage overhead of using Kamino-Tx in a replicated setting, we develop Kamino-Tx-Chain, a variant of Chain Replication where replicas perform in-place updates and do not maintain data copies locally; replicas in Kamino-Tx-Chain leverage other replicas as copies to roll back or forward for atomicity. Our results show that using Kamino-Tx increases throughput by up to 9.5x for unreplicated systems and up to 2.2x for replicated settings.",
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"title": "Atomic In-place Updates for Non-volatile Main Memories with Kamino-Tx",
"venue": "EuroSys",
"year": 2017
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"name": "Pablo D. Mininni"
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"paperAbstract": "Data reduction through compression is emerging as a promising approach to ease I/O costs for simulation codes on supercomputers. Typically, this compression is achieved by techniques that operate on individual time slices. However, as simulation codes advance in time, outputting multiple time slices as they go, the opportunity for compression incorporating the time dimension has not been extensively explored. Moreover, recent supercomputers are increasingly equipped with deeper memory hierarchies, including solid state drives and burst buffers, which creates the opportunity to temporarily store multiple time slices and then apply compression to them all at once, i.e., spatiotemporal compression. This paper explores the benefits of incorporating the time dimension into existing wavelet compression, including studying its key parameters and demonstrating its benefits in three axes: storage, accuracy, and temporal resolution. Our results demonstrate that temporal compression can improve each of these axes, and that the impact on performance for real systems, including tradeoffs in memory usage and execution time, is acceptable. We also demonstrate the benefits of spatiotemporal wavelet compression with real-world visualization use cases and tailored evaluation metrics.",
"pdfUrls": [
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"http://doi.ieeecomputersociety.org/10.1109/CLUSTER.2017.15"
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"title": "Spatiotemporal Wavelet Compression for Visualization of Scientific Simulation Data",
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"name": "Georgios I. Goumas"
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],
"doi": "10.1109/ICPP.2017.38",
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"paperAbstract": "This paper presents a low-overhead optimizer for the ubiquitous sparse matrix-vector multiplication (SpMV) kernel. Architectural diversity among different processors together with structural diversity among different sparse matrices lead to bottleneck diversity. This justifies an SpMV optimizer that is both matrix- and architecture-adaptive through runtime specialization. To this direction, we present an approach that first identifies the performance bottlenecks of SpMV for a given sparse matrix on the target platform either through profiling or by matrix property inspection, and then selects suitable optimizations to tackle those bottlenecks. Our optimization pool is based on the widely used Compressed Sparse Row (CSR) sparse matrix storage format and has low preprocessing overheads, making our overall approach practical even in cases where fast decision making and optimization setup is required. We evaluate our optimizer on three x86-based computing platforms and demonstrate that it is able to distinguish and appropriately optimize SpMV for the majority of matrices in a representative test suite, leading to significant speedups over the CSR and Inspector-Executor CSR SpMV kernels available in the latest release of the Intel MKL library.",
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"title": "Performance Analysis and Optimization of Sparse Matrix-Vector Multiplication on Modern Multi- and Many-Core Processors",
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"paperAbstract": "While virtualization only introduces a small overhead on machines with few cores, this is not the case on larger ones. Most of the overhead on the latter machines is caused by the Non-Uniform Memory Access (NUMA) architecture they are using. In order to reduce this overhead, this paper shows how NUMA placement heuristics can be implemented inside Xen. With an evaluation of 29 applications on a 48-core machine, we show that the NUMA placement heuristics can multiply the performance of 9 applications by more than 2.",
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"title": "An interface to implement NUMA policies in the Xen hypervisor",
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"paperAbstract": "In this paper, we study the fundamental problem of leader election in the mobile telephone model: a recently introduced variation of the classical telephone model modified to better describe the local peer-to-peercommunication services implemented in many popular smartphone operating systems. In more detail, the mobile telephone model differs from the classical telephone model in three ways: (1) each devicecan participate in at most one connection per round; (2) the network topology can undergo a parameterizedrate of change; and (3) devices can advertise a parameterized number of bits to their neighbors in each round before connection attempts are initiated. We begin by describing and analyzing a new leader election algorithm in this model that works under the harshest possible parameter assumptions: maximum rate of topology changes and no advertising bits. We then apply this result to resolve an open question from [Ghaffari, 2016] on the efficiency of PUSH-PULL rumor spreading under these conditions. We then turn our attention to the slightly easier case where devices can advertise a single bit in each round. We demonstrate a large gap in time complexity between these zero bit and one bit cases. In more detail, we describe and analyze a new algorithm that solves leader election with a time complexitythat includes the parameter bounding topology changes. For all values of this parameter, this algorithm is faster than the previous result, with a gap that grows quickly as the parameter increases (indicating lower rates of change). We conclude by describing and analyzing a modified version of this algorithmthat does not require the assumptionthat all devices start during the same round. This new version has a similar time complexity (the rounds required differ only by a polylogarithmic factor),but now requires slightly larger advertisement tags.",
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"title": "Leader Election in a Smartphone Peer-to-Peer Network",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"doi": "10.1145/3035918.3035924",
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"paperAbstract": "Influence maximization (<b>IM</b>) on social networks is one of the most active areas of research in computer science. While various IM techniques proposed over the last decade have definitely enriched the field, unfortunately, experimental reports on existing techniques fall short in validity and integrity since many comparisons are not based on a common platform or merely discussed in theory. In this paper, we perform an in-depth benchmarking study of <b>IM</b> techniques on social networks. Specifically, we design a benchmarking platform, which enables us to evaluate and compare the existing techniques systematically and thoroughly under identical experimental conditions. Our benchmarking results analyze and diagnose the inherent deficiencies of the existing approaches and surface the open challenges in <b>IM</b> even after a decade of research. More fundamentally, we unearth and debunk a series of myths and establish that there is no single state-of-the-art technique in IM. At best, a technique is the state of the art in only one aspect.",
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"title": "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study",
"venue": "SIGMOD Conference",
"year": 2017
},
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"name": "Youngsok Kim"
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"name": "Jae-Eon Jo"
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{
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"name": "Minsoo Rhu"
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{
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"name": "Hanjun Kim"
},
{
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"name": "Jangwoo Kim"
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"doi": "10.1145/3123939.3123968",
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"paperAbstract": "Graphics Processing Unit (GPU) vendors have been scaling single-GPU architectures to satisfy the ever-increasing user demands for faster graphics processing. However, as it gets extremely difficult to further scale single-GPU architectures, the vendors are aiming to achieve the scaled performance by simultaneously using multiple GPUs connected with newly developed, fast inter-GPU networks (e.g., NVIDIA NVLink, AMD XDMA). With fast inter-GPU networks, it is now promising to employ split frame rendering (SFR) which improves both frame rate and single-frame latency by assigning disjoint regions of a frame to different GPUs. Unfortunately, the scalability of current SFR implementations is seriously limited as they suffer from a large amount of redundant computation among GPUs.\n This paper proposes <i>GPUpd</i>, a novel multi-GPU architecture for fast and scalable SFR. With small hardware extensions, GPUpd introduces a new graphics pipeline stage called <i>Cooperative Projection & Distribution (C-PD)</i> where all GPUs cooperatively project 3D objects to 2D screen and efficiently redistribute the objects to their corresponding GPUs. C-PD not only eliminates the redundant computation among GPUs, but also incurs minimal inter-GPU network traffic by transferring object IDs instead of mid-pipeline outcomes between GPUs. To further reduce the redistribution overheads, GPUpd minimizes inter-GPU synchronizations by implementing batching and runahead-execution of draw commands. Our detailed cycle-level simulations with 8 real-world game traces show that GPUpd achieves a geomean speedup of 4.98X in single-frame latency with 16 GPUs, whereas the current SFR implementations achieve only 3.07X geomean speedup which saturates on 4 or more GPUs.",
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"title": "GPUpd: a fast and scalable multi-GPU architecture using cooperative projection and distribution",
"venue": "MICRO",
"year": 2017
},
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],
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"paperAbstract": "We reduce the cost of communication and synchronization in graph processing by analyzing the fastest way to process graphs: pushing the updates to a shared state or pulling the updates to a private state. We investigate the applicability of this push-pull dichotomy to various algorithms and its impact on complexity, performance, and the amount of used locks, atomics, and reads/writes. We consider 11 graph algorithms, 3 programming models, 2 graph abstractions, and various families of graphs. The conducted analysis illustrates surprising differences between push and pull variants of different algorithms in performance, speed of convergence, and code complexity; the insights are backed up by performance data from hardware counters. We use these findings to illustrate which variant is faster for each algorithm and to develop generic strategies that enable even higher speedups. Our insights can be used to accelerate graph processing engines or libraries on both massively-parallel shared-memory machines as well as distributed-memory systems.",
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"paperAbstract": "Multi-stream SSDs can isolate data with different life time to disparate erase blocks, thus reduce garbage collection overhead and improve overall SSD performance. Applications are responsible for management of these device-level steams such as stream open/close and data-to-stream mapping. This requires application changes, and the engineer deploying the solution needs to be able to individually identify the streams in their workload. Furthermore, when multiple applications are involved, such as in VM or containerized environments, stream management becomes more complex due to the limited number of streams a device can support, for example, allocating streams to applications or sharing streams across applications will cause additional overhead.\n To address these issues and reduce the overhead of stream management, this paper proposes automatic stream management algorithms that operate under the application layer. Our stream assignment techniques, called AutoStream, is based on run time workload detection and independent of the application(s). We implement our AutoStream prototype in NVMe Linux device driver and our performance evaluation shows up to 60% reduction on WAF(Write Amplification Factor) and up to 237% improvement on performance compared to a conventional SSD device.",
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"paperAbstract": "Garbage collection greatly improves programmer productivity and ensures memory safety. Manual memory management on the other hand often delivers better performance but is typically unsafe and can lead to system crashes or security vulnerabilities. We propose integrating safe manual memory management with garbage collection in the .NET runtime to get the best of both worlds. In our design, programmers can choose between allocating objects in the garbage collected heap or the manual heap. All existing applications run unmodified, and without any performance degradation, using the garbage collected heap. \n Our programming model for manual memory management is flexible: although objects in the manual heap can have a single owning pointer, we allow deallocation at any program point and concurrent sharing of these objects amongst all the threads in the program. Experimental results from our .NET CoreCLR implementation on real-world applications show substantial performance gains especially in multithreaded scenarios: up to 3x savings in peak working sets and 2x improvements in runtime.",
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"title": "Project snowflake: non-blocking safe manual memory management in .NET",
"venue": "PACMPL",
"year": 2017
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"name": "Zizhong Chen"
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"paperAbstract": "Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific research. In this work, we design a new error-controlled lossy compression algorithm for large-scale scientific data. Our key contribution is significantly improving the prediction hitting rate (or prediction accuracy) for each data point based on its nearby data values along multiple dimensions. We derive a series of multilayer prediction formulas and their unified formula in the context of data compression. One serious challenge is that the data prediction has to be performed based on the preceding decompressed values during the compression in order to guarantee the error bounds, which may degrade the prediction accuracy in turn. We explore the best layer for the prediction by considering the impact of compression errors on the prediction accuracy. Moreover, we propose an adaptive error-controlled quantization encoder, which can further improve the prediction hitting rate considerably. The data size can be reduced significantly after performing the variable-length encoding because of the uneven distribution produced by our quantization encoder. We evaluate the new compressor on production scientific data sets and compare it with many other state-of-the-art compressors: GZIP, FPZIP, ZFP, SZ-1.1, and ISABELA. Experiments show that our compressor is the best in class, especially with regard to compression factors (or bit-rates) and compression errors (including RMSE, NRMSE, and PSNR). Our solution is better than the second-best solution by more than a 2x increase in the compression factor and 3.8x reduction in the normalized root mean squared error on average, with reasonable error bounds and user-desired bit-rates.",
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},
"85086f14c5047dfa726f5e2c4adff609e0daf357": {
"authors": [
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"paperAbstract": "Despite the growing popularity of enterprise virtual desktop infrastructure (VDI), little is known about its storage traffic characteristics. In addition, no prior work has considered the detailed characteristics of virtual machine (VM) behavior on VDI. In this paper, we analyze the enterprise storage traffic on commercial office VDI using designated VMs. For 28 consecutive days, we gathered various types of traces, including a usage questionnaire and active and passive measurements. To characterize the storage traffic, we focused on two perspectives: fibre channel (FC) traffic and VM behavior. From the FC traffic perspective, we found that read traffic is dominant, although the applications are similar to those in a previous small-scale VDI. In particular, the write response time of large transactions, e.g.,128 KiB, is strongly affected by a slight decrease in cache hits during an update storm. From the VM behavior, we found that all active user VMs generate only 25% of traffic. Although a few VMs generate massive traffic, their impact is small. These characteristics are unique in comparison with the small-scale VDI. Our results have significant implications for designing the next generation of VDI and improving its performance.",
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"title": "Understanding storage traffic characteristics on enterprise virtual desktop infrastructure",
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"name": "Amin Mantrach"
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"name": "Xiao Bai"
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"doi": "10.1145/3077136.3080811",
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"paperAbstract": "Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specifically, the proposed architectures only consider the textual content appearing in a query-advertisement pair as input, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.",
"pdfUrls": [
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"title": "Deep Character-Level Click-Through Rate Prediction for Sponsored Search",
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"paperAbstract": "Shared Memory stands out as a sine qua non for parallel programming of many commercial and emerging multicore processors. It optimizes patterns of communication that benefit common programming styles. As parallel programming is now mainstream, those common programming styles are challenged with emerging applications that communicate often and involve large amount of data. Such applications include graph analytics and machine learning, and this paper focuses on these domains. We retain the shared memory model and introduce a set of lightweight in-hardware explicit messaging instructions in the instruction set architecture (ISA). A set of auxiliary communication models are proposed that utilize explicit messages to accelerate synchronization primitives, and efficiently move computation towards data. The results on a 256-core simulated multicore demonstrate that the proposed communication models improve performance and dynamic energy by an average of 4x and 42% respectively over traditional shared memory.",
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"title": "On Using Time Without Clocks via Zigzag Causality",
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"title": "Towards Implicit Visual Memory-Based Authentication",
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"paperAbstract": "System failures resulting from configuration errors are one of the major reasons for the compromised reliability of today's software systems. Although many techniques have been proposed for configuration error detection, these approaches can generally only be applied after an error has occurred. Proactively verifying configuration files is a challenging problem, because 1) software configurations are typically written in poorly structured and untyped “languages”, and 2) specifying rules for configuration verification is challenging in practice. This paper presents ConfigV, a verification framework for general software configurations. Our framework works as follows: in the pre-processing stage, we first automatically derive a specification. Once we have a specification, we check if a given configuration file adheres to that specification. The process of learning a specification works through three steps. First, ConfigV parses a training set of configuration files (not necessarily all correct) into a well-structured and probabilistically-typed intermediate representation. Second, based on the association rule learning algorithm, ConfigV learns rules from these intermediate representations. These rules establish relationships between the keywords appearing in the files. Finally, ConfigV employs rule graph analysis to refine the resulting rules. ConfigV is capable of detecting various configuration errors, including ordering errors, integer correlation errors, type errors, and missing entry errors. We evaluated ConfigV by verifying public configuration files on GitHub, and we show that ConfigV can detect known configuration errors in these files.",
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"title": "Synthesizing configuration file specifications with association rule learning",
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"paperAbstract": "Traditionally, <i>active storage</i> techniques have been proposed to move computation tasks to storage nodes in order to exploit data locality. However, we argue in this paper that active storage is ill-suited for cloud storage for two reasons: 1. <i>Lack of elasticity:</i> Computing can only scale out with the number of storage nodes; and 2. <i>Resource Contention:</i> Sharing compute resources can produce interferences in the storage system. Serverless computing is now emerging as a promising alternative for ensuring painless scalability, and also, for simplifying the development of disaggregated computing tasks.\n Here we present an innovative <i>data-driven serverless</i> computing middleware for object storage. It is a lightweight compute solution that allows users to create small, stateless functions that intercept and operate on data flows in a scalable manner without the need to manage a server or a runtime environment. We demonstrate through different use cases how our solution scales with minimal overhead, while getting rid of the resource contention problems incurred by active storage tasks.",
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"paperAbstract": "Video streaming dominates the Internet's overall traffic mix, with reports stating that it will constitute 90% of all consumer traffic by 2019. Most of this video is delivered by Content Delivery Networks (CDNs), and, while they optimize QoE metrics such as buffering ratio and start-up time, no single CDN provides optimal performance. In this paper we make the case for elastic CDNs, the ability to build virtual CDNs on-the-fly on top of shared, third-party infrastructure at a scale. To bring this idea closer to reality we begin by large-scale simulations to quantify the effects that elastic CDNs would have if deployed, and build and evaluate MiniCache, a specialized, minimalistic virtualized content cache that runs on the Xen hypervisor. MiniCache is able to serve content at rates of up to 32 Gb/s and handle up to 600K reqs/sec on a single CPU core, as well as boot in about 90 milliseconds on x86 and around 370 milliseconds on ARM32.",
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"title": "Unikernels Everywhere: The Case for Elastic CDNs",
"venue": "VEE",
"year": 2017
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"paperAbstract": "Personal Cloud Storage (PCS) is a very popular Internet service. It allows users to backup data to the cloud as well as to perform collaborative work while sharing content. Notably, content sharing is a key feature for PCS users. It however comes with extra costs for service providers, as shared files must be synchronized to multiple user devices, generating more downloads from cloud servers. Despite the increasing interest in this type of service, a thorough investigation on the costs and benefits of PCS for service providers and end users has not been conducted yet. To that end, we propose a model to analyze cost-benefit tradeoffs for both parties. We develop utility functions that capture, in an abstract level, the satisfaction of the service provider and users in various scenarios. Then, we apply our model to evaluate alternative policies for content sharing in PCS. We consider two alternative policies for the current PCS sharing architecture, which count on user collaboration to reduce providers' costs. Our results show that such policies are advantageous for providers and users, leading to 39% utility improvements for both parties, while requiring low commitment of resources from participating users.",
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"paperAbstract": "Motivated by the asymmetric read and write costs of emerging non-volatile memory technologies, we study lower bounds for the problems of sorting, permuting and multiplying a sparse matrix by a dense vector in the asymmetric external memory model (AEM). Given an AEM with internal (symmetric) memory of size M, transfers between symmetric and asymmetric memory in blocks of size B and the ratio ω between write and read costs, we show Ω(min (N, ωN/B log<sub>ω M/B</sub> N/B) lower bound for the cost of permuting N input elements. This lower bound also applies to the problem of sorting N elements. This proves that the existing sorting algorithms in the AEM model are optimal to within a constant factor for reasonable ranges of parameters N, M, B, and ω. We also show a lower bound of Ω(min {H, ω H/B log<sub>ω M/B</sub> N/ max{δ ,M}}) for the cost of multiplying an N x N matrix with at most H= δ N non-empty entries by a vector with N elements.",
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"paperAbstract": "Performance of future large-scale HPC systems will be limited by costs associated with scaling power. Some HPC centers are reaching the limits of their existent site power delivery infrastructure and are facing prohibitive upgrade costs. Others are reaching budgetary limits on their energy operating costs. Without a breakthrough in energy efficiency, the HPC industry may fail to maintain historical performance scaling rates and fall short of 2018-2020 Exascale performance goals by an estimated 23x margin. Overcoming this gap will require co-designed hardware and software system energy management solutions and increased collaboration between hardware vendors and the HPC software community. In this work, we introduce the Global Extensible Open Power Manager (GEOPM): a tree-hierarchical, plug-in extensible, open source runtime framework that we are contributing to the HPC community to accelerate collaboration and research toward co-designed energy management solutions. First results with an experimental power rebalancing optimization demonstrate up to 32% improvements in the runtime of CORAL system procurement benchmarks like miniFE and Nekbone in a power-limited Xeon Phi cluster. These promising initial results motivate further work with the community to extend GEOPM to new optimization strategies to achieve further speedups. Keywords\u2014runtime systems; scalability; control systems; tuning; power management; RAPL; P-states; DVFS; resource management; power-aware scheduling; performance optimization",
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"doi": "10.1109/CLOUD.2017.35",
"doiUrl": "https://doi.org/10.1109/CLOUD.2017.35",
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"paperAbstract": "Hadoop clusters have been transitioning from a dedicated cluster environment to a shared cluster environment. This trend has resulted in the YARN container abstraction that isolates computing tasks from physical resources. With YARN containers, Hadoop has expanded to support various distributed frameworks. However, it has been reported that Hadoop tasks suffer from a significant overhead of container relaunch. In order to reduce the container overhead without making significant changes to the existing YARN framework, we propose leveraging the input split, which is the logical representation of physical HDFS blocks. Our assorted block coalescing scheme combines multiple HDFS blocks and creates large input splits of various sizes, reducing the number of containers and their initialization overhead. Our experimental study shows the assorted block coalescing scheme reduces the container overhead by a large margin while it achieves good load balance and job scheduling fairness without impairing the degree of overlap between map phase and reduce phase.",
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"title": "Coalescing HDFS Blocks to Avoid Recurring YARN Container Overhead",
"venue": "2017 IEEE 10th International Conference on Cloud Computing (CLOUD)",
"year": 2017
},
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"authors": [
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"name": "Jaewon Lee"
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"name": "Hanhwi Jang"
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"name": "Jae-Eon Jo"
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"name": "Gyu-hyeon Lee"
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{
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],
"doi": "10.1109/ISPASS.2017.7975292",
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"paperAbstract": "Computer architects use a variety of workloads to measure system performance. For many workloads, the workload configuration determines the stress applied to the system and the corresponding performance behavior. Therefore, architects must make great efforts to explore and find the correct workload configuration before performing detailed analysis. However, such explorations become impossible for indevelopment systems which exist only as a software model. The existing system modeling platforms are either accurate but too slow, or fast but inaccurate to get workload-reported performance metrics (e.g., latency and throughput) which are necessary for configuring workloads. In this paper, we propose StressRight, a method to quickly model the first-order performance of full-system workloads and reconstruct the workload-reported performance metrics. Stress-Right allows to explore how the workload configurations affect the stress and performance. The key idea is to execute workloads on a fast but timing-agnostic platform (e.g. emulators), and efficiently reconstruct the timing/performance details by analyzing only the unique code blocks. Our evaluation using memcached and PARSEC shows that StressRight achieves 8∼45x speedup compared to a cycle-level simulator while maintaining good accuracy.",
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"title": "StressRight: Finding the right stress for accurate in-development system evaluation",
"venue": "2017 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)",
"year": 2017
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"authors": [
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"name": "Lata Narayanan"
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"paperAbstract": "We consider a problem of routing on directed paths and trees to a single destination, with rate-limited, adversarial traffic. In particular, we focus on local buffer management algorithms that ensure no packet loss, while minimizing the size of the required buffers. While a centralized algorithm for the problem that uses constant-sized buffers has been recently shown [21], there is no known <i>local</i> algorithm that achieves a sub-linear buffer size. In this paper we show tight bounds for the maximum buffer size needed by l-local algorithms for information gathering on directed paths and trees, where an algorithm is called l-local if the decision made by each node v depends only on the sizes of the buffers at most l hops away from v.\n We show three main results: A lower bound of Ω(c log n/l) for all l-local algorithms on both directed and undirected paths, where c is an upper bound on the link capacity and injection rate. A surprisingly simple 1-local algorithm for directed paths that uses buffers of size O(log n), when c=1. A natural 2-local extension of this algorithm to directed trees, for c=1, with the same asymptotic bound.\n Our Ω(log n) lower bound is significantly lower than the Ω(n) lower bound for greedy algorithms, and perhaps surprisingly, there is a matching upper bound. The algorithm that achieves it can be summarized in two lines: If the size of your buffer is odd, forward a message if your successor's buffer size is equal or lower. If your buffer size is even, forward a message only if your successor's buffer size is strictly lower. For trees, a simple arbitration between siblings is added.",
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"title": "Optimal Local Buffer Management for Information Gathering with Adversarial Traffic",
"venue": "SPAA",
"year": 2017
},
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"authors": [
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],
"name": "Xiang Lian"
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{
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"name": "Dongchul Kim"
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"doi": "10.1145/3035918.3035929",
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"paperAbstract": "In many real applications such as bioinformatics and biological network analysis, it has always been an important, yet challenging, topic to accurately infer/reconstruct <i>gene regulatory networks</i> (<b>GRNs</b>) from microarray data, and efficiently identify those matching <b>GRNs</b> with similar interaction structures for potential disease analysis and treatment tasks. Motivated by this, in this paper, we formalize the problem of <i>ad-hoc inference and matching over gene regulatory networks</i> (<b>IM-GRN</b>), which deciphers ad-hoc <b>GRN</b> graph structures online from gene feature databases (without full <b>GRN</b> materializations), and retrieves the inferred <b>GRNs</b> that are subgraph-isomorphic to a query <b>GRN</b> graph with high confidences. Specifically, we propose a novel probabilistic score to measure the possible interaction between any two genes (inferred from gene feature vectors), and thus model GRNs by probabilistic graphs, containing edge existence probabilities. In order to efficiently process <b>IM-GRN</b> queries, we propose effective reduction, pruning, and embedding strategies to significantly reduce the search space of <b>GRN</b> inference and matching, without materializing all <b>GRNs</b>. We also present an effective indexing mechanism and an efficient <b>IM-GRN</b> query processing algorithm by the index traversal. Finally, extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed <b>IM-GRN</b> query answering approaches over real/synthetic <b>GRN</b> data sets.",
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"http://doi.acm.org/10.1145/3035918.3035929"
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"title": "Efficient Ad-Hoc Graph Inference and Matching in Biological Databases",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Palden Lama"
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"doi": "10.1109/MASCOTS.2017.18",
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"paperAbstract": "Cloud-based services are increasingly popular for big data analytics due to the flexibility, scalability, and cost-effectiveness of provisioning elastic resources on-demand. However, data analytics-as-a-service suffers from the overheads of data movement between compute and storage clusters, due to their decoupled architecture in existing cloud infrastructure. In this work, we propose a novel approach of in-situ big data processing on cloud storage by dynamically offloading data-intensive jobs from compute cluster to storage cluster, and improve job throughput. However, it is challenging to achieve this goal since introducing additional workload on the storage cluster can significantly impact interactive web requests that fetch cloud storage data, with strict SLA (service-level agreement) for tail latency. In this work, we present MPLEX, a system that augments data analytics-as-a-service by efficiently multiplexing compute and storage cluster to improve job throughput without violating the SLA of cloud storage service in terms of tail response time. It applies an SLA-aware opportunistic job scheduling technique supported by a machine learning based prediction model to exploit the dynamic workload conditions in the compute, and storage cluster. Performance evaluations on an OpenStack Swift cluster, and an OpenStack based virtual cluster of Hadoop VMs built atop NSFCloud's Chameleon testbed show that MPLEX improves the Hadoop job throughput by up to 1.7X, while maintaining the SLA for cloud storage service requests.",
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"title": "MPLEX: In-Situ Big Data Processing with Compute-Storage Multiplexing",
"venue": "2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)",
"year": 2017
},
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"authors": [
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"name": "Dong Dai"
},
{
"ids": [
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"name": "Yong Chen"
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{
"ids": [
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"name": "Philip H. Carns"
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{
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"name": "John Jenkins"
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{
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"name": "Robert B. Ross"
}
],
"doi": "10.1109/PACT.2017.14",
"doiUrl": "https://doi.org/10.1109/PACT.2017.14",
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"paperAbstract": "Type inference is convenient by allowing programmers to elide type annotations, but this comes at the cost of often generating very confusing and opaque type error messages that are of little help to fix type errors. Though there have been many successful attempts at making type error messages better in the past thirty years, many classes of errors are still difficult to fix. In particular, current approaches still generate imprecise and uninformative error messages for type errors arising from errors in grouping constructs like parentheses and brackets. Worse, a recent study shows that these errors usually take more than 10 steps to fix and occur quite frequently (around 45% to 60% of all type errors) in programs written by students learning functional programming. We call this class of errors, <em>nonstructural errors</em>. \nWe solve this problem by developing Learnskell, a type error debugger that uses machine learning to help diagnose and deliver high quality error messages, for programs that contain nonstructural errors. While previous approaches usually report type errors on typing constraints or on the type level, Learnskell generates suggestions on the expression level. We have performed an evaluation on more than 1,500 type errors, and the result shows that Learnskell is quite precise. It can correctly capture 86% of all nonstructural errors and locate the error cause with a precision of 63%/87% with the first 1/3 messages, respectively. This is several times more than the precision of state-of-the-art compilers and debuggers. We have also studied the performance of Learnskell and found out that it scales to large programs.",
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"title": "Learning user friendly type-error messages",
"venue": "PACMPL",
"year": 2017
},
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"authors": [
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"ids": [
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"name": "Sarat Sreepathi"
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{
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"name": "Jitendra Kumar"
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{
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"name": "Richard Tran Mills"
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{
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"name": "Forrest M. Hoffman"
},
{
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"name": "Vamsi Sripathi"
},
{
"ids": [
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"name": "William W. Hargrove"
}
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"doi": "10.1109/CLUSTER.2017.88",
"doiUrl": "https://doi.org/10.1109/CLUSTER.2017.88",
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"paperAbstract": "A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like the Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.",
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"venue": "2017 IEEE International Conference on Cluster Computing (CLUSTER)",
"year": 2017
},
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{
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"name": "Fran\u00e7ois-Henry Rouet"
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"doi": "10.1109/IPDPS.2017.21",
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"paperAbstract": "We present the design and implementation of a parallel and fully algebraic preconditioner based on an approximate sparse factorization using low-rank matrix compression. The sparse factorization uses a multifrontal algorithm with fill-in occurring in dense frontal matrices. These frontal matrices are approximated as hierarchically semi-separable matrices, which are constructed using a randomized sampling technique. The resulting preconditioner has (close to) optimal complexity in terms of flops and memory usage for many discretized partial differential equations. We illustrate the robustness and performance of this new preconditioner for a number of unstructured grid problems. Initial results show that the rank-structured preconditioner could be a viable alternative to algebraic multigrid and incomplete LU, for instance. Our implementation uses MPI and OpenMP and supports real and complex arithmetic and 32 and 64 bit integers. We present a detailed performance analysis. The code is released as the STRUMPACK library with a BSD license, and a PETSc interface is available to allow for easy integration in existing applications.",
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"title": "A Robust Parallel Preconditioner for Indefinite Systems Using Hierarchical Matrices and Randomized Sampling",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Xun Jian"
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{
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"name": "Pavan Kumar Hanumolu"
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{
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"doi": "10.1109/HPCA.2017.60",
"doiUrl": "https://doi.org/10.1109/HPCA.2017.60",
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"paperAbstract": "As the amount of digital data the world generates explodes, data centers and HPC systems that process this big data will require high bandwidth and high capacity main memory. Unfortunately, conventional memory technologies either provide high memory capacity (e.g., DDRx memory) or high bandwidth (GDDRx memory), but not both. Memory networks, which provide both high bandwidth and high capacity memory by connecting memory modules together via a network of point-to-point links, are promising future memory candidates for data centers and HPCs. In this paper, we perform the first exploration to understand the power characteristics of memory networks. We find idle I/O links to be the biggest power contributor in memory networks. Subsequently, we study idle I/O power in more detail. We evaluate well-known circuit-level I/O power control mechanisms such as rapid on off, variable link width, and DVFS. We also adapt prior works on memory power management to memory networks. The adapted schemes together reduce I/O power by 32% and 21%, on average, for big and small networks, respectively. We also explore novel power management schemes specifically targeting memory networks, which yield another 29% and 17% average I/O power reduction for big and small networks, respectively.",
"pdfUrls": [
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"paperAbstract": "A service-oriented architecture (SOA)-based application is composed of a number of distributed and loosely-coupled services which are interconnected to accomplish a more complex functionality. The main security challenge in SOA is that we cannot trust the participating services in a service composition to behave as expected all the time. Moreover, the chain of all services involved in an end-to-end invocation may not be visible to the clients. As a result, any violation of the client's policies could remain undetected. To address these challenges in SOA, we propose the following contributions. First, we propose a new end-to-end security architecture for SOA based on a dynamic composite trust model. To maintain the dynamic trust, we designed a trusted-third party service called trust manager component, which collects and processes feedbacks from the actual execution of services. Second, we developed an end-to-end inter-service policy monitoring and enforcement framework (PME framework), which is able to dynamically intercept the interactions between services at runtime and react to the potentially malicious activities according to the client's policies. Third, we design an intra-service policy monitoring and enforcement framework based on taint analysis mechanism to monitor the flow of information within services and detect and prevent information disclosure attacks. These two frameworks together can provide an end-to-end visibility and security in SOA. Finally, we have extensively studied the correctness and performance of the proposed security frameworks based on a realistic SOA case study in a cloud environment. All experimental studies validate that the practicality and effectiveness of the presented solutions.",
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"paperAbstract": "Memory hardware errors may result from transient particle-induced faults as well as device defects due to aging. These errors are an important threat to computer system reliability as VLSI technologies continue to scale. Managing memory hardware errors is a critical component in developing an overall system dependability strategy. Memory error detection and correction are supported in a range of available hardware mechanisms. However, memory protections (particularly the more advanced ones) come at substantial costs in performance and energy usage. Moreover, the protection mechanisms are often a fixed, system-wide choice and can not easily adapt to different protection demand of different applications or memory regions.\n In this paper, we present a new RAIM (redundant array of independent memory) design that compared to the state-of-the-art implementation can easily provide high protection capability and the ability to selectively protect a subset of the memory. A straightforward implementation of the design can incur a substantial memory traffic overhead. We propose a few practical optimizations to mitigate this overhead. With these optimizations the proposed RAIM design offers significant advantages over existing RAIM design at lower or comparable costs.",
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"title": "Redundant memory array architecture for efficient selective protection",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
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"paperAbstract": "A popular community repository such as Docker Hub, PyPI, or RubyGems distributes tens of thousands of software projects to millions of users. The large number of projects and users make these repositories attractive targets for exploitation. After a repository compromise, a malicious party can launch a number of attacks on unsuspecting users, including rollback attacks that revert projects to obsolete and vulnerable versions. Unfortunately, due to the rapid rate at which packages are updated, existing techniques that protect against rollback attacks would cause each user to download 2\u20133 times the size of an average package in metadata each month, making them impractical to deploy. In this work, we develop a system called Mercury that uses a novel technique to compactly disseminate version information while still protecting against rollback attacks. Due to a different technique for dealing with key revocation, users are protected from rollback attacks, even if the software repository is compromised. This technique is bandwidth-efficient, especially when delta compression is used to transmit only the differences between previous and current lists of version information. An analysis we performed for the Python community shows that once Mercury is deployed on PyPI, each user will only download metadata each month that is about 3.5% the size of an average package. Our work has been incorporated into the latest versions of TUF, which is being integrated by Haskell, OCaml, RubyGems, Python, and CoreOS, and is being used in production by LEAP, Flynn, and Docker.",
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"paperAbstract": "Multicore CPUs and cheap co-processors such as GPUs create opportunities for vastly accelerating database queries. However, given the differences in their threading models, expected granularities of parallelism, and memory subsystems, effectively utilising all cores with all co-processors for an intensive query is very difficult. This paper introduces a novel templating methodology to create portable, yet architecture-aware, algorithms. We apply this methodology on the very compute-intensive task of calculating the *skycube*, a materialisation of exponentially many skyline query results, which finds applications in data exploration and multi-criteria decision making. We define three parallel templates, two that leverage insights from previous skycube research and a third that exploits a novel point-based paradigm to expose more data parallelism. An experimental study shows that, relative to the state-of-the-art that does not parallelise well due to its memory and cache requirements, our algorithms provide an order of magnitude improvement on either architecture and proportionately improve as more GPUs are added.",
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"paperAbstract": "Our understanding of exploit documents as a vector to deliver targeted malware is limited to a handful of studies done in collaboration with the Tibetans, Uyghurs, and political dissidents in the Middle East. In this measurement study, we present a complementary methodology relying only on publicly available data to capture and analyze targeted attacks with both greater scale and depth. In particular, we detect exploit documents uploaded over one year to a large anti-virus aggregator (VirusTotal) and then mine the social engineering information they embed to infer their likely targets and contextual information of the attacks. We identify attacks against two ethnic groups (Tibet and Uyghur) as well as 12 countries spanning America, Asia, and Europe. We then analyze the exploit documents dynamically in sandboxes to correlate and compare the exploited vulnerabilities and malware families targeting different groups. Finally, we use machine learning to infer the role of the uploaders of these documents to VirusTotal (i.e., attacker, targeted victim, or third-party), which enables their classification based only on their metadata, without any dynamic analysis. We make our datasets available to the academic community.",
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"paperAbstract": "Feature selection is one of the most important data mining research topics with many applications. In practical problems, features often have group structure to effect the outcomes. Thus, it is crucial to automatically identify homogenous groups of features for high-dimensional data analysis. Octagonal shrinkage and clustering algorithm for regression (OSCAR) is an important sparse regression approach with automatic feature grouping and selection by ℓ<sub>1</sub> norm and pairwise ℓ<sub>∞</sub> norm. However, due to over-complex representation of the penalty (especially the pairwise ℓ<sub>∞</sub> norm), so far OSCAR has no solution path algorithm which is mostly useful for tuning the model. To address this challenge, in this paper, we propose a groups-keeping solution path algorithm to solve the OSCAR model (OscarGKPath). Given a set of homogenous groups of features and an accuracy bound ε, OscarGKPath can fit the solutions in an interval of regularization parameters while keeping the feature groups. The entire solution path can be obtained by combining multiple such intervals. We prove that all solutions in the solution path produced by OscarGKPath can strictly satisfy the given accuracy bound ε. The experimental results on benchmark datasets not only confirm the effectiveness of our OscarGKPath algorithm, but also show the superiority of our OscarGKPath in cross validation compared with the existing batch algorithm.",
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"paperAbstract": "Service providers want to reduce datacenter costs by consolidating workloads onto fewer servers. At the same time, customers have performance goals, such as meeting tail latency Service Level Objectives (SLOs). Consolidating workloads while meeting tail latency goals is challenging, especially since workloads in production environments are often bursty. To limit the congestion when consolidating workloads, customers and service providers often agree upon rate limits. Ideally, rate limits are chosen to maximize the number of workloads that can be co-located while meeting each workload's SLO. In reality, neither the service provider nor customer knows how to choose rate limits. Customers end up selecting rate limits on their own in some ad hoc fashion, and service providers are left to optimize given the chosen rate limits.\n This paper describes WorkloadCompactor, a new system that uses workload traces to automatically choose rate limits simultaneously with selecting onto which server to place workloads. Our system meets customer tail latency SLOs while minimizing datacenter resource costs. Our experiments show that by optimizing the choice of rate limits, WorkloadCompactor reduces the number of required servers by 30--60% as compared to state-of-the-art approaches.",
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"paperAbstract": "Clustering results are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, Alternative Clustering algorithms can be used to solve such a problem. However, these suffer from at least one of the following problems: i) continuous covariates or non-linearly separable clusters cannot be handled; ii) assumptions are made about the distribution of the data; iii) one or more hyper-parameters need to be set. Here we propose a novel algorithm, named Kernel Conditional Clustering (KCC), whose objective is derived from a kernel based conditional dependence measure. KCC is parameter-light and makes no assumptions about the cluster structure, the covariates, or the distribution of the data. On both simulated and real-world datasets, the proposed KCC algorithm detects the ground truth cluster structures more accurately than state-of-the-art alternative clustering methods.",
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"venue": "2017 IEEE International Conference on Data Mining (ICDM)",
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"paperAbstract": "Location privacy continues to attract significant attentions in recent years, fueled by the rapid growth of locationbased services (LBSs) and smart mobile devices. Location obfuscation has been the dominating location privacy preserving approach, which transforms the exact location of a mobile user to a perturbed location before its public release. The notion of location privacy has evolved from user-defined location kanonymity to two statistical quantification based privacy notions: geo-indistinguishability and expected inference error. The former promotes differential location privacy but does not protect location against inference attacks of Bayesian adversary with using prior information, whereas the latter promotes the background inference resilient location privacy but does not guarantee differential location privacy with respect to geo-indistinguishability. In this paper we argue that geo-indistinguishability and expected inference error are two complementary notions for location privacy. We formally study the relationship between two privacy notions. By leveraging this relationship and a personalized error bound, we can effectively combine the two privacy notions. We develop PIVE, a two-phase dynamic differential location privacy framework. In Phase I, we take into account the user-defined inference error threshold and the prior knowledge about the user\u2019s location to determine a subset of locations as the protection location set for protecting the actual location by increasing adversary\u2019s expected location inference error. In Phase II, we generate pseudo-locations (i.e., perturbed locations) in the way that achieves differential privacy over the protection location set. This two-phase location obfuscation is constructed dynamically by leveraging the relationship between two privacy notions based on adversary\u2019s current prior information and user-specific privacy requirements on different locations and at different times. Experiments with real-world datasets demonstrate that our PIVE approach effectively guarantees the two privacy notions simultaneously and outperforms the existing mechanisms in terms of adaptive privacy protection in presence of skewed locations and computation efficiency.",
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"title": "Dynamic Differential Location Privacy with Personalized Error Bounds",
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"year": 2017
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"paperAbstract": "Distributed protocols such as Paxos play an important role in many computer systems. Therefore, a bug in a distributed protocol may have tremendous effects. Accordingly, a lot of effort has been invested in verifying such protocols. However, checking invariants of such protocols is undecidable and hard in practice, as it requires reasoning about an unbounded number of nodes and messages. Moreover, protocol actions and invariants involve both quantifier alternations and higher-order concepts such as set cardinalities and arithmetic. \nThis paper makes a step towards automatic verification of such protocols. We aim at a technique that can verify correct protocols and identify bugs in incorrect protocols. To this end, we develop a methodology for deductive verification based on effectively propositional logic (EPR)â\u0080\u0094a decidable fragment of first-order logic (also known as the Bernays-Sch\u00c3\u00b6nfinkel-Ramsey class). In addition to decidability, EPR also enjoys the finite model property, allowing to display violations as finite structures which are intuitive for users. Our methodology involves modeling protocols using general (uninterpreted) first-order logic, and then systematically transforming the model to obtain a model and an inductive invariant that are decidable to check. The steps of the transformations are also mechanically checked, ensuring the soundness of the method. We have used our methodology to verify the safety of Paxos, and several of its variants, including Multi-Paxos, Vertical Paxos, Fast Paxos, Flexible Paxos and Stoppable Paxos. To the best of our knowledge, this work is the first to verify these protocols using a decidable logic, and the first formal verification of Vertical Paxos, Fast Paxos and Stoppable Paxos.",
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"http://arxiv.org/abs/1710.07191"
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"title": "Paxos made EPR: decidable reasoning about distributed protocols",
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"year": 2017
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"authors": [
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"paperAbstract": "SQL is the de facto language for manipulating relational data. Though powerful, many users find it difficult to write SQL queries due to highly expressive constructs. \n While using the programming-by-example paradigm to help users write SQL queries is an attractive proposition, as evidenced by online help forums such as Stack Overflow, developing techniques for synthesizing SQL queries from given input-output (I/O) examples has been difficult, due to the large space of SQL queries as a result of its rich set of operators. \n \n In this paper, we present a new scalable and efficient algorithm for synthesizing SQL queries based on I/O examples. The key innovation of our algorithm is development of a language for abstract queries, i.e., queries with uninstantiated operators, that can be used to express a large space of SQL queries efficiently. Using abstract queries to represent the search space nicely decomposes the synthesis problem into two tasks: 1) searching for abstract queries that can potentially satisfy the given I/O examples, and 2) instantiating the found abstract queries and ranking the results. \n \n We have implemented this algorithm in a new tool called Scythe and evaluated it using 193 benchmarks collected from Stack Overflow. Our evaluation shows that Scythe can efficiently solve 74% of the benchmarks, most in just a few seconds, and the queries range from simple ones involving a single selection to complex queries with 6 nested subqueires.",
"pdfUrls": [
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"http://doi.acm.org/10.1145/3062341.3062365"
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"title": "Synthesizing highly expressive SQL queries from input-output examples",
"venue": "PLDI",
"year": 2017
},
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"paperAbstract": "In High Performance Computing, heterogeneity is now the normwith specialized accelerators like GPUs providing efficientcomputational power. The added complexity has led to the developmentof task-based runtime systems, which allow complex computations to beexpressed as task graphs, and rely on scheduling algorithms to performload balancing between all resources of the platforms. Developing goodscheduling algorithms, even on a single node, and analyzing them canthus have a very high impact on the performance of current HPCsystems. The special case of two types of resources (namely CPUs andGPUs) is of practical interest. HeteroPrio is such an algorithm whichhas been proposed in the context of fast multipole computations, andthen extended to general task graphs with very interesting results. Inthis paper, we provide a theoretical insight on the performance ofHeteroPrio, by proving approximation bounds compared to the optimalschedule in the case where all tasks are independent and for differentplatform sizes. Interestingly, this shows that spoliation allows toprove approximation ratios for a list scheduling algorithm on twounrelated resources, which is not possible otherwise. We also establishthat almost all our bounds are tight. Additionally, we provide anexperimental evaluation of HeteroPrio on real task graphs from denselinear algebra computation, which highlights the reasons explainingits good practical performance.",
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"title": "Approximation Proofs of a Fast and Efficient List Scheduling Algorithm for Task-Based Runtime Systems on Multicores and GPUs",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
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"paperAbstract": "The widely-studied radio network model [Chlamtac and Kutten, 1985] is a graph-based description that captures the inherent impact of collisions in wireless communication. In this model, the strong assumption is made that node v receives a message from a neighbor if and only if exactly one of its neighbors broadcasts. We relax this assumption by introducing a new noisy radio network model in which random faults occur at senders or receivers. Specifically, for a constant noise parameter p \u2208 [0, 1), either every sender has probability p of transmitting noise or every receiver of a single transmission in its neighborhood has probability p of receiving noise. We first study single-message broadcast algorithms in noisy radio networks and show that the Decay algorithm [Bar-Yehuda et al., 1992] remains robust in the noisy model while the diameter-linear algorithm of Gasieniec et al., 2007 does not. We give a modified version of the algorithm of Gasieniec et al., 2007 that is robust to sender and receiver faults, and extend both this modified algorithm and the Decay algorithm to robust multi-message broadcast algorithms, broadcasting \u03a9 ( 1 log n log log n ) and \u03a9 ( 1 log n ) messages per round, respectively. We next investigate the extent to which (network) coding improves throughput in noisy radio networks. In particular, we study the coding cap \u2013 the ratio of the throughput of coding to that of routing \u2013 in noisy radio networks. We address the previously perplexing result of Alon et al. 2014 that worst case coding throughput is no better than worst case routing throughput up to constants: we show that the worst case throughput performance of coding is, in fact, superior to that of routing \u2013 by a \u0398(log(n)) gap \u2013 provided receiver faults are introduced. However, we show that sender faults have little effect on throughput. In particular, we show that any coding or routing scheme for the noiseless setting can be transformed to be robust to sender faults with only a constant throughput overhead. These transformations imply that the results of Alon et al., 2014 carry over to noisy radio networks with sender faults as well. As a result, if sender faults are introduced then there exist topologies for which there is a \u0398(log logn) gap, but the worst case throughput across all topologies is \u0398 ( 1 log n ) for both coding and routing. \u2217Supported in part by the Israel Science Foundation (grant 1696/14) and the Binational Science Foundation (grant 2015803). \u2020Supported in part by the National Science Foundation through grants CCF-1527110 and CCF-1618280. ar X iv :1 70 5. 07 36 9v 1 [ cs .D C ] 2 1 M ay 2 01 7",
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"paperAbstract": "Near-threshold computing is emerging as a promising energy-efficient alternative for power-constrained environments. Unfortunately, aggressive reduction in supply voltage to the near-threshold range, albeit effective, faces a host of challenges. This includes higher relative leakage power and high error rates, particularly in dense SRAM structures such as on-chip caches. This paper presents an architecture that rethinks the cache hierarchy in near-threshold multiprocessors. Our design uses STT-RAM to implement all on-chip caches. STT-RAM has several advantages over SRAM at low voltages including low leakage, high density, and reliability. The design consolidates the private caches of near-threshold cores into shared L1 instruction/data caches organized in clusters. We find that our consolidated cache design can service more than 95% of incoming requests within a single cycle. We demonstrate that eliminating the coherence traffic associated with private caches results in a performance boost of 11%. In addition, we propose a hardware-based core management system that dynamically consolidates virtual cores into variable numbers of physical cores to increase resource efficiency. We demonstrate that this approach can save up to 33% in energy.",
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"title": "Respin: Rethinking Near-Threshold Multiprocessor Design with Non-volatile Memory",
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"paperAbstract": "We consider an agent-based model in which two types of agents interact locally over a graph and have a common intolerance threshold \u03c4 for changing their types with exponentially distributed waiting times. The model is equivalent to an unperturbed Schelling model of self-organized segregation, an Asynchronous Cellular Automata (ACA) with extended Moore neighborhoods, or a zero-temperature Ising model with Glauber dynamics, and has applications in the analysis of social and biological networks, and spin glasses systems. Some rigorous results were recently obtained in the theoretical computer science literature, and this work provides several extensions. We enlarge the intolerance interval leading to the formation of large segregated regions of agents of a single type from the known size > 0 to size \u2248 0.134. Namely, we show that for 0.433 < \u03c4 < 1/2 (and by symmetry 1/2 < \u03c4 < 0.567), the expected size of the largest segregated region containing an arbitrary agent is exponential in the size of the neighborhood. We further extend the interval leading to large segregated regions to size \u2248 0.312 considering \u201calmost segregated\u201d regions, namely regions where the ratio of the number of agents of one type and the number of agents of the other type vanishes quickly as the size of the neighborhood grows. In this case, we show that for 0.344 < \u03c4 \u2264 0.433 (and by symmetry for 0.567 \u2264 \u03c4 < 0.656) the expected size of the largest almost segregated region containing an arbitrary agent is exponential in the size of the neighborhood. This behavior is reminiscent of supercritical percolation, where small clusters of empty sites can be observed within any sufficiently large region of the occupied percolation cluster. The exponential bounds that we provide also imply that complete segregation, where agents of a single type cover the whole grid, does not occur with high probability for p = 1/2 and the range of tolerance considered. * An extended abstract of this paper with most proofs and some results omitted, has appeared in the Proceeding of PODC\u201917. This work was partially supported by Army Research Office (ARO), award number W911NF-15-1-0253. \u2020 PhD student, Department of Electrical and Computer Engineering, University of California, San Diego. E-mail address: homidvar@ucsd.edu. Address: 9500 Gilman Drive, Mail Code 0018, La Jolla, CA 92093. Phone: (858) 333-2933 \u2021 Professor, Department of Electrical and Computer Engineering, University of California, San Diego. E-mail address: mfrances@ucsd.edu. Address: 9500 Gilman Drive, Mail Code 0407, La Jolla, CA 92093. Phone: (858) 822-2284 ar X iv :1 70 5. 08 58 6v 1 [ cs .S I] 2 4 M ay 2 01 7",
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"title": "Self-organized Segregation on the Grid",
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"paperAbstract": "The USB protocol has become ubiquitous, supporting devices from high-powered computing devices to small embedded devices and control systems. USB's greatest feature, its openness and expandability, is also its weakness, and attacks such as BadUSB exploit the unconstrained functionality afforded to these devices as a vector for compromise. Fundamentally, it is virtually impossible to know whether a USB device is benign or malicious. This work introduces FirmUSB, a USB-specific firmware analysis framework that uses domain knowledge of the USB protocol to examine firmware images and determine the activity that they can produce. Embedded USB devices use microcontrollers that have not been well studied by the binary analysis community, and our work demonstrates how lifters into popular intermediate representations for analysis can be built, as well as the challenges of doing so. We develop targeting algorithms and use domain knowledge to speed up these processes by a factor of 7 compared to unconstrained fully symbolic execution. We also successfully find malicious activity in embedded 8051 firmwares without the use of source code. Finally, we provide insights into the challenges of symbolic analysis on embedded architectures and provide guidance on improving tools to better handle this important class of devices.",
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"paperAbstract": "Various defense schemes --- which determine the presence of an attack on the in-vehicle network --- have recently been proposed. However, they fail to identify <i>which</i> Electronic Control Unit (ECU) actually mounted the attack. Clearly, pinpointing the attacker ECU is essential for fast/efficient forensic, isolation, security patch, etc. To meet this need, we propose a novel scheme, called <i>Viden</i> (Voltage-based attacker identification), which can identify the attacker ECU by measuring and utilizing voltages on the in-vehicle network. The first phase of <i>Viden</i>, called <i>ACK learning</i>, determines whether or not the measured voltage signals really originate from the genuine message transmitter. <i>Viden</i> then exploits the voltage measurements to construct and update the transmitter ECUs' voltage profiles as their fingerprints. It finally uses the voltage profiles to identify the attacker ECU. Since <i>Viden</i> adapts its profiles to changes inside/outside of the vehicle, it can pinpoint the attacker ECU under various conditions. Moreover, its efficiency and design-compliance with modern in-vehicle network implementations make <i>Viden</i> practical and easily deployable. Our extensive experimental evaluations on both a CAN bus prototype and two real vehicles have shown that <i>Viden</i> can accurately fingerprint ECUs based solely on voltage measurements and thus identify the attacker ECU with a low false identification rate of 0.2%.",
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"paperAbstract": "Kernel matrices appear in machine learning and non-parametric statistics. Given N points in d dimensions and a kernel function that requires O(d) work to evaluate, we present an O(dN log N)-work algorithm for the approximate factorization of a regularized kernel matrix, a common computational bottleneck in the training phase of a learning task. With this factorization, solving a linear system with a kernel matrix can be done with O(N log N) work. Our algorithm only requires kernel evaluations and does not require that the kernel matrix admits an efficient global low rank approximation. Instead, our factorization only assumeslow-rank properties for the off-diagonal blocks under anappropriate row and column ordering. We also present a hybrid method that, when the factorization is prohibitively expensive, combines a partial factorization with iterative methods. As a highlight, we are able to approximately factorize a dense 11M-by-11M kernel matrixin 2 minutes on 3,072 x86 "Haswell" cores and a 4.5M-by-4.5M matrix in 1 minute using 4,352 "Knights Landing" cores.",
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"http://arxiv.org/abs/1701.02324",
"https://arxiv.org/pdf/1701.02324v1.pdf",
"https://doi.org/10.1109/IPDPS.2017.10"
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"title": "An N log N Parallel Fast Direct Solver for Kernel Matrices",
"venue": "2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)",
"year": 2017
},
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"paperAbstract": "Specialized hardware accelerators have performance and energy-efficiency advantages over general-purpose processors. To fully realize these benefits and aid programmability, accelerators may share a physical and virtual address space and full cache coherence with the host system. However, allowing accelerators -- particularly those designed by third parties -- to directly communicate with host coherence protocols poses several problems. Host coherence protocols are complex, vary between companies, and may be proprietary, increasing burden on accelerator designers. Bugs in the accelerator implementation may cause crashes and other serious consequences to the host system.\n We propose <i>Crossing Guard</i>, a coherence interface between the host coherence system and accelerators. The Crossing Guard interface provides the accelerator designer with a standardized set of coherence messages that are simple enough to aid in design of bug-free coherent caches. At the same time, they are sufficiently complex to allow customized and optimized accelerator caches with performance comparable to using the host protocol. The Crossing Guard hardware is implemented as part of the trusted host, and provides complete safety to the host coherence system, even in the presence of a pathologically buggy accelerator cache.",
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"title": "Crossing Guard: Mediating Host-Accelerator Coherence Interactions",
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"year": 2017
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"doi": "10.1109/HPCC-SmartCity-DSS.2017.22",
"doiUrl": "https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.22",
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"journalName": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"paperAbstract": "High end scientific applications in the form of workflows are being executed in cloud for various benefits. But with an increase in the processing capabilities of the cloud system, the energy consumption has also increased significantly. Thus energy efficient execution of these scientific workflows in cloud becomes essential. Existing research on energy efficient scheduling of scientific workflows in cloud mainly focus on reducing only the dynamic energy consumption of the compute nodes and uses DVFS technique. In this paper, we have proposed six different energy efficient scheduling approaches for a set of online scientific workflows in a cloud system considering both static and dynamic energy consumption of the compute nodes. These approaches are divided into two categories: non-splittable allocation of VMs on single host, and splittable allocation on multiple hosts. We have compared the performance of our proposed policies with state-of-art energy efficient scheduling policy, EnReal and found that the policies perform better than EnReal. All three scheduling policies with non-splittable VM allocation perform at par with EnReal in energy consumption but they do not require any migration of VMs. And all policies under splittable VM category perform significantly better than EnReal with an average energy reduction of 70%.",
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"title": "Energy Efficient Scheduling of Scientific Workflows in Cloud Environment",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
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"name": "Yutong Lu"
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"doi": "10.1109/IPDPS.2017.31",
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"title": "Comparing the Usability of Cryptographic APIs",
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"paperAbstract": "The design and presentation of a <i>Search Engine Results Page (SERP)</i> has been subject to much research. With many contemporary aspects of the SERP now under scrutiny, work still remains in investigating more traditional SERP components, such as the <i>result summary</i>. Prior studies have examined a variety of different aspects of result summaries, but in this paper we investigate the influence of result summary length on search behaviour, performance and user experience. To this end, we designed and conducted a within-subjects experiment using the <i>TREC AQUAINT</i> news collection with 53 participants. Using <i>Kullback-Leibler distance</i> as a measure of <i>information gain</i>, we examined result summaries of different lengths and selected four conditions where the change in information gain was the greatest: <i>(i)</i> title only; <i>(ii)</i> title plus one snippet; <i>(iii)</i> title plus two snippets; and <i>(iv)</i> title plus four snippets. Findings show that participants broadly preferred longer result summaries, as they were perceived to be more informative. However, their performance in terms of correctly identifying relevant documents was similar across all four conditions. Furthermore, while the participants felt that longer summaries were more informative, empirical observations suggest otherwise; while participants were more likely to click on relevant items given longer summaries, they also were more likely to click on non-relevant items. This shows that longer is not necessarily better, though participants perceived that to be the case - and second, they reveal a positive relationship between the length and informativeness of summaries and their attractiveness (i.e. clickthrough rates). These findings show that there are tensions between perception and performance when designing result summaries that need to be taken into account.",
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"title": "A Study of Snippet Length and Informativeness: Behaviour, Performance and User Experience",
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"paperAbstract": "This paper explores solutions for enabling efficient supports of <i>position independence</i> of pointer-based data structures on byte-addressable None-Volatile Memory (NVM). When a dynamic data structure (e.g., a linked list) gets loaded from persistent storage into main memory in different executions, the locations of the elements contained in the data structure could differ in the address spaces from one run to another. As a result, some special support must be provided to ensure that the pointers contained in the data structures always point to the correct locations, which is called position independence.\n This paper shows the insufficiency of traditional methods in supporting position independence on NVM. It proposes a concept called <i>implicit self-contained representations of pointers</i>, and develops two such representations named <i>off-holder</i> and <i>Region ID in Value (RIV)</i> to materialize the concept. Experiments show that the enabled representations provide much more efficient and flexible support of position independence for dynamic data structures, alleviating a major issue for effective data reuses on NVM.",
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"title": "Efficient support of position independence on non-volatile memory",
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"year": 2017
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"name": "Cheng Luo"
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"name": "Min Zhang"
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"name": "Shaoping Ma"
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"doi": "10.1145/3077136.3080795",
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"paperAbstract": "Mobile search engine result pages (SERPs) are becoming highly visual and heterogenous. Unlike the traditional ten-blue-link SERPs for desktop search, different verticals and cards occupy different amounts of space within the small screen. Hence, traditional retrieval measures that regard the SERP as a ranked list of homogeneous items are not adequate for evaluating the overall quality of mobile SERPs. Specifically, we address the following new problems in mobile search evaluation: (1) Different retrieved items have different heights within the scrollable SERP, unlike a ten-blue-link SERP in which results have similar heights with each other. Therefore, the traditional rank-based decaying functions are not adequate for mobile search metrics. (2) For some types of verticals and cards, the information that the user seeks is already embedded in the snippet, which makes clicking on those items to access the landing page unnecessary. (3) For some results with complex sub-components (and usually a large height), the total gain of the results cannot be obtained if users only read part of their contents. The benefit brought by the result is affected by user's reading behavior and the internal gain distribution (over the height) should be modeled to get a more accurate estimation. To tackle these problems, we conduct a lab-based user study to construct suitable user behavior model for mobile search evaluation. From the results, we find that the geometric heights of user's browsing trails can be adopted as a good signal of user effort. Based on these findings, we propose a new evaluation metric, Height-Biased Gain, which is calculated by summing up the product of gain distribution and discount factors that are both modeled in terms of result height. To evaluate the effectiveness of the proposed metric, we compare the agreement of evaluation metrics with side-by-side user preferences on a test collection composed of four mobile search engines. Experimental results show that HBG agrees with user preferences 85.33% of the time, which is better than all existing metrics.",
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"year": 2017
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"paperAbstract": "Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (<i>e.g.</i>, playlists and booklists). On one hand, user generated lists contain rich signal about item co-occurrence, as items within a list are usually gathered based on a specific theme. On the other hand, a user's preference over a list also indicate her preference over items within the list. We believe that 1) if the rich relevance signal within user generated lists can be properly leveraged, an enhanced recommendation for individual items can be provided, and 2) if user-item and user-list interactions are properly utilized, and the relationship between a list and its contained items is discovered, the performance of user-item and user-list recommendations can be mutually reinforced.\n Towards this end, we devise embedding factorization models, which extend traditional factorization method by incorporating item-item (item-item-list) co-occurrence with embedding-based algorithms. Specifically, we employ factorization model to capture users' preferences over items and lists, and utilize embedding-based models to discover the co-occurrence information among items and lists. The gap between the two types of models is bridged by sharing items' latent factors. Remarkably, our proposed framework is capable of solving the new-item cold-start problem, where items have never been consumed by users but exist in user generated lists. Overall performance comparisons and micro-level analyses demonstrate the promising performance of our proposed approaches.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3077136.3080779",
"http://www.comp.nus.edu.sg/~xiangnan/papers/sigir17-EmbeddingMF.pdf"
],
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"s2Url": "https://semanticscholar.org/paper/91fd133adf2bd15ab814351b3a9e9f13f2951e38",
"sources": [
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"title": "Embedding Factorization Models for Jointly Recommending Items and User Generated Lists",
"venue": "SIGIR",
"year": 2017
},
"924915ae02e621c10f257fb09a1dcc0202d9aad4": {
"authors": [
{
"ids": [
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"name": "Christian DeLozier"
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"name": "Ariel Eizenberg"
},
{
"ids": [
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"name": "Shiliang Hu"
},
{
"ids": [
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"name": "Gilles Pokam"
},
{
"ids": [
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"name": "Joseph Devietti"
}
],
"doi": "10.1145/3123939.3123947",
"doiUrl": "https://doi.org/10.1145/3123939.3123947",
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"Consistency model",
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"Speedup",
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"paperAbstract": "Cache contention in the form of false sharing and true sharing arises when threads overshare cache lines at high frequency. Such oversharing can reduce or negate the performance benefits of parallel execution. Prior systems for detecting and repairing cache contention lack efficiency in detection or repair, contain subtle memory consistency flaws, or require invasive changes to the program environment.\n In this paper, we introduce a new way to combat cache line oversharing via the Thread Memory Isolation (T<scp>mi</scp>) system. T<scp>mi</scp> operates completely in userspace, leveraging performance counters and the Linux ptrace mechanism to tread lightly on monitored applications, intervening only when necessary. T<scp>mi</scp>'s compatible-by-default design allows it to scale to real-world workloads, unlike previous proposals. T<scp>mi</scp> introduces a novel code-centric consistency model to handle cross-language memory consistency issues. T<scp>mi</scp> exploits the flexibility of code-centric consistency to efficiently repair false sharing while preserving strong consistency model semantics when necessary.\n T<scp>mi</scp> has minimal impact on programs without oversharing, slowing their execution by just 2% on average. We also evaluate T<scp>mi</scp> on benchmarks with known false sharing, and manually inject a false sharing bug into the leveldb key-value store from Google. For these programs, T<scp>mi</scp> provides an average speedup of 5.2x and achieves 88% of the speedup possible with manual source code fixes.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3123939.3123947",
"http://www.cis.upenn.edu/~delozier/docs/tmi_micro_2017.pdf"
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"title": "TMI: thread memory isolation for false sharing repair",
"venue": "MICRO",
"year": 2017
},
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"authors": [
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"name": "Qingyao Ai"
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"name": "Yongfeng Zhang"
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{
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"name": "Keping Bi"
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{
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"name": "Xu Chen"
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"name": "W. Bruce Croft"
}
],
"doi": "10.1145/3077136.3080813",
"doiUrl": "https://doi.org/10.1145/3077136.3080813",
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],
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"paperAbstract": "Product search is an important part of online shopping. In contrast to many search tasks, the objectives of product search are not confined to retrieving relevant products. Instead, it focuses on finding items that satisfy the needs of individuals and lead to a user purchase. The unique characteristics of product search make search personalization essential for both customers and e-shopping companies. Purchase behavior is highly personal in online shopping and users often provide rich feedback about their decisions (e.g. product reviews). However, the severe mismatch found in the language of queries, products and users make traditional retrieval models based on bag-of-words assumptions less suitable for personalization in product search. In this paper, we propose a hierarchical embedding model to learn semantic representations for entities (i.e. words, products, users and queries) from different levels with their associated language data. Our contributions are three-fold: (1) our work is one of the initial studies on personalized product search; (2) our hierarchical embedding model is the first latent space model that jointly learns distributed representations for queries, products and users with a deep neural network; (3) each component of our network is designed as a generative model so that the whole structure is explainable and extendable. Following the methodology of previous studies, we constructed personalized product search benchmarks with Amazon product data. Experiments show that our hierarchical embedding model significantly outperforms existing product search baselines on multiple benchmark datasets.",
"pdfUrls": [
"https://ciir-publications.cs.umass.edu/pub/web/getpdf.php?id=1260",
"http://doi.acm.org/10.1145/3077136.3080813"
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"title": "Learning a Hierarchical Embedding Model for Personalized Product Search",
"venue": "SIGIR",
"year": 2017
},
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"authors": [
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"name": "Yanqi Zhou"
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{
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"name": "Sameer Wagh"
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{
"ids": [
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],
"name": "Prateek Mittal"
},
{
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"name": "David Wentzlaff"
}
],
"doi": "10.1109/HPCA.2017.36",
"doiUrl": "https://doi.org/10.1109/HPCA.2017.36",
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"Cloud computing",
"Data center",
"Dynamic random-access memory",
"Memory timings",
"Multi-user",
"Network on a chip",
"Privacy",
"Schedule (project management)",
"Scheduling (computing)",
"Side-channel attack",
"Simulation",
"Time of arrival",
"Timing channel",
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"id": "9277d342b22fb55238ef3811e07ac2b226c9db78",
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"journalName": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
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"paperAbstract": "Information leaks based on timing side channels in computing devices have serious consequences for user security and privacy. In particular, malicious applications in multi-user systems such as data centers and cloud-computing environments can exploit memory timing as a side channel to infer a victim's program access patterns/phases. Memory timing channels can also be exploited for covert communications by an adversary. We propose Camouflage, a hardware solution to mitigate timing channel attacks not only in the memory system, but also along the path to and from the memory system (e.g. NoC, memory scheduler queues). Camouflage introduces the novel idea of shaping memory requests' and responses' inter-arrival time into a pre-determined distribution for security purposes, even creating additional fake traffic if needed. This limits untrusted parties (either cloud providers or co-scheduled clients) from inferring information from another security domain by probing the bus to and from memory, or analyzing memory response rate. We design three different memory traffic shaping mechanisms for different security scenarios by having Camouflage work on requests, responses, and bi-directional (both) traffic. Camouflage is complementary to ORAMs and can be optionally used in conjunction with ORAMs to protect information leaks via both memory access timing and memory access patterns. Camouflage offers a tunable trade-off between system security and system performance. We evaluate Camouflage's security and performance both theoretically and via simulations, and find that Camouflage outperforms state-of-the-art solutions in performance by up to 50%.",
"pdfUrls": [
"http://parallel.princeton.edu/papers/camouflage.pdf",
"http://doi.ieeecomputersociety.org/10.1109/HPCA.2017.36"
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"title": "Camouflage: Memory Traffic Shaping to Mitigate Timing Attacks",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
"927db6add0917c3c9a6e57d36f47d9a3f5f927b1": {
"authors": [
{
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"name": "Xiaodong Yu"
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{
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"name": "Kaixi Hou"
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{
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"name": "Hao Wang"
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{
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"name": "Wu-chun Feng"
}
],
"doi": "10.1109/IISWC.2017.8167767",
"doiUrl": "https://doi.org/10.1109/IISWC.2017.8167767",
"entities": [
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"Automata theory",
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"paperAbstract": "Programming Micron's Automata Processor (AP) requires expertise in both automata theory and the AP architecture, as programmers have to manually manipulate state transition elements (STEs) and their transitions with a low-level Automata Network Markup Language (ANML). When the required STEs of an application exceed the hardware capacity, multiple reconfigurations are needed. However, most previous AP-based designs limit the dataset size to fit into a single AP board and simply neglect the costly overhead of reconfiguration. This results in unfair performance comparisons between the AP and other processors. To address this issue, we propose a framework for the fast and fair evaluation of AP devices. Our framework provides a hierarchical approach that automatically generates automata for large datasets through user-defined paradigms and allows the use of cascadable macros to achieve highly optimized reconfigurations. We highlight the importance of counting the configuration time in the overall AP performance, which in turn, can provide better insight into identifying essential hardware features, specifically for large-scale problem sizes. Our framework shows that the AP can achieve up to 461x overall speedup fairly compared to CPU counterparts.",
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"title": "A framework for fast and fair evaluation of automata processing hardware",
"venue": "2017 IEEE International Symposium on Workload Characterization (IISWC)",
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"paperAbstract": "Large scale distributed systems are expected to consume huge amounts of energy. To solve this issue, shutdown policies constitute an appealing approach able to dynamically adapt the resource set to the actual workload. However, multiple constraints have to be taken into account for such policies to be applied on real infrastructures, in particular the time and energy cost of shutting down and waking up nodes, and power capping to avoid disruption of the system. In this paper, we propose models translating these various constraints into different shutdown policies that can be combined. Our models are validated through simulations on real workload traces and power measurements on real testbeds.",
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"paperAbstract": "Today's high-end virtual reality (VR) systems require a cable connection to stream high-definition videos from a PC or game console to the headset. This cable significantly limits the player's mobility and, hence, the user's VR experience. The high data rate requirement of this link (multiple Gbps) precludes its replacement by today's wireless systems, such as Wi-Fi. In this talk, I present MoVR, a system that creates a high data rate, millimeter wave (mmWave) link between the PC and the headset. Specifically, I will explain how we address the two key problems that prevent existing mmWave links from being used in VR systems. First, mmWave signals suffer from a blockage problem. i.e., they operate mainly in line-of-sight and can be blocked by simple obstacles such as the player lifting her hand in front of the headset. Second, mmWave radios use highly directional antennas with very narrow beams; they work only when the transmitter\u2019s beam is aligned with the receiver\u2019s beam. Any small movement of the headset can break the alignment and stall the data stream.",
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"paperAbstract": "In spite of the multicore revolution, high single thread performance still plays an important role in ensuring a decentoverall gain. Look-ahead is a proven strategy in uncoveringimplicit parallelism; however, a conventional out-of-ordercore quickly becomes resource-inefficient when looking beyond a short distance. An effective approach is to use an in-dependent look-ahead thread running on a separate contextguided by a program slice known as the skeleton. We observethat fixed heuristics to generate skeletons are often suboptimal. As a consequence, look-ahead agent is not able to targetsufficient bottlenecks to reap all the benefits it should.In this paper, we present DRUT, a holistic hardware-software solution, which achieves good single thread performance by tuning the look-ahead skeleton efficiently. First, we propose a number of dynamic transformations to branchbased code modules (we call them Do-It-Yourself or DIY)that enable a faster look-ahead thread without compromisingthe quality of the look-ahead. Second, we extend our tuningmechanism to any arbitrary code region and use a profile-driven technique to tune the skeleton for the whole program.Assisted by the aforementioned techniques, look-aheadthread improves the performance of a baseline decoupledlook-ahead by up to 1.93× with a geometric mean of 1.15×. Our techniques, combined with the weak dependence removal technique, improve the performance of a baselinelook-ahead by up to 2.12× with a geometric mean of 1.20×. This is an impressive performance gain of 1.61× over thesingle-thread baseline, which is much better compared toconventional Turbo Boost with a comparable energy budget.",
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"paperAbstract": "We study the online constrained ranking problem motivated by an application to web-traffic shaping: an online stream of sessions arrive in which, within each session, we are asked to rank items. The challenge involves optimizing the ranking in each session so that local vs. global objectives are controlled: within each session one wishes to maximize a reward (local) while satisfying certain constraints over the entire set of sessions (global). A typical application of this setup is that of page optimization in a web portal. We wish to rank items so that not only is user engagement maximized in each session, but also other business constraints (such as the number of views/clicks delivered to various publishing partners) are satisfied.\n We describe an online algorithm for performing this optimization. A novel element of our approach is the use of linear programming duality and connections to the celebrated Hungarian algorithm. This framework enables us to determine a set of <i>shadow prices</i> for each traffic-shaping constraint that can then be used directly in the final ranking function to assign near-optimal rankings. The (dual) linear program can be solved off-line periodically to determine the prices. At serving time these prices are used as weights to compute weighted rank-scores for the items, and the simplicity of the approach facilitates scalability to web applications. We provide rigorous theoretical guarantees for the performance of our online algorithm and validate our approach using numerical experiments on real web-traffic data from a prominent internet portal.",
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"title": "Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping",
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"year": 2017
},
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"authors": [
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{
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{
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"name": "Andre Pawlowski"
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{
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"name": "Thorsten Holz"
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{
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"doi": "10.1109/SP.2017.66",
"doiUrl": "https://doi.org/10.1109/SP.2017.66",
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"paperAbstract": "Modern vehicles are required to comply with a range of environmental regulations limiting the level of emissions for various greenhouse gases, toxins and particulate matter. To ensure compliance, regulators test vehicles in controlled settings and empirically measure their emissions at the tailpipe. However, the black box nature of this testing and the standardization of its forms have created an opportunity for evasion. Using modern electronic engine controllers, manufacturers can programmatically infer when a car is undergoing an emission test and alter the behavior of the vehicle to comply with emission standards, while exceeding them during normal driving in favor of improved performance. While the use of such a defeat device by Volkswagen has brought the issue of emissions cheating to the public's attention, there have been few details about the precise nature of the defeat device, how it came to be, and its effect on vehicle behavior. In this paper, we present our analysis of two families of software defeat devices for diesel engines: one used by the Volkswagen Group to pass emissions tests in the US and Europe, and a second that we have found in Fiat Chrysler Automobiles. To carry out this analysis, we developed new static analysis firmware forensics techniques necessary to automatically identify known defeat devices and confirm their function. We tested about 900 firmware images and were able to detect a potential defeat device in more than 400 firmware images spanning eight years. We describe the precise conditions used by the firmware to detect a test cycle and how it affects engine behavior. This work frames the technical challenges faced by regulators going forward and highlights the important research agenda in providing focused software assurance in the presence of adversarial manufacturers.",
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"title": "How They Did It: An Analysis of Emission Defeat Devices in Modern Automobiles",
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},
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"paperAbstract": "For data durability, many applications rely on synchronous operations such as an fsync() system call. However, latency-sensitive synchronous operations can be delayed under the compound transaction scheme of the current journaling technique. Because a compound transaction includes irrelevant data and metadata, as well as the data and metadata of fsynced file, the latency of an fsync call can be unexpectedly long. In this paper, we first analyze various factors that may delay an fsync operation, and propose a novel hybrid journaling technique, called ijournaling, which journals only the corresponding file-level transaction for an fsync call, while recording a normal journal transaction during periodic journaling. The file-level transaction journal has only the related metadata updates of the fsynced file. By removing several factors detrimental to fsync latency, the proposed technique can reduce the fsync latency, mitigate the interference between fsync-intensive threads, and provide high manycore scalability. Experiments using a smartphone and a desktop computer showed significant improvements in fsync latency through the use of ijournaling.",
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"title": "iJournaling: Fine-Grained Journaling for Improving the Latency of Fsync System Call",
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{
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{
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"name": "Michael Laurenzano"
},
{
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"name": "Scott A. Mahlke"
},
{
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"name": "Lingjia Tang"
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{
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"name": "Jason Mars"
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],
"doi": "10.1145/3123939.3123970",
"doiUrl": "https://doi.org/10.1145/3123939.3123970",
"entities": [
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"Artificial neural network",
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"paperAbstract": "Deep neural networks (DNNs) are key computational building blocks for emerging classes of web services that interact in real time with users via voice, images and video inputs. Although GPUs have gained popularity as a key accelerator platform for deep learning workloads, the increasing demand for DNN computation leaves a significant gap between the compute capabilities of GPU-enabled datacenters and the compute needed to service demand.\n The state-of-the-art techniques to improve DNN performance have significant limitations in bridging the gap on real systems. Current network pruning techniques remove computation, but the resulting networks map poorly to GPU architectures, yielding no performance benefit or even slowdowns. Meanwhile, current bandwidth optimization techniques focus on reducing off-chip bandwidth while overlooking on-chip bandwidth, a key DNN bottleneck.\n To address these limitations, this work introduces DeftNN, a GPU DNN execution framework that targets the key architectural bottlenecks of DNNs on GPUs to automatically and transparently improve execution performance. DeftNN is composed of two novel optimization techniques - (1) synapse vector elimination, a technique that identifies non-contributing synapses in the DNN and carefully transforms data and removes the computation and data movement of these synapses while fully utilizing the GPU to improve performance, and (2) near-compute data fission, a mechanism for scaling down the on-chip data movement requirements within DNN computations. Our evaluation of DeftNN spans 6 state-of-the-art DNNs. By applying both optimizations in concert, DeftNN is able to achieve an average speedup of 2.1X on real GPU hardware. We also introduce a small additional hardware unit per GPU core to facilitate efficient data fission operations, increasing the speedup achieved by DeftNN to 2.6X.",
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"http://doi.acm.org/10.1145/3123939.3123970"
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"title": "DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission",
"venue": "MICRO",
"year": 2017
},
"93ee8f084498d1f667a4e9c1ea2671c4dd4e5f91": {
"authors": [
{
"ids": [
"2062558"
],
"name": "Daniel Genkin"
},
{
"ids": [
"2172728"
],
"name": "Luke Valenta"
},
{
"ids": [
"2133826"
],
"name": "Yuval Yarom"
}
],
"doi": "10.1145/3133956.3134029",
"doiUrl": "https://doi.org/10.1145/3133956.3134029",
"entities": [
"Chosen-ciphertext attack",
"Ciphertext",
"Computer security",
"Email",
"Encryption",
"Libgcrypt",
"Mathematical structure",
"Microarchitecture",
"Montgomery modular multiplication",
"Pretty Good Privacy",
"Public-key cryptography",
"SWAP (instrument)",
"Side-channel attack",
"Spectral leakage"
],
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"paperAbstract": "In recent years, applications increasingly adopt security primitives designed with better countermeasures against side channel attacks. A concrete example is Libgcrypt's implementation of ECDH encryption with Curve25519. The implementation employs the Montgomery ladder scalar-by-point multiplication, uses the unified, branchless Montgomery double-and-add formula and implements a constant-time argument swap within the ladder. However, Libgcrypt's field arithmetic operations are not implemented in a constant-time side-channel-resistant fashion.\n Based on the secure design of Curve25519, users of the curve are advised that there is no need to perform validation of input points. In this work we demonstrate that when this recommendation is followed, the mathematical structure of Curve25519 facilitates the exploitation of side-channel weaknesses.\n We demonstrate the effect of this vulnerability on three software applications---encrypted git, email and messaging---that use Libgcrypt. In each case, we show how to craft malicious OpenPGP files that use the Curve25519 point of order 4 as a chosen ciphertext to the ECDH encryption scheme. We find that the resulting interactions of the point at infinity, order-2, and order-4 elements in the Montgomery ladder scalar-by-point multiplication routine create side channel leakage that allows us to recover the private key in as few as 11 attempts to access such malicious files.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3133956.3134029",
"https://obj.umiacs.umd.edu/papers_for_stories/genkin_ACMCCS2017.pdf",
"https://eprint.iacr.org/2017/806.pdf",
"http://eprint.iacr.org/2017/806"
],
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"title": "May the Fourth Be With You: A Microarchitectural Side Channel Attack on Several Real-World Applications of Curve25519",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
"93f45cdc7bb6d291d77ce1f987cd724e06d5dd3c": {
"authors": [
{
"ids": [
"33877556"
],
"name": "Michael A. Bender"
},
{
"ids": [
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"name": "Martin Farach-Colton"
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{
"ids": [
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"name": "Rob Johnson"
},
{
"ids": [
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"name": "Simon Mauras"
},
{
"ids": [
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"name": "Tyler Mayer"
},
{
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"2652269"
],
"name": "Cynthia A. Phillips"
},
{
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"name": "Helen Xu"
}
],
"doi": "10.1145/3034786.3056117",
"doiUrl": "https://doi.org/10.1145/3034786.3056117",
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"Block size (cryptography)",
"Data dictionary",
"Data structure",
"Database",
"Graph coloring",
"Insertion sort",
"Random-access memory",
"Range query (data structures)",
"Skip list",
"With high probability"
],
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"paperAbstract": "The skip list is an elegant dictionary data structure that is commonly deployed in RAM. A skip list with <i>N</i> elements supports searches, inserts, and deletes in <i>O</i>(log <i>N</i>) operations with high probability (w.h.p.) and range queries returning <i>K</i> elements in <i>O</i>(log <i>N</i> + <i>K</i>) operations w.h.p.\n A seemingly natural way to generalize the skip list to external memory with block size <i>B</i> is to \"promote\" with probability 1/<i>B</i>, rather than 1/2. However, there are practical and theoretical obstacles to getting the skip list to retain its efficient performance, space bounds, and high-probability guarantees.\n We give an external-memory skip list that achieves write-optimized bounds. That is, for 0 < ε < 1, range queries take <i>O</i>(log<sub><i>B</i>ε</sub> <i>N</i> + <i>K/B</i>) I/Os w.h.p. and insertions and deletions take <i>O</i>((log<sup><i>B</i>ε</sup> <i>N</i>) / <i>B</i><sup>1-ε</sup>) amortized I/Os w.h.p.\n Our write-optimized skip list inherits the virtue of simplicity from RAM skip lists. Moreover, it matches or beats the asymptotic bounds of prior write-optimized data structures such as the <i>B</i><sup>ε</sup> & tree or LSM trees, which are deployed in high-performance databases and file systems.\n The main technical challenge in proving our bounds comes from the fact that there are so few levels in the skip list, an aspect of the data structure that is essential to getting strong external-memory bounds. We use extremal-graph coloring to show that it is possible to decompose paths in the skip list into uncorrelated groups, regardless of the insertion/deletion pattern. Thus, we achieve our bounds by averaging over these uncorrelated paths rather than by averaging over uncorrelated levels, as in the standard skip list.",
"pdfUrls": [
"http://supertech.csail.mit.edu/papers/BenderFaJo17.pdf",
"http://doi.acm.org/10.1145/3034786.3056117"
],
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"sources": [
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],
"title": "Write-Optimized Skip Lists",
"venue": "PODS",
"year": 2017
},
"945ddbcbda534c6a3a135ce3de71c5fd7e3ec069": {
"authors": [
{
"ids": [
"2073481"
],
"name": "Xingbo Wu"
},
{
"ids": [
"2571439"
],
"name": "Fan Ni"
},
{
"ids": [
"1804354"
],
"name": "Song Jiang"
}
],
"doi": "10.1145/3127479.3127483",
"doiUrl": "https://doi.org/10.1145/3127479.3127483",
"entities": [
"Attribute\u2013value pair",
"CPU cache",
"Cache (computing)",
"Central processing unit",
"Commodity computing",
"Computer",
"Computer data storage",
"Data access",
"Data item",
"Data structure",
"Database index",
"Dynamic random-access memory",
"Experiment",
"Hash table",
"Locality of reference",
"Throughput",
"Web search engine"
],
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"paperAbstract": "With the ever increasing DRAM capacity in commodity computers, applications tend to store large amount of data in main memory for fast access. Accordingly, efficient traversal of index structures to locate requested data becomes crucial to their performance. The index data structures grow so large that only a fraction of them can be cached in the CPU cache. The CPU cache can leverage access locality to keep the most frequently used part of an index in it for fast access. However, the traversal on the index to a target data during a search for a data item can result in significant false temporal and spatial localities, which make CPU cache space substantially underutilized. In this paper we show that even for highly skewed accesses the index traversal incurs excessive cache misses leading to suboptimal data access performance. To address the issue, we introduce Search Lookaside Buffer (<b>SLB</b>) to selectively cache only the search results, instead of the index itself. SLB can be easily integrated with any index data structure to increase utilization of the limited CPU cache resource and improve throughput of search requests on a large data set. We integrate SLB with various index data structures and applications. Experiments show that SLB can improve throughput of the index data structures by up to an order of magnitude. Experiments with real-world key-value traces also show up to 73% throughput improvement on a hash table.",
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"title": "Search lookaside buffer: efficient caching for index data structures",
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"paperAbstract": "The sparse directory has emerged as a critical component for supporting the shared memory abstraction in multi-and many-core chip-multiprocessors. Recent research efforts have explored ways to reduce the number of entries in the sparse directory. These include tracking coherence of private regions at a coarse grain, not tracking blocks that belong to pages identified as private by the operating system (OS), and not tracking a subset of blocks that are speculated to be private by the hardware. These techniques require support for multi-grain coherence, assistance of OS, or broadcast-based recovery on sharing an untracked block that is wrongly speculated as private. In this paper, wedesign a robust minimally-sized sparse directory that can offer adequate performance while enjoying the simplicity, scalability, and OS-independence of traditional broadcast-free block-grain coherence. We begin our exploration with a naive design that does not have a sparse directory and the location/sharers of a block are tracked by borrowing a portion of the block's last-level cache (LLC) data way. Such a design, however, lengthens the critical path from two transactions to three transactions (two hops to three hops) for the blocks that experience frequent shared read accesses. We address this problem by architecting a tiny sparse directory that dynamically identifies and tracks a selected subset of the blocks that experience a large volume of shared accesses. We augment the tiny directory proposal with an option of selectively spilling into the LLC space for tracking the coherence of the critical shared blocks that the tiny directory fails to accommodate. Detailed simulation-based study on a 128-core system with a large set of multi-threaded applications spanning scientific, general-purpose, and commercial computing shows that our coherence tracking proposal operating with (1/32)x to (1/256)x sparse directories offers performance within a percentage of a traditional 2x sparse directory.",
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"title": "Tiny Directory: Efficient Shared Memory in Many-Core Systems with Ultra-Low-Overhead Coherence Tracking",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
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"name": "Huan Chen"
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"doi": "10.1145/3143361.3143391",
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"paperAbstract": "SDN controllers demand tight performance guarantees over the control plane actions performed by switches. For example, traffic engineering techniques that frequently reconfigure the network require guarantees on the speed of reconfiguring the network. Initial experiments show that poor performance of Ternary Content-Addressable Memory (TCAM) control actions (e.g., rule insertion) can inflate application performance by a factor of 2x! Yet, modern switches provide no guarantees for these important control plane actions -- inserting, modifying, or deleting rules.\n In this paper, we present the design and evaluation of Hermes, a practical and immediately deployable framework that offers a novel method for partitioning and optimizing switch TCAM to enable performance guarantees. Hermes builds on recent studies on switch performance and provides guarantees by trading-off a nominal amount of TCAM space for assured performance. We evaluated Hermes using large-scale simulations. Our evaluations show that with less than 5% overheads, Hermes provides 5ms insertion guarantees that translates into an improvement of application level metrics by up to 80%. Hermes is more than 50% better than existing state of the art techniques and provides significant improvement for traditional networks running BGP.",
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"title": "Hermes: Providing Tight Control over High-Performance SDN Switches",
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"paperAbstract": "Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE, which provides natural and high-level constructs for processing streaming data. Our language has a novel integration of linguistic constructs from two distinct programming paradigms: streaming extensions of relational query languages and quantitative extensions of regular expressions. The former allows the programmer to employ relational constructs to partition the input data by keys and to integrate data streams from different sources, while the latter can be used to exploit the logical hierarchy in the input stream for modular specifications. \n We first present the core language with a small set of combinators, formal semantics, and a decidable type system. We then show how to express a number of common patterns with illustrative examples. Our compilation algorithm translates the high-level query into a streaming algorithm with precise complexity bounds on per-item processing time and total memory footprint. We also show how to integrate approximation algorithms into our framework. We report on an implementation in Java, and evaluate it with respect to existing high-performance engines for processing streaming data. Our experimental evaluation shows that (1) StreamQRE allows more natural and succinct specification of queries compared to existing frameworks, (2) the throughput of our implementation is higher than comparable systems (for example, two-to-four times greater than RxJava), and (3) the approximation algorithms supported by our implementation can lead to substantial memory savings.",
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"https://www.cis.upenn.edu/~alur/PLDI17.pdf",
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"title": "StreamQRE: modular specification and efficient evaluation of quantitative queries over streaming data",
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"year": 2017
},
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"name": "Nicolas Michael"
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"name": "Nitin Ramannavar"
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"name": "Yixiao Shen"
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"name": "Sheetal Patil"
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{
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"doi": "10.1145/3030207.3044530",
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"paperAbstract": "The evolution of cloud-computing imposes many challenges on performance testing and requires not only a different approach and methodology of performance evaluation and analysis, but also specialized tools and frameworks to support such work. In traditional performance testing, typically a single workload was run against a static test configuration. The main metrics derived from such experiments included throughput, response times, and system utilization at steady-state. While this may have been sufficient in the past, where in many cases a single application was run on dedicated hardware, this approach is no longer suitable for cloud-based deployments. Whether private or public cloud, such environments typically host a variety of applications on distributed shared hardware resources, simultaneously accessed by a large number of tenants running heterogeneous workloads. The number of tenants as well as their activity and resource needs dynamically change over time, and the cloud infrastructure reacts to this by reallocating existing or provisioning new resources. Besides metrics such as the number of tenants and overall resource utilization, performance testing in the cloud must be able to answer many more questions: How is the quality of service of a tenant impacted by the constantly changing activity of other tenants? How long does it take the cloud infrastructure to react to changes in demand, and what is the effect on tenants while it does so? How well are service level agreements met? What is the resource consumption of individual tenants? How can global performance metrics on application- and system-level in a distributed system be correlated to an individual tenant's perceived performance?\n In this paper we present <i>CloudPerf</i>, a performance test framework specifically designed for distributed and dynamic multi-tenant environments, capable of answering all of the above questions, and more. CloudPerf consists of a distributed harness, a protocol-independent load generator and workload modeling framework, an extensible statistics framework with live-monitoring and post-analysis tools, interfaces for cloud deployment operations, and a rich set of both low-level as well as high-level workloads from different domains.",
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"paperAbstract": "Achieving high performance on modern systems is challenging. Even with a detailed profile from a performance tool, writing or refactoring a program to remove its performance issues is still a daunting task for application programmers: it demands lots of program optimization expertise that is often system specific.\n Vendors often provide some detailed optimization guides to assist programmers in the process. However, these guides are frequently hundreds of pages long, making it difficult for application programmers to master and memorize all the rules and guidelines and properly apply them to a specific problem instance.\n In this work, we develop a framework named Egeria to alleviate the difficulty. Through Egeria, one can easily construct an advising tool for a certain high performance computing (HPC) domain (e.g., GPU programming) by providing Egeria with a optimization guide or other related documents for the target domain. An advising tool produced by Egeria provides a concise list of essential rules automatically extracted from the documents. At the same time, the advising tool serves as a question-answer agent that can interactively offers suggestions for specific optimization questions. Egeria is made possible through a distinctive multi-layered design that leverages natural language processing techniques and extends them with knowledge of HPC domains and how to extract information relevant to code optimization Experiments on CUDA, OpenCL, and Xeon Phi programming guides demonstrate, both qualitatively and quantitatively, the usefulness of Egeria for HPC.",
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"name": "T. V. Lakshman"
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"paperAbstract": "Stream processing pipelines operated by current big data streaming frameworks present two problems. First, the pipelines are not flexible, controllable, and programmable enough to accommodate dynamic streaming application needs. Second, the application-level data routing over the pipelines do not exhibit optimal performance for increasingly common one-to-many communication. To address these problems, we propose an SDN-based real-time big data streaming framework called Typhoon, that tightly integrates SDN functionality into a real-time stream framework. By partially offloading application-layer data routing and control to the network layer via SDN interfaces and protocols, Typhoon provides on-the-fly programmability of both the application and network layers, and achieve high-performance data routing. In addition, Typhoon SDN controller exposes cross-layer information, from both the application and the network, to SDN control plane applications to extend the framework's functionality. We introduce several SDN control plane applications to illustrate these benefits.",
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"paperAbstract": "Graphics Processing Units (GPUs) are increasingly used to accelerate portions of general-purpose applications. Higher level language extensions have been proposed to help non-experts bridge the gap between a host and the GPU's threading model. Recent updates to the OpenMP standard allow a user to parallelize code on a GPU using the well known fork-join programming model for CPUs. Mapping this model to the architecturally visible threading model of typical GPUs has been challenging. In this work we propose a novel approach using the technique of Warp Specialization. We show how to specialize one warp (a unit of 32 GPU threads) to handle sequential code on a GPU. When this master warp reaches a user-specified parallel region, it awakens unused GPU warps to collectively execute the parallel code. Based on this method, we have implemented a Clang-based, OpenMP 4.5 compliant, open source compiler for GPUs. Our work achieves a 3.6x (and up to 32x) performance improvement over a baseline that does not exploit fork-join parallelism on an NVIDIA k40m GPU across a set of 25 kernels. Compared to state-of-the-art compilers (Clang-ykt, GCC-OpenMP, GCC-OpenACC) our work is 2.1 - 7.6x faster. Our proposed technique is simpler to implement, robust, and performant.",
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"paperAbstract": "Big data systems such as Spark are built around the idea of splitting an iterative parallel program into tiny tasks with other aspects of system design built around this basic design principle. Unfortunately, in spite of immense engineering effort, tiny tasks have unavoidably large overheads. We use the example of logistic regression -- a common machine learning primitive -- to compare the performance of Spark to different designs that converge to a hand-coded parallel MPI-based implementation. We conclude that Spark leaves orders of magnitude performance on the table, due to its insistence on setting the granularity of a task to a single iteration. We counter a common argument for the tiny task approach --namely better resilience to faults -- by demonstrating that optimum job checkpoint intervals are far longer than the duration of the tiny tasks favored in Spark's design. We propose an alternative approach that relies on an auto-parallelizing compiler tightly integrated with the MPI runtime, illustrating the opposite end of the spectrum where task granularities are as large as possible.",
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"title": "BatchDB: Efficient Isolated Execution of Hybrid OLTP+OLAP Workloads for Interactive Applications",
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"year": 2017
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"venue": "2017 IEEE 10th International Conference on Cloud Computing (CLOUD)",
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"paperAbstract": "Online underground economy is an important channel that connects the merchants of illegal products and their buyers, which is also constantly monitored by legal authorities. As one common way for evasion, the merchants and buyers together create a vocabulary of jargons (called "black keywords" in this paper) to disguise the transaction (e.g., "smack" is one street name for "heroin" [1]). Black keywords are often "unfriendly" to the outsiders, which are created by either distorting the original meaning of common words or tweaking other black keywords. Understanding black keywords is of great importance to track and disrupt the underground economy, but it is also prohibitively difficult: the investigators have to infiltrate the inner circle of criminals to learn their meanings, a task both risky and time-consuming. In this paper, we make the first attempt towards capturing and understanding the ever-changing black keywords. We investigated the underground business promoted through blackhat SEO (search engine optimization) and demonstrate that the black keywords targeted by the SEOers can be discovered through a fully automated approach. Our insights are two-fold: first, the pages indexed under black keywords are more likely to contain malicious or fraudulent content (e.g., SEO pages) and alarmed by off-the-shelf detectors, second, people tend to query multiple similar black keywords to find the merchandise. Therefore, we could infer whether a search keyword is "black" by inspecting the associated search results and then use the related search queries to extend our findings. To this end, we built a system called KDES (Keywords Detection and Expansion System), and applied it to the search results of Baidu, China's top search engine. So far, we have already identified 478,879 black keywords which were clustered under 1,522 core words based on text similarity. We further extracted the information like emails, mobile phone numbers and instant messenger IDs from the pages and domains relevant to the underground business. Such information helps us gain better understanding about the underground economy of China in particular. In addition, our work could help search engine vendors purify the search results and disrupt the channel of the underground market. Our co-authors from Baidu compared our results with their blacklist, found many of them (e.g., long-tail and obfuscated keywords) were not in it, and then added them to Baidu's internal blacklist.",
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"title": "How to Learn Klingon without a Dictionary: Detection and Measurement of Black Keywords Used by the Underground Economy",
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"paperAbstract": "Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses.\n Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns.\n In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks.",
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"title": "POISED: Spotting Twitter Spam Off the Beaten Paths",
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"paperAbstract": "A large amount of valuable information resides in decentralized social graphs, where no entity has access to the complete graph structure. Instead, each user maintains locally a limited view of the graph. For example, in a phone network, each user keeps a contact list locally in her phone, and does not have access to other users' contacts. The contact lists of all users form an implicit social graph that could be very useful to study the interaction patterns among different populations. However, due to privacy concerns, one could not simply collect the unfettered local views from users and reconstruct a decentralized social network.\n In this paper, we investigate techniques to ensure local differential privacy of individuals while collecting structural information and generating representative synthetic social graphs. We show that existing local differential privacy and synthetic graph generation techniques are insufficient for preserving important graph properties, due to excessive noise injection, inability to retain important graph structure, or both. Motivated by this, we propose LDPGen, a novel multi-phase technique that incrementally clusters users based on their connections to different partitions of the whole population. Every time a user reports information, LDPGen carefully injects noise to ensure local differential privacy.We derive optimal parameters in this process to cluster structurally-similar users together. Once a good clustering of users is obtained, LDPGen adapts existing social graph generation models to construct a synthetic social graph.\n We conduct comprehensive experiments over four real datasets to evaluate the quality of the obtained synthetic graphs, using a variety of metrics, including (i) important graph structural measures; (ii) quality of community discovery; and (iii) applicability in social recommendation. Our experiments show that the proposed technique produces high-quality synthetic graphs that well represent the original decentralized social graphs, and significantly outperform those from baseline approaches.",
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"http://doi.acm.org/10.1145/3133956.3134086"
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"title": "Generating Synthetic Decentralized Social Graphs with Local Differential Privacy",
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"year": 2017
},
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"name": "Emily Johnston"
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"name": "Michael Pradel"
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"name": "Yulissa Arroyo-Paredes"
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"paperAbstract": "Identifier names are often used by developers to convey additional information about the meaning of a program over and above the semantics of the programming language itself. We present an algorithm that uses this information to detect argument selection defects, in which the programmer has chosen the wrong argument to a method call in Java programs. We evaluate our algorithm at Google on 200 million lines of internal code and 10 million lines of predominantly open-source external code and find defects even in large, mature projects such as OpenJDK, ASM, and the MySQL JDBC. The precision and recall of the algorithm vary depending on a sensitivity threshold. Higher thresholds increase precision, giving a true positive rate of 85%, reporting 459 true positives and 78 false positives. Lower thresholds increase recall but lower the true positive rate, reporting 2,060 true positives and 1,207 false positives. We show that this is an order of magnitude improvement on previous approaches. By analyzing the defects found, we are able to quantify best practice advice for API design and show that the probability of an argument selection defect increases markedly when methods have more than five arguments.",
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"title": "Detecting argument selection defects",
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"name": "Siyang Li"
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"name": "Youyou Lu"
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"name": "Jiwu Shu"
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"name": "Yang Hu"
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"name": "Tao Li"
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"doi": "10.1145/3126908.3126928",
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"paperAbstract": "Key-Value stores provide scalable metadata service for distributed file systems. However, the metadata's organization itself, which is organized using a directory tree structure, does not fit the key-value access pattern, thereby limiting the performance. To address this issue, we propose a distributed file system with a loosely-coupled metadata service, LocoFS, to bridge the performance gap between file system metadata and key-value stores. LocoFS is designed to decouple the dependencies between different kinds of metadata with two techniques. First, LocoFS decouples the directory content and structure, which organizes file and directory index nodes in a flat space while reversely indexing the directory entries. Second, it decouples the file metadata to further improve the key-value access performance. Evaluations show that LocoFS with eight nodes boosts the metadata throughput by 5 times, which approaches 93% throughput of a single-node key-value store, compared to 18% in the state-of-the-art IndexFS.",
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"title": "LocoFS: a loosely-coupled metadata service for distributed file systems",
"venue": "SC",
"year": 2017
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{
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"doi": "10.1145/3077136.3080827",
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"paperAbstract": "To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of <i>ephemeral</i> (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine.",
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"title": "Efficient & Effective Selective Query Rewriting with Efficiency Predictions",
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"paperAbstract": "The Oak Ridge Leadership Computing Facility (OLCF) runs Titan, the No. 4 supercomputer in the world, to deliver over four billion compute core hours every year to several scientific domains, in their pursuit of leadership science. In this paper, we analyze four years worth of heterogeneous log data sources from the OLCF resource fabric, capturing metadata on entities such as users (2,546), scientific project allocations (674), jobs (1,352,402) and publications (1,146), to derive insights into the trends in core hour usage and publications, across 35 science domains. We have constructed a scalable graph to represent the OLCF entities and apply rich graph analytics for our analysis. Based on this, we have analyzed the metadata across five dimensions, namely (1) quantitative analysis of Titan system usage, (2) quantitative analysis of OLCF publications, (3) correlation analysis between system usage and publications, (4) text analysis to derive OLCF research trends, and (5) utilization of graph mining for association analysis. To the best of our knowledge, our work is the first of its kind to apply graph- based big data techniques to provide comprehensive insights on an HPC center's core hour usage and users' publication trends. Our results provide valuable details into an HPC center's core allocation program, measuring the productivity of scientific domains, the interplay between core usage and research output, accelerating collaboration, and in predicting new connections between resource entities.",
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"paperAbstract": "Consider the following random process: we are given n queues, into which elements of increasing labels are inserted uniformly at random. To remove an element, we pick two queues at random, and remove the element of lower label (higher priority) among the two. The cost of a removal is the rank of the label removed, among labels still present in any of the queues, that is, the distance from the optimal choice at each step. Variants of this strategy are prevalent in state-of-the-art concurrent priority queue implementations. Nonetheless, it is not known whether such implementations provide any rank guarantees, even in a sequential model. We answer this question, showing that this strategy provides surprisingly strong guarantees: Although the single-choice process, where we always insert and remove from a single randomly chosen queue, has degrading cost, going to infinity as we increase the number of steps, in the two choice process, the expected rank of a removed element is O (n) while the expected worst-case cost is O (n logn). These bounds are tight, and hold irrespective of the number of steps for which we run the process. The argument is based on a new technical connection between \u201cheavily loaded\" ballsinto-bins processes and priority scheduling. Our analytic results inspire a new concurrent priority queue implementation, which improves upon the state of the art in terms of practical performance.",
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"paperAbstract": "RDMA (Remote Direct Memory Access) is a technology that enables user applications to perform direct data transfer between the virtual memory of processes on remote endpoints, without operating system involvement or intermediate data copies. Achieving zero intermediate data copies using RDMA requires specialized network interface hardware. Software RDMA drivers emulate RDMA semantics in software to allow the use of RDMA without investing in such hardware, although they cannot perform zero-copy transfers. Nonetheless, software RDMA drivers are useful for research, application development, testing, debugging, or as a less expensive desktopclient for a centralized RDMA server application running on RDMA-capable hardware.Existing software RDMA drivers perform data transfer in the kernel. Data Plane Development Kit (DPDK) provides a framework for mapping Ethernet interface cards into userspace and performing bulk packet transfers. This in turn allows a software RDMA driver to perform data transfer in userspace. We present our software RDMA driver, urdma, that performs data transfer in userspace, discuss its design and implementation, and demonstrate that it can achieve lower small message latency than existing kernel-based implementations while maintaining high bandwidth utilization for large messages.",
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"paperAbstract": "60 GHz millimeter-wave networking has emerged as the next frontier technology to provide multi-Gbps wireless connectivity. However, the intrinsic directionality and limited field-of-view of 60 GHz antennas make the links extremely sensitive to user mobility and orientation change. Hence, seamless coverage, even at room level, becomes challenging. In this paper, we propose Pia, a robust 60 GHz network architecture that can provide seamless coverage and mobility support at multi-Gbps bitrate. Pia comprises multiple cooperating access points (APs). It leverages the pose information on mobile clients to proactively select the AP and manage multi-link spatial reuse. These decisions require a model of the pose/location of the APs and ambient reflectors. We address these challenges through a set of AP-pose sensing and compressive angle estimation algorithms that fuse the pose measurement with link quality measurement on the client. We have implemented Pia using commodity 60 GHz platforms. Our experiments show that Pia reduces the occurrence of link outage by 6.3x and improves the spatial sharing capacity by 76%, compared to conventional schemes that only use in-band information for adaptation.",
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"title": "Pose Information Assisted 60 GHz Networks: Towards Seamless Coverage and Mobility Support",
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"year": 2017
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"paperAbstract": "Networks-on-Chip (NoCs) in chip multiprocessors are prone to within-die process variation as they span the whole chip. To tolerate variation, their voltages (Vdd) carry over-provisioned guardbands. As a result, prior work has proposed to save energy by operating at reduced Vdd while occasionally suffering and fixing errors. Unfortunately, these proposals use heuristic controller designs that provide no error bounds guarantees.In this work, we develop a scheme that dynamically minimizes the Vdd of groups of routers in a variation-prone NoC using formal control-theoretic methods. The scheme, called Sthira, saves substantial energy while guaranteeing the stability and convergence of error rates. We also enhance the scheme with a low-cost secondary network that retransmits erroneous packets for higher energy efficiency. The enhanced scheme is called Sthira+. We evaluate Sthira and Sthira+ with simulations of NoCs with 64-100 routers. In an NoC with 8 routers per Vdd domain, our schemes reduce the average energy consumptionof the NoC by 27%; in a futuristic NoC with one router per Vdd domain, Sthira+ and Sthira reduce the average energy consumption by 36% and 32%, respectively. The performance impact is negligible. These are significant savings over the state-of-the-art. We conclude that formal control is essential, and that the cheaper Sthira is more cost-effective than Sthira+.",
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},
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"paperAbstract": "Use-after-free vulnerabilities due to dangling pointers are an important and growing threat to systems security. While various solutions exist to address this problem, none of them is sufficiently practical for real-world adoption. Some can be bypassed by attackers, others cannot support complex multithreaded applications prone to dangling pointers, and the remainder have prohibitively high overhead. One major source of overhead is the need to synchronize threads on every pointer write due to pointer tracking.\n In this paper, we present DangSan, a use-after-free detection system that scales efficiently to large numbers of pointer writes as well as to many concurrent threads. To significantly reduce the overhead of existing solutions, we observe that pointer tracking is write-intensive but requires very few reads. Moreover, there is no need for strong consistency guarantees as inconsistencies can be reconciled at read (i.e., object deallocation) time. Building on these intuitions, DangSan's design mimics that of log-structured file systems, which are ideally suited for similar workloads. Our results show that DangSan can run heavily multithreaded applications, while introducing only half the overhead of previous multithreaded use-after-free detectors.",
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"paperAbstract": "OLAP tools have been extensively used by enterprises to make better and faster decisions. Nevertheless, they require users to specify group-by attributes and know precisely what they are looking for. This paper takes the first attempt towards automatically extracting top-<i>k</i> <i>insights</i> from multi-dimensional data. This is useful not only for non-expert users, but also reduces the manual effort of data analysts. In particular, we propose the concept of <i>insight</i> which captures interesting observation derived from aggregation results in multiple steps (e.g., rank by a dimension, compute the percentage of measure by a dimension). An example insight is: ``Brand B's rank (across brands) falls along the year, in terms of the increase in sales''. Our problem is to compute the top-<i>k</i> insights by a score function. It poses challenges on (i) the effectiveness of the result and (ii) the efficiency of computation. We propose a meaningful scoring function for insights to address (i). Then, we contribute a computation framework for top-<i>k</i> insights, together with a suite of optimization techniques (i.e., pruning, ordering, specialized cube, and computation sharing) to address (ii). Our experimental study on both real data and synthetic data verifies the effectiveness and efficiency of our proposed solution.",
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"paperAbstract": "Power reduction is one of the primary tasks for designing modern processors, especially for high-performance throughput processors such as GPU due to their high power budget. In this paper, we propose a novel circuit-architecture co-design scheme to harvest enormous power savings for GPU on-chip SRAM and interconnects. We propose a new 8T SRAM that exhibits asymmetric energy consumption for bit value 0/1, in terms of read, write and standby. We name this feature <i>Bit-Value-Favor</i> (BVF). To harvest the power benefits from BVF on GPUs, we propose three coding methods at architectural level to maximize the occurrence of bit-1s over bit-0s in the on-chip data and instruction streams, leading to substantial chip-level power reduction. Experimental results across a large spectrum of 58 representative GPU applications demonstrate that our proposed BVF design can bring an average of 21% and 24% chip power reduction under 28nm and 40nm process technologies, with negligible design overhead. Further sensitivity studies show that the effectiveness of our design is robust to DVFS, warp scheduling policies and different SRAM capacities.",
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"http://doi.acm.org/10.1145/3123939.3123944"
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"title": "BVF: enabling significant on-chip power savings via bit-value-favor for throughput processors",
"venue": "MICRO",
"year": 2017
},
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"name": "Amanieu D'Antras"
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"name": "Cosmin Gorgovan"
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"name": "Jim D. Garside"
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"name": "John Goodacre"
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{
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"name": "Mikel Luj\u00e1n"
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"doi": "10.1145/3050748.3050756",
"doiUrl": "https://doi.org/10.1145/3050748.3050756",
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"paperAbstract": "Current computer architectures --- ARM, MIPS, PowerPC, SPARC, x86 --- have evolved from a 32-bit architecture to a 64-bit one. Computer architects often consider whether it could be possible to eliminate hardware support for a subset of the instruction set as to reduce hardware complexity, which could improve performance, reduce power usage and accelerate processor development. This paper considers the scenario where we want to eliminate 32-bit hardware support from the ARMv8 architecture.\n Dynamic binary translation can be used for this purpose and generally comes in one of two forms: application-level translators that translate a single user mode process on top of a native operating system, and system-level translators that translate an entire operating system and all its processes.\n Application-level translators can have good performance but is not totally transparent; system-level translators may be 100% compatible but performance suffers. HyperMAMBO-X64 uses a new approach that gets the best of both worlds, being able to run the translator as an application under the hypervisor but still react to the behavior of guest operating systems. It works with complete transparency with regards to the virtualized system whilst delivering performance close to that provided by hardware execution.\n A key factor in the low overhead of HyperMAMBO-X64 is its deep integration with the virtualization and memory management features of ARMv8. These are exploited to support caching of translations across multiple address spaces while ensuring that translated code remains consistent with the source instructions it is based on. We show how these attributes are achieved without sacrificing either performance or accuracy.",
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"title": "HyperMAMBO-X64: Using Virtualization to Support High-Performance Transparent Binary Translation",
"venue": "VEE",
"year": 2017
},
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"name": "Dimitrios Skarlatos"
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"name": "Nam Sung Kim"
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"name": "Josep Torrellas"
}
],
"doi": "10.1145/3123939.3124540",
"doiUrl": "https://doi.org/10.1145/3123939.3124540",
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"paperAbstract": "To reduce the memory requirements of virtualized environments, modern hypervisors are equipped with the capability to search the memory address space and merge identical pages --- a process called page deduplication. This process uses a combination of data hashing and exhaustive comparison of pages, which consumes processor cycles and pollutes caches.\n In this paper, we present a lightweight hardware mechanism that augments the memory controller and performs the page merging process with minimal hypervisor involvement. Our concept, called <i>PageForge</i>, is effective. It compares pages in the memory controller, and repurposes the Error Correction Codes (ECC) engine to generate accurate and inexpensive ECC-based hash keys. We evaluate PageForge with simulations of a 10-core processor with a virtual machine (VM) on each core, running a set of applications from the TailBench suite. When compared with RedHat's KSM, a state-of-the-art software implementation of page merging, PageForge attains identical savings in memory footprint while substantially reducing the overhead. Compared to a system without same-page merging, PageForge reduces the memory footprint by an average of 48%, enabling the deployment of twice as many VMs for the same physical memory. Importantly, it keeps the average latency overhead to 10%, and the 95<sup>th</sup> percentile tail latency to 11%. In contrast, in KSM, these latency overheads are 68% and 136%, respectively.",
"pdfUrls": [
"http://skarlat2.web.engr.illinois.edu/publications/pageForge_micro17.pdf",
"http://iacoma.cs.uiuc.edu/iacoma-papers/PRES/present_micro17_1.pdf",
"http://doi.acm.org/10.1145/3123939.3124540",
"http://iacoma.cs.uiuc.edu/iacoma-papers/micro17_1.pdf"
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"s2Url": "https://semanticscholar.org/paper/9bce69cb320f770ba693ee0313514e49cc4ca1a0",
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"title": "Pageforge: a near-memory content-aware page-merging architecture",
"venue": "MICRO",
"year": 2017
},
"9bd40ca73af180ed147c097a2d017c4334c692ff": {
"authors": [
{
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"name": "Vinay Vasista"
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{
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"name": "Kumudha Narasimhan"
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"name": "Siddharth Bhat"
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{
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"name": "Uday Bondhugula"
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],
"doi": "10.1145/3126908.3126968",
"doiUrl": "https://doi.org/10.1145/3126908.3126968",
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"paperAbstract": "The Geometric Multigrid (GMG) method is widely used in numerical analysis to accelerate the convergence of partial differential equations solvers using a hierarchy of grid discretizations. Multiple grid sizes and recursive expression of multigrid cycles make the task of program optimization tedious. A high-level language that aids domain experts for GMG with effective optimization and parallelization support is thus valuable.\n We demonstrate how high performance can be achieved along with enhanced programmability for GMG, with new language/optimization support in the PolyMage DSL framework. We compare our approach with (a) hand-optimized code, (b) hand-optimized code in conjunction with polyhedral optimization techniques, and (c) the existing PolyMage optimizer adapted to multigrid. We use benchmarks varying in multigrid cycle structure and smoothing steps for evaluation. On a 24-core Intel Xeon Haswell multicore system, our automatically optimized codes achieve a mean improvement of 3.2x over straightforward parallelization, and 1.31x over the PolyMage optimizer.",
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"title": "Optimizing geometric multigrid method computation using a DSL approach",
"venue": "SC",
"year": 2017
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"name": "Feilong Liu"
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"name": "Lingyan Yin"
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{
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"name": "Spyros Blanas"
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],
"doi": "10.1145/3064176.3064202",
"doiUrl": "https://doi.org/10.1145/3064176.3064202",
"entities": [
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"paperAbstract": "The commoditization of high-performance networking has sparked research interest in the RDMA capability of this hardware. One-sided RDMA primitives, in particular, have generated substantial excitement due to the ability to directly access remote memory from within an application without involving the TCP/IP stack or the remote CPU. This paper considers how to leverage RDMA to improve the analytical performance of parallel database systems. To shuffle data efficiently using RDMA, one needs to consider a complex design space that includes (1) the number of open connections, (2) the contention for the shared network interface, (3) the RDMA transport function, and (4) how much memory should be reserved to exchange data between nodes during query processing. We contribute six designs that capture salient trade-offs in this design space. We comprehensively evaluate how transport-layer decisions impact the query performance of a database system for different generations of InfiniBand. We find that a shuffling operator that uses the RDMA Send/Receive transport function over the Unreliable Datagram transport service can transmit data up to 4× faster than an RDMA-capable MPI implementation in a 16-node cluster. The response time of TPC-H queries improves by as much as 2×.",
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"title": "Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems",
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"paperAbstract": "Full duplex techniques can potentially double the channel capacity and achieve lower delays by empowering two radios to simultaneously transmit in thesame frequency band.However, full duplex is only available between two adjacent nodes within the communication range. In this paper, we present BiPass to break this limitation.With the help of full duplex capable relays, weenable simultaneous bidirectional in-band cut-through transmissions between two far apart nodes, so they can do full duplex communications as if they were within each other's transmission range.To design such a system, we analyze interference patterns and propose a loop-back interference cancellation strategy. We identify the power amplification problem at relay nodes and develop an algorithm to solve it. We also develop a routing algorithm, an opportunistic forwarding scheme, and a real-time feedback strategy to leverage this system in ad-hoc networks.To evaluate the real world performance of BiPass, we build a prototype and conduct experiments using software defined radios. We show that BiPass can achieve 1.6x median throughput gain over state-of-the-art one-way cut-through systems, and 4.09x gain over the decode-and-forward scheme. Our simulations further reveal that even when the data traffic is not bidirectional, BiPass has 1.36x throughput gain and 47\\% delay reduction overone-way cut-through systemsin large networks.",
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"paperAbstract": "In order to keep the costs of operating in-memory storage on the public cloud low, we devise novel ideas and enabling modeling and optimization techniques for combining conventional Amazon EC2 instances with the cheaper spot and burstable instances. Whereas a naturally appealing way of using failure-prone spot instances is to selectively store unpopular (\"cold\") content, we show that a form of \"hot-cold mixing\" across regular and spot instances might be more cost-effective. To overcome performance degradation resulting from spot instance revocations, we employ a highly available passive backup using the recently emergent burstable instances. We show how the idiosyncratic resource allocations of burstable instances make them ideal candidates for such a backup. We implement all our ideas in an EC2-based memcached prototype. Using simulations and live experiments on our prototype, we show that (i) our hot-cold mixing, informed by our modeling of spot prices, helps improve cost savings by 50-80% compared to only using regular instances, and (ii) our burstable-based backup helps reduce performance degradation during spot revocation, e.g., the 95% latency during failure recovery improves by 25% compared to a backup based on regular instances.",
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"paperAbstract": "In today's large-scale High Performance Computing (HPC) systems, an increasing portion of the computing capacity is wasted due to failures and recoveries. It is expected that exascale machines will decrease the mean time between failures to a few hours, making fault tolerance a major challenge. This work explores novel methodologies to fault tolerance that achieve forward recovery, power-awareness, and scalability. The proposed model, referred to as Rejuvenating Shadows, is able to deal with multiple types of failure and maintain consistent level of resilience. An implementation is provided for MPI, and empirically evaluated with various benchmark applications that represent a wide range of HPC workloads. The results demonstrate Rejuvenating Shadows' ability to tolerate high failure rates, and to outperform in-memory checkpointing/restart in both execution time and resource utilization.",
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"paperAbstract": "Tailoring the operating voltage to fine-grain temporal changes in the power and performance needs of the workload can effectively enhance power efficiency. Therefore, power-limited computing platforms of today widely deploy integrated (i.e., on-chip) voltage regulation which enables fast fine-grain voltage control. Voltage regulators convert and distribute power from an external energy source to the processor. Unfortunately, power conversion loss is inevitable and projected integrated regulator designs are unlikely to eliminate this loss even asymptotically. Reconfigurable power delivery by selective shut-down, i.e., gating, of distributed on-chip regulators in response to spatio-temporal changes in power demand can sustain operation at the minimum conversion loss. However, even the minimum conversion loss is sizable, and as conversion loss gets dissipated as heat, on-chip regulators can easily cause thermal emergencies due to their small footprint.\n Although reconfigurable distributed on-chip power delivery is emerging as a new design paradigm to enforce sustained operation at minimum possible power conversion loss, thermal implications have been overlooked at the architectural level. This paper hence provides a thermal characterization. We introduce ThermoGater, an architectural governor for a collection of practical, thermally-aware regulator gating policies to mitigate (if not prevent) regulator-induced thermal emergencies, which also consider potential implications for voltage noise. Practical ThermoGater policies can not only sustain minimum power conversion loss throughout execution effectively, but also keep the maximum temperature (thermal gradient) across chip within 0.6°C (0.3°C) on average in comparison to thermally-optimal oracular regulator gating, while the maximum voltage noise stays within 1.0% of the best case voltage noise profile.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3079856.3080250",
"http://people.ece.umn.edu/~ukarpuzc/Karpuzcu_files/thermoGaterTalk.pdf",
"http://altai.ece.umn.edu/Main_files/isca17.pdf"
],
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"sources": [
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"title": "ThermoGater: Thermally-aware on-chip voltage regulation",
"venue": "2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA)",
"year": 2017
},
"9c7218c8effa7691d507b08d4b222c403ce26c4a": {
"authors": [
{
"ids": [
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],
"name": "Jean Karim Zinzindohou\u00e9"
},
{
"ids": [
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],
"name": "Karthikeyan Bhargavan"
},
{
"ids": [
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],
"name": "Jonathan Protzenko"
},
{
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],
"name": "Benjamin Beurdouche"
}
],
"doi": "10.1145/3133956.3134043",
"doiUrl": "https://doi.org/10.1145/3133956.3134043",
"entities": [
"64-bit computing",
"Algorithm",
"Application programming interface",
"Assembly language",
"Authentication",
"Clang",
"CompCert",
"Compiler",
"Correctness (computer science)",
"Cryptographic primitive",
"Cryptography",
"Drop-in replacement",
"Encryption",
"Fastest",
"GNU Compiler Collection",
"Hash function",
"Hash-based message authentication code",
"High- and low-level",
"Library",
"Memory safety",
"Message authentication",
"Microsoft CryptoAPI",
"Novell Storage Services",
"OpenSSL",
"Poly1305",
"Programming language",
"SHA-2",
"Transport Layer Security",
"Type signature"
],
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"journalName": "IACR Cryptology ePrint Archive",
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"paperAbstract": "HACL* is a verified portable C cryptographic library that implements modern cryptographic primitives such as the ChaCha20 and Salsa20 encryption algorithms, Poly1305 and HMAC message authentication, SHA-256 and SHA-512 hash functions, the Curve25519 elliptic curve, and Ed25519 signatures.\n HACL* is written in the F* programming language and then compiled to readable C code. The F* source code for each cryptographic primitive is verified for memory safety, mitigations against timing side-channels, and functional correctness with respect to a succinct high-level specification of the primitive derived from its published standard. The translation from F* to C preserves these properties and the generated C code can itself be compiled via the CompCert verified C compiler or mainstream compilers like GCC or CLANG. When compiled with GCC on 64-bit platforms, our primitives are as fast as the fastest pure C implementations in OpenSSL and libsodium, significantly faster than the reference C code in TweetNaCl, and between 1.1x-5.7x slower than the fastest hand-optimized vectorized assembly code in SUPERCOP.\n HACL* implements the NaCl cryptographic API and can be used as a drop-in replacement for NaCl libraries like libsodium and TweetNaCl. HACL* provides the cryptographic components for a new mandatory ciphersuite in TLS 1.3 and is being developed as the main cryptographic provider for the miTLS verified implementation. Primitives from HACL* are also being integrated within Mozilla's NSS cryptographic library. Our results show that writing fast, verified, and usable C cryptographic libraries is now practical.",
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"https://eprint.iacr.org/2017/536.pdf",
"http://doi.acm.org/10.1145/3133956.3134043",
"http://eprint.iacr.org/2017/536"
],
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"sources": [
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],
"title": "HACL*: A Verified Modern Cryptographic Library",
"venue": "CCS",
"year": 2017
},
"9cf7699e39fa3aad6b60aa69822dda6d06f600d7": {
"authors": [
{
"ids": [
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"name": "Junfeng Zhou"
},
{
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"name": "Shijie Zhou"
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{
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],
"name": "Jeffrey Xu Yu"
},
{
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"name": "Hao Wei"
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{
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"name": "Ziyang Chen"
},
{
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],
"name": "Xian Tang"
}
],
"doi": "10.1145/3035918.3035927",
"doiUrl": "https://doi.org/10.1145/3035918.3035927",
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"Algorithm",
"Connected component (graph theory)",
"Database",
"Directed acyclic graph",
"Directed graph",
"Graph (discrete mathematics)",
"Graph operations",
"Reachability",
"Strongly connected component",
"Time complexity",
"Transitive reduction"
],
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"paperAbstract": "Answering reachability queries is one of the fundamental graph operations. The existing approaches build indexes and answer reachability queries on a directed acyclic graph (<i>DAG</i>) <i>G</i>, which is constructed by coalescing each strongly connected component of the given directed graph G into a node of <i>G</i>. Considering that <i>G</i> can still be large to be processed efficiently, there are studies to further reduce <i>G</i> to a smaller graph. However, these approaches suffer from either inefficiency in answering reachability queries, or cannot scale to large graphs.\n In this paper, we study <i>DAG</i> reduction to accelerate reachability query processing, which reduces the size of <i>G</i> by computing transitive reduction (<i>TR</i>) followed by computing equivalence reduction (<i>ER</i>). For <i>ER</i>, we propose a divide-and-conquer algorithm, namely <i>linear-ER</i>. Given the result <i>G<sup>t</sup></i> of <i>TR</i>, <i>linear-ER</i> gets a smaller <i>DAG G</i><sup>ε</sup> in linear time based on equivalence relationship between nodes in <i>G</i>. Our <i>DAG</i> reduction approaches (<i>TR</i> and <i>ER</i>) significantly improve the cost of time and space, and can be scaled to large graphs. We confirm the efficiency of our approaches by extensive experimental studies for <i>TR</i>, <i>ER</i>, and reachability query processing using 20 real datasets.",
"pdfUrls": [
"http://doi.acm.org/10.1145/3035918.3035927"
],
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"sources": [
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"title": "DAG Reduction: Fast Answering Reachability Queries",
"venue": "SIGMOD Conference",
"year": 2017
},
"9cff27c2c79e0532174e9b2b145cf6cffe10b0e8": {
"authors": [
{
"ids": [
"1695071"
],
"name": "Bo Wan"
},
{
"ids": [
"4523297"
],
"name": "Haizhao Luo"
},
{
"ids": [
"9368082"
],
"name": "Kaiqi Zhou"
},
{
"ids": [
"6916241"
],
"name": "Xi Li"
},
{
"ids": [
"1722340"
],
"name": "Chao Wang"
},
{
"ids": [
"2798613"
],
"name": "Xianglan Chen"
},
{
"ids": [
"8453780"
],
"name": "Xuehai Zhou"
}
],
"doi": "10.1109/HPCC-SmartCity-DSS.2017.75",
"doiUrl": "https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.75",
"entities": [
"Autopilot",
"Best, worst and average case",
"Performance",
"Programming paradigm",
"Real-time computing",
"Real-time operating system",
"Responsiveness"
],
"id": "9cff27c2c79e0532174e9b2b145cf6cffe10b0e8",
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"journalName": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
"journalPages": "578-585",
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"paperAbstract": "Real-time systems need reliable guarantees for the satisfaction of their timing constraints. However, novel speed-up hardware architectures and software mechanism, which target improving average-case performances, ignore and sometimes worsen the ability to obtain guarantees. An alternative approach is the Logical Execution Time (LET) model, but there are some deficiencies in existing LET-based development tools. In this paper, we propose a novel LET-based time-aware programming framework called TipFrame. The framework introduces Servants to improve the responsiveness of LET-based periodic tasks further. The runtime makes behaviors in the system level consistent with the semantics of LET model for predictability. TipFrame implements in C language providing time-aware programming interfaces called TipFrame-C. The programming paradigm of TipFrame-C is described using an autopilot avionic control system. Evaluation results demonstrate that our approach is effective and efficient to construct LET-based real-time systems.",
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"https://doi.org/10.1109/HPCC-SmartCity-DSS.2017.75"
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"title": "A Time-Aware Programming Framework for Constructing Predictable Real-Time Systems",
"venue": "2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)",
"year": 2017
},
"9d6ef820108deea58af84ef1385fa50f1687bfcb": {
"authors": [
{
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],
"name": "Pengjie Ren"
},
{
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"name": "Zhumin Chen"
},
{
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],
"name": "Zhaochun Ren"
},
{
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"name": "Furu Wei"
},
{
"ids": [
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],
"name": "Jun Ma"
},
{
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],
"name": "Maarten de Rijke"
}
],
"doi": "10.1145/3077136.3080792",
"doiUrl": "https://doi.org/10.1145/3077136.3080792",
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"paperAbstract": "Log-structured merge (LSM) data stores enable to store and process large volumes of data while maintaining good performance. They mitigate the I/O bottleneck by absorbing updates in a memory layer and transferring them to the disk layer in sequential batches. Yet, the LSM architecture fundamentally requires elements to be in sorted order. As the amount of data in memory grows, maintaining this sorted order becomes increasingly costly. Contrary to intuition, existing LSM systems could actually lose throughput with larger memory components.\n In this paper, we introduce FloDB, an LSM memory component architecture which allows throughput to scale on modern multicore machines with ample memory sizes. The main idea underlying FloDB is essentially to bootstrap the traditional LSM architecture by adding a small in-memory buffer layer on top of the memory component. This buffer offers low-latency operations, masking the write latency of the sorted memory component. Integrating this buffer in the classic LSM memory component to obtain FloDB is not trivial and requires revisiting the algorithms of the user-facing LSM operations (search, update, scan). FloDB's two layers can be implemented with state-of-the-art, highly-concurrent data structures. This way, as we show in the paper, FloDB eliminates significant synchronization bottlenecks in classic LSM designs, while offering a rich LSM API.\n We implement FloDB as an extension of LevelDB, Google's popular LSM key-value store. We compare FloDB's performance to that of state-of-the-art LSMs. In short, FloDB's performance is up to one order of magnitude higher than that of the next best-performing competitor in a wide range of multi-threaded workloads.",
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"https://infoscience.epfl.ch/record/227333/files/flodb-infoscience.pdf",
"http://doi.acm.org/10.1145/3064176.3064193",
"https://infoscience.epfl.ch/record/227333/files/flodb-eurosys-presentation-conference.compressed.pdf"
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"title": "FloDB: Unlocking Memory in Persistent Key-Value Stores",
"venue": "EuroSys",
"year": 2017
},
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"authors": [
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"name": "Sebastian Werner"
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"name": "Javier Navaridas"
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{
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"name": "Mikel Luj\u00e1n"
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"doi": "10.1109/HPCA.2017.23",
"doiUrl": "https://doi.org/10.1109/HPCA.2017.23",
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"paperAbstract": "Optical on-chip communication is considered a promising candidate to overcome latency and energy bottlenecks of electrical interconnects. Although recently proposed hybrid Networks-on-chip (NoCs), which implement both electrical and optical links, improve power efficiency, they often fail to combine these two interconnect technologies efficiently and suffer from considerable laser power overheads caused by high-bandwidth optical links. We argue that these overheads can be avoided by inserting a higher quantity of low-bandwidth optical links in a topology, as this yields lower optical loss and in turn laser power. Moreover, when optimally combined with electrical links for short distances, this can be done without trading off latency. We present the effectiveness of this concept with Lego, our hybrid, mesh-based NoC that provides high power efficiency by utilizing electrical links for local traffic, and low-bandwidth optical links for long distances. Electrical links are placed systematically to outweigh the serialization delay introduced by the optical links, simplify router microarchitecture, and allow to save optical resources. Our routing algorithm always chooses the link that offers the lowest latency and energy. Compared to state-of-the-art proposals, Lego increases throughput-per-watt by at least 40%, and lowers latency by 35% on average for synthetic traffic. On SPLASH-2/PARSEC workloads, Lego improves power efficiency by at least 37% (up to 3.5x).",
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"title": "Designing Low-Power, Low-Latency Networks-on-Chip by Optimally Combining Electrical and Optical Links",
"venue": "2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)",
"year": 2017
},
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"name": "Mohsen Ghaffari"
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"name": "Fabian Kuhn"
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"name": "Hsin-Hao Su"
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"doi": "10.1145/3087801.3087827",
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"paperAbstract": "We present a randomized distributed algorithm that computes a minimum spanning tree in \u03c4mix (G ) \u00b7 2O ( \u221a logn log logn) ) rounds, in any n-node graphG with mixing time \u03c4mix (G ). This result provides a sub-polynomial complexity for a wide range of graphs of practical interest, and goes below the celebrated \u03a9\u0303(D + \u221a n) lower bound of Das Sarma et al. [STOC\u201911] which holds for some worst-case general graphs. The core novelty in this result is a distributed method for permutation routing. In this problem, one is given a number of sourcedestination pairs, and we should deliver one packet from each source to its destination, all in parallel, in the shortest span of time possible. Our algorithm allows us to route and deliver all these packets in \u03c4mix (G ) \u00b7 2O ( \u221a logn log logn) rounds, assuming that each node v is the source or destination for at most dG (v ) packets. The main technical ingredient in this routing result is a certain hierarchical embedding of good-expansion random graphs on the base graph, which we believe can be of interest well beyond this work.",
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"title": "Distributed MST and Routing in Almost Mixing Time",
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"paperAbstract": "Nested virtualization, the ability to run a virtual machine inside another virtual machine, is increasingly important because of the need to deploy virtual machines running software stacks on top of virtualized cloud infrastructure. As ARM servers make inroads in cloud infrastructure deployments, supporting nested virtualization on ARM is a key requirement, which has been met recently with the introduction of nested virtualization support to the ARM architecture. We build the first hypervisor to use ARM nested virtualization support and show that despite similarities between ARM and x86 nested virtualization support, performance on ARM is much worse than on x86. This is due to excessive traps to the hypervisor caused by differences in non-nested virtualization support. To address this problem, we introduce a novel paravirtualization technique to rapidly prototype architectural changes for virtualization and evaluate their performance impact using existing hardware. Using this technique, we propose Nested Virtualization Extensions for ARM (NEVE), a set of simple architectural changes to ARM that can be used by software to coalesce and defer traps by logging the results of hypervisor instructions until the results are actually needed by the hypervisor or virtual machines. We show that NEVE allows hypervisors running real application workloads to provide an order of magnitude better performance than current ARM nested virtualization support and up to three times less overhead than x86 nested virtualization. NEVE will be included in ARMv8.4, the next version of the ARM architecture.",
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"paperAbstract": "De novo genome assembly is one of the most important and challenging computational problems in modern genomics; further, it shares algorithms and communication patterns important to other graph analytic and irregular applications. Unlike simulations, it has no floating point arithmetic and is dominated by small memory transactions within and between computing nodes. In this work, we focus on the highly scalable HipMer assembler and identify the dominant algorithms and communication patterns, also using microbenchmarks to capture the workload. We evaluate HipMer on a variety of platforms from the latest HPC systems to ethernet clusters. HipMer performs well on all single node systems, including the Xeon Phi manycore architecture. Given large enough problems, it also demonstrates excellent scaling across nodes in an HPC system, but requires a high speed network with low overhead and high injection rates. Our results shed light on the architectural features that are most important for achieving good parallel efficiency on this and related problems. AQ1",
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"title": "Performance Characterization of De Novo Genome Assembly on Leading Parallel Systems",
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"paperAbstract": "To improve the security of user-chosen Android screen lock patterns, we propose a novel system-guided pattern lock scheme called "SysPal" that mandates the use of a small number of randomly selected points while selecting a pattern. Users are given the freedom to use those mandated points at any position. We conducted a large-scale online study with 1,717 participants to evaluate the security and usability of three SysPal policies, varying the number of mandatory points that must be used (upon selecting a pattern) from one to three. Our results suggest that the two SysPal policies that mandate the use of one and two points can help users select significantly more secure patterns compared to the current Android policy: 22.58% and 23.19% fewer patterns were cracked. Those two SysPal policies, however, did not show any statistically significant inferiority in pattern recall success rate (the percentage of participants who correctly recalled their pattern after 24 hours). In our lab study, we asked participants to install our screen unlock application on their own Android device, and observed their real-life phone unlock behaviors for a day. Again, our lab study did not show any statistically significant difference in memorability for those two SysPal policies compared to the current Android policy.",
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"paperAbstract": "With rapidly increasing parallelism, DRAM performance and power have surfaced as primary constraints from consumer electronics to high performance computing (HPC) for a variety of applications, including bulk-synchronous data-parallel applications which are key drivers for multi-core, with examples including image processing, climate modeling, physics simulation, gaming, face recognition, and many others. We present the last-level collective prefetcher (LLCP), a purely hardware last-level cache (LLC) prefetcher that exploits the highly correlated prefetch patterns of data-parallel algorithms that would otherwise not be recognized by a prefetcher that is oblivious to data parallelism. LLCP generates prefetches on behalf of multiple cores in memory address order to maximize DRAM efficiency and bandwidth, and can prefetch from multiple memory pages without expensive translations. Compared to well-established other prefetchers, LLCP improves execution time by 5.5% on average (10% maximum), increases DRAM bandwidth by 9% to 18%, decreases DRAM rank energy by 6%, produces 27% more timely prefetches, and increases coverage by 25% at minimum.",
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"paperAbstract": "Modern Integrated Circuits (ICs) employ several classes of countermeasures to mitigate physical attacks. Recently, a powerful semi-invasive attack relying on optical contactless probing has been introduced, which can assist the attacker in circumventing the integrated countermeasures and probe the secret data on a chip. This attack can be mounted using IC debug tools from the backside of the chip. The first published attack based on this technique was conducted against a proof-of-concept hardware implementation on a Field Programmable Gate Array (FPGA). Therefore, the success of optical probing techniques against a real commercial device without any knowledge of the hardware implementation is still questionable. The aim of this work is to assess the threat of optical contactless probing in a real attack scenario. To this end, we conduct an optical probing attack against the bitstream encryption feature of a common FPGA. We demonstrate that the adversary is able to extract the plaintext data containing sensitive design information and intellectual property (IP). In contrast to previous optical attacks from the IC backside, our attack does not require any device preparation or silicon polishing, which makes it a non-invasive attack. Additionally, we debunk the myth that small technology sizes are unsusceptible to optical attacks, as we use an optical resolution of about 1 um to successfully attack a 28 nm device. Based on our time measurements, an attacker needs less than 10 working days to conduct the optical analysis and reverse-engineer the security-related parts of the hardware. Finally, we propose and discuss potential countermeasures, which could make the attack more challenging.",
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"title": "On the Power of Optical Contactless Probing: Attacking Bitstream Encryption of FPGAs",
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"paperAbstract": "In this paper, we propose a browser fingerprinting technique that can track users not only within a single browser but also across different browsers on the same machine. Specifically, our approach utilizes many novel OS and hardware level features, such as those from graphics cards, CPU, and installed writing scripts. We extract these features by asking browsers to perform tasks that rely on corresponding OS and hardware functionalities. Our evaluation shows that our approach can successfully identify 99.24% of users as opposed to 90.84% for state of the art on single-browser fingerprinting against the same dataset. Further, our approach can achieve higher uniqueness rate than the only cross-browser approach in the literature with similar stability.",
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"paperAbstract": "Program obfuscation is a powerful security primitive with many applications. White-box cryptography studies a particular subset of program obfuscation targeting keyed pseudorandom functions (PRFs), a core component of systems such as mobile payment and digital rights management. Although the white-box obfuscators currently used in practice do not come with security proofs and are thus routinely broken, recent years have seen an explosion of <i>cryptographic</i> techniques for obfuscation, with the goal of avoiding this build-and-break cycle.\n In this work, we explore in detail cryptographic program obfuscation and the related primitive of multi-input functional encryption (MIFE). In particular, we extend the 5Gen framework (CCS 2016) to support circuit-based MIFE and program obfuscation, implementing both existing and new constructions. We then evaluate and compare the efficiency of these constructions in the context of PRF obfuscation.\n As part of this work we (1) introduce a novel instantiation of MIFE that works directly on functions represented as arithmetic circuits, (2) use a known transformation from MIFE to obfuscation to give us an obfuscator that performs better than all prior constructions, and (3) develop a compiler for generating circuits optimized for our schemes. Finally, we provide detailed experiments, demonstrating, among other things, the ability to obfuscate a PRF with a 64-bit key and 12 bits of input (containing 62k gates) in under 4 hours, with evaluation taking around 1 hour. This is by far the most complex function obfuscated to date.",
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"title": "5Gen-C: Multi-input Functional Encryption and Program Obfuscation for Arithmetic Circuits",
"venue": "IACR Cryptology ePrint Archive",
"year": 2017
},
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"name": "Antonio Mallia"
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"name": "Giuseppe Ottaviano"
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"name": "Elia Porciani"
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"name": "Nicola Tonellotto"
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"paperAbstract": "Query processing is one of the main bottlenecks in large-scale search engines. Retrieving the top <i>k</i> most relevant documents for a given query can be extremely expensive, as it involves scoring large amounts of documents. Several <i>dynamic pruning</i> techniques have been introduced in the literature to tackle this problem, such as BlockMaxWAND, which splits the inverted index into constant- sized blocks and stores the maximum document-term scores per block; this information can be used during query execution to safely skip low-score documents, producing many-fold speedups over exhaustive methods.\n We introduce a refinement for BlockMaxWAND that uses variable- sized blocks, rather than constant-sized. We set up the problem of deciding the block partitioning as an optimization problem which maximizes how accurately the block upper bounds represent the underlying scores, and describe an efficient algorithm to find an approximate solution, with provable approximation guarantees. rough an extensive experimental analysis we show that our method significantly outperforms the state of the art roughly by a factor 2×. We also introduce a compressed data structure to represent the additional block information, providing a compression ratio of roughly 50%, while incurring only a small speed degradation, no more than 10% with respect to its uncompressed counterpart.",
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"title": "Faster BlockMax WAND with Variable-sized Blocks",
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"title": "Automatic program inversion using symbolic transducers",
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"paperAbstract": "Existing network policy abstractions handle basic group based reachability and access control list based security policies. However, QoS policies as well as dynamic policies are also important and not representing them in the high level policy abstraction poses serious limitations. At the same time, efficiently configuring and composing group based QoS and dynamic policies present significant technical challenges, such as (a) maintaining group granularity during configuration, (b) dealing with network-bandwidth contention among policies from distinct writers and (c) dealing with multiple path changes corresponding to dynamically changing policies, group membership and end-point mobility. In this paper we propose Janus, a system which makes two major contributions. First, we extend the prior policy graph abstraction model to represent complex QoS and dynamic tateful/temporal policies. Second, we convert the policy configuration problem into an optimization problem with the goal of maximizing the number of satisfied and configured policies, and minimizing the number of path changes under dynamic environments. To solve this, Janus presents several novel heuristic algorithms. We evaluate our system using a diverse set of bandwidth policies and network topologies. Our experiments demonstrate that Janus can achieve near-optimal solutions in a reasonable amount of time.",
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"title": "Supporting Diverse Dynamic Intent-based Policies using Janus",
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"name": "Witawas Srisa-an"
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"paperAbstract": "Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the state-of-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linux-based NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic "lesser evil" algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energy-efficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.",
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"title": "Energy-Efficient I/O Thread Schedulers for NVMe SSDs on NUMA",
"venue": "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)",
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"name": "Divya Muthukumaran"
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"paperAbstract": "Trusted execution support in modern CPUs, as offered by Intel SGX enclaves, can protect applications in untrusted environments. While prior work has shown that legacy applications can run in their entirety inside enclaves, this results in a large trusted computing base (TCB). Instead, we explore an approach in which we partition an application and use an enclave to protect only security-sensitive data and functions, thus obtaining a smaller TCB. We describe Glamdring, the first source-level partitioning framework that secures applications written in C using Intel SGX. A developer first annotates securitysensitive application data. Glamdring then automatically partitions the application into untrusted and enclave parts: (i) to preserve data confidentiality, Glamdring uses dataflow analysis to identify functions that may be exposed to sensitive data; (ii) for data integrity, it uses backward slicing to identify functions that may affect sensitive data. Glamdring then places security-sensitive functions inside the enclave, and adds runtime checks and cryptographic operations at the enclave boundary to protect it from attack. Our evaluation of Glamdring with the Memcached store, the LibreSSL library, and the Digital Bitbox bitcoin wallet shows that it achieves small TCB sizes and has acceptable performance overheads.",
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"title": "Glamdring: Automatic Application Partitioning for Intel SGX",
"venue": "USENIX Annual Technical Conference",
"year": 2017
},
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"authors": [
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"paperAbstract": "In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a <i>weight vector</i>. The score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting, the user input is a vector (equivalently, a point) in a <i>d</i>-dimensional preference space, where <i>d</i> is the number of data attributes. In this paper we study the problem of determining in which regions of the preference space the weight vector should lie so that a given option (<i>focal record</i>) is among the top-<i>k</i> score-wise. In effect, these regions capture all possible user profiles for which the focal record is highly preferable, and are therefore essential in market impact analysis, potential customer identification, profile-based marketing, targeted advertising, etc. We refer to our problem as <i>k-Shortlist Preference Region</i> identification (<i>k</i>SPR), and exploit its computational geometric nature to develop a framework for its efficient (and exact) processing. Using real and synthetic benchmarks, we show that our most optimized algorithm outperforms by three orders of magnitude a competitor we constructed from previous work on a different problem.",
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"sources": [
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"title": "Determining the Impact Regions of Competing Options in Preference Space",
"venue": "SIGMOD Conference",
"year": 2017
},
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"authors": [
{
"ids": [
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"name": "Meng Luo"
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"name": "Oleksii Starov"
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"name": "Nima Honarmand"
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],
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"paperAbstract": "Much of recent research on mobile security has focused on malicious applications. Although mobile devices have powerful browsers that are commonly used by users and are vulnerable to at least as many attacks as their desktop counterparts, mobile web security has not received the attention that it deserves from the community. In particular, there is no longitudinal study that investigates the evolution of mobile browser vulnerabilities over the diverse set of browsers that are available out there. In this paper, we undertake the first such study, focusing on UI vulnerabilities among mobile browsers. We investigate and quantify vulnerabilities to 27 UI-related attacks---compiled from previous work and augmented with new variations of our own---across 128 browser families and 2,324 individual browser versions spanning a period of more than 5 years. In the process, we collect an extensive dataset of browser versions, old and new, from multiple sources. We also design and implement a browser-agnostic testing framework, called <i>Hindsight</i>, to automatically expose browsers to attacks and evaluate their vulnerabilities. We use <i>Hindsight</i> to conduct the tens of thousands of individual attacks that were needed for this study. We discover that 98.6% of the tested browsers are vulnerable to at least one of our attacks and that the average mobile web browser is becoming <i>less secure</i> with each passing year. Overall, our findings support the conclusion that mobile web security has been ignored by the community and must receive more attention.",
"pdfUrls": [
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"title": "Hindsight: Understanding the Evolution of UI Vulnerabilities in Mobile Browsers",
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"year": 2017
},
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"paperAbstract": "Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with the training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing-based technique to drastically reduce the amount of computation needed to train and test neural networks. Our approach combines two recent ideas, Adaptive Dropout and Randomized Hashing for Maximum Inner Product Search (MIPS), to select the nodes with the highest activations efficiently. Our new algorithm for deep learning reduces the overall computational cost of the forward and backward propagation steps by operating on significantly fewer nodes. As a consequence, our algorithm uses only 5% of the total multiplications, while keeping within 1% of the accuracy of the original model on average. A unique property of the proposed hashing-based back-propagation is that the updates are always sparse. Due to the sparse gradient updates, our algorithm is ideally suited for asynchronous, parallel training, leading to near-linear speedup, as the number of cores increases. We demonstrate the scalability and sustainability (energy efficiency) of our proposed algorithm via rigorous experimental evaluations on several datasets.",
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"sources": [
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"title": "Scalable and Sustainable Deep Learning via Randomized Hashing",
"venue": "KDD",
"year": 2017
},
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"name": "Tao Guo"
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"name": "Xin Cao"
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"name": "Gao Cong"
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"name": "Jiaheng Lu"
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"name": "Xuemin Lin"
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"doi": "10.1145/3035918.3035920",
"doiUrl": "https://doi.org/10.1145/3035918.3035920",
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