ns3-simulation/queue_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals = [50, 70, 90]  #[60, 80, 100, 120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame = pd.read_csv(f\"{vals[0]}.csv\", names=[\"t\", \"delay\", \"size\", \"wl\", \"app\"])\n",
    "sns.displot(\n",
    "    data=frame,\n",
    "    kind='ecdf',\n",
    "    x=\"size\",\n",
    "    hue=\"wl\"\n",
    ").set(\n",
    "    ylim=(0, 0.6)\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottleneck = '/NodeList/0/DeviceList/0/$ns3::CsmaNetDevice/TxQueue/PacketsInQueue'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_bottleneck(filename):\n",
    "    \"\"\"Plot the bottleneck queue.\"\"\"\n",
    "    queuesize = pd.read_csv(filename, names=[\"node\", 'time', \"queue\"])\n",
    "    bottleneck_queuesize = queuesize[queuesize['node'] == bottleneck]\n",
    "\n",
    "    bottleneck_queuesize.plot(x=\"time\", y=\"queue\", legend=False, title=bottleneck)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for val in vals:\n",
    "    plot_bottleneck(f\"{val}_queue.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = []\n",
    "names = [\"node\", 'time', \"queue\"]\n",
    "for val in vals:\n",
    "    frames.append(\n",
    "        pd.read_csv(f\"{val}_queue.csv\", names=names).assign(load=val)\n",
    "    )\n",
    "all_frames = pd.concat(frames, ignore_index=True)\n",
    "\n",
    "all_frames = all_frames[all_frames['node'] == bottleneck].drop(columns=['node'])\n",
    "\n",
    "(\n",
    "    all_frames\n",
    "    .groupby(\"load\")\n",
    "    ['queue']\n",
    "    .describe()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = []\n",
    "names = [\"t\", \"delay\", \"size\", \"workload\", \"app\"]\n",
    "for val in vals:\n",
    "    frames.append(\n",
    "        pd.read_csv(f\"{val}.csv\", names=names).assign(load=val)\n",
    "    )\n",
    "all_frames = pd.concat(frames, ignore_index=True)\n",
    "\n",
    "(\n",
    "    all_frames\n",
    "    #.transform(lambda df: df * 1000) # s to ms\n",
    "    .groupby(\"load\")\n",
    "    ['delay']\n",
    "    .describe()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(all_frames.groupby('load')['size'].sum()) * 8 / 59 / 1024 / 1024"
   ]
  }
 ],
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