logos-storage-research/analysis/failure_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Codex",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LzbZW-vg0z9S"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Codex Platform Structure\n",
        "\n",
        "This work attemps to simulate as many details as possible of the Codex platform. The details about the aspects we want to model can be found in the following notes: https://hackmd.io/yTRlMInIRi6fG-nk984ULQ\n",
        "\n",
        "First, we want to build a model of what we expect the Codex platform to look like, in terms of number of nodes, storage used and available, file size distribution, among others."
      ],
      "metadata": {
        "id": "BT9ssm0WZkZL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import random as random\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [15, 8]\n",
        "\n",
        "\n",
        "#@title Platform parameters\n",
        "\n",
        "#@markdown Number of peers in the network\n",
        "NetworkSize = 5000 #@param {type:\"slider\", min:1, max:10000, step:1}\n",
        "#@markdown Total storage provided in the system (Petabytes)\n",
        "StorageProvided = 5 #@param {type:\"slider\", min:1, max:100, step:1}\n",
        "#@markdown Total storage in use (Petabytes)\n",
        "StorageUsed = 2 #@param {type:\"slider\", min:1, max:100, step:1}\n",
        "#@markdown Variance in storage offered by nodes (%)\n",
        "StorageVariance = 40 #@param {type:\"slider\", min:1, max:50, step:1}\n",
        "#@markdown Mean file size (Kilobytes)\n",
        "MeanFileSize = 4096 #@param {type:\"slider\", min:1, max:1048576, step:128}\n",
        "#@markdown Block size for erasure coding (Kilobytes)\n",
        "BlockSize = 4 #@param {type:\"slider\", min:1, max:32, step:1}\n",
        "\n",
        "totalUsed = 0\n",
        "totalFree = 0\n",
        "nodesProvided = [0]*NetworkSize\n",
        "nodesUsed = [0]*NetworkSize\n",
        "nodesFree = [0]*NetworkSize\n",
        "for i in range(NetworkSize):\n",
        "  sign = random.randint(-1,1)\n",
        "  variance = random.random() * StorageVariance / 100\n",
        "  nodesProvided[i] = (StorageProvided*1024/NetworkSize)+(sign*variance*(StorageProvided*1024/NetworkSize))\n",
        "  nodesUsed[i] = (StorageUsed*1024/NetworkSize)+(sign*variance*(StorageUsed*1024/NetworkSize))\n",
        "  nodesFree[i] = nodesProvided[i] - nodesUsed[i]\n",
        "  totalUsed += nodesUsed[i]\n",
        "  totalFree += nodesFree[i]\n",
        "\n",
        "def plotDist(nodesUsed, nodesFree):\n",
        "  x = np.arange(len(nodesFree))\n",
        "  plt.rcParams['figure.figsize'] = [15, 8]\n",
        "  plt.subplot(211)\n",
        "  plt.xlabel(\"Nodes\")\n",
        "  plt.ylabel(\"Storage Provided (Terabytes)\")\n",
        "  plt.bar(x-1, nodesUsed, label=\"Used\")\n",
        "  plt.bar(x-1, nodesFree, label=\"Free\", bottom=nodesUsed)\n",
        "  plt.legend(loc=\"upper left\")\n",
        "\n",
        " \n",
        "print(\"Total storage used: %f Terabytes\" % totalUsed)\n",
        "print(\"Total storage free: %f Terabytes\" % totalFree)\n",
        "print(\"Total number of blocks in the system: %i\" % (totalUsed*1024*1024/BlockSize)) \n",
        "print(\"Total number of files in the system: %i\" % (totalUsed*1024*1024/MeanFileSize)) \n",
        "plotDist(nodesUsed, nodesFree)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "1mfYz2oeaWZS",
        "outputId": "5617edd4-99a7-4822-c916-2292d965793d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total storage used: 2054.255442 Terabytes\n",
            "Total storage free: 3081.383163 Terabytes\n",
            "Total number of blocks in the system: 538510738\n",
            "Total number of files in the system: 525889\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Failure Model\n",
        "\n",
        "Here we try to capture the most common type of failures and their impact in the system."
      ],
      "metadata": {
        "id": "0BdiiYaXNpnR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Failure model parameters\n",
        "\n",
        "#@markdown Annual Failure Rate (% of network nodes down per year)\n",
        "AnnualFailureRate = 3 #@param {type:\"slider\", min:1, max:10, step:1}\n",
        "#@markdown Number of correlated failures event per year\n",
        "CorrelatedFailureEvents = 1 #@param {type:\"slider\", min:0, max:10, step:1}\n",
        "#@markdown Number of nodes affected by correlated failures \n",
        "CorrelatedFailureImpact = 100 #@param {type:\"slider\", min:2, max:1000, step:1}\n",
        "\n",
        "AnnualNodesFailed = NetworkSize*AnnualFailureRate/100\n",
        "MeanTimeBetweenFailures = 365*24/AnnualNodesFailed\n",
        "\n",
        "print(\"Number of nodes failed per year: %i\" % AnnualNodesFailed)\n",
        "print(\"Mean time between failures: %i hours\" % MeanTimeBetweenFailures)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75TtMGepOxl7",
        "outputId": "72c29478-8c72-4eb9-fc1a-bb5cc637f553"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of nodes failed per year: 150\n",
            "Mean time between failures: 58 hours\n"
          ]
        }
      ]
    }
  ]
}