{
 "cells": [
  {
   "source": [
    "# Theodolite Analysis - Demand Metric\n",
    "\n",
    "This notebook applies Theodolite's *demand* metric to describe scalability of a SUT based on Theodolite measurement data.\n",
    "\n",
    "Theodolite's *demand* metric is a function, mapping load intensities to the minimum required resources (e.g., instances) that are required to process this load. With this notebook, the *demand* metric function is approximated by a map of tested load intensities to their minimum required resources.\n",
    "\n",
    "The final output when running this notebook will be a CSV file, providig this mapping. It can be used to create nice plots of a system's scalability using the `demand-metric-plot.ipynb` notebook."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "In the following cell, we need to specifiy:\n",
    "\n",
    "* `exp_id`: The experiment id  that is to be analyzed.\n",
    "* `warmup_sec`: The number of seconds which are to be ignored in the beginning of each experiment.\n",
    "* `max_lag_trend_slope`: The maximum tolerable increase in queued messages per second.\n",
    "* `measurement_dir`: The directory where the measurement data files are to be found.\n",
    "* `results_dir`: The directory where the computed demand CSV files are to be stored."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_id = 200\n",
    "warmup_sec = 60\n",
    "max_lag_trend_slope = 2000\n",
    "measurement_dir = '<path-to>/measurements'\n",
    "results_dir = '<path-to>/results'\n"
   ]
  },
  {
   "source": [
    "With the following call, we compute our demand mapping."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.demand import demand\n",
    "\n",
    "demand = demand(exp_id, measurement_dir, max_lag_trend_slope, warmup_sec)"
   ]
  },
  {
   "source": [
    "We might already want to plot a simple visualization here:"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "demand.plot(kind='line',x='load',y='resources')"
   ]
  },
  {
   "source": [
    "Finally we store the results in a CSV file."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "demand.to_csv(os.path.join(results_dir, f'exp{exp_id}_demand.csv'), index=False)"
   ]
  }
 ],
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