notebooks/zeph_savings_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probe saving analysis\n",
    "\n",
    "*Hypothesis*: Zeph will be able to see almost the same as complete discovery\n",
    "(= full IPv4 routable prefixes) but with a much-reduced probing budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "\n",
    "exp_dir = Path(\"../resources/data/measurements/exp1/\")\n",
    "\n",
    "with (exp_dir / \"zeph_10.txt\").open(\"r\") as fd:\n",
    "    zeph_10_uuids = [uuid.strip() for uuid in fd.readlines()]\n",
    "with (exp_dir / \"zeph_25.txt\").open(\"r\") as fd:\n",
    "    zeph_25_uuids = [uuid.strip() for uuid in fd.readlines()]\n",
    "with (exp_dir / \"zeph_50.txt\").open(\"r\") as fd:\n",
    "    zeph_50_uuids = [uuid.strip() for uuid in fd.readlines()]\n",
    "with (exp_dir / \"zeph_75.txt\").open(\"r\") as fd:\n",
    "    zeph_75_uuids = [uuid.strip() for uuid in fd.readlines()]\n",
    "with (exp_dir / \"zeph_100.txt\").open(\"r\") as fd:\n",
    "    zeph_100_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": 4,
   "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Estimated computing time: ~7 minutes\n",
    "\n",
    "zeph_10_n_links = []\n",
    "for uuid in zeph_10_uuids:\n",
    "    zeph_10_n_links.append(len(get_links(uuid)))\n",
    "zeph_25_n_links = []\n",
    "for uuid in zeph_25_uuids:\n",
    "    zeph_25_n_links.append(len(get_links(uuid)))\n",
    "zeph_50_n_links = []\n",
    "for uuid in zeph_50_uuids:\n",
    "    zeph_50_n_links.append(len(get_links(uuid)))\n",
    "zeph_75_n_links = []\n",
    "for uuid in zeph_75_uuids:\n",
    "    zeph_75_n_links.append(len(get_links(uuid)))\n",
    "zeph_100_n_links = []\n",
    "for uuid in zeph_100_uuids:\n",
    "    zeph_100_n_links.append(len(get_links(uuid)))\n",
    "\n",
    "print(zeph_10_n_links)\n",
    "print(zeph_25_n_links)\n",
    "print(zeph_50_n_links)\n",
    "print(zeph_75_n_links)\n",
    "print(zeph_100_n_links)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"Zeph 10%\": zeph_10_n_links,\n",
    "    \"Zeph 25%\": zeph_25_n_links,\n",
    "    \"Zeph 50%\": zeph_50_n_links,\n",
    "    \"Zeph 75%\": zeph_75_n_links,\n",
    "    \"Zeph 100%\": zeph_100_n_links,\n",
    "})\n",
    "\n",
    "ax = sns.lineplot(data=data)\n",
    "\n",
    "ax.set_xlabel(\"Cycles\", fontsize=14)\n",
    "ax.set_ylabel(\"Number of distinct links\", fontsize=14)\n",
    "ax.set(ylim=(0, 3_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/exp1_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": [
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    }
   ],
   "source": [
    "# Estimated computing time: ~3 minutes\n",
    "\n",
    "zeph_10_n_nodes = []\n",
    "for uuid in zeph_10_uuids:\n",
    "    zeph_10_n_nodes.append(len(get_nodes(uuid)))\n",
    "zeph_25_n_nodes = []\n",
    "for uuid in zeph_25_uuids:\n",
    "    zeph_25_n_nodes.append(len(get_nodes(uuid)))\n",
    "zeph_50_n_nodes = []\n",
    "for uuid in zeph_50_uuids:\n",
    "    zeph_50_n_nodes.append(len(get_nodes(uuid)))\n",
    "zeph_75_n_nodes = []\n",
    "for uuid in zeph_75_uuids:\n",
    "    zeph_75_n_nodes.append(len(get_nodes(uuid)))\n",
    "zeph_100_n_nodes = []\n",
    "for uuid in zeph_100_uuids:\n",
    "    zeph_100_n_nodes.append(len(get_nodes(uuid)))\n",
    "\n",
    "print(zeph_10_n_nodes)\n",
    "print(zeph_25_n_nodes)\n",
    "print(zeph_50_n_nodes)\n",
    "print(zeph_75_n_nodes)\n",
    "print(zeph_100_n_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
      ],
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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",
    "    \"Zeph 10%\": zeph_10_n_nodes,\n",
    "    \"Zeph 25%\": zeph_25_n_nodes,\n",
    "    \"Zeph 50%\": zeph_50_n_nodes,\n",
    "    \"Zeph 75%\": zeph_75_n_nodes,\n",
    "    \"Zeph 100%\": zeph_100_n_nodes,\n",
    "})\n",
    "\n",
    "ax = sns.lineplot(data=data)\n",
    "\n",
    "ax.set_xlabel(\"Cycles\", fontsize=14)\n",
    "ax.set_ylabel(\"Number of distinct nodes\", fontsize=14)\n",
    "ax.set(ylim=(0, 1_200_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/exp1_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": [
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   ],
   "source": [
    "zeph_10_n_probes = []\n",
    "for uuid in zeph_10_uuids:\n",
    "    zeph_10_n_probes.append(get_statistic(uuid, \"packets_sent\"))\n",
    "zeph_25_n_probes = []\n",
    "for uuid in zeph_25_uuids:\n",
    "    zeph_25_n_probes.append(get_statistic(uuid, \"packets_sent\"))\n",
    "zeph_50_n_probes = []\n",
    "for uuid in zeph_50_uuids:\n",
    "    zeph_50_n_probes.append(get_statistic(uuid, \"packets_sent\"))\n",
    "zeph_75_n_probes = []\n",
    "for uuid in zeph_75_uuids:\n",
    "    zeph_75_n_probes.append(get_statistic(uuid, \"packets_sent\"))\n",
    "zeph_100_n_probes = []\n",
    "for uuid in zeph_100_uuids:\n",
    "    zeph_100_n_probes.append(get_statistic(uuid, \"packets_sent\"))\n",
    "\n",
    "print(zeph_10_n_probes)\n",
    "print(zeph_25_n_probes)\n",
    "print(zeph_50_n_probes)\n",
    "print(zeph_75_n_probes)\n",
    "print(zeph_100_n_probes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame({\n",
    "    \"Zeph 10%\": zeph_10_n_probes,\n",
    "    \"Zeph 25%\": zeph_25_n_probes,\n",
    "    \"Zeph 50%\": zeph_50_n_probes,\n",
    "    \"Zeph 75%\": zeph_75_n_probes,\n",
    "    \"Zeph 100%\": zeph_100_n_probes,\n",
    "})\n",
    "\n",
    "ax = sns.lineplot(data=data)\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_00_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/exp1_probes.pdf\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "772e1080b1fbf5200c669858b2c09332e229694cf709b93af0edeea0c9b8a739"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.1"
  },
  "metadata": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}