notebooks/zeph_topology_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Internet topology analysis\n",
    "\n",
    "*Hypothesis*: Zeph will work at scale with Diamond-Miner. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "\n",
    "exp_dir = Path(\"../resources/data/measurements/exp3/\")\n",
    "\n",
    "with (exp_dir / \"production.txt\").open(\"r\") as fd:\n",
    "    production_uuids = [uuid.strip() for uuid in fd.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Iris API / database credentials\n",
    "import json\n",
    "import requests\n",
    "from zeph.drivers import create_auth_header\n",
    "\n",
    "def get_database_url(url, headers):\n",
    "    req = requests.get(url + \"/users/me/services\", headers=headers)\n",
    "    if req.status_code != 200:\n",
    "        raise ValueError(\"Unable to get the database URL\")\n",
    "    creds = req.json()[\"clickhouse\"]\n",
    "    return f\"{creds['base_url']}?database={creds['database']}&user={creds['username']}&password={creds['password']}\"\n",
    "\n",
    "config = json.load(open(\"../config.json\"))\n",
    "headers = create_auth_header(config[\"iris_url\"], config[\"iris_username\"], config[\"iris_password\"])\n",
    "database_url = get_database_url(config[\"iris_url\"], headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "matplotlib.rcParams['pdf.fonttype'] = 42\n",
    "matplotlib.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from diamond_miner.queries import GetLinks\n",
    "from zeph.drivers import get_previous_measurement_agents\n",
    "\n",
    "def sanitize_uuid(uuid):\n",
    "    return str(uuid).replace(\"-\", \"_\")\n",
    "\n",
    "\n",
    "def get_links(measurement_uuid):\n",
    "    agents_uuid = get_previous_measurement_agents(config[\"iris_url\"], measurement_uuid, headers)\n",
    "    \n",
    "    distinct_links = set()\n",
    "    for agent_uuid in agents_uuid:\n",
    "        measurement_id = sanitize_uuid(measurement_uuid) + \"__\" + sanitize_uuid(agent_uuid)\n",
    "        for link in GetLinks().execute_iter(database_url, measurement_id):\n",
    "            distinct_links.add((link[\"near_addr\"], link[\"far_addr\"])) \n",
    "    return distinct_links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[1;36m10436832\u001b[0m,\n",
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       "\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimated computing time: ~10 minutes\n",
    "\n",
    "production_n_links = []\n",
    "for uuid in production_uuids:\n",
    "    production_n_links.append(len(get_links(uuid)))\n",
    "production_zeph_n_links = []\n",
    "\n",
    "print(production_n_links)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "\n",
    "class OOMFormatter(ticker.ScalarFormatter):\n",
    "    def __init__(self, order=0, fformat=\"%1.1f\", offset=True, mathText=True):\n",
    "        self.oom = order\n",
    "        self.fformat = fformat\n",
    "        ticker.ScalarFormatter.__init__(self, useOffset=offset, useMathText=mathText)\n",
    "    \n",
    "    def _set_order_of_magnitude(self):\n",
    "        self.orderOfMagnitude = self.oom\n",
    "    \n",
    "    def _set_format(self, vmin=None, vmax=None):\n",
    "        self.format = self.fformat\n",
    "        if self._useMathText:\n",
    "            self.format = r'$\\mathdefault{%s}$' % self.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"production\": production_n_links,\n",
    "})\n",
    "\n",
    "ax = sns.lineplot(data=data, legend = False)\n",
    "\n",
    "ax.set_xlabel(\"Cycles\", fontsize=14)\n",
    "ax.set_ylabel(\"Number of distinct links\", fontsize=14)\n",
    "\n",
    "ax.set(ylim=(0, 25_000_000))\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(range(0, 10)))\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(range(1, 11)))\n",
    "\n",
    "ax.yaxis.set_major_formatter(OOMFormatter(6, \"%g\", mathText=False))\n",
    "ax.grid()\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../resources/figures/exp3_links.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from diamond_miner.queries import GetNodes\n",
    "\n",
    "def get_nodes(measurement_uuid):\n",
    "    agents_uuid = get_previous_measurement_agents(config[\"iris_url\"], measurement_uuid, headers)\n",
    "    \n",
    "    distinct_nodes = set()\n",
    "    for agent_uuid in agents_uuid:\n",
    "        measurement_id = sanitize_uuid(measurement_uuid) + \"__\" + sanitize_uuid(agent_uuid)\n",
    "        for node in GetNodes().execute_iter(database_url, measurement_id):\n",
    "            distinct_nodes.add(node[\"reply_src_addr\"]) \n",
    "    return distinct_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2090101</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2505044</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2778420</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2827698</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2909473</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3025584</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3117979</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3195673</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3224954</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3288325</span><span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m[\u001b[0m\u001b[1;36m2090101\u001b[0m, \u001b[1;36m2505044\u001b[0m, \u001b[1;36m2778420\u001b[0m, \u001b[1;36m2827698\u001b[0m, \u001b[1;36m2909473\u001b[0m, \u001b[1;36m3025584\u001b[0m, \u001b[1;36m3117979\u001b[0m, \u001b[1;36m3195673\u001b[0m, \u001b[1;36m3224954\u001b[0m, \u001b[1;36m3288325\u001b[0m\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimated computing time: ~5 minutes\n",
    "\n",
    "production_n_nodes = []\n",
    "for uuid in production_uuids:\n",
    "    production_n_nodes.append(len(get_nodes(uuid)))\n",
    "production_zeph_n_nodes = []\n",
    "\n",
    "print(production_n_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">Figure</span><span style=\"color: #000000; text-decoration-color: #000000\"> size 432x288 with </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #000000; text-decoration-color: #000000\"> Axes</span><span style=\"font-weight: bold\">&gt;</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"production\": production_n_nodes,\n",
    "})\n",
    "\n",
    "ax = sns.lineplot(data=data, legend = False)\n",
    "\n",
    "ax.set_xlabel(\"Cycles\", fontsize=14)\n",
    "ax.set_ylabel(\"Number of distinct nodes\", fontsize=14)\n",
    "ax.set(ylim=(0, 5_000_000))\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(range(0, 10)))\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(range(1, 11)))\n",
    "ax.grid()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../resources/figures/exp3_nodes.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests \n",
    "\n",
    "def get_statistic(measurement_uuid, statistic):    \n",
    "    data = requests.get(config[\"iris_url\"] + f\"/measurements/{measurement_uuid}\", headers=headers)\n",
    "\n",
    "    packets_sent = 0\n",
    "    for agent in data.json()[\"agents\"]:\n",
    "        for probing_round in agent[\"probing_statistics\"].values():\n",
    "            packets_sent += probing_round.get(statistic, 0)\n",
    "    return packets_sent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7607107064</span>,\n",
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       "<span style=\"font-weight: bold\">]</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[1;36m7607107064\u001b[0m,\n",
       "    \u001b[1;36m8368742164\u001b[0m,\n",
       "    \u001b[1;36m10251834898\u001b[0m,\n",
       "    \u001b[1;36m7667765432\u001b[0m,\n",
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       "    \u001b[1;36m9734247518\u001b[0m,\n",
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       "    \u001b[1;36m10730631999\u001b[0m,\n",
       "    \u001b[1;36m10582564340\u001b[0m,\n",
       "    \u001b[1;36m12077540429\u001b[0m\n",
       "\u001b[1m]\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "production_n_probes = []\n",
    "for uuid in production_uuids:\n",
    "    production_n_probes.append(get_statistic(uuid, \"packets_sent\"))\n",
    "\n",
    "print(production_n_probes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"production\": production_n_probes,\n",
    "})\n",
    "\n",
    "ax = sns.lineplot(data=data, legend = False)\n",
    "\n",
    "ax.set_xlabel(\"Cycles\", fontsize=14)\n",
    "ax.set_ylabel(\"Number of probes sent\", fontsize=14)\n",
    "ax.set(ylim=(0, 15_000_000_000))\n",
    "\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(range(0, 10)))\n",
    "ax.xaxis.set_major_formatter(ticker.FixedFormatter(range(1, 11)))\n",
    "\n",
    "ax.yaxis.set_major_formatter(OOMFormatter(9, \"%g\", mathText=False))\n",
    "\n",
    "ax.grid()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"../resources/figures/exp3_probes.pdf\")\n",
    "plt.show()"
   ]
  }
 ],
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