Performance_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting Graph of Get and Set Operations corresponding to the number of calls for different storage systems."
      ],
      "metadata": {
        "id": "s-5-hTdxyu0I"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "6pTasQAYubFL",
        "outputId": "520050ec-e899-4b9d-96b3-2044bb90550e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "noOfCalls = [100, 1000, 10000, 100000]\n",
        "timing = {\n",
        "    \"VM Boot Disk\": [0.06586241722106934, 0.30051445960998535, 3.050597906112671, 76.04047966003418],\n",
        "    \"GCS\": [11.349967241287231,0,0,0],\n",
        "    \"Redis\": [0.06280279159545898, 0.524960994720459, 5.177708625793457, 55.4121527671814]\n",
        "}\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "barWidth = 0.2\n",
        "index = range(len(noOfCalls))\n",
        "\n",
        "for label, times in timing.items():\n",
        "    plt.bar([i + barWidth * list(timing.keys()).index(label) for i in index], times, barWidth, label=label)\n",
        "\n",
        "plt.xlabel(\"Number of Calls\")\n",
        "plt.ylabel(\"Time (seconds)\")\n",
        "plt.title(\"SET Operation Times for Different Number of Calls\")\n",
        "plt.xticks([i + barWidth for i in index], noOfCalls)\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "noOfCalls = [100, 1000, 10000, 100000]\n",
        "timing = {\n",
        "    \"VM Disk\": [0.016910076141357422, 0.1670088768005371, 1.783681869506836, 19.073380708694458],\n",
        "    \"GCS\": [4.974316120147705, 39.159087896347046, 361.5159077644348, 3631.36093211174],\n",
        "    \"Redis\": [0.09749174118041992, 0.8340539932250977, 8.183197021484375, 83.096999168396]\n",
        "}\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "barWidth = 0.2\n",
        "index = range(len(noOfCalls))\n",
        "\n",
        "for label, times in timing.items():\n",
        "    plt.bar([i + barWidth * list(timing.keys()).index(label) for i in index], times, barWidth, label=label)\n",
        "\n",
        "plt.xlabel(\"Number of Calls\")\n",
        "plt.ylabel(\"Time (seconds)\")\n",
        "plt.title(\"GET Operation Times for Different Number of Calls\")\n",
        "plt.xticks([i + barWidth for i in index], noOfCalls)\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "KlXYrJsGvihR",
        "outputId": "9de177ae-e3e9-4905-ae0d-5bb1d6f5d4f8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y8dzuGXhyorM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}