age_cache_simulation/aoi_cache_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71f85f2a-423f-44d2-b80d-da9ac8d3961a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import simpy\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from enum import Enum\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "# Types of cache\n",
    "class CacheType(Enum):\n",
    "    LRU = 1\n",
    "    RANDOM_EVICTION = 2\n",
    "experiment_name = ''\n",
    "\n",
    "# Constants\n",
    "SEED = 42\n",
    "DATABASE_OBJECTS = 100 # Number of objects in the database\n",
    "ACCESS_COUNT_LIMIT = 10   # Total time to run the simulation\n",
    "EXPERIMENT_BASE_DIR = \"./experiments/\"\n",
    "TEMP_BASE_DIR = \"./.aoi_cache/\"\n",
    "\n",
    "ZIPF_CONSTANT = 2      # Shape parameter for the Zipf distribution (controls skewness) Needs to be: 1< \n",
    "\n",
    "# Set random seeds\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "\n",
    "# Initialize simulation environment\n",
    "env = simpy.Environment()\n",
    "\n",
    "os.makedirs(TEMP_BASE_DIR, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a37d7a3-3e11-4b89-8dce-6091dd38b16f",
   "metadata": {},
   "source": [
    "How to set certain parameters for specific scenarios\n",
    "\n",
    "\n",
    "| Name                 | Cache Capacity       | MAX_REFRESH_RATE | cache_type                | CACHE_TTL |\n",
    "| -------------------- | -------------------- | ---------------- | ------------------------- | --------- |\n",
    "| Default              | DATABASE_OBJECTS     | 1<               | CacheType.LRU             | 5         |\n",
    "| No Refresh           | DATABASE_OBJECTS     | 0                | CacheType.LRU             | 5         |\n",
    "| Infinite TTL         | DATABASE_OBJECTS / 2 | 0                | CacheType.LRU             | 0         |\n",
    "| Random Eviction (RE) | DATABASE_OBJECTS / 2 | 1<               | CacheType.RANDOM_EVICTION | 5         |\n",
    "| RE without Refresh   | DATABASE_OBJECTS / 2 | 0                | CacheType.RANDOM_EVICTION | 5         |\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d0ab5b1-162a-42c8-80a3-d31f763101f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration (Just example, will be overwritten in next block\n",
    "\n",
    "CACHE_CAPACITY = DATABASE_OBJECTS   # Maximum number of objects the cache can hold\n",
    "\n",
    "# MAX_REFRESH_RATE is used as the maximum for a uniform\n",
    "# distribution for mu.\n",
    "# If MAX_REFRESH_RATE is 0, we do not do any refreshes.\n",
    "MAX_REFRESH_RATE = 0\n",
    "\n",
    "cache_type = CacheType.LRU\n",
    "\n",
    "# CACHE_TTL is used to determin which TTL to set when an\n",
    "# object is pulled into the cache\n",
    "# If CACHE_TTL is set to 0, the TTL is infinite\n",
    "CACHE_TTL = 5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ff299ca-ec65-453b-b167-9a0f7728a207",
   "metadata": {},
   "outputs": [],
   "source": [
    "configurations = {\n",
    "    \"default\": (DATABASE_OBJECTS, 10, CacheType.LRU, 5),\n",
    "    \"No Refresh\": (DATABASE_OBJECTS, 0, CacheType.LRU, 5),\n",
    "    \"Infinite TTL\": (int(DATABASE_OBJECTS / 2), 0, CacheType.LRU, 0),\n",
    "    \"Random Eviction\": (int(DATABASE_OBJECTS / 2), 10, CacheType.RANDOM_EVICTION, 5),\n",
    "    \"RE without Refresh\": (int(DATABASE_OBJECTS / 2), 0, CacheType.RANDOM_EVICTION, 5),\n",
    "    \"No Refresh (0.5s ttl)\": (DATABASE_OBJECTS, 0, CacheType.LRU, 0.5),\n",
    "    \"No Refresh (1.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.LRU, 1),\n",
    "    \"No Refresh (2.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.LRU, 2),\n",
    "    \"No Refresh (3.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.LRU, 3),\n",
    "    \"No Refresh (4.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.LRU, 4),\n",
    "    \"No Refresh (5.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.LRU, 5),\n",
    "}\n",
    "\n",
    "experiment_name = \"No Refresh (0.5s ttl)\"\n",
    "config = configurations[experiment_name]\n",
    "\n",
    "CACHE_CAPACITY = config[0]\n",
    "MAX_REFRESH_RATE = config[1]\n",
    "cache_type = config[2]\n",
    "CACHE_TTL = config[3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5cea042f-e9fc-4a1e-9750-de212ca70601",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Database:\n",
    "    def __init__(self):\n",
    "        # Each object now has a specific refresh rate 'mu'\n",
    "        self.data = {i: f\"Object {i}\" for i in range(1, DATABASE_OBJECTS + 1)}\n",
    "        self.lambda_values = {i: np.random.zipf(ZIPF_CONSTANT) for i in range(1, DATABASE_OBJECTS + 1)}  # Request rate 'lambda' for each object\n",
    "        # Refresh rate 'mu' for each object\n",
    "        if MAX_REFRESH_RATE == 0:\n",
    "            self.mu_values = {i: 0 for i in range(1,DATABASE_OBJECTS + 1)}    \n",
    "        else:\n",
    "            self.mu_values = {i: np.random.uniform(1, MAX_REFRESH_RATE) for i in range(1, DATABASE_OBJECTS + 1)}\n",
    "        self.next_request = {i: np.random.exponential(self.lambda_values[i]) for i in range(1, DATABASE_OBJECTS + 1)}\n",
    "\n",
    "\n",
    "    def get_object(self, obj_id):\n",
    "        # print(f\"[{env.now:.2f}] Database: Fetched {self.data.get(obj_id, 'Unknown')} for ID {obj_id}\")\n",
    "        return self.data.get(obj_id, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "499bf543-b2c6-4e4d-afcc-0a6665ce3ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cache:\n",
    "    def __init__(self, env, db, cache_type):\n",
    "        self.cache_type = cache_type\n",
    "        self.env = env\n",
    "        self.db = db\n",
    "        self.storage = {}  # Dictionary to store cached objects\n",
    "        self.ttl = {}      # Dictionary to store TTLs\n",
    "        self.age = {}      # Dictionary to store age of each object\n",
    "        self.cache_size_over_time = []  # To record cache state at each interval\n",
    "        self.cache_next_request_over_time = []\n",
    "        self.request_log = {i: [] for i in range(1, DATABASE_OBJECTS + 1)}\n",
    "        self.hits = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)}  # Track hits per object\n",
    "        self.misses = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)}  # Track misses per object\n",
    "        self.cumulative_age = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)}  # Track cumulative age per object\n",
    "        self.access_count = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)}  # Track access count per object\n",
    "        self.next_refresh = {}  # Track the next refresh time for each cached object\n",
    "        \n",
    "    def get(self, obj_id):\n",
    "        if obj_id in self.storage and \\\n",
    "        (self.ttl[obj_id] > env.now or CACHE_TTL == 0):\n",
    "            # Cache hit: increment hit count and update cumulative age\n",
    "            self.hits[obj_id] += 1\n",
    "            self.cumulative_age[obj_id] += self.age[obj_id]\n",
    "            self.access_count[obj_id] += 1\n",
    "        else:\n",
    "            # Cache miss: increment miss count\n",
    "            self.misses[obj_id] += 1\n",
    "            self.access_count[obj_id] += 1\n",
    "            \n",
    "            # Fetch the object from the database if it’s not in cache\n",
    "            obj = self.db.get_object(obj_id)\n",
    "            \n",
    "            # If the cache is full, evict the oldest object\n",
    "            if len(self.storage) >= CACHE_CAPACITY:\n",
    "                if self.cache_type == CacheType.LRU:\n",
    "                    self.evict_oldest()\n",
    "                elif self.cache_type == CacheType.RANDOM_EVICTION:\n",
    "                    self.evict_random()\n",
    "            \n",
    "            # Add the object to cache, set TTL, reset age, and schedule next refresh\n",
    "            self.storage[obj_id] = obj\n",
    "            if CACHE_TTL != 0:\n",
    "                self.ttl[obj_id] = env.now + CACHE_TTL\n",
    "            else:\n",
    "                self.ttl[obj_id] = 0\n",
    "            self.age[obj_id] = 0\n",
    "            if MAX_REFRESH_RATE != 0:\n",
    "                self.next_refresh[obj_id] = env.now + np.random.exponential(self.db.mu_values[obj_id])  # Schedule refresh\n",
    "\n",
    "        \n",
    "    def evict_oldest(self):\n",
    "        \"\"\"Remove the oldest item from the cache to make space.\"\"\"\n",
    "        oldest_id = max(self.age, key=self.age.get)  # Find the oldest item by age\n",
    "        print(f\"[{env.now:.2f}] Cache: Evicting oldest object {oldest_id} to make space at {self.ttl[oldest_id]:.2f}\")\n",
    "        del self.storage[oldest_id]\n",
    "        del self.ttl[oldest_id]\n",
    "        del self.age[oldest_id]\n",
    "\n",
    "    def evict_random(self):\n",
    "        \"\"\"Remove a random item from the cache to make space.\"\"\"\n",
    "        random_id = np.random.choice(list(self.storage.keys()))  # Select a random key from the cache\n",
    "        print(f\"[{env.now:.2f}] Cache: Evicting random object {random_id} to make space at {self.ttl[random_id]:.2f}\")\n",
    "        del self.storage[random_id]\n",
    "        del self.ttl[random_id]\n",
    "        del self.age[random_id]\n",
    "        \n",
    "    def refresh_object(self, obj_id):\n",
    "        \"\"\"Refresh the object from the database to keep it up-to-date. TTL is increased on refresh.\"\"\"\n",
    "        obj = self.db.get_object(obj_id)\n",
    "        self.storage[obj_id] = obj\n",
    "        if CACHE_TTL != 0:\n",
    "            self.ttl[obj_id] = env.now + CACHE_TTL\n",
    "        else:\n",
    "            self.ttl[obj_id] = 0\n",
    "        self.age[obj_id] = 0\n",
    "        # print(f\"[{env.now:.2f}] Cache: Refreshed object {obj_id}\")\n",
    "        \n",
    "    def age_objects(self):\n",
    "        \"\"\"Increment age of each cached object.\"\"\"\n",
    "        for obj_id in list(self.age.keys()):\n",
    "            if CACHE_TTL != 0:\n",
    "                if self.ttl[obj_id] > env.now:\n",
    "                    self.age[obj_id] += 1\n",
    "                    # print(f\"[{env.now:.2f}] Cache: Object {obj_id} aged to {self.age[obj_id]}\")\n",
    "                else:\n",
    "                        # Remove object if its TTL expired\n",
    "                        # print(f\"[{env.now:.2f}] Cache: Object {obj_id} expired\")\n",
    "                        del self.storage[obj_id]\n",
    "                        del self.ttl[obj_id]\n",
    "                        del self.age[obj_id]\n",
    "            else:\n",
    "                self.age[obj_id] += 1\n",
    "                \n",
    "    def record_cache_state(self):\n",
    "        \"\"\"Record the current cache state (number of objects in cache) over time.\"\"\"\n",
    "        self.cache_size_over_time.append((env.now, len(self.storage)))\n",
    "        self.cache_next_request_over_time.append((env.now, self.db.next_request.copy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7286d498-aa6c-4efb-bb28-fe29736eab64",
   "metadata": {},
   "outputs": [],
   "source": [
    "def age_cache_process(env, cache):\n",
    "    \"\"\"Process that ages cache objects over time, removes expired items, and refreshes based on object-specific intervals.\"\"\"\n",
    "    while True:\n",
    "        cache.age_objects()  # Age objects and remove expired ones\n",
    "\n",
    "\n",
    "        if MAX_REFRESH_RATE != 0:\n",
    "            # Refresh objects based on their individual refresh intervals\n",
    "            for obj_id in list(cache.storage.keys()):\n",
    "                # Check if it's time to refresh this object based on next_refresh\n",
    "                if env.now >= cache.next_refresh[obj_id]:\n",
    "                    cache.refresh_object(obj_id)\n",
    "                    # Schedule the next refresh based on the object's mu\n",
    "                    cache.next_refresh[obj_id] = env.now + np.random.exponential(cache.db.mu_values[obj_id])\n",
    "        \n",
    "        cache.record_cache_state()  # Record cache state at each time step\n",
    "        yield env.timeout(1)  # Run every second\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "687f5634-8edf-4337-b42f-bbb292d47f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def client_request_process(env, cache, event):\n",
    "    \"\"\"Client process that makes requests for objects from the cache.\"\"\"\n",
    "    lowest_lambda_object = max(cache.db.lambda_values.items(), key=lambda x: x[1])\n",
    "    lowest_lambda_object = [lowest_lambda_object] if isinstance(lowest_lambda_object, int) else lowest_lambda_object\n",
    "    while True:\n",
    "        obj_id, next_request = min(cache.db.next_request.items(), key=lambda x: x[1])\n",
    "        yield env.timeout(next_request - env.now)\n",
    "        if env.now >= next_request:\n",
    "            # print(f\"[{env.now:.2f}] Client: Requesting object {obj_id}\")\n",
    "            cache.get(obj_id)\n",
    "            \n",
    "            # print(f\"[{env.now:.2f}] Client: Schedule next request for {obj_id}\")\n",
    "            next_request = env.now + np.random.exponential(cache.db.lambda_values[obj_id])\n",
    "            cache.request_log[obj_id].append(next_request)\n",
    "            cache.db.next_request[obj_id] = next_request\n",
    "        if all(cache.access_count[obj] >= ACCESS_COUNT_LIMIT for obj in lowest_lambda_object):\n",
    "            event.succeed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c8516830-9880-4d9e-a91b-000338baf9d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Instantiate components\n",
    "db = Database()\n",
    "cache = Cache(env, db, cache_type)\n",
    "stop_event = env.event()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ba34b36-9ed5-4996-9600-11dfd25d8e60",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 434 ms, sys: 57.9 ms, total: 492 ms\n",
      "Wall time: 491 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Start processes\n",
    "env.process(age_cache_process(env, cache))\n",
    "env.process(client_request_process(env, cache, stop_event))\n",
    "\n",
    "# Run the simulation\n",
    "env.run(until=stop_event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b6f7c1f-ea54-4496-bb9a-370cee2d2751",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object 1: Hit Rate = 0.34, Average Age = 0.26\n",
      "Object 2: Hit Rate = 0.10, Average Age = 0.36\n",
      "Object 3: Hit Rate = 0.32, Average Age = 0.24\n",
      "Object 4: Hit Rate = 0.35, Average Age = 0.26\n",
      "Object 5: Hit Rate = 0.19, Average Age = 0.24\n",
      "Object 6: Hit Rate = 0.32, Average Age = 0.25\n",
      "Object 7: Hit Rate = 0.10, Average Age = 0.21\n",
      "Object 8: Hit Rate = 0.32, Average Age = 0.26\n",
      "Object 9: Hit Rate = 0.34, Average Age = 0.24\n",
      "Object 10: Hit Rate = 0.34, Average Age = 0.22\n",
      "Object 11: Hit Rate = 0.22, Average Age = 0.24\n",
      "Object 12: Hit Rate = 0.35, Average Age = 0.31\n",
      "Object 13: Hit Rate = 0.32, Average Age = 0.23\n",
      "Object 14: Hit Rate = 0.34, Average Age = 0.27\n",
      "Object 15: Hit Rate = 0.20, Average Age = 0.20\n",
      "Object 16: Hit Rate = 0.18, Average Age = 0.20\n",
      "Object 17: Hit Rate = 0.30, Average Age = 0.25\n",
      "Object 18: Hit Rate = 0.33, Average Age = 0.25\n",
      "Object 19: Hit Rate = 0.12, Average Age = 0.27\n",
      "Object 20: Hit Rate = 0.35, Average Age = 0.26\n",
      "Object 21: Hit Rate = 0.35, Average Age = 0.21\n",
      "Object 22: Hit Rate = 0.35, Average Age = 0.29\n",
      "Object 23: Hit Rate = 0.35, Average Age = 0.20\n",
      "Object 24: Hit Rate = 0.20, Average Age = 0.18\n",
      "Object 25: Hit Rate = 0.35, Average Age = 0.26\n",
      "Object 26: Hit Rate = 0.32, Average Age = 0.21\n",
      "Object 27: Hit Rate = 0.34, Average Age = 0.24\n",
      "Object 28: Hit Rate = 0.09, Average Age = 0.17\n",
      "Object 29: Hit Rate = 0.34, Average Age = 0.25\n",
      "Object 30: Hit Rate = 0.32, Average Age = 0.28\n",
      "Object 31: Hit Rate = 0.33, Average Age = 0.26\n",
      "Object 32: Hit Rate = 0.09, Average Age = 0.31\n",
      "Object 33: Hit Rate = 0.35, Average Age = 0.25\n",
      "Object 34: Hit Rate = 0.07, Average Age = 0.30\n",
      "Object 35: Hit Rate = 0.33, Average Age = 0.26\n",
      "Object 36: Hit Rate = 0.32, Average Age = 0.30\n",
      "Object 37: Hit Rate = 0.37, Average Age = 0.22\n",
      "Object 38: Hit Rate = 0.14, Average Age = 0.39\n",
      "Object 39: Hit Rate = 0.05, Average Age = 0.20\n",
      "Object 40: Hit Rate = 0.33, Average Age = 0.27\n",
      "Object 41: Hit Rate = 0.12, Average Age = 0.30\n",
      "Object 42: Hit Rate = 0.07, Average Age = 0.25\n",
      "Object 43: Hit Rate = 0.22, Average Age = 0.32\n",
      "Object 44: Hit Rate = 0.33, Average Age = 0.25\n",
      "Object 45: Hit Rate = 0.30, Average Age = 0.34\n",
      "Object 46: Hit Rate = 0.33, Average Age = 0.27\n",
      "Object 47: Hit Rate = 0.02, Average Age = 0.00\n",
      "Object 48: Hit Rate = 0.32, Average Age = 0.24\n",
      "Object 49: Hit Rate = 0.31, Average Age = 0.27\n",
      "Object 50: Hit Rate = 0.33, Average Age = 0.23\n",
      "Object 51: Hit Rate = 0.12, Average Age = 0.25\n",
      "Object 52: Hit Rate = 0.04, Average Age = 0.33\n",
      "Object 53: Hit Rate = 0.33, Average Age = 0.27\n",
      "Object 54: Hit Rate = 0.33, Average Age = 0.25\n",
      "Object 55: Hit Rate = 0.35, Average Age = 0.24\n",
      "Object 56: Hit Rate = 0.38, Average Age = 0.26\n",
      "Object 57: Hit Rate = 0.31, Average Age = 0.26\n",
      "Object 58: Hit Rate = 0.00, Average Age = 0.00\n",
      "Object 59: Hit Rate = 0.19, Average Age = 0.15\n",
      "Object 60: Hit Rate = 0.31, Average Age = 0.20\n",
      "Object 61: Hit Rate = 0.13, Average Age = 0.00\n",
      "Object 62: Hit Rate = 0.35, Average Age = 0.25\n",
      "Object 63: Hit Rate = 0.32, Average Age = 0.23\n",
      "Object 64: Hit Rate = 0.25, Average Age = 0.34\n",
      "Object 65: Hit Rate = 0.35, Average Age = 0.28\n",
      "Object 66: Hit Rate = 0.04, Average Age = 0.33\n",
      "Object 67: Hit Rate = 0.34, Average Age = 0.21\n",
      "Object 68: Hit Rate = 0.00, Average Age = 0.00\n",
      "Object 69: Hit Rate = 0.33, Average Age = 0.33\n",
      "Object 70: Hit Rate = 0.33, Average Age = 0.26\n",
      "Object 71: Hit Rate = 0.23, Average Age = 0.23\n",
      "Object 72: Hit Rate = 0.33, Average Age = 0.24\n",
      "Object 73: Hit Rate = 0.34, Average Age = 0.29\n",
      "Object 74: Hit Rate = 0.33, Average Age = 0.20\n",
      "Object 75: Hit Rate = 0.10, Average Age = 0.29\n",
      "Object 76: Hit Rate = 0.20, Average Age = 0.30\n",
      "Object 77: Hit Rate = 0.21, Average Age = 0.21\n",
      "Object 78: Hit Rate = 0.17, Average Age = 0.20\n",
      "Object 79: Hit Rate = 0.04, Average Age = 0.00\n",
      "Object 80: Hit Rate = 0.34, Average Age = 0.28\n",
      "Object 81: Hit Rate = 0.30, Average Age = 0.26\n",
      "Object 82: Hit Rate = 0.06, Average Age = 0.12\n",
      "Object 83: Hit Rate = 0.18, Average Age = 0.25\n",
      "Object 84: Hit Rate = 0.34, Average Age = 0.29\n",
      "Object 85: Hit Rate = 0.33, Average Age = 0.26\n",
      "Object 86: Hit Rate = 0.19, Average Age = 0.31\n",
      "Object 87: Hit Rate = 0.33, Average Age = 0.20\n",
      "Object 88: Hit Rate = 0.21, Average Age = 0.25\n",
      "Object 89: Hit Rate = 0.35, Average Age = 0.23\n",
      "Object 90: Hit Rate = 0.33, Average Age = 0.28\n",
      "Object 91: Hit Rate = 0.22, Average Age = 0.31\n",
      "Object 92: Hit Rate = 0.20, Average Age = 0.22\n",
      "Object 93: Hit Rate = 0.14, Average Age = 0.26\n",
      "Object 94: Hit Rate = 0.32, Average Age = 0.29\n",
      "Object 95: Hit Rate = 0.20, Average Age = 0.31\n",
      "Object 96: Hit Rate = 0.34, Average Age = 0.21\n",
      "Object 97: Hit Rate = 0.33, Average Age = 0.27\n",
      "Object 98: Hit Rate = 0.00, Average Age = 0.00\n",
      "Object 99: Hit Rate = 0.12, Average Age = 0.33\n",
      "Object 100: Hit Rate = 0.23, Average Age = 0.25\n"
     ]
    }
   ],
   "source": [
    "statistics = []\n",
    "# Calculate and print hit rate and average age for each object\n",
    "for obj_id in range(1, DATABASE_OBJECTS + 1):\n",
    "    if cache.access_count[obj_id] != 0:\n",
    "        hit_rate = cache.hits[obj_id] / max(1, cache.access_count[obj_id])  # Avoid division by zero\n",
    "        avg_age = cache.cumulative_age[obj_id] / max(1, cache.hits[obj_id])  # Only average over hits\n",
    "        print(f\"Object {obj_id}: Hit Rate = {hit_rate:.2f}, Average Age = {avg_age:.2f}\")\n",
    "        statistics.append({\"obj_id\": obj_id,\"hit_rate\": hit_rate,\"avg_age\": avg_age})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2d18372-cdba-4151-ae32-5bf45466bf94",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats = pd.DataFrame(statistics)\n",
    "stats.to_csv(f\"{TEMP_BASE_DIR}/hit_age.csv\",index=False)\n",
    "stats.drop(\"obj_id\", axis=1).describe().to_csv(f\"{TEMP_BASE_DIR}/overall_hit_age.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "80971714-44f1-47db-9e89-85be7c885bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>access_count</th>\n",
       "      <th>hits</th>\n",
       "      <th>misses</th>\n",
       "      <th>mu</th>\n",
       "      <th>lambda</th>\n",
       "      <th>hit_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>664</td>\n",
       "      <td>224</td>\n",
       "      <td>440</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>212</td>\n",
       "      <td>22</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>673</td>\n",
       "      <td>218</td>\n",
       "      <td>455</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>711</td>\n",
       "      <td>246</td>\n",
       "      <td>465</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>345</td>\n",
       "      <td>66</td>\n",
       "      <td>279</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>31.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>351</td>\n",
       "      <td>70</td>\n",
       "      <td>281</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>717</td>\n",
       "      <td>243</td>\n",
       "      <td>474</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>664</td>\n",
       "      <td>216</td>\n",
       "      <td>448</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>11.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>181</td>\n",
       "      <td>21</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>22.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    access_count  hits  misses  mu  lambda  hit_rate\n",
       "1            664   224     440   0       1     10.38\n",
       "2            212    22     190   0       3     32.39\n",
       "3            673   218     455   0       1     34.60\n",
       "4            711   246     465   0       1     19.13\n",
       "5            345    66     279   0       2     31.74\n",
       "..           ...   ...     ...  ..     ...       ...\n",
       "95           351    70     281   0       2     33.89\n",
       "96           717   243     474   0       1     32.53\n",
       "97           664   216     448   0       1      0.00\n",
       "98            23     0      23   0      37     11.60\n",
       "99           181    21     160   0       4     22.85\n",
       "\n",
       "[99 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "access_count = pd.DataFrame.from_dict(cache.access_count, orient='index', columns=['access_count'])\n",
    "hits = pd.DataFrame.from_dict(cache.hits, orient='index', columns=['hits'])\n",
    "misses = pd.DataFrame.from_dict(cache.misses, orient='index', columns=['misses'])\n",
    "mu = pd.DataFrame.from_dict(db.mu_values, orient='index', columns=['mu'])\n",
    "lmbda = pd.DataFrame.from_dict(db.lambda_values, orient='index', columns=['lambda'])\n",
    "hit_rate = pd.DataFrame(np.round((hits.to_numpy()/access_count.to_numpy())*100,2), columns=['hit_rate'])\n",
    "\n",
    "merged = access_count.merge(hits, left_index=True, right_index=True).merge(misses, left_index=True, right_index=True)\\\n",
    "    .merge(mu, left_index=True, right_index=True).merge(lmbda, left_index=True, right_index=True)\\\n",
    "    .merge(hit_rate, left_index=True, right_index=True)\n",
    "merged.to_csv(f\"{TEMP_BASE_DIR}/details.csv\")\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01f8f9ee-c278-4a22-8562-ba02e77f5ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract recorded data for plotting\n",
    "times, cache_sizes = zip(*cache.cache_size_over_time)\n",
    "\n",
    "# Plot the cache size over time\n",
    "plt.figure(figsize=(30, 5))\n",
    "plt.plot(times, cache_sizes, label=\"Objects in Cache\")\n",
    "plt.xlabel(\"Time (s)\")\n",
    "plt.ylabel(\"Number of Cached Objects\")\n",
    "plt.title(\"Number of Objects in Cache Over Time\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig(f\"{TEMP_BASE_DIR}/objects_in_cache_over_time.pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f30a0497-9b2e-4ea9-8ebf-6687de19aaa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "# Count occurrences of each number\n",
    "count = Counter(list(db.lambda_values.values()))\n",
    "\n",
    "# Separate the counts into two lists for plotting\n",
    "x = list(count.keys())  # List of unique numbers\n",
    "y = list(count.values())  # List of their respective counts\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(x, y, color='skyblue')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Number')\n",
    "plt.ylabel('Occurrences')\n",
    "plt.title('Occurance of each lambda in db')\n",
    "plt.savefig(f\"{TEMP_BASE_DIR}/lambda_distribution.pdf\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00a12eea-c805-4209-9143-48fa65619873",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "# Count occurrences of each number\n",
    "count = Counter(np.array(list(db.mu_values.values())).round(0))\n",
    "\n",
    "# Separate the counts into two lists for plotting\n",
    "x = list(count.keys())  # List of unique numbers\n",
    "y = list(count.values())  # List of their respective counts\n",
    "\n",
    "# Plot the data\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(x, y, color='skyblue')\n",
    "\n",
    "# Adding labels and title\n",
    "plt.xlabel('Number')\n",
    "plt.ylabel('Occurrences')\n",
    "plt.title('Occurance of each mu in db (rounded)')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "adbfeb40-76bd-4224-ac45-65c7b2b2cb7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_requests(object_id: int):\n",
    "    mu = db.mu_values[object_id]\n",
    "    lmb = db.lambda_values[object_id]\n",
    "    rq_log = np.array(cache.request_log[object_id])\n",
    "    df = rq_log[1:] - rq_log[:-1]\n",
    "    pd.DataFrame(df, columns=[f\"{object_id}, mu:{mu:.2f}, lambda: {lmb:.2f}\"]).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f550686-3463-4e50-be83-ceafb27512b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_rate(object_id: int):\n",
    "    # Calculate time intervals between consecutive events\n",
    "    intervals = np.diff(np.array(cache.request_log[object_id]))  # Differences between each event time\n",
    "    \n",
    "    # Calculate the rate per second for each interval\n",
    "    rates = 1 / intervals  # Inverse of the time interval gives rate per second\n",
    "    \n",
    "    # Optional: Calculate the average event rate over all intervals\n",
    "    average_rate = np.mean(rates)\n",
    "    print(\"Average event rate per second:\", average_rate)\n",
    "    print(\"The mu is: \", db.lambda_values[object_id])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b47990b1-0231-43ac-8bc5-8340abe4a8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(EXPERIMENT_BASE_DIR, exist_ok=True)\n",
    "folder_name = experiment_name.replace(\" \", \"_\").replace(\"(\", \"\").replace(\")\", \"\").replace(\".\", \"_\")\n",
    "folder_path = os.path.join(EXPERIMENT_BASE_DIR, folder_name)\n",
    "os.makedirs(folder_path, exist_ok=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "db83cad4-7cc6-4702-ae3a-d1af30a561d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_names = os.listdir(TEMP_BASE_DIR)\n",
    "    \n",
    "for file_name in file_names:\n",
    "    shutil.move(os.path.join(TEMP_BASE_DIR, file_name), folder_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "graphs",
   "language": "python",
   "name": "graphs"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}