{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "# Estimation de la latence et de la capacité d’une connexion à partir de mesures asymétriques (sujet 4)" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Pour notre analyse sur les caractéristiques d'une connexion nous utiliserons un modèle simplifié qui néglige certains détails.\n", "Ce modèle permet de mettre en relation le **temps d'envoi $T$** (en secondes) d'un message de **taille $S$** (en octets) par une connexion de **latence $L$** (en secondes) et de **capacité $C$** (en octets/seconde) par la formule suivante : $$T(S) = L + S/C$$" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Nous allons donc tenter de déterminer la latence et la capacité d'une connection à partir de deux jeux de données brutes, qui ont été obtenus pour deux connexions différentes avec l'outil `ping`." ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "## Analyse des données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dépendances" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Connexion courte à l'intérieur d'un campus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Récupération et formatage des données" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "raw_data_1 = pd.read_csv(\"http://mescal.imag.fr/membres/arnaud.legrand/teaching/2014/RICM4_EP_ping/liglab2.log.gz\",sep=' ',header=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données brutes sont stockées dans une variable `raw_data_1`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def convert_date(raw_date):\n", " return float(raw_date[1:-1])-1421761682\n", "\n", "def convert_size(raw_size):\n", " return int(raw_size)\n", "\n", "def convert_time(raw_time):\n", " if(pd.isna(raw_time)):\n", " return None\n", " return float(raw_time[5:])\n", "\n", "def format_data(raw_data):\n", " output_data = {}\n", " output_data[\"date\"] = [convert_date(d) for d in raw_data[0]]\n", " output_data[\"size\"] = [convert_size(s) for s in raw_data[1]]\n", " output_data[\"time\"] = [convert_time(t) for t in raw_data[8]]\n", " return output_data\n", "\n", "data_1 = format_data(raw_data_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données brutes sont formatée pour obtenir la date, la taille et le temps d'envoi de chaque mesure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Temps de transmission en fonction du temps" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def display_graph(start=0,end=-1):\n", " plt.plot(data_1[\"date\"][start:end],data_1[\"time\"][start:end])\n", " plt.xlabel(\"Date (sec)\")\n", " plt.ylabel(\"Temps d'envoi (ms)\")\n", " plt.title(\"Évolution du temps de transmission au cours du temps\")\n", " plt.show()\n", "\n", "display_graph()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'évolution du temps de transmission est représenté ici sur toute la plage de données.\n", "On constate une tendance du temps de transmission entre 50ms et 100ms.\n", "Cependant on constate également que cette valeur est très variable, avec des pics à plus de 250ms et des creux avoisinants les 1ms.\n", "Il semble probable que ces fluctuations ne soient pas seulement dûes à la taille des messages mais également à d'autres paramètres extérieurs." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "display_graph(20300,20500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En observant ici les données sur une échelle plus petite, on constate le même phénomène de fluctuation importante du temps de transmission." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Temps de transmission en fonction de la taille des messages" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(data_1[\"size\"],data_1[\"time\"])\n", "plt.xlabel(\"Taille du message (octet)\")\n", "plt.ylabel(\"Temps d'envoi (ms)\")\n", "plt.title(\"Évolution du temps de transmission au cours du temps\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_code_all_hidden": true, "kernelspec": { "display_name": "Python 3", "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }