aptstore_popularity_prediction_algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# APTSTORE : POPULARITY PREDICTION ALGORITHM (PPA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This algorithm is proposed in the following paper. I, Dayanand Raut (IIT Roorkee [Mtech 2020-2022]), have implemented it in python and simulated the data to run the algorithm and display its result.\n",
    "\n",
    "###### K. R. Krish, A. Khasymski, A. R. Butt, S. Tiwari and M. Bhandarkar, \"AptStore: Dynamic storage management for hadoop,\" 2013 IEEE International Conference on Cluster Computing (CLUSTER), 2013, pp. 1-5, doi: 10.1109/CLUSTER.2013.6702657.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries such as pandas, matlibplot and random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plot\n",
    "import random as rd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation of PPA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_popularity(accessed_files, deleted_files, popularity,block_numbers, l, c, s, Pmin, Pmax):\n",
    "    P = {}\n",
    "    IP = 0\n",
    "    Pred = {}\n",
    "    avg_popularity = find_average_popularity(popularity)\n",
    "    for f in set(accessed_files):\n",
    "        P[f] = [0]\n",
    "        \n",
    "    for k in range(0, len(accessed_files)-1):\n",
    "        f = accessed_files[k]\n",
    "        i = len(P[f])-1\n",
    "        \n",
    "        if(i==0):\n",
    "            P[f].append(avg_popularity)\n",
    "            continue\n",
    "        \n",
    "        else:\n",
    "            a = find_access_interval(f,k,accessed_files)\n",
    "            b = find_block_numbers(f, block_numbers)\n",
    "            calc = P[f][i] + c / (a*b*l*P[f][i])\n",
    "            P[f].append(calc)\n",
    "            \n",
    "        if(P[f][i]<Pmin):\n",
    "            P[f][i] = Pmin \n",
    "                    \n",
    "        if(P[f][i]>Pmax):           \n",
    "            P[f][i] = Pmax \n",
    "        \n",
    "        IP = IP + P[f][i+1] - P[f][i]\n",
    "                \n",
    "    for f in deleted_files:\n",
    "        #avg = find_average_popularity(popularity)\n",
    "        IP = IP + avg_popularity - popularity[f]\n",
    "        del P[f]\n",
    "        del popularity[f]\n",
    "        del block_numbers[f]\n",
    "        \n",
    "    MIP = IP / len(P)\n",
    " \n",
    "    for f in P:\n",
    "        i = len(P[f]) - 1\n",
    "        P[f][i] = P[f][i] - MIP/s\n",
    "        Pred[f] = P[f][i] + popularity[f]\n",
    "        popularity[f] = P[f][i]          \n",
    "        \n",
    "    \n",
    "    return Pred\n",
    " \n",
    "#--------------------------HELPER FUNCTIONS-----------------------------        \n",
    "def find_average_popularity(popularity):\n",
    "    sum = 0\n",
    "    for f in popularity:\n",
    "        sum = sum + popularity[f]\n",
    "        \n",
    "    return sum/len(popularity)\n",
    "\n",
    "\n",
    "def find_access_interval(f,k,files):\n",
    "    key = accessed_files[k]\n",
    "    k -= 1\n",
    "    count  =  1\n",
    "    while(k>=0):\n",
    "        if(key==files[k]):\n",
    "            break\n",
    "        k -= 1\n",
    "        count += 1\n",
    "        \n",
    "    return count * 10\n",
    "\n",
    "def find_block_numbers(f, block_number):\n",
    "    return block_number[f]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_popularity(accessed_files, Pmin, Pmax):\n",
    "    p = {}\n",
    "    for f in set(accessed_files):\n",
    "        p[f] = rd.uniform(Pmin, Pmax)\n",
    "        \n",
    "    return p\n",
    "        \n",
    "        \n",
    "def simulate_block_number(accessed_files, Bmin, Bmax):\n",
    "    b = {}\n",
    "    for f in set(accessed_files):\n",
    "        b[f] = rd.randint(Bmin, Bmax)\n",
    "        \n",
    "    return b\n",
    "\n",
    "def simulate_deleted_files(accessed_files):\n",
    "    deleted = set()\n",
    "    files = list(set(accessed_files))\n",
    "    n = rd.randint(1, int(len(files)/3) + 1)\n",
    "    for i in range(0, n):\n",
    "        x = rd.choice(files)\n",
    "        deleted.add(x)\n",
    "        \n",
    "    return deleted  \n",
    "\n",
    "def simulate_accessed_files(number_of_files): \n",
    "    accessed_files = []\n",
    "    n = rd.randint(number_of_files+5, 2 * number_of_files)\n",
    "    for i in range(0, n):\n",
    "        accessed_files.append(rd.randint(1, number_of_files))\n",
    "        \n",
    "    return accessed_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_popularity(popularity, block_numbers):\n",
    "    print(\"Files \\t\\t Popularity \\t Number_of_blocks\")\n",
    "    print(\"-----------------------------------------------\")\n",
    "    for f in popularity:\n",
    "        print(\"file\", f, \":\\t\", round(popularity[f],3),\"\\t\\t\", block_numbers[f] )\n",
    "        \n",
    "        \n",
    "def print_predicted_popularity(pred):\n",
    "    print(\"Files \\t\\t Predicted_Popularity\")\n",
    "    print(\"------------------------------------\")\n",
    "    for f in pred:\n",
    "        print(\"file\", f, \":\\t\", round(pred[f],3))\n",
    "        \n",
    "def count_frequency(accessed_files):\n",
    "    freq = {}\n",
    "    for f in accessed_files:\n",
    "        if f not in freq:\n",
    "            freq[f] = 1\n",
    "        else:\n",
    "            freq[f] += 1\n",
    "            \n",
    "    return freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SIMULATION AND VISUALIZATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accessing files:  [8, 3, 8, 5, 6, 8, 5, 4, 9, 7, 5, 4, 4, 1, 8, 7, 2]\n",
      "Deleting files:  [2, 6]\n",
      "\n",
      "\n",
      "   Files  Initial_Popularity  Block_numbers  Frequency  Predicted_Popularity\n",
      "0      1               0.465             47          1             -0.699146\n",
      "1      2               0.874             45          1                   NaN\n",
      "2      3               0.294             22          1             -0.870283\n",
      "3      4               0.210              4          3             14.066784\n",
      "4      5               0.900             30          3              2.365740\n",
      "5      6               0.314             49          1                   NaN\n",
      "6      7               0.627             10          2              2.919645\n",
      "7      8               0.363             13          4              7.618028\n",
      "8      9               0.773              5          1             -0.391488\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWqEYDXFJnSc9L+k9Jv5L0t/n8npIek7Q6v+3R+uWamVmNYlrgfwRGR8QngKHAOEnHA9cAiyLicGBRft/MzNpIowEemXfzux3znwAmAPPy+fOAia1SoZmZ1auoPnBJFZKWAxuBxyLiOeCgiNgAkN/2ab0yzcysrspiVoqI3cBQSR8Bfizp6GIPIGkGMAOgf//+zSrSrCUm9R1bsn3N37CwZPsya6kmfQslIrYCi4FxwFuS+gLktxsb2Ob2iBgeEcN79+7dwnLNzKxGMd9C6Z23vJHUBTgVeBV4CJiSrzYFeLC1ijQzsz0V04XSF5gnqYIs8BdExMOSngUWSJoGrAPOacU6zcysjkYDPCJWAMPqmb8ZGNMaRZmZWeN8JqaZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpYoB7iZWaIc4GZmiXKAm5klygFuZpaoynIXYM03qe/Ykuxn/oaFJdmPmbUtt8DNzBLlADczS5QD3MwsUQ5wM7NENRrgkg6V9ISkVyT9StIX8/k9JT0maXV+26P1yzUzsxrFtMB3AV+OiEHA8cBfS/o4cA2wKCIOBxbl983MrI00GuARsSEiXsqn3wFeAQ4BJgDz8tXmARNbq0gzM9tTk/rAJVUDw4DngIMiYgNkIQ/0KXVxZmbWsKIDXNKBwAPAVRGxrQnbzZC0VNLSTZs2NadGMzOrR1EBLqkjWXjfGxE/yme/JalvvrwvsLG+bSPi9ogYHhHDe/fuXYqazcyM4r6FIuD7wCsRcUvBooeAKfn0FODB0pdnZmYNKWYslBOBi4CXJS3P530V+DawQNI0YB1wTuuUaGZm9Wk0wCPiKUANLB5T2nLMzKxYPhPTzCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLVGW5CzAz25tJfceWZD/zNywsyX7aE7fAzcwS5QA3M0uUA9zMLFEOcDOzRDnAzcwS5QA3M0uUA9zMLFEOcDOzRDUa4JLulLRR0sqCeT0lPSZpdX7bo3XLNDOzuoppgc8FxtWZdw2wKCIOBxbl983MrA01GuARsQTYUmf2BGBePj0PmFjiuszMrBHN7QM/KCI2AOS3fUpXkpmZFaPVB7OSNAOYAdC/f//WPpyZNVOpBo2CfXPgqPaouS3wtyT1BchvNza0YkTcHhHDI2J47969m3k4MzOrq7kB/hAwJZ+eAjxYmnLMzKxYxXyNcD7wLHCEpPWSpgHfBk6TtBo4Lb9vZmZtqNE+8IiY1MCiMSWuxczMmsBnYpqZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolygJuZJcoBbmaWKAe4mVmiHOBmZolq9Ysam5nta0p1AeiWXvzZLXAzs0Q5wM3MEuUANzNLlPvAraTaS9+g2f7ALXAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0Q5wM3MEuUANzNLlAPczCxRDnAzs0S1KMAljZP0a0m/kXRNqYoyM7PGNTvAJVUAtwGfBj4OTJL08VIVZmZme9eSFvhxwG8i4rWI+BPwr8CE0pRlZmaNUUQ0b0PpbGBcRFya378I+GREXFFnvRnAjPzuEcCvm1/uB/QC3i7RvkrFNRXHNRWvPdblmopTypoGRETvujNbckUe1TNvj/8GEXE7cHsLjlP/waWlETG81PttCddUHNdUvPZYl2sqTlvU1JIulPXAoQX3+wFvtqwcMzMrVksC/AXgcEkDJR0AnAc8VJqyzMysMc3uQomIXZKuAB4FKoA7I+JXJauscSXvlikB11Qc11S89liXaypOq9fU7A8xzcysvHwmpplZohzg1uok1feNJTNrIQe4tbrI++mUK3c97ZWkbpI6lbuOQpL6t7eaACRV+ncpkQCX1EFSu6pV0ickXSzplHLXUkPSpyRNl3RSuWupkdczArIgj4IPXcr5ByjpekkfLtfxG/B/gY/VPC+SupW5HoA7gG+Uu4hCkiYD1wCfy++3iyCXdIykv5A0WdLnJB3W6sdM7UPMfAyW96OMhUs6BrgB+BPwPrAFuDwi3suXq63rk3QscAvwO+DPgK8DXYEDgZ9HRJufpZaPjfMi8DTwDvAfwL8Bfx0RX27regrqOha4KyKG5A2DIcAI4I/Av0fE5jLUNAKYGxFHSeoInAOcAHwU+HFE3FeGmo4FfgKsBnYB34iI5/Jlbf47XlDTPwM/B4YB88nOR6kCfhYRi9u6pryuY4BvAa+TnX35GWAp8HxE/EtrHbddtWrrI+mLkv5B0lEAEbG74C155zK9vZsGPBIRZwJTgQOAsXlNfYELy1DTRcBPI+J8sl/ua4GzyIJpVls/T/kf+H+R/VIvAuYA3YH7gAskXSDpo21ZU4Hzge/n0+cB15G9fsdQhucq9yngl/n0xWSv5zNk//BGS+pXhprOB74ZEaOBJ4Er8gCljA2oacB9EfE3ZA2CGUAX4L+B8yX1LFNdl5Jlwl8Bfwc8ArwFnCDp2tZ6l9DuAxz4KjAQmCvpCUlXSTokX3YmcFoZahpG9sdFRGwCFgCfz5ddDJTjlN4xwMP59BnAP+Xj1FxH1opr066egj/wBcBIoHNEfJWsdbI4r/ezbVlTgbOBYfk/kPOA2RFxDvBN4BDgf5WhpgUAkgbkNVwfEfMjYgHZ+Rpnl6GmM4HH8+k7yM6+vlvSF8pQS431QLWk3mR/azdGxDeBfwR6An9RprrWAYdK6p4P7ncI8BIwExhKdqZ66UVEu/0hG/zqQbJugCqyt5X3Ai8D9wPvASPauKYKYBRwaJ35DwCXkbU2h5XjuSqYPqrOsv8oR00Fx/8YMBc4CXgt/+XuAXQtUz1DyYZC/i1ZIHyo3M8V2dhCXyEb7G0p2UkgXfNlL7Z1TUAn4OR8ukPB/FPyv8Gvl+m16w/8NH9Obsv/5g7Ilz0PHFOmugbkmXQP2buVBWSNFoAVwNDWOG677wOX1BkgInYUzPsw2duUsRExqEx1VUTEbkkdIuJ9SYeTvW36n4g4thw11amvpq7RwLcj4rgy1zEeuBz4Q0T8ZTlqqY+kIyPi1Xz6FOCGcj1XeQ0fJftw7hKyhsvTwKaImFmumvK6avu8JY0DLomIz5Wxni7AQcAsoDPZ5z27I+IzZaypF9m1EboAyyJio6RPA7MiYkSrHLO9B3hdNb9Ikm4mC8tZ7aCmmjD/e+CtiJhd7pqg9p/fZ8laKPPKXEsFcDLw+4h4qeY5K2dNhfIPM8cDHyn3c1VDUnegV0T8tty1tFf5ZwOfJHsH80JErC1zSR8g6Xjg4Ij4UavsP7UAr5H3FW6JiHfKXUuNmq86RsT75a6lRl5TRKovtLU75foGiu0p2QA3M9vfpfAtFDMzq4cD3MwsUQ5wM7NEOcBtnyZpt6TlBT/Vkp7Jl1VLWlnuGs2aqyUXNTZLwfaIGFpn3siyVGJWYm6B235H0rv1zKuQdKOkFyStkPT5fH5fSUvy1vtKSZ9q+4rN6ucWuO3rukhank+/HhENjb8yjezEsBH5YFZPS1pINrbGoxFxfX4y0ofaoGazojjAbV9XXxdKfcYCQyTVDBrVHTgceAG4Mx/i9ScRsbyhHZi1NQe4WUbAlRHx6B4LpFFkI/PdI+nGiLi7zaszq4f7wM0yjwJ/lbe0kfTnkrrmQzZsjIg7yMYQP6acRZoVcgvcLPM9oBp4KR98fxMwkWwArq9I2gm8C0wuV4FmdXksFDOzRLkLxcwsUQ5wM7NEOcDNzBLlADczS5QD3MwsUQ5wM7NEOcDNzBLlADczS9T/A98SYT5T2TcdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   File  Predicted_Popularity\n",
      "0     1                -0.699\n",
      "1     3                -0.870\n",
      "2     4                14.067\n",
      "3     5                 2.366\n",
      "4     7                 2.920\n",
      "5     8                 7.618\n",
      "6     9                -0.391\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pmin = 0.1\n",
    "Pmax = 0.9\n",
    "accessed_files = simulate_accessed_files(9)\n",
    "popularity = simulate_popularity(accessed_files, Pmin, Pmax)\n",
    "Bmin = 1\n",
    "Bmax = 50\n",
    "block_numbers = simulate_block_number(accessed_files, Bmin, Bmax)\n",
    "c = 10000\n",
    "l = 9\n",
    "s = 5\n",
    "frequency = count_frequency(accessed_files)\n",
    "popularity_data = [[k,round(popularity[k],3), block_numbers[k], frequency[k]] for k in popularity ]\n",
    "\n",
    "#print_popularity(popularity, block_numbers)\n",
    "#print(popularity_data)\n",
    "\n",
    "# Showing the simulated result in graph\n",
    "# Bar graph of popularity\n",
    "print(\"Accessing files: \", accessed_files)\n",
    "deleted_files = list(simulate_deleted_files(accessed_files))\n",
    "print(\"Deleting files: \", deleted_files)\n",
    "print()\n",
    "\n",
    "# predict the popularity\n",
    "pred = predict_popularity(accessed_files, deleted_files, popularity,block_numbers, l, c, s, Pmin, Pmax)\n",
    "print()\n",
    "#print_predicted_popularity(pred)\n",
    "\n",
    "for x in popularity_data:\n",
    "    f = x[0]\n",
    "    if f in pred:\n",
    "        x.append(pred[f])\n",
    "    else:\n",
    "        x.append(None)\n",
    "\n",
    "        \n",
    "popularity_df = pd.DataFrame(popularity_data, columns = ['Files', 'Initial_Popularity', 'Block_numbers', 'Frequency', 'Predicted_Popularity'])\n",
    "\n",
    "print(popularity_df)\n",
    "popularity_df.plot.bar(x=\"Files\", y=\"Initial_Popularity\", color='#FFC300', rot=70, title=\"Popularity of Files\");\n",
    "plot.show(block=True)\n",
    "\n",
    "#Bar Graph of block numbers\n",
    "popularity_df.plot.bar(x=\"Files\", y=\"Block_numbers\", color='#581845', rot=70, title=\"Number of blocks of file\");\n",
    "plot.show(block=True)\n",
    "\n",
    "#Bar Graph of block numbers\n",
    "popularity_df.plot.bar(x=\"Files\", y=\"Frequency\", color='#FF5733', rot=70, title=\"Frequency of access\");\n",
    "plot.show(block=True)\n",
    "\n",
    "        \n",
    "# showing the result in graph\n",
    "predicted_data  = [[k, round(pred[k],3)] for k in pred]\n",
    "predicted_df = pd.DataFrame(predicted_data, columns=['File', 'Predicted_Popularity'])\n",
    "print(predicted_df)\n",
    "predicted_df.plot.barh(x=\"File\", y=\"Predicted_Popularity\", color='g', rot=70, title=\"Predicted Popularity of file\");\n",
    "plot.show(block=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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