k8s-distributions-iot-edge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Kubernetes Distributions Serverless Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Abstract\n",
    "Serverless computing, especially when deployed at the edge of the network is seen as an enabling technology for the future development of more complex IoT systems. However, special care must be taken when deploying new edge infrastructures for serverless workloads in terms of resource usage and network connectivity. Inefficient utilization of the available computing resources might easily cancel out the benefits acquired by moving the equipment closer to the edge, namely the reduced communication latency. Containers, together with the Kubernetes container orchestrator are used by many serverless platforms today. We evaluate the performance of three different Kubernetes distributions – full-fledged Kubernetes, K3s, and MicroK8s when deployed in a resource constrained environment at the edge. We use the OpenFaaS serverless platform and employ 14 different benchmarks divided into three separate categories to evaluate various aspects of the execution performance of the distributions. Four different test types are performed focusing on cold-start latency, serial execution performance, parallel execution using a single replica, and parallel execution utilizing different autoscaling strategies. Our results show that the two Kubernetes distributions specifically designed for deployment on edge nodes, K3s and MicroK8s, show better performance in comparison to a more complex full-fledged deployment."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Analysis\n",
    "\n",
    "The following libraries need to be installed in the environment before continuing with the data analysis."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install pandas\n",
    "!pip install matplotlib\n",
    "!pip install numpy\n",
    "!pip install scipy\n",
    "!pip install seaborn"
   ],
   "metadata": {
    "collapsed": false
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import re\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import math"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "TEST_PLATFORMS = ['kubespray', 'k3s', 'microk8s']\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 01 - Analyzing Cold Start Delays\n",
    "\n",
    "We evaluate the cold start delays for different platforms across the 14 different function types."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "df_cold_start_container = pd.DataFrame()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "kubespray_cold_start_container = pd.read_csv('data/kubespray/01-cold-start/pod_startup_times/kubespray-delay-all.csv')\n",
    "k3s_cold_start_container = pd.read_csv('data/k3s/01-cold-start/pod_startup_times/k3s-delay-all.csv')\n",
    "microk8s_cold_start_container = pd.read_csv('data/microk8s/01-cold-start/pod_startup_times/microk8s-delay-all.csv')\n",
    "\n",
    "kubespray_cold_start_container['platform'] = 'kubespray'\n",
    "k3s_cold_start_container['platform'] = 'k3s'\n",
    "microk8s_cold_start_container['platform']='microk8s'\n",
    "\n",
    "df_cold_start_container = df_cold_start_container.append(kubespray_cold_start_container)\n",
    "df_cold_start_container = df_cold_start_container.append(k3s_cold_start_container)\n",
    "df_cold_start_container = df_cold_start_container.append(microk8s_cold_start_container)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Increasing the default number of shown rows before starting with the data exploration..."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 250)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Exploring the data by looking at the mean cold start delay per platform."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              delay\n",
      "platform           \n",
      "k3s        5.627143\n",
      "kubespray  6.467857\n",
      "microk8s   5.622857\n"
     ]
    }
   ],
   "source": [
    "print(df_cold_start_container.groupby(['platform']).mean())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Exploring the data by looking at the mean cold start delay per platform and app."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              delay\n",
      "platform  app                      \n",
      "k3s       chameleon            5.73\n",
      "          dd                   5.62\n",
      "          float-operation      5.69\n",
      "          gzip-compression     5.66\n",
      "          image-processing     5.59\n",
      "          json-dumps-loads     5.54\n",
      "          linpack              5.55\n",
      "          matmul               5.67\n",
      "          model-training       5.51\n",
      "          pyaes                5.45\n",
      "          random-disk-io       5.71\n",
      "          s3-download-speed    5.56\n",
      "          sequential-disk-io   5.94\n",
      "          video-processing     5.56\n",
      "kubespray chameleon            6.52\n",
      "          dd                   6.31\n",
      "          float-operation      6.27\n",
      "          gzip-compression     6.50\n",
      "          image-processing     6.56\n",
      "          json-dumps-loads     6.50\n",
      "          linpack              6.44\n",
      "          matmul               6.36\n",
      "          model-training       6.66\n",
      "          pyaes                6.46\n",
      "          random-disk-io       6.60\n",
      "          s3-download-speed    6.54\n",
      "          sequential-disk-io   6.28\n",
      "          video-processing     6.55\n",
      "microk8s  chameleon            5.69\n",
      "          dd                   5.65\n",
      "          float-operation      5.76\n",
      "          gzip-compression     5.55\n",
      "          image-processing     5.62\n",
      "          json-dumps-loads     5.74\n",
      "          linpack              5.60\n",
      "          matmul               5.60\n",
      "          model-training       5.46\n",
      "          pyaes                5.65\n",
      "          random-disk-io       5.49\n",
      "          s3-download-speed    5.72\n",
      "          sequential-disk-io   5.58\n",
      "          video-processing     5.61\n"
     ]
    }
   ],
   "source": [
    "print(df_cold_start_container.groupby(['platform', 'app']).mean())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Plotting the average cold start delay per platform"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cold_start_mean_bars = sns.barplot(data=df_cold_start_container.groupby(['platform']).mean().reset_index(), x='platform', y='delay', order=TEST_PLATFORMS, errwidth=0, palette=['lightseagreen', 'gold', 'darkorange'])\n",
    "cold_start_mean_bars.set_xlabel('Platform', fontsize=12)\n",
    "cold_start_mean_bars.set_ylabel('Avg. container start-up time (s)', fontsize=12)\n",
    "cold_start_mean_bars.tick_params(labelsize=15)\n",
    "for ax in cold_start_mean_bars.patches:\n",
    "    cold_start_mean_bars.annotate(format(ax.get_height(), '.3f'), (ax.get_x() + ax.get_width() / 2., ax.get_height()), ha = 'center', va = 'center', xytext = (0, -20), textcoords = 'offset points', fontsize=15)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "fig = cold_start_mean_bars.get_figure()\n",
    "fig.savefig('01-01-cold_start_mean_bars', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   },
   "execution_count": 9,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "Further exploration by using box plots to see more details about the data. Extreme outliers have been eliminated to save space on the chart. The median is shown with a pink line."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cold_start_boxplot = sns.boxplot(data=df_cold_start_container[df_cold_start_container['delay']<=17], x=\"delay\", y=\"platform\", width=0.8, medianprops=dict(color=\"deeppink\", alpha=1.0, linewidth=2.5), order=TEST_PLATFORMS, palette=['lightseagreen', 'gold', 'darkorange'])\n",
    "cold_start_boxplot.set_xlabel('Container start-up time (s)', fontsize=12)\n",
    "cold_start_boxplot.set_ylabel('Platform', fontsize=12)\n",
    "cold_start_boxplot.tick_params(labelsize=15)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "fig = cold_start_boxplot.get_figure()\n",
    "fig.savefig('01-02-cold_start_boxplot', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "It would be beneficial to visualise the average response times per function. We can use a heatmap for this purpose."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_cold_start = df_cold_start_container[['app', 'delay', 'platform']]\n",
    "test_cold_start = test_cold_start.groupby(['app', 'platform']).mean().reset_index()\n",
    "cold_start_heatmap = sns.heatmap(data=test_cold_start.pivot(index='app', columns='platform', values='delay'), annot=True, annot_kws={'fontsize':13, 'weight': 'bold'})\n",
    "cold_start_heatmap.set_xlabel('Platform', fontsize=13)\n",
    "cold_start_heatmap.set_ylabel('Test', fontsize=13)\n",
    "cold_start_heatmap.tick_params(labelsize=13)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "fig = cold_start_heatmap.get_figure()\n",
    "fig.savefig('01-03-cold_start_heatmap', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Testing whether the cold-start delays for the different platforms follow the same distribution using a nonparametric three-way Kruskal-Wallis test:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reject H0 - different distribution\n"
     ]
    }
   ],
   "source": [
    "data_kubespray = kubespray_cold_start_container['delay']\n",
    "data_k3s = k3s_cold_start_container['delay']\n",
    "data_microk8s = microk8s_cold_start_container['delay']\n",
    "alpha = 0.05\n",
    "statistic, pvalue = stats.kruskal(data_kubespray,data_k3s, data_microk8s)\n",
    "if pvalue > alpha:\n",
    "    print('Fail to reject H0 - same distribution')\n",
    "else:\n",
    "    print('Reject H0 - different distribution')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The null hypothesis failed to be rejected. Let’s test whether K3s and MicroK8s delays have the same distribution, using a nonparametric Mann-Whitney U-test:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is: 0.20193473974729836\n",
      "Fail to reject H0 - same distribution\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "statistic, pvalue = stats.mannwhitneyu(data_k3s,data_microk8s)\n",
    "print(f'The p-value is: {pvalue}')\n",
    "if pvalue > alpha:\n",
    "    print('Fail to reject H0 - same distribution')\n",
    "else:\n",
    "    print('Reject H0 - different distribution')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "There is not enough evidence to support the case that the cold start delays of K3s and MicroK8s platforms follow different distributions.\n",
    "\n",
    "In conclusion, Kubespray offers a higher cold-start delay than either K3s or MicroK8s. There is no evidence suggesting difference between the cold-start performance of K3s and MicroK8s."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 02 - Analyzing Serial Execution\n",
    "\n",
    "To analyze the serial execution performance, each function has been executed for 5 minutes across all three Kubernetes distributions."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "df_serial = pd.DataFrame()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Loading the data into a single data frame..."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "if os.path.sep == '/':\n",
    "    regex_escape = '/'\n",
    "else:\n",
    "    regex_escape = '\\\\\\\\'\n",
    "\n",
    "test_type= '02'\n",
    "test_type_description = 'serial_execution'\n",
    "\n",
    "for test_platform in TEST_PLATFORMS:\n",
    "    platform_serial_files = glob.glob(f'data/{test_platform}/02-serial-execution/*.csv')\n",
    "    for file in platform_serial_files:\n",
    "        test_name = re.findall(f'{regex_escape}(.*).csv', file)[0]\n",
    "        df_tmp = pd.read_csv(file)\n",
    "        df_tmp['test_type']=test_type\n",
    "        df_tmp['test_type_description']=test_type_description\n",
    "        df_tmp['test_platform']=test_platform\n",
    "        df_tmp['test_name']=test_name\n",
    "        df_serial = df_serial.append(df_tmp)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Exploring the mean response time for the various tests across the three different platforms:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                               response-time  \\\n",
      "test_name                 test_type_description test_platform                  \n",
      "chameleon-serial          serial_execution      k3s                22.030736   \n",
      "                                                kubespray          22.573714   \n",
      "                                                microk8s           22.216171   \n",
      "dd-serial                 serial_execution      k3s                 1.027233   \n",
      "                                                kubespray           1.318654   \n",
      "                                                microk8s            1.038141   \n",
      "float-operation-serial    serial_execution      k3s                 7.233514   \n",
      "                                                kubespray           7.482924   \n",
      "                                                microk8s            7.131577   \n",
      "gzip-compression-serial   serial_execution      k3s                 3.721601   \n",
      "                                                kubespray           3.850758   \n",
      "                                                microk8s            3.824784   \n",
      "image-processing-serial   serial_execution      k3s                 5.675247   \n",
      "                                                kubespray           5.751743   \n",
      "                                                microk8s            5.649320   \n",
      "json-dumps-loads-serial   serial_execution      k3s                 2.181569   \n",
      "                                                kubespray           2.303899   \n",
      "                                                microk8s            2.165884   \n",
      "linpack-serial            serial_execution      k3s                 3.371846   \n",
      "                                                kubespray           3.967341   \n",
      "                                                microk8s            3.374785   \n",
      "matmul-serial             serial_execution      k3s                 8.230176   \n",
      "                                                kubespray           8.439036   \n",
      "                                                microk8s            8.190332   \n",
      "model-training-serial     serial_execution      k3s                 6.482445   \n",
      "                                                kubespray           6.685331   \n",
      "                                                microk8s            6.495434   \n",
      "pyaes-serial              serial_execution      k3s                 1.147724   \n",
      "                                                kubespray           1.189124   \n",
      "                                                microk8s            1.167760   \n",
      "random-disk-io-serial     serial_execution      k3s                 2.798792   \n",
      "                                                kubespray           2.934476   \n",
      "                                                microk8s            2.755274   \n",
      "s3-download-speed-serial  serial_execution      k3s                 4.116149   \n",
      "                                                kubespray           3.682005   \n",
      "                                                microk8s            4.069986   \n",
      "sequential-disk-io-serial serial_execution      k3s                 1.912592   \n",
      "                                                kubespray           1.844182   \n",
      "                                                microk8s            1.705905   \n",
      "video-processing-serial   serial_execution      k3s                44.327171   \n",
      "                                                kubespray          45.082500   \n",
      "                                                microk8s           44.104086   \n",
      "\n",
      "                                                               DNS+dialup  \\\n",
      "test_name                 test_type_description test_platform               \n",
      "chameleon-serial          serial_execution      k3s              0.002214   \n",
      "                                                kubespray        0.002457   \n",
      "                                                microk8s         0.002621   \n",
      "dd-serial                 serial_execution      k3s              0.000711   \n",
      "                                                kubespray        0.000444   \n",
      "                                                microk8s         0.000354   \n",
      "float-operation-serial    serial_execution      k3s              0.000855   \n",
      "                                                kubespray        0.000659   \n",
      "                                                microk8s         0.000826   \n",
      "gzip-compression-serial   serial_execution      k3s              0.000359   \n",
      "                                                kubespray        0.000362   \n",
      "                                                microk8s         0.000448   \n",
      "image-processing-serial   serial_execution      k3s              0.000547   \n",
      "                                                kubespray        0.000666   \n",
      "                                                microk8s         0.000631   \n",
      "json-dumps-loads-serial   serial_execution      k3s              0.000492   \n",
      "                                                kubespray        0.000494   \n",
      "                                                microk8s         0.000458   \n",
      "linpack-serial            serial_execution      k3s              0.000327   \n",
      "                                                kubespray        0.000453   \n",
      "                                                microk8s         0.000369   \n",
      "matmul-serial             serial_execution      k3s              0.000805   \n",
      "                                                kubespray        0.000925   \n",
      "                                                microk8s         0.000708   \n",
      "model-training-serial     serial_execution      k3s              0.000715   \n",
      "                                                kubespray        0.000704   \n",
      "                                                microk8s         0.000881   \n",
      "pyaes-serial              serial_execution      k3s              0.000387   \n",
      "                                                kubespray        0.000421   \n",
      "                                                microk8s         0.000360   \n",
      "random-disk-io-serial     serial_execution      k3s              0.000578   \n",
      "                                                kubespray        0.000560   \n",
      "                                                microk8s         0.000639   \n",
      "s3-download-speed-serial  serial_execution      k3s              0.000385   \n",
      "                                                kubespray        0.000333   \n",
      "                                                microk8s         0.000457   \n",
      "sequential-disk-io-serial serial_execution      k3s              0.000350   \n",
      "                                                kubespray        0.000382   \n",
      "                                                microk8s         0.000387   \n",
      "video-processing-serial   serial_execution      k3s              0.004100   \n",
      "                                                kubespray        0.004129   \n",
      "                                                microk8s         0.005300   \n",
      "\n",
      "                                                                    DNS  \\\n",
      "test_name                 test_type_description test_platform             \n",
      "chameleon-serial          serial_execution      k3s            0.000114   \n",
      "                                                kubespray      0.000093   \n",
      "                                                microk8s       0.000093   \n",
      "dd-serial                 serial_execution      k3s            0.000012   \n",
      "                                                kubespray      0.000018   \n",
      "                                                microk8s       0.000012   \n",
      "float-operation-serial    serial_execution      k3s            0.000038   \n",
      "                                                kubespray      0.000032   \n",
      "                                                microk8s       0.000033   \n",
      "gzip-compression-serial   serial_execution      k3s            0.000015   \n",
      "                                                kubespray      0.000015   \n",
      "                                                microk8s       0.000019   \n",
      "image-processing-serial   serial_execution      k3s            0.000023   \n",
      "                                                kubespray      0.000026   \n",
      "                                                microk8s       0.000022   \n",
      "json-dumps-loads-serial   serial_execution      k3s            0.000017   \n",
      "                                                kubespray      0.000017   \n",
      "                                                microk8s       0.000019   \n",
      "linpack-serial            serial_execution      k3s            0.000013   \n",
      "                                                kubespray      0.000017   \n",
      "                                                microk8s       0.000017   \n",
      "matmul-serial             serial_execution      k3s            0.000032   \n",
      "                                                kubespray      0.000047   \n",
      "                                                microk8s       0.000035   \n",
      "model-training-serial     serial_execution      k3s            0.000023   \n",
      "                                                kubespray      0.000029   \n",
      "                                                microk8s       0.000030   \n",
      "pyaes-serial              serial_execution      k3s            0.000013   \n",
      "                                                kubespray      0.000011   \n",
      "                                                microk8s       0.000015   \n",
      "random-disk-io-serial     serial_execution      k3s            0.000024   \n",
      "                                                kubespray      0.000026   \n",
      "                                                microk8s       0.000024   \n",
      "s3-download-speed-serial  serial_execution      k3s            0.000016   \n",
      "                                                kubespray      0.000017   \n",
      "                                                microk8s       0.000018   \n",
      "sequential-disk-io-serial serial_execution      k3s            0.000013   \n",
      "                                                kubespray      0.000015   \n",
      "                                                microk8s       0.000013   \n",
      "video-processing-serial   serial_execution      k3s            0.000186   \n",
      "                                                kubespray      0.000186   \n",
      "                                                microk8s       0.000186   \n",
      "\n",
      "                                                               Request-write  \\\n",
      "test_name                 test_type_description test_platform                  \n",
      "chameleon-serial          serial_execution      k3s                 0.000050   \n",
      "                                                kubespray           0.000021   \n",
      "                                                microk8s            0.000043   \n",
      "dd-serial                 serial_execution      k3s                 0.000007   \n",
      "                                                kubespray           0.000009   \n",
      "                                                microk8s            0.000008   \n",
      "float-operation-serial    serial_execution      k3s                 0.000024   \n",
      "                                                kubespray           0.000017   \n",
      "                                                microk8s            0.000023   \n",
      "gzip-compression-serial   serial_execution      k3s                 0.000021   \n",
      "                                                kubespray           0.000009   \n",
      "                                                microk8s            0.000005   \n",
      "image-processing-serial   serial_execution      k3s                 0.000009   \n",
      "                                                kubespray           0.000019   \n",
      "                                                microk8s            0.000026   \n",
      "json-dumps-loads-serial   serial_execution      k3s                 0.000014   \n",
      "                                                kubespray           0.000014   \n",
      "                                                microk8s            0.000009   \n",
      "linpack-serial            serial_execution      k3s                 0.000009   \n",
      "                                                kubespray           0.000007   \n",
      "                                                microk8s            0.000009   \n",
      "matmul-serial             serial_execution      k3s                 0.000038   \n",
      "                                                kubespray           0.000017   \n",
      "                                                microk8s            0.000019   \n",
      "model-training-serial     serial_execution      k3s                 0.000017   \n",
      "                                                kubespray           0.000007   \n",
      "                                                microk8s            0.000019   \n",
      "pyaes-serial              serial_execution      k3s                 0.000004   \n",
      "                                                kubespray           0.000007   \n",
      "                                                microk8s            0.000007   \n",
      "random-disk-io-serial     serial_execution      k3s                 0.000008   \n",
      "                                                kubespray           0.000013   \n",
      "                                                microk8s            0.000014   \n",
      "s3-download-speed-serial  serial_execution      k3s                 0.000016   \n",
      "                                                kubespray           0.000018   \n",
      "                                                microk8s            0.000016   \n",
      "sequential-disk-io-serial serial_execution      k3s                 0.000005   \n",
      "                                                kubespray           0.000015   \n",
      "                                                microk8s            0.000007   \n",
      "video-processing-serial   serial_execution      k3s                 0.000100   \n",
      "                                                kubespray           0.000171   \n",
      "                                                microk8s            0.000100   \n",
      "\n",
      "                                                               Response-delay  \\\n",
      "test_name                 test_type_description test_platform                   \n",
      "chameleon-serial          serial_execution      k3s                 22.028279   \n",
      "                                                kubespray           22.571036   \n",
      "                                                microk8s            22.213329   \n",
      "dd-serial                 serial_execution      k3s                  1.026368   \n",
      "                                                kubespray            1.318043   \n",
      "                                                microk8s             1.037634   \n",
      "float-operation-serial    serial_execution      k3s                  7.232433   \n",
      "                                                kubespray            7.482061   \n",
      "                                                microk8s             7.130565   \n",
      "gzip-compression-serial   serial_execution      k3s                  3.721053   \n",
      "                                                kubespray            3.850221   \n",
      "                                                microk8s             3.824166   \n",
      "image-processing-serial   serial_execution      k3s                  5.674534   \n",
      "                                                kubespray            5.750898   \n",
      "                                                microk8s             5.648502   \n",
      "json-dumps-loads-serial   serial_execution      k3s                  2.180914   \n",
      "                                                kubespray            2.303232   \n",
      "                                                microk8s             2.165256   \n",
      "linpack-serial            serial_execution      k3s                  3.371354   \n",
      "                                                kubespray            3.966724   \n",
      "                                                microk8s             3.374248   \n",
      "matmul-serial             serial_execution      k3s                  8.229157   \n",
      "                                                kubespray            8.437922   \n",
      "                                                microk8s             8.189432   \n",
      "model-training-serial     serial_execution      k3s                  6.481551   \n",
      "                                                kubespray            6.684436   \n",
      "                                                microk8s             6.494362   \n",
      "pyaes-serial              serial_execution      k3s                  1.147185   \n",
      "                                                kubespray            1.188547   \n",
      "                                                microk8s             1.167242   \n",
      "random-disk-io-serial     serial_execution      k3s                  2.798044   \n",
      "                                                kubespray            2.933748   \n",
      "                                                microk8s             2.754462   \n",
      "s3-download-speed-serial  serial_execution      k3s                  4.115590   \n",
      "                                                kubespray            3.681502   \n",
      "                                                microk8s             4.069357   \n",
      "sequential-disk-io-serial serial_execution      k3s                  1.912076   \n",
      "                                                kubespray            1.843624   \n",
      "                                                microk8s             1.705353   \n",
      "video-processing-serial   serial_execution      k3s                 44.322743   \n",
      "                                                kubespray           45.077957   \n",
      "                                                microk8s            44.098300   \n",
      "\n",
      "                                                               Response-read  \\\n",
      "test_name                 test_type_description test_platform                  \n",
      "chameleon-serial          serial_execution      k3s                 0.000121   \n",
      "                                                kubespray           0.000114   \n",
      "                                                microk8s            0.000121   \n",
      "dd-serial                 serial_execution      k3s                 0.000102   \n",
      "                                                kubespray           0.000104   \n",
      "                                                microk8s            0.000101   \n",
      "float-operation-serial    serial_execution      k3s                 0.000124   \n",
      "                                                kubespray           0.000122   \n",
      "                                                microk8s            0.000109   \n",
      "gzip-compression-serial   serial_execution      k3s                 0.000116   \n",
      "                                                kubespray           0.000108   \n",
      "                                                microk8s            0.000106   \n",
      "image-processing-serial   serial_execution      k3s                 0.000104   \n",
      "                                                kubespray           0.000108   \n",
      "                                                microk8s            0.000107   \n",
      "json-dumps-loads-serial   serial_execution      k3s                 0.000103   \n",
      "                                                kubespray           0.000103   \n",
      "                                                microk8s            0.000105   \n",
      "linpack-serial            serial_execution      k3s                 0.000106   \n",
      "                                                kubespray           0.000104   \n",
      "                                                microk8s            0.000103   \n",
      "matmul-serial             serial_execution      k3s                 0.000103   \n",
      "                                                kubespray           0.000108   \n",
      "                                                microk8s            0.000108   \n",
      "model-training-serial     serial_execution      k3s                 0.000104   \n",
      "                                                kubespray           0.000107   \n",
      "                                                microk8s            0.000113   \n",
      "pyaes-serial              serial_execution      k3s                 0.000105   \n",
      "                                                kubespray           0.000102   \n",
      "                                                microk8s            0.000102   \n",
      "random-disk-io-serial     serial_execution      k3s                 0.000102   \n",
      "                                                kubespray           0.000108   \n",
      "                                                microk8s            0.000107   \n",
      "s3-download-speed-serial  serial_execution      k3s                 0.000101   \n",
      "                                                kubespray           0.000104   \n",
      "                                                microk8s            0.000107   \n",
      "sequential-disk-io-serial serial_execution      k3s                 0.000106   \n",
      "                                                kubespray           0.000106   \n",
      "                                                microk8s            0.000104   \n",
      "video-processing-serial   serial_execution      k3s                 0.000200   \n",
      "                                                kubespray           0.000200   \n",
      "                                                microk8s            0.000329   \n",
      "\n",
      "                                                               status-code  \\\n",
      "test_name                 test_type_description test_platform                \n",
      "chameleon-serial          serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "dd-serial                 serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "float-operation-serial    serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "gzip-compression-serial   serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "image-processing-serial   serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "json-dumps-loads-serial   serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "linpack-serial            serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "matmul-serial             serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "model-training-serial     serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "pyaes-serial              serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "random-disk-io-serial     serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "s3-download-speed-serial  serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "sequential-disk-io-serial serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "video-processing-serial   serial_execution      k3s                  200.0   \n",
      "                                                kubespray            200.0   \n",
      "                                                microk8s             200.0   \n",
      "\n",
      "                                                                   offset  \n",
      "test_name                 test_type_description test_platform              \n",
      "chameleon-serial          serial_execution      k3s            143.015321  \n",
      "                                                kubespray      146.552671  \n",
      "                                                microk8s       143.933950  \n",
      "dd-serial                 serial_execution      k3s            149.383834  \n",
      "                                                kubespray      145.352330  \n",
      "                                                microk8s       148.376303  \n",
      "float-operation-serial    serial_execution      k3s            148.248402  \n",
      "                                                kubespray      149.715893  \n",
      "                                                microk8s       149.785193  \n",
      "gzip-compression-serial   serial_execution      k3s            148.834462  \n",
      "                                                kubespray      147.969435  \n",
      "                                                microk8s       149.007675  \n",
      "image-processing-serial   serial_execution      k3s            147.634858  \n",
      "                                                kubespray      149.498270  \n",
      "                                                microk8s       149.795217  \n",
      "json-dumps-loads-serial   serial_execution      k3s            149.504781  \n",
      "                                                kubespray      149.736444  \n",
      "                                                microk8s       149.538542  \n",
      "linpack-serial            serial_execution      k3s            148.746978  \n",
      "                                                kubespray      152.015376  \n",
      "                                                microk8s       149.368675  \n",
      "matmul-serial             serial_execution      k3s            148.383124  \n",
      "                                                kubespray      147.465072  \n",
      "                                                microk8s       147.336311  \n",
      "model-training-serial     serial_execution      k3s            149.217464  \n",
      "                                                kubespray      147.509347  \n",
      "                                                microk8s       149.664330  \n",
      "pyaes-serial              serial_execution      k3s            149.892137  \n",
      "                                                kubespray      150.020768  \n",
      "                                                microk8s       149.412824  \n",
      "random-disk-io-serial     serial_execution      k3s            149.861559  \n",
      "                                                kubespray      149.950182  \n",
      "                                                microk8s       148.663418  \n",
      "s3-download-speed-serial  serial_execution      k3s            147.643304  \n",
      "                                                kubespray      146.202538  \n",
      "                                                microk8s       149.866134  \n",
      "sequential-disk-io-serial serial_execution      k3s            148.699298  \n",
      "                                                kubespray      148.490180  \n",
      "                                                microk8s       149.033180  \n",
      "video-processing-serial   serial_execution      k3s            132.728229  \n",
      "                                                kubespray      135.901000  \n",
      "                                                microk8s       132.167143  \n"
     ]
    }
   ],
   "source": [
    "print(df_serial.groupby(['test_name', 'test_type_description', 'test_platform']).mean())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Calculating the total number of executions for each test during the 5 minutes test interval:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_name                  test_type_description  test_platform\n",
      "chameleon-serial           serial_execution       k3s               14\n",
      "                                                  kubespray         14\n",
      "                                                  microk8s          14\n",
      "dd-serial                  serial_execution       k3s              293\n",
      "                                                  kubespray        228\n",
      "                                                  microk8s         290\n",
      "float-operation-serial     serial_execution       k3s               42\n",
      "                                                  kubespray         41\n",
      "                                                  microk8s          43\n",
      "gzip-compression-serial    serial_execution       k3s               81\n",
      "                                                  kubespray         78\n",
      "                                                  microk8s          79\n",
      "image-processing-serial    serial_execution       k3s               53\n",
      "                                                  kubespray         53\n",
      "                                                  microk8s          54\n",
      "json-dumps-loads-serial    serial_execution       k3s              138\n",
      "                                                  kubespray        131\n",
      "                                                  microk8s         139\n",
      "linpack-serial             serial_execution       k3s               89\n",
      "                                                  kubespray         76\n",
      "                                                  microk8s          89\n",
      "matmul-serial              serial_execution       k3s               37\n",
      "                                                  kubespray         36\n",
      "                                                  microk8s          37\n",
      "model-training-serial      serial_execution       k3s               47\n",
      "                                                  kubespray         45\n",
      "                                                  microk8s          47\n",
      "pyaes-serial               serial_execution       k3s              262\n",
      "                                                  kubespray        253\n",
      "                                                  microk8s         257\n",
      "random-disk-io-serial      serial_execution       k3s              108\n",
      "                                                  kubespray        103\n",
      "                                                  microk8s         109\n",
      "s3-download-speed-serial   serial_execution       k3s               73\n",
      "                                                  kubespray         82\n",
      "                                                  microk8s          74\n",
      "sequential-disk-io-serial  serial_execution       k3s              157\n",
      "                                                  kubespray        163\n",
      "                                                  microk8s         176\n",
      "video-processing-serial    serial_execution       k3s                7\n",
      "                                                  kubespray          7\n",
      "                                                  microk8s           7\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_serial.groupby(['test_name', 'test_type_description', 'test_platform']).size())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Using bar plots to better visualize the number of executions in a 5 minute interval"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "    response-time  DNS+dialup     DNS  Request-write  Response-delay  \\\n0         22.7557      0.0344  0.0013         0.0001         22.7210   \n1         22.4511      0.0000  0.0000         0.0000         22.4508   \n2         22.4753      0.0000  0.0000         0.0000         22.4751   \n3         22.4550      0.0000  0.0000         0.0000         22.4549   \n4         22.6566      0.0000  0.0000         0.0000         22.6564   \n..            ...         ...     ...            ...             ...   \n2         43.9139      0.0000  0.0000         0.0000         43.9132   \n3         42.8895      0.0000  0.0000         0.0004         42.8889   \n4         44.4614      0.0000  0.0000         0.0001         44.4612   \n5         44.5180      0.0000  0.0000         0.0000         44.5169   \n6         44.3824      0.0000  0.0000         0.0000         44.3822   \n\n    Response-read  status-code    offset test_type test_type_description  \\\n0          0.0001          200    0.0050        02      serial_execution   \n1          0.0002          200   22.7608        02      serial_execution   \n2          0.0001          200   45.2118        02      serial_execution   \n3          0.0001          200   67.6871        02      serial_execution   \n4          0.0001          200   90.1421        02      serial_execution   \n..            ...          ...       ...       ...                   ...   \n2          0.0006          200   88.5670        02      serial_execution   \n3          0.0001          200  132.4809        02      serial_execution   \n4          0.0001          200  175.3704        02      serial_execution   \n5          0.0010          200  219.8318        02      serial_execution   \n6          0.0001          200  264.3497        02      serial_execution   \n\n   test_platform                test_name  \n0      kubespray         chameleon-serial  \n1      kubespray         chameleon-serial  \n2      kubespray         chameleon-serial  \n3      kubespray         chameleon-serial  \n4      kubespray         chameleon-serial  \n..           ...                      ...  \n2       microk8s  video-processing-serial  \n3       microk8s  video-processing-serial  \n4       microk8s  video-processing-serial  \n5       microk8s  video-processing-serial  \n6       microk8s  video-processing-serial  \n\n[4126 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>response-time</th>\n      <th>DNS+dialup</th>\n      <th>DNS</th>\n      <th>Request-write</th>\n      <th>Response-delay</th>\n      <th>Response-read</th>\n      <th>status-code</th>\n      <th>offset</th>\n      <th>test_type</th>\n      <th>test_type_description</th>\n      <th>test_platform</th>\n      <th>test_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>22.7557</td>\n      <td>0.0344</td>\n      <td>0.0013</td>\n      <td>0.0001</td>\n      <td>22.7210</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0050</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>kubespray</td>\n      <td>chameleon-serial</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>22.4511</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>22.4508</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>22.7608</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>kubespray</td>\n      <td>chameleon-serial</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>22.4753</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>22.4751</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>45.2118</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>kubespray</td>\n      <td>chameleon-serial</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>22.4550</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>22.4549</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>67.6871</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>kubespray</td>\n      <td>chameleon-serial</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>22.6566</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>22.6564</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>90.1421</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>kubespray</td>\n      <td>chameleon-serial</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>43.9139</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>43.9132</td>\n      <td>0.0006</td>\n      <td>200</td>\n      <td>88.5670</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>microk8s</td>\n      <td>video-processing-serial</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>42.8895</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0004</td>\n      <td>42.8889</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>132.4809</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>microk8s</td>\n      <td>video-processing-serial</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>44.4614</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0001</td>\n      <td>44.4612</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>175.3704</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>microk8s</td>\n      <td>video-processing-serial</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>44.5180</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>44.5169</td>\n      <td>0.0010</td>\n      <td>200</td>\n      <td>219.8318</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>microk8s</td>\n      <td>video-processing-serial</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>44.3824</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>44.3822</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>264.3497</td>\n      <td>02</td>\n      <td>serial_execution</td>\n      <td>microk8s</td>\n      <td>video-processing-serial</td>\n    </tr>\n  </tbody>\n</table>\n<p>4126 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_serial"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x504 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,7))\n",
    "serial_execution_count_plot = sns.countplot(x='test_name', hue='test_platform', data=df_serial, palette=['lightseagreen', 'gold', 'darkorange'])\n",
    "\n",
    "for ax in serial_execution_count_plot.patches:\n",
    "    serial_execution_count_plot.annotate(format(ax.get_height(), '.0f'), (ax.get_x() + ax.get_width() / 2., ax.get_height()), ha = 'center', va = 'center', xytext = (0, 5), textcoords = 'offset points', fontsize=13)\n",
    "serial_execution_count_plot.set_xticklabels(serial_execution_count_plot.get_xticklabels(), rotation=45, horizontalalignment='right', fontsize=15)\n",
    "serial_execution_count_plot.set_ylabel('Number of executions in a 5 minute interval', fontsize=14)\n",
    "serial_execution_count_plot.set_xlabel('Test name', fontsize=14)\n",
    "serial_execution_count_plot.legend(fontsize=15)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "fig = serial_execution_count_plot.get_figure()\n",
    "fig.savefig('02-03-serial_execution_count_plot', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Visualizing the average response time for each test function during the 5 minutes test interval:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x864 with 14 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "serial_execution_avg_bar_plot = sns.FacetGrid(df_serial, col=\"test_name\", hue='test_platform', col_wrap=4, sharey=False, palette=['lightseagreen', 'gold', 'darkorange'],\n",
    "                                              sharex=False)\n",
    "serial_execution_avg_bar_plot = serial_execution_avg_bar_plot.map(sns.barplot, 'test_platform', 'response-time',\n",
    "                                                                  order=TEST_PLATFORMS, errwidth=0)\n",
    "serial_execution_avg_bar_plot.set_titles('{col_name}', size=12)\n",
    "serial_execution_avg_bar_plot.fig.tight_layout(w_pad=3)\n",
    "for ax in serial_execution_avg_bar_plot.axes.ravel():\n",
    "    ax.set_ylabel('Avg. Response Time (s)', fontsize=12)\n",
    "    ax.set_xlabel('')\n",
    "    ax.tick_params(labelsize=12)\n",
    "    for p in ax.patches:\n",
    "        ax.annotate(format(p.get_height(), '.2f'), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center',\n",
    "                    va='center', xytext=(0, -15), textcoords='offset points', fontsize=15)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "serial_execution_avg_bar_plot.savefig('02-04-serial_execution_avg_bar_plot', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Testing whether there are significant differences in the response times between the three platforms"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chameleon-serial Reject H0 - different distribution\n",
      "dd-serial Reject H0 - different distribution\n",
      "float-operation-serial Reject H0 - different distribution\n",
      "gzip-compression-serial Reject H0 - different distribution\n",
      "image-processing-serial Reject H0 - different distribution\n",
      "json-dumps-loads-serial Reject H0 - different distribution\n",
      "linpack-serial Reject H0 - different distribution\n",
      "matmul-serial Reject H0 - different distribution\n",
      "model-training-serial Reject H0 - different distribution\n",
      "pyaes-serial Reject H0 - different distribution\n",
      "random-disk-io-serial Reject H0 - different distribution\n",
      "s3-download-speed-serial Reject H0 - different distribution\n",
      "sequential-disk-io-serial Reject H0 - different distribution\n",
      "video-processing-serial Fail to reject H0 - same distribution\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "test_names = df_serial['test_name'].unique()\n",
    "for i in test_names:\n",
    "    statistic, pvalue = stats.kruskal(\n",
    "        df_serial[df_serial['test_platform'].eq('kubespray') & df_serial['test_name'].eq(i)]['response-time'],\n",
    "        df_serial[df_serial['test_platform'].eq('k3s') & df_serial['test_name'].eq(i)]['response-time'],\n",
    "        df_serial[df_serial['test_platform'].eq('microk8s') & df_serial['test_name'].eq(i)]['response-time'])\n",
    "    if pvalue > alpha:\n",
    "        print(f'{i} Fail to reject H0 - same distribution')\n",
    "    else:\n",
    "        print(f'{i} Reject H0 - different distribution')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Same distribution for `video-processing`. Let's test using a two way Mann-Whitney test only `k3s` and `microk8s`."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chameleon-serial Reject H0 - different distribution\n",
      "dd-serial Reject H0 - different distribution\n",
      "float-operation-serial Reject H0 - different distribution\n",
      "gzip-compression-serial Reject H0 - different distribution\n",
      "image-processing-serial Fail to reject H0 - same distribution\n",
      "json-dumps-loads-serial Reject H0 - different distribution\n",
      "linpack-serial Fail to reject H0 - same distribution\n",
      "matmul-serial Reject H0 - different distribution\n",
      "model-training-serial Reject H0 - different distribution\n",
      "pyaes-serial Reject H0 - different distribution\n",
      "random-disk-io-serial Reject H0 - different distribution\n",
      "s3-download-speed-serial Fail to reject H0 - same distribution\n",
      "sequential-disk-io-serial Reject H0 - different distribution\n",
      "video-processing-serial Fail to reject H0 - same distribution\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "test_names = df_serial['test_name'].unique()\n",
    "for i in test_names:\n",
    "    statistic, pvalue = stats.mannwhitneyu(df_serial[df_serial['test_platform'].eq('k3s') & df_serial['test_name'].eq(i)]['response-time'], df_serial[df_serial['test_platform'].eq('microk8s') & df_serial['test_name'].eq(i)]['response-time'])\n",
    "    if pvalue > alpha:\n",
    "        print(f'{i} Fail to reject H0 - same distribution')\n",
    "    else:\n",
    "        print(f'{i} Reject H0 - different distribution')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The null hypothesis fails to be rejected for:\n",
    "\n",
    " - `image-processing`\n",
    " - `linpack`\n",
    " - `s3-download-speed`\n",
    " - `video-processing`"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 03 Analyzing parallel execution text with a single replica\n",
    "\n",
    "This test has been characterized by 20 workers each executing 1 request at the same time and meassuring the time required to receive a response for all 20 requests."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "df_parallel_single_worker = pd.DataFrame()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Loading the data into a single data frame..."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "if os.path.sep == '/':\n",
    "    regex_escape = '/'\n",
    "else:\n",
    "    regex_escape = '\\\\\\\\'\n",
    "\n",
    "test_type= '03'\n",
    "test_type_description = '20_worker_1_replica_parallel'\n",
    "\n",
    "for test_platform in TEST_PLATFORMS:\n",
    "    platform_serial_files = glob.glob(f'data/{test_platform}/03-parallel-20-worker-1-replica/*.csv')\n",
    "    for file in platform_serial_files:\n",
    "        test_name = re.findall(f'{regex_escape}(.*).csv', file)[0]\n",
    "        df_tmp = pd.read_csv(file)\n",
    "        df_tmp['test_type']=test_type\n",
    "        df_tmp['test_type_description']=test_type_description\n",
    "        df_tmp['test_platform']=test_platform\n",
    "        df_tmp['test_name']=test_name\n",
    "        df_parallel_single_worker = df_parallel_single_worker.append(df_tmp)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Renaming the test name column in order to have better chart labels..."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "df_parallel_single_worker['test_name'] = df_parallel_single_worker['test_name'].replace(['-parallel-20-worker-1-replica'], '', regex=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Exploring the total time taken for each platform to finish the test..."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                               response-time  \\\n",
      "test_name          test_type_description        test_platform                  \n",
      "chameleon          20_worker_1_replica_parallel k3s                 794.9436   \n",
      "                                                kubespray           779.0099   \n",
      "                                                microk8s            807.1679   \n",
      "dd                 20_worker_1_replica_parallel k3s                  21.6157   \n",
      "                                                kubespray            26.1697   \n",
      "                                                microk8s             21.3832   \n",
      "float-operation    20_worker_1_replica_parallel k3s                 261.9761   \n",
      "                                                kubespray           236.0120   \n",
      "                                                microk8s            259.6878   \n",
      "gzip-compression   20_worker_1_replica_parallel k3s                 114.8600   \n",
      "                                                kubespray           116.9336   \n",
      "                                                microk8s            119.3774   \n",
      "image-processing   20_worker_1_replica_parallel k3s                  32.3392   \n",
      "                                                kubespray            32.5597   \n",
      "                                                microk8s             31.5562   \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s                  57.3012   \n",
      "                                                kubespray            60.0063   \n",
      "                                                microk8s             58.1128   \n",
      "linpack            20_worker_1_replica_parallel k3s                 138.4108   \n",
      "                                                kubespray           146.3967   \n",
      "                                                microk8s            133.4826   \n",
      "matmul             20_worker_1_replica_parallel k3s                 164.2887   \n",
      "                                                kubespray           173.7760   \n",
      "                                                microk8s            163.3823   \n",
      "model-training     20_worker_1_replica_parallel k3s                 131.1210   \n",
      "                                                kubespray           128.6725   \n",
      "                                                microk8s            133.6591   \n",
      "pyaes              20_worker_1_replica_parallel k3s                  42.5737   \n",
      "                                                kubespray            41.0035   \n",
      "                                                microk8s             44.0335   \n",
      "random-disk-io     20_worker_1_replica_parallel k3s                  95.5815   \n",
      "                                                kubespray            93.3366   \n",
      "                                                microk8s             81.2139   \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s                  78.4787   \n",
      "                                                kubespray            70.0163   \n",
      "                                                microk8s             73.6779   \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s                  38.2119   \n",
      "                                                kubespray            38.1442   \n",
      "                                                microk8s             46.7550   \n",
      "video-processing   20_worker_1_replica_parallel k3s                 325.3667   \n",
      "                                                kubespray           331.6865   \n",
      "                                                microk8s            321.1512   \n",
      "\n",
      "                                                               DNS+dialup  \\\n",
      "test_name          test_type_description        test_platform               \n",
      "chameleon          20_worker_1_replica_parallel k3s                0.1066   \n",
      "                                                kubespray          0.1562   \n",
      "                                                microk8s           0.1720   \n",
      "dd                 20_worker_1_replica_parallel k3s                0.1644   \n",
      "                                                kubespray          0.1312   \n",
      "                                                microk8s           0.1048   \n",
      "float-operation    20_worker_1_replica_parallel k3s                0.1455   \n",
      "                                                kubespray          0.1245   \n",
      "                                                microk8s           0.1686   \n",
      "gzip-compression   20_worker_1_replica_parallel k3s                0.1194   \n",
      "                                                kubespray          0.1697   \n",
      "                                                microk8s           0.1478   \n",
      "image-processing   20_worker_1_replica_parallel k3s                0.1245   \n",
      "                                                kubespray          0.1641   \n",
      "                                                microk8s           0.1161   \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s                0.1469   \n",
      "                                                kubespray          0.1359   \n",
      "                                                microk8s           0.1488   \n",
      "linpack            20_worker_1_replica_parallel k3s                0.1302   \n",
      "                                                kubespray          0.1262   \n",
      "                                                microk8s           0.1295   \n",
      "matmul             20_worker_1_replica_parallel k3s                0.1251   \n",
      "                                                kubespray          0.1571   \n",
      "                                                microk8s           0.1250   \n",
      "model-training     20_worker_1_replica_parallel k3s                0.1094   \n",
      "                                                kubespray          0.1322   \n",
      "                                                microk8s           0.1283   \n",
      "pyaes              20_worker_1_replica_parallel k3s                0.1659   \n",
      "                                                kubespray          0.2164   \n",
      "                                                microk8s           0.1457   \n",
      "random-disk-io     20_worker_1_replica_parallel k3s                0.1150   \n",
      "                                                kubespray          0.1214   \n",
      "                                                microk8s           0.1360   \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s                0.1673   \n",
      "                                                kubespray          0.1391   \n",
      "                                                microk8s           0.1350   \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s                0.1476   \n",
      "                                                kubespray          0.1185   \n",
      "                                                microk8s           0.1297   \n",
      "video-processing   20_worker_1_replica_parallel k3s                0.1315   \n",
      "                                                kubespray          0.1275   \n",
      "                                                microk8s           0.1368   \n",
      "\n",
      "                                                                  DNS  \\\n",
      "test_name          test_type_description        test_platform           \n",
      "chameleon          20_worker_1_replica_parallel k3s            0.0018   \n",
      "                                                kubespray      0.0015   \n",
      "                                                microk8s       0.0017   \n",
      "dd                 20_worker_1_replica_parallel k3s            0.0014   \n",
      "                                                kubespray      0.0015   \n",
      "                                                microk8s       0.0016   \n",
      "float-operation    20_worker_1_replica_parallel k3s            0.0016   \n",
      "                                                kubespray      0.0019   \n",
      "                                                microk8s       0.0019   \n",
      "gzip-compression   20_worker_1_replica_parallel k3s            0.0014   \n",
      "                                                kubespray      0.0016   \n",
      "                                                microk8s       0.0026   \n",
      "image-processing   20_worker_1_replica_parallel k3s            0.0014   \n",
      "                                                kubespray      0.0017   \n",
      "                                                microk8s       0.0018   \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s            0.0016   \n",
      "                                                kubespray      0.0016   \n",
      "                                                microk8s       0.0017   \n",
      "linpack            20_worker_1_replica_parallel k3s            0.0019   \n",
      "                                                kubespray      0.0016   \n",
      "                                                microk8s       0.0014   \n",
      "matmul             20_worker_1_replica_parallel k3s            0.0025   \n",
      "                                                kubespray      0.0014   \n",
      "                                                microk8s       0.0015   \n",
      "model-training     20_worker_1_replica_parallel k3s            0.0017   \n",
      "                                                kubespray      0.0016   \n",
      "                                                microk8s       0.0019   \n",
      "pyaes              20_worker_1_replica_parallel k3s            0.0014   \n",
      "                                                kubespray      0.0019   \n",
      "                                                microk8s       0.0017   \n",
      "random-disk-io     20_worker_1_replica_parallel k3s            0.0015   \n",
      "                                                kubespray      0.0016   \n",
      "                                                microk8s       0.0014   \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s            0.0015   \n",
      "                                                kubespray      0.0015   \n",
      "                                                microk8s       0.0017   \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s            0.0015   \n",
      "                                                kubespray      0.0015   \n",
      "                                                microk8s       0.0016   \n",
      "video-processing   20_worker_1_replica_parallel k3s            0.0016   \n",
      "                                                kubespray      0.0017   \n",
      "                                                microk8s       0.0016   \n",
      "\n",
      "                                                               Request-write  \\\n",
      "test_name          test_type_description        test_platform                  \n",
      "chameleon          20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0010   \n",
      "                                                microk8s              0.0006   \n",
      "dd                 20_worker_1_replica_parallel k3s                   0.0007   \n",
      "                                                kubespray             0.0008   \n",
      "                                                microk8s              0.0005   \n",
      "float-operation    20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0002   \n",
      "gzip-compression   20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0002   \n",
      "image-processing   20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0007   \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0003   \n",
      "                                                microk8s              0.0009   \n",
      "linpack            20_worker_1_replica_parallel k3s                   0.0009   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0012   \n",
      "matmul             20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0010   \n",
      "                                                microk8s              0.0001   \n",
      "model-training     20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0004   \n",
      "                                                microk8s              0.0008   \n",
      "pyaes              20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0005   \n",
      "random-disk-io     20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0005   \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0004   \n",
      "                                                microk8s              0.0002   \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0002   \n",
      "video-processing   20_worker_1_replica_parallel k3s                   0.0011   \n",
      "                                                kubespray             0.0011   \n",
      "                                                microk8s              0.0004   \n",
      "\n",
      "                                                               Response-delay  \\\n",
      "test_name          test_type_description        test_platform                   \n",
      "chameleon          20_worker_1_replica_parallel k3s                  794.8368   \n",
      "                                                kubespray            778.8535   \n",
      "                                                microk8s             806.9958   \n",
      "dd                 20_worker_1_replica_parallel k3s                   21.5089   \n",
      "                                                kubespray             26.0969   \n",
      "                                                microk8s              21.3110   \n",
      "float-operation    20_worker_1_replica_parallel k3s                  261.8305   \n",
      "                                                kubespray            235.9278   \n",
      "                                                microk8s             259.5211   \n",
      "gzip-compression   20_worker_1_replica_parallel k3s                  114.8044   \n",
      "                                                kubespray            116.8463   \n",
      "                                                microk8s             119.2667   \n",
      "image-processing   20_worker_1_replica_parallel k3s                   32.3056   \n",
      "                                                kubespray             32.4140   \n",
      "                                                microk8s              31.4413   \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s                   57.1741   \n",
      "                                                kubespray             59.8889   \n",
      "                                                microk8s              57.9638   \n",
      "linpack            20_worker_1_replica_parallel k3s                  138.3674   \n",
      "                                                kubespray            146.2908   \n",
      "                                                microk8s             133.3622   \n",
      "matmul             20_worker_1_replica_parallel k3s                  164.1820   \n",
      "                                                kubespray            173.6187   \n",
      "                                                microk8s             163.2772   \n",
      "model-training     20_worker_1_replica_parallel k3s                  131.0122   \n",
      "                                                kubespray            128.5602   \n",
      "                                                microk8s             133.5823   \n",
      "pyaes              20_worker_1_replica_parallel k3s                   42.4217   \n",
      "                                                kubespray             40.8791   \n",
      "                                                microk8s              43.9232   \n",
      "random-disk-io     20_worker_1_replica_parallel k3s                   95.5453   \n",
      "                                                kubespray             93.2151   \n",
      "                                                microk8s              81.0973   \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s                   78.3357   \n",
      "                                                kubespray             69.8770   \n",
      "                                                microk8s              73.5623   \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s                   38.1037   \n",
      "                                                kubespray             38.0631   \n",
      "                                                microk8s              46.6250   \n",
      "video-processing   20_worker_1_replica_parallel k3s                  325.3228   \n",
      "                                                kubespray            331.5588   \n",
      "                                                microk8s             321.0523   \n",
      "\n",
      "                                                               Response-read  \\\n",
      "test_name          test_type_description        test_platform                  \n",
      "chameleon          20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0002   \n",
      "dd                 20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0001   \n",
      "float-operation    20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0002   \n",
      "gzip-compression   20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0002   \n",
      "image-processing   20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0001   \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0002   \n",
      "linpack            20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0002   \n",
      "matmul             20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0001   \n",
      "model-training     20_worker_1_replica_parallel k3s                   0.0010   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0002   \n",
      "pyaes              20_worker_1_replica_parallel k3s                   0.0001   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0001   \n",
      "random-disk-io     20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0002   \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0002   \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0001   \n",
      "                                                microk8s              0.0002   \n",
      "video-processing   20_worker_1_replica_parallel k3s                   0.0002   \n",
      "                                                kubespray             0.0002   \n",
      "                                                microk8s              0.0004   \n",
      "\n",
      "                                                               status-code  \\\n",
      "test_name          test_type_description        test_platform                \n",
      "chameleon          20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "dd                 20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "float-operation    20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "gzip-compression   20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "image-processing   20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "linpack            20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "matmul             20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "model-training     20_worker_1_replica_parallel k3s                    500   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "pyaes              20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "random-disk-io     20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "video-processing   20_worker_1_replica_parallel k3s                    200   \n",
      "                                                kubespray              200   \n",
      "                                                microk8s               200   \n",
      "\n",
      "                                                               offset  \\\n",
      "test_name          test_type_description        test_platform           \n",
      "chameleon          20_worker_1_replica_parallel k3s            0.0011   \n",
      "                                                kubespray      0.0011   \n",
      "                                                microk8s       0.0013   \n",
      "dd                 20_worker_1_replica_parallel k3s            0.0010   \n",
      "                                                kubespray      0.0016   \n",
      "                                                microk8s       0.0011   \n",
      "float-operation    20_worker_1_replica_parallel k3s            0.0010   \n",
      "                                                kubespray      0.0012   \n",
      "                                                microk8s       0.0011   \n",
      "gzip-compression   20_worker_1_replica_parallel k3s            0.0011   \n",
      "                                                kubespray      0.0012   \n",
      "                                                microk8s       0.0012   \n",
      "image-processing   20_worker_1_replica_parallel k3s            0.0010   \n",
      "                                                kubespray      0.0009   \n",
      "                                                microk8s       0.0011   \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s            0.0012   \n",
      "                                                kubespray      0.0012   \n",
      "                                                microk8s       0.0011   \n",
      "linpack            20_worker_1_replica_parallel k3s            0.0010   \n",
      "                                                kubespray      0.0012   \n",
      "                                                microk8s       0.0013   \n",
      "matmul             20_worker_1_replica_parallel k3s            0.0017   \n",
      "                                                kubespray      0.0010   \n",
      "                                                microk8s       0.0010   \n",
      "model-training     20_worker_1_replica_parallel k3s            0.0012   \n",
      "                                                kubespray      0.0010   \n",
      "                                                microk8s       0.0011   \n",
      "pyaes              20_worker_1_replica_parallel k3s            0.0014   \n",
      "                                                kubespray      0.0011   \n",
      "                                                microk8s       0.0012   \n",
      "random-disk-io     20_worker_1_replica_parallel k3s            0.0011   \n",
      "                                                kubespray      0.0011   \n",
      "                                                microk8s       0.0013   \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s            0.0011   \n",
      "                                                kubespray      0.0011   \n",
      "                                                microk8s       0.0012   \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s            0.0011   \n",
      "                                                kubespray      0.0011   \n",
      "                                                microk8s       0.0010   \n",
      "video-processing   20_worker_1_replica_parallel k3s            0.0011   \n",
      "                                                kubespray      0.0010   \n",
      "                                                microk8s       0.0010   \n",
      "\n",
      "                                                              test_type  \n",
      "test_name          test_type_description        test_platform            \n",
      "chameleon          20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "dd                 20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "float-operation    20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "gzip-compression   20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "image-processing   20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "json-dumps-loads   20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "linpack            20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "matmul             20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "model-training     20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "pyaes              20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "random-disk-io     20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "s3-download-speed  20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "sequential-disk-io 20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n",
      "video-processing   20_worker_1_replica_parallel k3s                  03  \n",
      "                                                kubespray            03  \n",
      "                                                microk8s             03  \n"
     ]
    }
   ],
   "source": [
    "print(df_parallel_single_worker.groupby(['test_name', 'test_type_description', 'test_platform']).max())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Visualizing the data above using bar charts..."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "             test_name         test_type_description test_platform  \\\n0            chameleon  20_worker_1_replica_parallel           k3s   \n1            chameleon  20_worker_1_replica_parallel     kubespray   \n2            chameleon  20_worker_1_replica_parallel      microk8s   \n3                   dd  20_worker_1_replica_parallel           k3s   \n4                   dd  20_worker_1_replica_parallel     kubespray   \n5                   dd  20_worker_1_replica_parallel      microk8s   \n6      float-operation  20_worker_1_replica_parallel           k3s   \n7      float-operation  20_worker_1_replica_parallel     kubespray   \n8      float-operation  20_worker_1_replica_parallel      microk8s   \n9     gzip-compression  20_worker_1_replica_parallel           k3s   \n10    gzip-compression  20_worker_1_replica_parallel     kubespray   \n11    gzip-compression  20_worker_1_replica_parallel      microk8s   \n12    image-processing  20_worker_1_replica_parallel           k3s   \n13    image-processing  20_worker_1_replica_parallel     kubespray   \n14    image-processing  20_worker_1_replica_parallel      microk8s   \n15    json-dumps-loads  20_worker_1_replica_parallel           k3s   \n16    json-dumps-loads  20_worker_1_replica_parallel     kubespray   \n17    json-dumps-loads  20_worker_1_replica_parallel      microk8s   \n18             linpack  20_worker_1_replica_parallel           k3s   \n19             linpack  20_worker_1_replica_parallel     kubespray   \n20             linpack  20_worker_1_replica_parallel      microk8s   \n21              matmul  20_worker_1_replica_parallel           k3s   \n22              matmul  20_worker_1_replica_parallel     kubespray   \n23              matmul  20_worker_1_replica_parallel      microk8s   \n24      model-training  20_worker_1_replica_parallel           k3s   \n25      model-training  20_worker_1_replica_parallel     kubespray   \n26      model-training  20_worker_1_replica_parallel      microk8s   \n27               pyaes  20_worker_1_replica_parallel           k3s   \n28               pyaes  20_worker_1_replica_parallel     kubespray   \n29               pyaes  20_worker_1_replica_parallel      microk8s   \n30      random-disk-io  20_worker_1_replica_parallel           k3s   \n31      random-disk-io  20_worker_1_replica_parallel     kubespray   \n32      random-disk-io  20_worker_1_replica_parallel      microk8s   \n33   s3-download-speed  20_worker_1_replica_parallel           k3s   \n34   s3-download-speed  20_worker_1_replica_parallel     kubespray   \n35   s3-download-speed  20_worker_1_replica_parallel      microk8s   \n36  sequential-disk-io  20_worker_1_replica_parallel           k3s   \n37  sequential-disk-io  20_worker_1_replica_parallel     kubespray   \n38  sequential-disk-io  20_worker_1_replica_parallel      microk8s   \n39    video-processing  20_worker_1_replica_parallel           k3s   \n40    video-processing  20_worker_1_replica_parallel     kubespray   \n41    video-processing  20_worker_1_replica_parallel      microk8s   \n\n    response-time  DNS+dialup     DNS  Request-write  Response-delay  \\\n0        794.9436      0.1066  0.0018         0.0001        794.8368   \n1        779.0099      0.1562  0.0015         0.0010        778.8535   \n2        807.1679      0.1720  0.0017         0.0006        806.9958   \n3         21.6157      0.1644  0.0014         0.0007         21.5089   \n4         26.1697      0.1312  0.0015         0.0008         26.0969   \n5         21.3832      0.1048  0.0016         0.0005         21.3110   \n6        261.9761      0.1455  0.0016         0.0001        261.8305   \n7        236.0120      0.1245  0.0019         0.0001        235.9278   \n8        259.6878      0.1686  0.0019         0.0002        259.5211   \n9        114.8600      0.1194  0.0014         0.0001        114.8044   \n10       116.9336      0.1697  0.0016         0.0002        116.8463   \n11       119.3774      0.1478  0.0026         0.0002        119.2667   \n12        32.3392      0.1245  0.0014         0.0001         32.3056   \n13        32.5597      0.1641  0.0017         0.0001         32.4140   \n14        31.5562      0.1161  0.0018         0.0007         31.4413   \n15        57.3012      0.1469  0.0016         0.0001         57.1741   \n16        60.0063      0.1359  0.0016         0.0003         59.8889   \n17        58.1128      0.1488  0.0017         0.0009         57.9638   \n18       138.4108      0.1302  0.0019         0.0009        138.3674   \n19       146.3967      0.1262  0.0016         0.0002        146.2908   \n20       133.4826      0.1295  0.0014         0.0012        133.3622   \n21       164.2887      0.1251  0.0025         0.0002        164.1820   \n22       173.7760      0.1571  0.0014         0.0010        173.6187   \n23       163.3823      0.1250  0.0015         0.0001        163.2772   \n24       131.1210      0.1094  0.0017         0.0002        131.0122   \n25       128.6725      0.1322  0.0016         0.0004        128.5602   \n26       133.6591      0.1283  0.0019         0.0008        133.5823   \n27        42.5737      0.1659  0.0014         0.0002         42.4217   \n28        41.0035      0.2164  0.0019         0.0001         40.8791   \n29        44.0335      0.1457  0.0017         0.0005         43.9232   \n30        95.5815      0.1150  0.0015         0.0001         95.5453   \n31        93.3366      0.1214  0.0016         0.0001         93.2151   \n32        81.2139      0.1360  0.0014         0.0005         81.0973   \n33        78.4787      0.1673  0.0015         0.0001         78.3357   \n34        70.0163      0.1391  0.0015         0.0004         69.8770   \n35        73.6779      0.1350  0.0017         0.0002         73.5623   \n36        38.2119      0.1476  0.0015         0.0001         38.1037   \n37        38.1442      0.1185  0.0015         0.0001         38.0631   \n38        46.7550      0.1297  0.0016         0.0002         46.6250   \n39       325.3667      0.1315  0.0016         0.0011        325.3228   \n40       331.6865      0.1275  0.0017         0.0011        331.5588   \n41       321.1512      0.1368  0.0016         0.0004        321.0523   \n\n    Response-read  status-code  offset test_type  \n0          0.0002          200  0.0011        03  \n1          0.0002          200  0.0011        03  \n2          0.0002          200  0.0013        03  \n3          0.0001          200  0.0010        03  \n4          0.0001          200  0.0016        03  \n5          0.0001          200  0.0011        03  \n6          0.0001          200  0.0010        03  \n7          0.0001          200  0.0012        03  \n8          0.0002          200  0.0011        03  \n9          0.0002          200  0.0011        03  \n10         0.0002          200  0.0012        03  \n11         0.0002          200  0.0012        03  \n12         0.0001          200  0.0010        03  \n13         0.0002          200  0.0009        03  \n14         0.0001          200  0.0011        03  \n15         0.0002          200  0.0012        03  \n16         0.0002          200  0.0012        03  \n17         0.0002          200  0.0011        03  \n18         0.0001          200  0.0010        03  \n19         0.0002          200  0.0012        03  \n20         0.0002          200  0.0013        03  \n21         0.0002          200  0.0017        03  \n22         0.0002          200  0.0010        03  \n23         0.0001          200  0.0010        03  \n24         0.0010          500  0.0012        03  \n25         0.0001          200  0.0010        03  \n26         0.0002          200  0.0011        03  \n27         0.0001          200  0.0014        03  \n28         0.0001          200  0.0011        03  \n29         0.0001          200  0.0012        03  \n30         0.0002          200  0.0011        03  \n31         0.0001          200  0.0011        03  \n32         0.0002          200  0.0013        03  \n33         0.0002          200  0.0011        03  \n34         0.0002          200  0.0011        03  \n35         0.0002          200  0.0012        03  \n36         0.0002          200  0.0011        03  \n37         0.0001          200  0.0011        03  \n38         0.0002          200  0.0010        03  \n39         0.0002          200  0.0011        03  \n40         0.0002          200  0.0010        03  \n41         0.0004          200  0.0010        03  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>test_name</th>\n      <th>test_type_description</th>\n      <th>test_platform</th>\n      <th>response-time</th>\n      <th>DNS+dialup</th>\n      <th>DNS</th>\n      <th>Request-write</th>\n      <th>Response-delay</th>\n      <th>Response-read</th>\n      <th>status-code</th>\n      <th>offset</th>\n      <th>test_type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>chameleon</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>794.9436</td>\n      <td>0.1066</td>\n      <td>0.0018</td>\n      <td>0.0001</td>\n      <td>794.8368</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>chameleon</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>779.0099</td>\n      <td>0.1562</td>\n      <td>0.0015</td>\n      <td>0.0010</td>\n      <td>778.8535</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>chameleon</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>807.1679</td>\n      <td>0.1720</td>\n      <td>0.0017</td>\n      <td>0.0006</td>\n      <td>806.9958</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0013</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>dd</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>21.6157</td>\n      <td>0.1644</td>\n      <td>0.0014</td>\n      <td>0.0007</td>\n      <td>21.5089</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>dd</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>26.1697</td>\n      <td>0.1312</td>\n      <td>0.0015</td>\n      <td>0.0008</td>\n      <td>26.0969</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0016</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>dd</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>21.3832</td>\n      <td>0.1048</td>\n      <td>0.0016</td>\n      <td>0.0005</td>\n      <td>21.3110</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>float-operation</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>261.9761</td>\n      <td>0.1455</td>\n      <td>0.0016</td>\n      <td>0.0001</td>\n      <td>261.8305</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>float-operation</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>236.0120</td>\n      <td>0.1245</td>\n      <td>0.0019</td>\n      <td>0.0001</td>\n      <td>235.9278</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>float-operation</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>259.6878</td>\n      <td>0.1686</td>\n      <td>0.0019</td>\n      <td>0.0002</td>\n      <td>259.5211</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>gzip-compression</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>114.8600</td>\n      <td>0.1194</td>\n      <td>0.0014</td>\n      <td>0.0001</td>\n      <td>114.8044</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>gzip-compression</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>116.9336</td>\n      <td>0.1697</td>\n      <td>0.0016</td>\n      <td>0.0002</td>\n      <td>116.8463</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>gzip-compression</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>119.3774</td>\n      <td>0.1478</td>\n      <td>0.0026</td>\n      <td>0.0002</td>\n      <td>119.2667</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>image-processing</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>32.3392</td>\n      <td>0.1245</td>\n      <td>0.0014</td>\n      <td>0.0001</td>\n      <td>32.3056</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>image-processing</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>32.5597</td>\n      <td>0.1641</td>\n      <td>0.0017</td>\n      <td>0.0001</td>\n      <td>32.4140</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0009</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>image-processing</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>31.5562</td>\n      <td>0.1161</td>\n      <td>0.0018</td>\n      <td>0.0007</td>\n      <td>31.4413</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>json-dumps-loads</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>57.3012</td>\n      <td>0.1469</td>\n      <td>0.0016</td>\n      <td>0.0001</td>\n      <td>57.1741</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>json-dumps-loads</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>60.0063</td>\n      <td>0.1359</td>\n      <td>0.0016</td>\n      <td>0.0003</td>\n      <td>59.8889</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>json-dumps-loads</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>58.1128</td>\n      <td>0.1488</td>\n      <td>0.0017</td>\n      <td>0.0009</td>\n      <td>57.9638</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>linpack</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>138.4108</td>\n      <td>0.1302</td>\n      <td>0.0019</td>\n      <td>0.0009</td>\n      <td>138.3674</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>linpack</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>146.3967</td>\n      <td>0.1262</td>\n      <td>0.0016</td>\n      <td>0.0002</td>\n      <td>146.2908</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>linpack</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>133.4826</td>\n      <td>0.1295</td>\n      <td>0.0014</td>\n      <td>0.0012</td>\n      <td>133.3622</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0013</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>matmul</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>164.2887</td>\n      <td>0.1251</td>\n      <td>0.0025</td>\n      <td>0.0002</td>\n      <td>164.1820</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0017</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>matmul</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>173.7760</td>\n      <td>0.1571</td>\n      <td>0.0014</td>\n      <td>0.0010</td>\n      <td>173.6187</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>matmul</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>163.3823</td>\n      <td>0.1250</td>\n      <td>0.0015</td>\n      <td>0.0001</td>\n      <td>163.2772</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>model-training</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>131.1210</td>\n      <td>0.1094</td>\n      <td>0.0017</td>\n      <td>0.0002</td>\n      <td>131.0122</td>\n      <td>0.0010</td>\n      <td>500</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>model-training</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>128.6725</td>\n      <td>0.1322</td>\n      <td>0.0016</td>\n      <td>0.0004</td>\n      <td>128.5602</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>model-training</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>133.6591</td>\n      <td>0.1283</td>\n      <td>0.0019</td>\n      <td>0.0008</td>\n      <td>133.5823</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>pyaes</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>42.5737</td>\n      <td>0.1659</td>\n      <td>0.0014</td>\n      <td>0.0002</td>\n      <td>42.4217</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0014</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>pyaes</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>41.0035</td>\n      <td>0.2164</td>\n      <td>0.0019</td>\n      <td>0.0001</td>\n      <td>40.8791</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>pyaes</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>44.0335</td>\n      <td>0.1457</td>\n      <td>0.0017</td>\n      <td>0.0005</td>\n      <td>43.9232</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>random-disk-io</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>95.5815</td>\n      <td>0.1150</td>\n      <td>0.0015</td>\n      <td>0.0001</td>\n      <td>95.5453</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>random-disk-io</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>93.3366</td>\n      <td>0.1214</td>\n      <td>0.0016</td>\n      <td>0.0001</td>\n      <td>93.2151</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>random-disk-io</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>81.2139</td>\n      <td>0.1360</td>\n      <td>0.0014</td>\n      <td>0.0005</td>\n      <td>81.0973</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0013</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>s3-download-speed</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>78.4787</td>\n      <td>0.1673</td>\n      <td>0.0015</td>\n      <td>0.0001</td>\n      <td>78.3357</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>s3-download-speed</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>70.0163</td>\n      <td>0.1391</td>\n      <td>0.0015</td>\n      <td>0.0004</td>\n      <td>69.8770</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>s3-download-speed</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>73.6779</td>\n      <td>0.1350</td>\n      <td>0.0017</td>\n      <td>0.0002</td>\n      <td>73.5623</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0012</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>sequential-disk-io</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>38.2119</td>\n      <td>0.1476</td>\n      <td>0.0015</td>\n      <td>0.0001</td>\n      <td>38.1037</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>sequential-disk-io</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>38.1442</td>\n      <td>0.1185</td>\n      <td>0.0015</td>\n      <td>0.0001</td>\n      <td>38.0631</td>\n      <td>0.0001</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>sequential-disk-io</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>46.7550</td>\n      <td>0.1297</td>\n      <td>0.0016</td>\n      <td>0.0002</td>\n      <td>46.6250</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>video-processing</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>k3s</td>\n      <td>325.3667</td>\n      <td>0.1315</td>\n      <td>0.0016</td>\n      <td>0.0011</td>\n      <td>325.3228</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0011</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>video-processing</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>kubespray</td>\n      <td>331.6865</td>\n      <td>0.1275</td>\n      <td>0.0017</td>\n      <td>0.0011</td>\n      <td>331.5588</td>\n      <td>0.0002</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>video-processing</td>\n      <td>20_worker_1_replica_parallel</td>\n      <td>microk8s</td>\n      <td>321.1512</td>\n      <td>0.1368</td>\n      <td>0.0016</td>\n      <td>0.0004</td>\n      <td>321.0523</td>\n      <td>0.0004</td>\n      <td>200</td>\n      <td>0.0010</td>\n      <td>03</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_parallel_single_worker.groupby(['test_name', 'test_type_description', 'test_platform']).max().reset_index()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 864x864 with 14 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parallel_single_worker_total_bar_plot = sns.FacetGrid(\n",
    "    df_parallel_single_worker.groupby(['test_name', 'test_type_description', 'test_platform']).max().reset_index(),\n",
    "    col=\"test_name\", hue='test_platform',col_wrap=4, sharey=False, palette=['gold', 'lightseagreen', 'darkorange'],\n",
    "    sharex=False)\n",
    "parallel_single_worker_total_bar_plot = parallel_single_worker_total_bar_plot.map(sns.barplot, 'test_platform',\n",
    "                                                                                  'response-time',\n",
    "                                                                                  order=TEST_PLATFORMS)\n",
    "parallel_single_worker_total_bar_plot.set_titles('{col_name}', size=12)\n",
    "parallel_single_worker_total_bar_plot.fig.tight_layout(w_pad=3)\n",
    "for ax in parallel_single_worker_total_bar_plot.axes.ravel():\n",
    "    ax.set_ylabel('Max Response Time (s)', fontsize=12)\n",
    "    ax.set_xlabel('')\n",
    "    ax.tick_params(labelsize=12)\n",
    "    for p in ax.patches:\n",
    "        ax.annotate(format(p.get_height(), '.2f'), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center',\n",
    "                    va='center', xytext=(0, -15), textcoords='offset points', fontsize=13)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "parallel_single_worker_total_bar_plot.savefig('03-04-parallel_single_worker_total_bar_plot', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Inferring how many requests can be served by a single replica at the same time for the following tests:\n",
    "\n",
    "- `float-operation`\n",
    "- `chameleon`\n",
    "- `gzip-compression`\n",
    "- `image-processing`\n",
    "- `linpack`\n",
    "- `matmul`\n",
    "- `model-training`\n",
    "- `pyaes`\n",
    "- `video-processing`\n",
    "\n",
    "These tests have been chosen because they do not depend on a network element, and are CPU bound."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 648x648 with 9 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relevant_tests = ['float-operation', 'chameleon', 'gzip-compression', 'image-processing', 'linpack', 'matmul',\n",
    "                  'model-training', 'pyaes', 'video-processing']\n",
    "concurrency_inferrence_hist = sns.FacetGrid(df_parallel_single_worker[\n",
    "                                                df_parallel_single_worker['test_platform'].eq('k3s') &\n",
    "                                                df_parallel_single_worker['test_name'].isin(relevant_tests)],\n",
    "                                            col=\"test_name\", hue='test_platform', col_wrap=3, sharey=False,\n",
    "                                            sharex=False)\n",
    "concurrency_inferrence_hist = concurrency_inferrence_hist.map(sns.histplot, 'response-time', bins=5)\n",
    "concurrency_inferrence_hist.set_titles('{col_name}', size=13)\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "for ax in concurrency_inferrence_hist.axes.ravel():\n",
    "    ax.set_xlabel('Execution Time (s)', fontsize=12)\n",
    "    ax.set_ylabel('Count', fontsize=12)\n",
    "    for p in ax.patches:\n",
    "        ax.annotate(format(p.get_height(), '.2f'), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center',\n",
    "                    va='center', xytext=(0, -15), textcoords='offset points', fontsize=13)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "concurrency_inferrence_hist.savefig('03-06-concurrency_inferrence_hist', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Four requests at the same time. This value is also explained in the OpenFaaS docs and it is automatically determined based on the number of cores that the machine has."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 04 - Analyzing autoscaling behavior using the native Alertmanager rules for OpenFaaS\n",
    "\n",
    "The default rule tests the number of successful invocations per second for the last 10 seconds and if this number is larger than 5, it scales up the number of function instances up to a preconfigured maximum. This means that even when executing requests at a constant rate, say 6 per second, it is possible to obtain the maximum number of allowed replicas, same as when testing with a much larger number of requests per second, for example 30 or 40. We have tested this behavior by executing 1 request per second from 6 concurrent workers for more than 200 seconds, successfully reaching the defined threshold for the maximum number of replicas.\n",
    "\n",
    "Since the default scaling rule does not take into account the current number of deployed replicas, instead it relies solely on the number of requests per second, it is binary in nature. Under a consistent load it either scales to the maximum number of configured replicas or it does not scale at all. However, as a result of using well-known third-party applications for the actual monitoring and rule definitions, administrators should be able with relative ease to change the default behavior in order to better reflect their needs."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 05 - Analyzing autoscaling behavior using the Kubernetes Horizontal Pod Autoscaler (HPA)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "df_hpa_parallel = pd.DataFrame()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Loading all the data into a single data frame..."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "tests = ['02_c=2-48+2_q=1_m=5', '03_c=40_-2_q=1_m=5', '04_c=varied_q=1_m=5']\n",
    "\n",
    "test_type = '05'\n",
    "test_type_description = 'hpa_parallel'\n",
    "\n",
    "if os.path.sep == '/':\n",
    "    regex_escape = '/'\n",
    "else:\n",
    "    regex_escape = '\\\\\\\\'\n",
    "\n",
    "for platform in TEST_PLATFORMS:\n",
    "    for test in tests:\n",
    "        csv_files = glob.glob(f'data/{platform}/05-hpa-autoscaling/results/{test}/*scaling-c*.csv')\n",
    "        for i in csv_files:\n",
    "            test_name = re.findall(f'{regex_escape}(.*)-scaling.*.csv', i)[0]\n",
    "            test_concurrency = re.findall(f'{regex_escape}.*-scaling-c(\\d+).*.csv', i)[0]\n",
    "            df_tmp = pd.read_csv(i)\n",
    "            df_tmp = df_tmp[['response-time']]\n",
    "            df_tmp['test_type'] = test_type\n",
    "            df_tmp['test_type_description'] = test_type_description\n",
    "            df_tmp['test_platform'] = platform\n",
    "            df_tmp['test_name'] = test_name\n",
    "            df_tmp['test_concurrency'] = test_concurrency\n",
    "            df_tmp['test_iteration'] = test\n",
    "            df_hpa_parallel = df_hpa_parallel.append(df_tmp)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The three tests that we will look at are:\n",
    "\n",
    "- start with a single replica and increase the number of concurrent requests per second from 2 to 48. The number of concurrent requests per second is increased by 2 every 5 minutes.\n",
    "- start with a single replica and decrease the number of concurrent requests per second from 40 to 1. The number of concurrent requests per second is decreased by 2 every 5 minutes.\n",
    "- start with a single replica and vary the number of concurrent requests per second. The number of concurrent requests per second is changed every 5 minutes. This is the sequence for the number of concurrent requests per second that was used: `[8, 1, 20, 4, 40, 24, 1, 4, 16, 1, 36, 32]`"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 730.25x216 with 3 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "concurrency_mean = df_hpa_parallel.groupby(\n",
    "    ['test_name', 'test_type', 'test_type_description', 'test_iteration', 'test_platform',\n",
    "     'test_concurrency']).mean().reset_index()\n",
    "concurrency_mean['test_concurrency'] = pd.to_numeric(concurrency_mean['test_concurrency'])\n",
    "concurrency_mean['test_iteration'] = concurrency_mean['test_iteration'].replace(['02_c=2-48+2_q=1_m=5'], 'Concurrency from 2 to 48, step +2')\n",
    "concurrency_mean['test_iteration'] = concurrency_mean['test_iteration'].replace(['03_c=40_-2_q=1_m=5'], 'Concurrency from 40 to 2, step -2')\n",
    "concurrency_mean['test_iteration'] = concurrency_mean['test_iteration'].replace(['04_c=varied_q=1_m=5'], 'Varied concurrency')\n",
    "hpa_concurrency_line_plot = sns.FacetGrid(concurrency_mean, col=\"test_iteration\", hue='test_platform', col_wrap=3,\n",
    "                                          sharey=False, sharex=False, legend_out=True, hue_order=TEST_PLATFORMS, palette=['lightseagreen', 'gold', 'darkorange'])\n",
    "hpa_concurrency_line_plot = hpa_concurrency_line_plot.map(sns.lineplot, 'test_concurrency', 'response-time', linewidth = 2)\n",
    "hpa_concurrency_line_plot.set_titles('{col_name}', size=13)\n",
    "hpa_concurrency_line_plot.add_legend()\n",
    "c = 0\n",
    "for ax in hpa_concurrency_line_plot.axes.ravel():\n",
    "    ax.set_xlabel('Concurrency', fontsize=12)\n",
    "    ax.set_ylabel('Avg. Response Time (s)', fontsize=12)\n",
    "    ax.tick_params(labelsize=12)\n",
    "    if c == 0:\n",
    "        ax.set_xticks(range(0, 49, 4))\n",
    "    else:\n",
    "        ax.set_xticks(range(0, 41, 4))\n",
    "    c += 1\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "hpa_concurrency_line_plot.savefig('05-07-hpa_concurrency_line_plot', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Visualizing how the number of replicas has changed with the change in the number of concurrent requests across time. The step chart below only shows the results for an initial load of 4 up to a maximum of 44 concurrent requsts per second, where the number of requests per second is increased by 4 every 5 minutes.\n",
    "\n",
    "The raw data for this chart is available in `{platform_name}/05-hpa-autoscaling/results/01_c=4-48+4_q=1_m=5/float-operation-scaling.csv`."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "concurrency_4 = pd.DataFrame({\n",
    "    'load': [4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44],\n",
    "    'kubespray': [1, 2, 3, 5, 5, 6, 7, 8, 8, 10, 10],\n",
    "    'k3s': [2, 3, 3, 4, 5, 6, 8, 8, 8, 10, 10],\n",
    "    'microk8s': [2, 3, 3, 4, 5, 6, 8, 8, 10, 10, 10]\n",
    "})\n",
    "sns.set_style(\"ticks\",{'axes.grid' : True})\n",
    "concurrency_4_step_plot = sns.lineplot(data=pd.melt(concurrency_4, ['load'], var_name='platform'), x='load', y='value', hue='platform', drawstyle='steps-pre', palette=['lightseagreen', 'gold', 'darkorange'], linewidth = 3)\n",
    "concurrency_4_step_plot.set_xticks(range(4, 45))\n",
    "concurrency_4_step_plot.tick_params(labelsize=13)\n",
    "concurrency_4_step_plot.set_ylabel('Max. Num. Replicas', fontsize=12)\n",
    "concurrency_4_step_plot.set_xlabel('Concurrency', fontsize=12)\n",
    "concurrency_4_step_plot.legend(fontsize=13)\n",
    "\n",
    "for ind, label in enumerate(concurrency_4_step_plot.get_xticklabels()):\n",
    "    if int(ind) % 4 == 0:  # keep every 4th tick label because that's the rate of increase\n",
    "        label.set_visible(True)\n",
    "    else:\n",
    "        label.set_visible(False)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "fig = concurrency_4_step_plot.get_figure()\n",
    "fig.savefig('05-08-concurrency_4_step_plot', bbox_inches='tight', dpi=300)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We see that the scale-up pattern of both K3s and MicroK8s is closely matched, with a difference shown only between the `32` and `36` concurrent load, while Kubespray is faster to scale up for `12` concurrent requests, but slower to scale up for `4` and `24`."
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}