evaluation/Main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Imports"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import datetime"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Variables and Utils"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import sharedVariables\n",
    "from sharedVariables import *  \n",
    "\n",
    "def to_human_readable(size):\n",
    "    power = 1000\n",
    "    n = 0\n",
    "    mem = {0 : 'B', 1: 'KB', 2: 'MB', 3: 'GB', 4: 'TB'}\n",
    "    while size > power:\n",
    "        size /=  power\n",
    "        n += 1\n",
    "    return \"{0:.2f} {1}\".format(size, mem[n])\n",
    "\n",
    "todaystring = datetime.datetime.today().strftime('%Y%m%d')\n",
    "outdir = f\"data/{todaystring}\"\n",
    "if not os.path.exists(outdir):\n",
    "    os.mkdir(outdir)\n",
    "\n",
    "sync_timings = list(pd.to_datetime(['2021-07-21T01:40:12.495194358Z', '2021-07-21T01:36:06.906168096Z',\n",
    "       '2021-07-21T01:32:20.769974404Z', '2021-07-21T01:28:46.650947007Z',\n",
    "       '2021-07-21T01:25:24.874192553Z', '2021-07-21T01:18:48.444698374Z',\n",
    "       '2021-07-21T01:14:52.702846072Z', '2021-07-21T01:11:17.139797585Z',\n",
    "       '2021-07-21T01:07:51.620853144Z']))\n",
    "base_timings = list(pd.to_datetime(['2021-07-21T01:37:44.391046353Z', '2021-07-21T01:34:09.592287642Z',\n",
    "       '2021-07-21T01:30:33.719478397Z', '2021-07-21T01:27:08.165382341Z',\n",
    "       '2021-07-21T01:23:47.975327731Z', '2021-07-21T01:19:58.095786286Z',\n",
    "       '2021-07-21T01:16:22.724758295Z', '2021-07-21T01:12:47.119141410Z',\n",
    "       '2021-07-21T01:09:06.449328442Z']))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Data Preparation"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "def filterDataForLocalTraffic(df): \n",
    "    df = df[df[\"jsonPayload.connection.src_ip\"].str.contains(r'^10\\.')]\n",
    "    df = df[df[\"jsonPayload.connection.dest_ip\"].str.contains(r'^10\\.')]\n",
    "    return df\n",
    "\n",
    "# df_sync = loadData('data/20210721/experiment-syncmesh-with-latency-3.log')\n",
    "# df_base = loadData('data/20210721/experiment-baseline-with-latency-3.log')\n",
    "df_sync = loadDataCSV(f'{outdir}/experiment-syncmesh-with-latency-3.csv')\n",
    "df_base = loadDataCSV(f'{outdir}/experiment-baseline-with-latency-3.csv')\n",
    "\n",
    "# df_sync.info()\n",
    "\n",
    "orig_df = df_sync\n",
    "\n",
    "df_sync = filterDataForLocalTraffic(df_sync)\n",
    "df_base = filterDataForLocalTraffic(df_base)\n",
    "\n",
    "df_base.to_csv(f\"test.csv\")\n",
    "# TODO: Filter Entries for port specific traffic\n",
    "\n",
    "\n",
    "\n",
    "nodes = list(dict.fromkeys(df_base[\"jsonPayload.connection.dest_ip\"].unique().tolist() + df_base[\"jsonPayload.connection.src_ip\"].unique().tolist()))\n",
    "nodes = [node for node in nodes if node not in [ip_client, ip_server, ip_orchestrator]]\n",
    "# print(nodes)\n",
    "\n",
    "# Print Table\n",
    "# df[[\"jsonPayload.connection.src_ip\", \"jsonPayload.connection.src_port\", \"jsonPayload.connection.dest_ip\", \"jsonPayload.bytes_sent\", \"jsonPayload.rtt_msec\"]].unique()\n",
    "# df_sync.index\n",
    "\n"
   ],
   "outputs": [
    {
     "output_type": "error",
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File data/20210801/experiment-syncmesh-with-latency-3.csv does not exist: 'data/20210801/experiment-syncmesh-with-latency-3.csv'",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-2c35add44466>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m# df_sync = loadData('data/20210721/experiment-syncmesh-with-latency-3.log')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m# df_base = loadData('data/20210721/experiment-baseline-with-latency-3.log')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mdf_sync\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadDataCSV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{outdir}/experiment-syncmesh-with-latency-3.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mdf_base\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloadDataCSV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'{outdir}/experiment-baseline-with-latency-3.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Develop\\GitHub\\DSPJ2021\\syncmesh\\evaluation\\sharedVariables.py\u001b[0m in \u001b[0;36mloadDataCSV\u001b[1;34m(file)\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mloadDataCSV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m     \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m     \u001b[1;31m# df = df.astype({\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File data/20210801/experiment-syncmesh-with-latency-3.csv does not exist: 'data/20210801/experiment-syncmesh-with-latency-3.csv'"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "\n",
    "def calcSubset(df, ports = [8080,27017]):\n",
    "    # df = df[df['jsonPayload.connection.dest_port'].isin(ports)]\n",
    "    # df = df[df['jsonPayload.connection.src_port'].isin(ports)]\n",
    "\n",
    "\n",
    "    fromClient = df[df['jsonPayload.connection.src_ip']==ip_client]\n",
    "    toServer = df[df['jsonPayload.connection.dest_ip']==ip_server]\n",
    "\n",
    "\n",
    "    fromClientDataset = {}\n",
    "    toServerDataset = {}\n",
    "    betweenNodesDataset = {}\n",
    "    for node in nodes: \n",
    "        fromClientDataset[node] = fromClient[fromClient['jsonPayload.connection.dest_ip']==node]\n",
    "\n",
    "    nodeCombinations = list(itertools.combinations(nodes, r=2))\n",
    "    for combination in nodeCombinations: \n",
    "        # Use both src_ip and dest_ip as they are not request related\n",
    "        fromNode = df[df['jsonPayload.connection.src_ip'].isin(combination)]\n",
    "        # fromAnyNode = df[df[\"jsonPayload.connection.src_ip\"].isin(nodes)]\n",
    "        betweenNodesDataset[\"between \" + combination[0] + \" and \" + combination[1]] = fromNode[fromNode['jsonPayload.connection.dest_ip'].isin(combination)]\n",
    "\n",
    "    for node in nodes: \n",
    "        toServerDataset[node] = toServer[toServer['jsonPayload.connection.src_ip']==node]\n",
    "    \n",
    "    return fromClientDataset, toServerDataset, betweenNodesDataset\n",
    "\n",
    "fromClientDataset, toServerDataset, betweenNodesDataset = calcSubset(df_base, [27017])\n",
    "\n",
    "# print(nodeCombinations)\n",
    "# toServer.head()\n",
    "# toServerDataset[\"10.2.0.12\"].head()\n",
    "# betweenNodesDataset['between 10.1.0.11 and 10.3.0.13'].info()\n",
    "# fromClientDataset"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Results"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## General"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "fig.suptitle('Round Trip Time (Overall)')\n",
    "base_remove0 = df_base[df_base['jsonPayload.bytes_sent']!=0]\n",
    "sync_remove0 = df_sync[df_sync['jsonPayload.bytes_sent']!=0]\n",
    "ax1.hist(base_remove0['jsonPayload.rtt_msec'])\n",
    "ax1.set_title(\"Baseline\")\n",
    "ax1.set_ylabel(\"Requests\")\n",
    "ax1.set_xlabel(\"Round Trip Time (msec)\")\n",
    "ax2.hist(sync_remove0['jsonPayload.rtt_msec'])\n",
    "ax2.set_title(\"Syncmesh\")\n",
    "ax2.set_ylabel(\"Requests\")\n",
    "ax2.set_xlabel(\"Round Trip Time (msec)\")\n",
    "fig.savefig(\"out/roundtrip_overall.png\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Case: Baseline (Central Mongo)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "fig, ax = plt.subplots()\n",
    "# ax.set_ylim(0,500)\n",
    "for key in toServerDataset: \n",
    "    ax.plot(toServerDataset[key].index, toServerDataset[key][\"jsonPayload.bytes_sent\"], label=\"from \" + key, marker='o', linestyle='None')\n",
    "# set title and legend\n",
    "ax.set_title('Scenario: Baseline\\nBandwidth to Server')\n",
    "ax.set_ylabel(\"Bytes\")\n",
    "ax.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.legend()\n",
    "for timing in base_timings: \n",
    "    ax.vlines(timing, 0, 999999, linestyle='--', alpha=0.2)\n",
    "fig.savefig(\"out/bandwidth_to_server.png\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Case: Syncmesh"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "fromClientDataset, toServerDataset, betweenNodesDataset = calcSubset(df_sync, [8080])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "fig, ax = plt.subplots()\n",
    "# plot histogram\n",
    "binwidth = 10\n",
    "\n",
    "# ax.hist(fromClientTo4['jsonPayload.bytes_sent'], bins=range(min(fromClientTo4['jsonPayload.bytes_sent']), max(fromClientTo4['jsonPayload.bytes_sent']) + binwidth, binwidth))\n",
    "# ax.set_title(\"Package Size Histogram for Client to 4\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "fig, ax = plt.subplots()\n",
    "# ax.set_ylim(0,500)\n",
    "for key in fromClientDataset: \n",
    "    ax.plot(fromClientDataset[key].index, fromClientDataset[key][\"jsonPayload.bytes_sent\"], label=\"to \" + key, marker='o', linestyle='None')\n",
    "# set title and legend\n",
    "ax.set_title('Scenario: Syncmesh\\nBandwidth to Client')\n",
    "ax.set_ylabel(\"Bytes\")\n",
    "ax.set_xlabel(\"Time\")\n",
    "ax.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "ax.legend()\n",
    "for timing in list(pd.to_datetime(sync_timings)): \n",
    "    ax.vlines(timing, 0, 9999, linestyle='--', alpha=0.2)\n",
    "# fig.savefig(\"out/bandwidth_from_client.png\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(15, 6))\n",
    "\n",
    "# ax.set_ylim(0,500)\n",
    "for key in betweenNodesDataset: \n",
    "    ax.plot(betweenNodesDataset[key].index, betweenNodesDataset[key][\"jsonPayload.bytes_sent\"], label=key, marker='o', linestyle='None')\n",
    "# set title and legend\n",
    "ax.set_title('Scenario: Syncmesh\\nBandwidth between Nodes')\n",
    "ax.set_ylabel(\"Bytes\")\n",
    "ax.set_xlabel(\"Time\")\n",
    "box = ax.get_position()\n",
    "# ax.set_position([box.x0, box.y0, box.width * 0.4, box.height])\n",
    "ax.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "ax.legend(loc='upper left')\n",
    "\n",
    "fig.savefig(\"out/bandwidth_between_nodes.png\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Comparison"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "experiments = {}\n",
    "\n",
    "# Sliding window:\n",
    "n = 2\n",
    "base_windows = [base_timings[i:i+n] for i in range(len(base_timings)-n+1)]\n",
    "sync_windows = [sync_timings[i:i+n] for i in range(len(sync_timings)-n+1)]\n",
    "\n",
    "i = 0\n",
    "for base_window, sync_window in zip(base_windows, sync_windows):\n",
    "    # experiment = {}\n",
    "    print(base_window)\n",
    "    df_base_window = df_base.loc[str(base_window[0]):str(base_window[1])]\n",
    "    df_sync_window = df_sync.loc[str(sync_window[0]):str(sync_window[1])]\n",
    "    # print(df_base_window.info())\n",
    "    experiments[i] = {}\n",
    "    experiments[i][\"baseAggregateBytes\"] =  df_base_window[\"jsonPayload.bytes_sent\"].sum()\n",
    "    experiments[i][\"baseAggregateRTT\"] = df_base_window[\"jsonPayload.rtt_msec\"].mean()\n",
    "    experiments[i][\"syncAggregateBytes\"] = df_sync_window[\"jsonPayload.bytes_sent\"].sum()\n",
    "    experiments[i][\"syncAggregateRTT\"] = df_sync_window[\"jsonPayload.rtt_msec\"].mean()\n",
    "    i += 1\n",
    "\n",
    "# timedfbaseline.info()\n",
    "print(experiments)\n",
    "\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4, figsize=(15,5))\n",
    "cases = ['Baseline', 'Syncmesh']\n",
    "values1 = [experiments[0][\"baseAggregateBytes\"], experiments[0][\"syncAggregateBytes\"]]\n",
    "values2 = [experiments[1][\"baseAggregateBytes\"], experiments[1][\"syncAggregateBytes\"]]\n",
    "values3 = [experiments[2][\"baseAggregateBytes\"], experiments[2][\"syncAggregateBytes\"]]\n",
    "values4 = [experiments[3][\"baseAggregateBytes\"], experiments[3][\"syncAggregateBytes\"]]\n",
    "fig.suptitle('Aggregated Bandwidth\\nScenario: Collect')\n",
    "ax1.bar(cases,values1)\n",
    "ax1.set_title('30 Days')\n",
    "ax1.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax1.get_position()\n",
    "ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax2.bar(cases,values2)\n",
    "ax2.set_title('14 Days')\n",
    "ax2.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax2.get_position()\n",
    "ax2.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax3.bar(cases,values3)\n",
    "ax3.set_title('7 Days')\n",
    "ax3.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax3.get_position()\n",
    "ax3.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax4.bar(cases,values4)\n",
    "ax4.set_title('1 Day')\n",
    "ax4.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax4.get_position()\n",
    "ax4.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "# ax.set_ylabel(\"Bytes\")\n",
    "# ax.set_xlabel(\"Time\")\n",
    "fig.savefig(\"out/bandwith_aggregate_comparison_collect.png\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[Timestamp('2021-07-21 01:37:44.391046353+0000', tz='UTC'), Timestamp('2021-07-21 01:34:09.592287642+0000', tz='UTC')]\n",
      "[Timestamp('2021-07-21 01:34:09.592287642+0000', tz='UTC'), Timestamp('2021-07-21 01:30:33.719478397+0000', tz='UTC')]\n",
      "[Timestamp('2021-07-21 01:30:33.719478397+0000', tz='UTC'), Timestamp('2021-07-21 01:27:08.165382341+0000', tz='UTC')]\n",
      "[Timestamp('2021-07-21 01:27:08.165382341+0000', tz='UTC'), Timestamp('2021-07-21 01:23:47.975327731+0000', tz='UTC')]\n",
      "[Timestamp('2021-07-21 01:23:47.975327731+0000', tz='UTC'), Timestamp('2021-07-21 01:19:58.095786286+0000', tz='UTC')]\n",
      "[Timestamp('2021-07-21 01:19:58.095786286+0000', tz='UTC'), Timestamp('2021-07-21 01:16:22.724758295+0000', tz='UTC')]\n",
      "[Timestamp('2021-07-21 01:16:22.724758295+0000', tz='UTC'), Timestamp('2021-07-21 01:12:47.119141410+0000', tz='UTC')]\n",
      "[Timestamp('2021-07-21 01:12:47.119141410+0000', tz='UTC'), Timestamp('2021-07-21 01:09:06.449328442+0000', tz='UTC')]\n",
      "{0: {'baseAggregateBytes': 19541385, 'baseAggregateRTT': 77.03846153846153, 'syncAggregateBytes': 84296941, 'syncAggregateRTT': 174.7659574468085}, 1: {'baseAggregateBytes': 8937661, 'baseAggregateRTT': 49.233333333333334, 'syncAggregateBytes': 41412468, 'syncAggregateRTT': 87.47826086956522}, 2: {'baseAggregateBytes': 5258150, 'baseAggregateRTT': 50.96551724137931, 'syncAggregateBytes': 21864906, 'syncAggregateRTT': 55.355555555555554}, 3: {'baseAggregateBytes': 786751, 'baseAggregateRTT': 64.84, 'syncAggregateBytes': 2830878, 'syncAggregateRTT': 37.38235294117647}, 4: {'baseAggregateBytes': 72758522, 'baseAggregateRTT': 79.56666666666666, 'syncAggregateBytes': 167909422, 'syncAggregateRTT': 98.16363636363636}, 5: {'baseAggregateBytes': 36215547, 'baseAggregateRTT': 80.72727272727273, 'syncAggregateBytes': 144674395, 'syncAggregateRTT': 83.47826086956522}, 6: {'baseAggregateBytes': 22246795, 'baseAggregateRTT': 72.67857142857143, 'syncAggregateBytes': 75962312, 'syncAggregateRTT': 71.92682926829268}, 7: {'baseAggregateBytes': 2446561, 'baseAggregateRTT': 44.71875, 'syncAggregateBytes': 9999703, 'syncAggregateRTT': 58.55882352941177}}\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "## Bandwidth Aggregated\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4, figsize=(15,5))\n",
    "cases = ['Baseline', 'Syncmesh']\n",
    "values1 = [experiments[4][\"baseAggregateBytes\"], experiments[4][\"syncAggregateBytes\"]]\n",
    "values2 = [experiments[5][\"baseAggregateBytes\"], experiments[5][\"syncAggregateBytes\"]]\n",
    "values3 = [experiments[6][\"baseAggregateBytes\"], experiments[6][\"syncAggregateBytes\"]]\n",
    "values4 = [experiments[7][\"baseAggregateBytes\"], experiments[7][\"syncAggregateBytes\"]]\n",
    "fig.suptitle('Aggregated Bandwidth\\nScenario: Aggregate')\n",
    "ax1.bar(cases,values1)\n",
    "ax1.set_title('30 Days')\n",
    "ax1.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax1.get_position()\n",
    "ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax2.bar(cases,values2)\n",
    "ax2.set_title('14 Days')\n",
    "ax2.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax2.get_position()\n",
    "ax2.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax3.bar(cases,values3)\n",
    "ax3.set_title('7 Days')\n",
    "ax3.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax3.get_position()\n",
    "ax3.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax4.bar(cases,values4)\n",
    "ax4.set_title('1 Day')\n",
    "ax4.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax4.get_position()\n",
    "ax4.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "# ax.set_ylabel(\"Bytes\")\n",
    "# ax.set_xlabel(\"Time\")\n",
    "fig.savefig(\"out/bandwith_aggregated_comparison_aggregate.png\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4, figsize=(20,5))\n",
    "cases = ['Baseline', 'Syncmesh']\n",
    "values1 = [experiments[0][\"baseAggregateRTT\"], experiments[0][\"syncAggregateRTT\"]]\n",
    "values2 = [experiments[1][\"baseAggregateRTT\"], experiments[1][\"syncAggregateRTT\"]]\n",
    "values3 = [experiments[2][\"baseAggregateRTT\"], experiments[2][\"syncAggregateRTT\"]]\n",
    "values4 = [experiments[3][\"baseAggregateRTT\"], experiments[3][\"syncAggregateRTT\"]]\n",
    "fig.suptitle('Mean Round Trip Time\\nScenario: Collect')\n",
    "ax1.bar(cases,values1)\n",
    "ax1.set_title('30 Days')\n",
    "ax1.set_ylabel(\"msec\")\n",
    "ax2.bar(cases,values2)\n",
    "ax2.set_title('14 Days')\n",
    "ax2.set_ylabel(\"msec\")\n",
    "ax3.bar(cases,values3)\n",
    "ax3.set_title('7 Days')\n",
    "ax3.set_ylabel(\"msec\")\n",
    "ax4.bar(cases,values4)\n",
    "ax4.set_title('1 Day')\n",
    "ax4.set_ylabel(\"msec\")\n",
    "# ax.set_ylabel(\"Bytes\")\n",
    "# ax.set_xlabel(\"Time\")\n",
    "fig.savefig(\"out/rtt_mean_comparison_collect.png\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4, figsize=(20,5))\n",
    "cases = ['Baseline', 'Syncmesh']\n",
    "values1 = [experiments[4][\"baseAggregateRTT\"], experiments[4][\"syncAggregateRTT\"]]\n",
    "values2 = [experiments[5][\"baseAggregateRTT\"], experiments[5][\"syncAggregateRTT\"]]\n",
    "values3 = [experiments[6][\"baseAggregateRTT\"], experiments[6][\"syncAggregateRTT\"]]\n",
    "values4 = [experiments[7][\"baseAggregateRTT\"], experiments[7][\"syncAggregateRTT\"]]\n",
    "fig.suptitle('Mean Round Trip Time\\nScenario: Aggregate')\n",
    "ax1.bar(cases,values1)\n",
    "ax1.set_title('30 Days')\n",
    "ax1.set_ylabel(\"msec\")\n",
    "ax2.bar(cases,values2)\n",
    "ax2.set_title('14 Days')\n",
    "ax2.set_ylabel(\"msec\")\n",
    "ax3.bar(cases,values3)\n",
    "ax3.set_title('7 Days')\n",
    "ax3.set_ylabel(\"msec\")\n",
    "ax4.bar(cases,values4)\n",
    "ax4.set_title('1 Day')\n",
    "ax4.set_ylabel(\"msec\")\n",
    "# ax.set_ylabel(\"Bytes\")\n",
    "# ax.set_xlabel(\"Time\")\n",
    "fig.savefig(\"out/rtt_mean_comparison_aggregate.png\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "baseBytes = df_base[\"jsonPayload.bytes_sent\"].sum()\n",
    "syncBytes = df_sync[\"jsonPayload.bytes_sent\"].sum()\n",
    "\n",
    "fig, (ax1) = plt.subplots(1,1)\n",
    "cases = ['Baseline', 'Syncmesh']\n",
    "values1 = [baseBytes, syncBytes]\n",
    "fig.suptitle('Aggregated Bandwidth\\nScenario: Collect')\n",
    "ax1.bar(cases,values1)\n",
    "ax1.set_title('30 Days')\n",
    "ax1.set_yticklabels([to_human_readable(v) if v >= 0 else ' ' for v in  \n",
    "                    ax.get_yticks(minor=False)])\n",
    "box = ax1.get_position()\n",
    "# ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "# ax.set_xlabel(\"Time\")\n",
    "fig.savefig(\"out/overall_bandwidth.png\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "3352614b04f6a771ec3a392566a3763a34cdc96a177f762027aa589f02ec5f67"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.3 32-bit"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}