Sprint Demo-6-Copy0.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Name Based Routing Project"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Addressable Problem:\n"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "-Reduce the effects of DDoS attack\n",
      "\n",
      "-Speed up content delivery\n",
      "\n",
      "-And open up new markets by routing with named strings instead of DNS and IP."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "What's new/different?"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "A new DDoS Solution:"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Different from Siteprotect\n",
      "-- its too late when an attack converges on a site of interest\n",
      "-- Name Base Routing allows for isolating attacks regionally"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Our solution:"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Improve Traditional IP by using Name Based Routing techniques \n",
      "    (does not require new or additional hardware)\n",
      "    \n",
      "\n",
      "    \n",
      "-Create a parent/child hierachy relationship between network routers\n",
      "-Any arbitrary string can be registered as a Domain name\n",
      "-Allows for delivery of requests from multiple sources\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Our Hypothesis:"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "-We believes it will beat standard IP for DDoS attacks\n",
      "\n",
      "-It will deliver content faster than the best current content delivery method (Akamai-style content delivery)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Setup:"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "- Probability p of a request arising at any leaf node (1/total machine count)\n",
      "\n",
      "- 2 Queues:\n",
      "    -- The forward queue, which queues up requests, \n",
      "    -- The return queue, which queues up return requests.\n",
      "\n",
      "- Heat Map \n",
      "    -- Combines both queues into one for display purposes only.    \n",
      "\n",
      "- Ticks \n",
      "    -- (Used as unit of time)\n",
      "    -- We don't want to use CPU time.\n",
      "\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Progress:"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "    Past:"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "-Graphical User Interface \n",
      "    Illustrates differences betweeb Name Based Routing and traditional IP \n",
      "    -- requests 'video'\n",
      "    -- DDoS     'video'\n",
      "    -- NB performance while supporting delivery from multiple sources 'video'\n",
      "    -- IP performance under a spike in requests for particular content 'video'\n",
      "    \n",
      "\n",
      "-Discrete Event Simulator \n",
      "    -- collected stats on Round Trip Time for a level 4 Name Based Network (1400 machines) vs IP (1400 machines)\n",
      "    -- Simulation that collects Rount Trip Time to fulfill a request for IP and NB for:\n",
      "        --- 4 levels (1,400 machines)\n",
      "        --- 5 levels (16,807 machines)\n",
      "        --- 6 levels (117,648 machines)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Current:"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "-Graphical User Interface\n",
      "    -- Build Javascript gui to demonstrate conflict resolution in Name Based Routing\n",
      "    -- Further built upon Attack video for Traditional IP\n",
      "\n",
      "-Discreate Event Simulator\n",
      "    -- Compiled Simulations for probability (p = .2, .4, .6, .8) of node receiving a request\n",
      "        -- probability (q = .2, .4, .6, .8) of a node being chosen to publish a content source\n",
      "    "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 0
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 0
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pylab as py\n",
      "import pandas as pd\n",
      "\n",
      "\n",
      "\n",
      "def make_filename(system,levels, p,q):\n",
      "    filename = system + '_rtt-l=' + str(levels) + '-p=' +  str(p) + '-q=' + str(q) + '.csv'\n",
      "    return filename\n",
      "\n",
      "def get_pq_means(system,levels, p_max, q_max):\n",
      "    total_means = []\n",
      "    p_max*=10\n",
      "    q_max*=10\n",
      "#     print p_max\n",
      "#     print q_max\n",
      "    for p in [x for x in range(2,int(p_max)+1) if x%2==0]:\n",
      "        for q in [y for y in range(2,int(q_max)+1) if y%2==0]:\n",
      "#             print p \n",
      "#             print q\n",
      "            cur_mean = []\n",
      "            p_dec = float(p)/10\n",
      "            q_dec = float(q)/10\n",
      "            print p_dec\n",
      "            print q_dec\n",
      "            filename = make_filename(system,levels,p_dec,q_dec)\n",
      "            print 'opening file ' + filename\n",
      "            with open(filename, 'rb') as rtt:\n",
      "                reader = csv.reader(rtt)\n",
      "                for row in reader:\n",
      "                    cur_mean.append(int(row[0]))\n",
      "            total_means.append(int(sum(cur_mean)/len(cur_mean)))\n",
      "    return total_means\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nb_means = get_pq_means('nb',4,.8,.8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.2\n",
        "0.2\n",
        "opening file nb_rtt-l=4-p=0.2-q=0.2.csv\n",
        "0.2\n",
        "0.4\n",
        "opening file nb_rtt-l=4-p=0.2-q=0.4.csv\n",
        "0.2\n",
        "0.6\n",
        "opening file nb_rtt-l=4-p=0.2-q=0.6.csv\n",
        "0.2\n",
        "0.8\n",
        "opening file nb_rtt-l=4-p=0.2-q=0.8.csv\n",
        "0.4\n",
        "0.2\n",
        "opening file nb_rtt-l=4-p=0.4-q=0.2.csv\n",
        "0.4\n",
        "0.4\n",
        "opening file nb_rtt-l=4-p=0.4-q=0.4.csv\n",
        "0.4\n",
        "0.6\n",
        "opening file nb_rtt-l=4-p=0.4-q=0.6.csv\n",
        "0.4\n",
        "0.8\n",
        "opening file nb_rtt-l=4-p=0.4-q=0.8.csv\n",
        "0.6\n",
        "0.2\n",
        "opening file nb_rtt-l=4-p=0.6-q=0.2.csv\n",
        "0.6\n",
        "0.4\n",
        "opening file nb_rtt-l=4-p=0.6-q=0.4.csv\n",
        "0.6\n",
        "0.6\n",
        "opening file nb_rtt-l=4-p=0.6-q=0.6.csv\n",
        "0.6\n",
        "0.8\n",
        "opening file nb_rtt-l=4-p=0.6-q=0.8.csv\n",
        "0.8\n",
        "0.2\n",
        "opening file nb_rtt-l=4-p=0.8-q=0.2.csv\n",
        "0.8\n",
        "0.4\n",
        "opening file nb_rtt-l=4-p=0.8-q=0.4.csv\n",
        "0.8\n",
        "0.6\n",
        "opening file nb_rtt-l=4-p=0.8-q=0.6.csv\n",
        "0.8\n",
        "0.8\n",
        "opening file nb_rtt-l=4-p=0.8-q=0.8.csv\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nb_means"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[144, 149, 148, 29, 96, 114, 123, 126, 136, 201, 241, 297, 153, 234, 284, 299]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for x in range(2,3):\n",
      "    print x\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
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    }
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  }
 ]
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