1055 lines
299 KiB
Plaintext
1055 lines
299 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "71f85f2a-423f-44d2-b80d-da9ac8d3961a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import simpy\n",
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"import random\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"from enum import Enum\n",
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"import os\n",
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"import shutil\n",
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"from tqdm import tqdm\n",
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"import math\n",
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"\n",
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"# Types of cache\n",
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"class CacheType(Enum):\n",
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" LRU = 1\n",
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" RANDOM_EVICTION = 2\n",
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" TTL = 3\n",
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"\n",
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"# Constants\n",
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"SEED = 42\n",
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"DATABASE_OBJECTS = 100 # Number of objects in the database\n",
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"ACCESS_COUNT_LIMIT = 100 # Total time to run the simulation\n",
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"EXPERIMENT_BASE_DIR = \"./experiments/\"\n",
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"TEMP_BASE_DIR = \"./.aoi_cache/\"\n",
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"\n",
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"ZIPF_CONSTANT = 2 # Shape parameter for the Zipf distribution (controls skewness) Needs to be: 1< \n",
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"\n",
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"# Set random seeds\n",
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"random.seed(SEED)\n",
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"np.random.seed(SEED)\n",
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"\n",
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"os.makedirs(TEMP_BASE_DIR, exist_ok=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9a37d7a3-3e11-4b89-8dce-6091dd38b16f",
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"metadata": {},
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"source": [
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"How to set certain parameters for specific scenarios\n",
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"\n",
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"\n",
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"| Name | Cache Capacity | MAX_REFRESH_RATE | cache_type | CACHE_TTL |\n",
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"| -------------------- | -------------------- | ---------------- | ------------------------- | --------- |\n",
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"| Default | DATABASE_OBJECTS | 1< | CacheType.LRU | 5 |\n",
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"| No Refresh | DATABASE_OBJECTS | 0 | CacheType.LRU | 5 |\n",
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"| Infinite TTL | DATABASE_OBJECTS / 2 | 0 | CacheType.LRU | 0 |\n",
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"| Random Eviction (RE) | DATABASE_OBJECTS / 2 | 1< | CacheType.RANDOM_EVICTION | 5 |\n",
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"| RE without Refresh | DATABASE_OBJECTS / 2 | 0 | CacheType.RANDOM_EVICTION | 5 |\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "3d0ab5b1-162a-42c8-80a3-d31f763101f1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Configuration (Just example, will be overwritten in next block\n",
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"CACHE_CAPACITY = DATABASE_OBJECTS # Maximum number of objects the cache can hold\n",
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"\n",
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"# MAX_REFRESH_RATE is used as the maximum for a uniform\n",
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"# distribution for mu.\n",
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"# If MAX_REFRESH_RATE is 0, we do not do any refreshes.\n",
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"MAX_REFRESH_RATE = 0\n",
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"\n",
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"cache_type = CacheType.LRU\n",
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"\n",
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"# CACHE_TTL is used to determin which TTL to set when an\n",
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"# object is pulled into the cache\n",
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"# If CACHE_TTL is set to 0, the TTL is infinite\n",
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"CACHE_TTL = 5\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "3ff299ca-ec65-453b-b167-9a0f7728a207",
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"metadata": {},
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"outputs": [],
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"source": [
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"configurations = {\n",
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" \"default\": (DATABASE_OBJECTS, 10, CacheType.LRU, 5),\n",
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" \"No Refresh\": (DATABASE_OBJECTS, 0, CacheType.LRU, 5),\n",
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" \"Infinite TTL\": (int(DATABASE_OBJECTS / 2), 0, CacheType.LRU, 0),\n",
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" \"Random Eviction\": (int(DATABASE_OBJECTS / 2), 10, CacheType.RANDOM_EVICTION, 5),\n",
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" \"RE without Refresh\": (int(DATABASE_OBJECTS / 2), 0, CacheType.RANDOM_EVICTION, 5),\n",
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" \"No Refresh (0.5s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 0.5),\n",
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" \"No Refresh (1.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 1),\n",
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" \"No Refresh (2.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 2),\n",
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" \"No Refresh (3.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 3),\n",
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" \"No Refresh (4.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 4),\n",
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" \"No Refresh (5.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 5),\n",
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"}\n",
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"\n",
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"experiment_name = \"No Refresh (0.5s ttl)\"\n",
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"config = configurations[experiment_name]\n",
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"\n",
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"CACHE_CAPACITY = config[0]\n",
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"MAX_REFRESH_RATE = config[1]\n",
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"cache_type = config[2]\n",
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"CACHE_TTL = config[3]\n",
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"\n",
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"if cache_type == CacheType.TTL:\n",
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" assert CACHE_TTL > 0, \"Needs CACHE_TTL to be greater than 0 when using TTL-Cache.\"\n",
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" assert CACHE_CAPACITY >= DATABASE_OBJECTS, \"Cache Size needs to be greater or equal to the amount of Database Objects.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "5cea042f-e9fc-4a1e-9750-de212ca70601",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Database:\n",
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" def __init__(self):\n",
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" # Each object now has a specific refresh rate 'mu'\n",
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" self.data = {i: f\"Object {i}\" for i in range(1, DATABASE_OBJECTS + 1)}\n",
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" self.lambda_values = {i: np.random.zipf(ZIPF_CONSTANT) for i in range(1, DATABASE_OBJECTS + 1)} # Request rate 'lambda' for each object\n",
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" # Refresh rate 'mu' for each object\n",
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" if MAX_REFRESH_RATE == 0:\n",
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" self.mu_values = {i: 0 for i in range(1,DATABASE_OBJECTS + 1)} \n",
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" else:\n",
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" self.mu_values = {i: np.random.uniform(1, MAX_REFRESH_RATE) for i in range(1, DATABASE_OBJECTS + 1)}\n",
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" self.next_request = {i: np.random.exponential(1/self.lambda_values[i]) for i in range(1, DATABASE_OBJECTS + 1)}\n",
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"\n",
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"\n",
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" def get_object(self, obj_id):\n",
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" # print(f\"[{env.now:.2f}] Database: Fetched {self.data.get(obj_id, 'Unknown')} for ID {obj_id}\")\n",
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" return self.data.get(obj_id, None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "499bf543-b2c6-4e4d-afcc-0a6665ce3ae1",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Cache:\n",
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" def __init__(self, env, db, cache_type):\n",
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" self.cache_type = cache_type\n",
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" self.env = env\n",
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" self.db = db\n",
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" self.storage = {} # Dictionary to store cached objects\n",
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" self.ttl = {} # Dictionary to store TTLs\n",
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" self.initial_fetch = {} # Dictionary to store when an object was fetched from the databse to determine the age\n",
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" self.cache_size_over_time = [] # To record cache state at each interval\n",
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" self.cache_next_request_over_time = []\n",
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" self.request_log = {i: [] for i in range(1, DATABASE_OBJECTS + 1)}\n",
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" self.hits = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Track hits per object\n",
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" self.misses = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Track misses per object\n",
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" self.cumulative_age = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Track cumulative age per object\n",
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" self.access_count = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Track access count per object\n",
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" self.next_refresh = {} # Track the next refresh time for each cached object\n",
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" self.object_start_time = {} # Used as helper variable to determine the starting time of an object in the cache\n",
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" self.cumulative_cache_time = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)} # Stores the cumulative time the object has spent between its eviction and when it was first pulled into the cache\n",
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" \n",
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" def get(self, obj_id):\n",
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" if obj_id in self.storage:\n",
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" # Cache hit: Refresh TTL if TTL-Cache\n",
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" if self.cache_type == CacheType.TTL:\n",
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" if self.ttl[obj_id] > env.now:\n",
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" self.ttl[obj_id] = env.now + CACHE_TTL\n",
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" \n",
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" # Cache hit: increment hit count and update cumulative age\n",
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" self.hits[obj_id] += 1\n",
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" self.access_count[obj_id] += 1\n",
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" \n",
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" self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n",
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"\n",
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" # Cache hit: Refresh database object on hit\n",
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" # self.initial_fetch[obj_id] = env.now\n",
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" else:\n",
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" assert obj_id not in self.storage.keys(), \"Found object in cache on miss.\"\n",
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" assert obj_id not in self.initial_fetch.keys(), \"Found age timer on miss.\"\n",
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" assert obj_id not in self.object_start_time.keys(), \"Found cache time ratio timer on miss.\"\n",
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" # Cache miss: Add TTL if TTL-Cache\n",
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" # When full cache: If non-TTL-Cache: Evict. If TTL-Cache: Don't add to Cache.\n",
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" if self.cache_type == CacheType.TTL:\n",
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" assert obj_id not in self.ttl.keys(), \"Found cache time ratio timer on miss.\"\n",
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" self.ttl[obj_id] = env.now + CACHE_TTL\n",
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" else:\n",
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" if len(self.storage) == DATABASE_OBJECTS:\n",
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" if self.cache_type == CacheType.LRU:\n",
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" self.evict_oldest()\n",
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" elif self.cache_type == CacheType.RANDOM_EVICTION:\n",
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" self.evict_random()\n",
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" elif self.cache-type == CacheType.TTL:\n",
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" return\n",
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" \n",
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" # Cache miss: increment miss count\n",
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" self.misses[obj_id] += 1\n",
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" self.access_count[obj_id] += 1\n",
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" \n",
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" # Cache miss: Fetch the object from the database\n",
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" self.storage[obj_id] = self.db.get_object(obj_id)\n",
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" self.object_start_time[obj_id] = env.now\n",
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" \n",
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" self.initial_fetch[obj_id] = env.now\n",
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" self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n",
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" \n",
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" if MAX_REFRESH_RATE != 0:\n",
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" self.next_refresh[obj_id] = env.now + np.random.exponential(1/self.db.mu_values[obj_id]) # Schedule refresh\n",
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" \n",
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" def evict_oldest(self):\n",
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" \"\"\"Remove the oldest item from the cache to make space.\"\"\"\n",
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" oldest_id = min(self.initial_fetch, key=self.initial_fetch.get) # Find the oldest item by age\n",
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" print(f\"[{env.now:.2f}] Cache: Evicting oldest object {oldest_id} to make space at {self.ttl[oldest_id]:.2f}\")\n",
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" self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
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" del self.storage[oldest_id]\n",
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" del self.initial_fetch[oldest_id]\n",
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" del self.object_start_time[obj_id]\n",
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"\n",
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" def evict_random(self):\n",
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" \"\"\"Remove a random item from the cache to make space.\"\"\"\n",
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" random_id = np.random.choice(list(self.storage.keys())) # Select a random key from the cache\n",
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" print(f\"[{env.now:.2f}] Cache: Evicting random object {random_id} to make space at {self.ttl[random_id]:.2f}\")\n",
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" self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
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" del self.storage[random_id]\n",
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" del self.initial_fetch[random_id]\n",
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" del self.object_start_time[obj_id]\n",
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" \n",
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" def refresh_object(self, obj_id):\n",
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" \"\"\"Refresh the object from the database to keep it up-to-date. TTL is increased on refresh.\"\"\"\n",
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" obj = self.db.get_object(obj_id)\n",
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" self.storage[obj_id] = obj\n",
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" if self.cache_type == CacheType.TTL:\n",
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" self.ttl[obj_id] = env.now + CACHE_TTL\n",
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" self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
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" # print(f\"[{env.now:.2f}] Cache: Refreshed object {obj_id}\")\n",
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" \n",
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" def check_expired(self):\n",
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" \"\"\"Increment age of each cached object.\"\"\"\n",
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" if self.cache_type == CacheType.TTL:\n",
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" for obj_id in list(self.ttl.keys()):\n",
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" if self.ttl[obj_id] <= env.now:\n",
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" # Remove object if its TTL expired\n",
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" # print(f\"[{env.now:.2f}] Cache: Object {obj_id} expired\")\n",
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" self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
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" del self.storage[obj_id]\n",
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" del self.ttl[obj_id]\n",
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" del self.initial_fetch[obj_id]\n",
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" del self.object_start_time[obj_id]\n",
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"\n",
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" \n",
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" def record_cache_state(self):\n",
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" \"\"\"Record the current cache state (number of objects in cache) over time.\"\"\"\n",
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" self.cache_size_over_time.append((env.now, len(self.storage)))\n",
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" self.cache_next_request_over_time.append((env.now, self.db.next_request.copy()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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||
"id": "7286d498-aa6c-4efb-bb28-fe29736eab64",
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"metadata": {},
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"outputs": [],
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"source": [
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"def age_cache_process(env, cache):\n",
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" \"\"\"Process that ages cache objects over time, removes expired items, and refreshes based on object-specific intervals.\"\"\"\n",
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" while True:\n",
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" if cache.cache_type == CacheType.TTL:\n",
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" cache.check_expired() # Remove expired objects\n",
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"\n",
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" if MAX_REFRESH_RATE != 0:\n",
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" # Refresh objects based on their individual refresh intervals\n",
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" for obj_id in list(cache.storage.keys()):\n",
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" # Check if it's time to refresh this object based on next_refresh\n",
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" if env.now >= cache.next_refresh[obj_id]:\n",
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" cache.refresh_object(obj_id)\n",
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" # Schedule the next refresh based on the object's mu\n",
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" cache.next_refresh[obj_id] = env.now + np.random.exponential(1/cache.db.mu_values[obj_id])\n",
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" \n",
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" cache.record_cache_state() # Record cache state at each time step\n",
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" yield env.timeout(0.05) # Run every second"
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]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 7,
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||
"id": "687f5634-8edf-4337-b42f-bbb292d47f0f",
|
||
"metadata": {},
|
||
"outputs": [],
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||
"source": [
|
||
"def client_request_process(env, cache, event):\n",
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" \"\"\"Client process that makes requests for objects from the cache.\"\"\"\n",
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" last_print = 0\n",
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" with tqdm(total=ACCESS_COUNT_LIMIT, desc=\"Progress\", leave=True) as pbar:\n",
|
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" while True:\n",
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" obj_id, next_request = min(cache.db.next_request.items(), key=lambda x: x[1])\n",
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" yield env.timeout(next_request - env.now)\n",
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"\n",
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" # For progress bar\n",
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" if (int(env.now) % 1) == 0 and int(env.now) != last_print:\n",
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" last_print = int(env.now)\n",
|
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" pbar.n = min(cache.access_count.values())\n",
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" pbar.refresh()\n",
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" \n",
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" if env.now >= next_request:\n",
|
||
" # print(f\"[{env.now:.2f}] Client: Requesting object {obj_id}\")\n",
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" cache.get(obj_id)\n",
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" \n",
|
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" # print(f\"[{env.now:.2f}] Client: Schedule next request for {obj_id}\")\n",
|
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" next_request = env.now + np.random.exponential(1/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",
|
||
" \n",
|
||
" # Simulation stop condition\n",
|
||
" if all(access_count >= ACCESS_COUNT_LIMIT for access_count in cache.access_count.values()):\n",
|
||
" print(f\"Simulation ended after {env.now} seconds.\")\n",
|
||
" for obj_id in cache.storage.keys():\n",
|
||
" cache.cumulative_cache_time[obj_id] += (env.now - cache.object_start_time[obj_id])\n",
|
||
" event.succeed()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "c8516830-9880-4d9e-a91b-000338baf9d6",
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Initialize simulation environment\n",
|
||
"env = simpy.Environment()\n",
|
||
"\n",
|
||
"# 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
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||
},
|
||
"outputs": [
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||
{
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||
"name": "stderr",
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||
"output_type": "stream",
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||
"text": [
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"Progress: 99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 99/100 [00:00<00:00, 158.42it/s]"
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]
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},
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{
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"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Simulation ended after 123.41758277479552 seconds.\n",
|
||
"CPU times: user 550 ms, sys: 98.6 ms, total: 648 ms\n",
|
||
"Wall time: 632 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)\n",
|
||
"simulation_end_time = env.now"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "3b6f7c1f-ea54-4496-bb9a-370cee2d2751",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Object 1: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.08\n",
|
||
"Object 2: Hit Rate = 0.80, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.80, Average Age = 0.73, Expected Age = 0.32\n",
|
||
"Object 3: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.16, Expected Age = 0.07\n",
|
||
"Object 4: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.15, Expected Age = 0.07\n",
|
||
"Object 5: Hit Rate = 0.60, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.29, Expected Age = 0.18\n",
|
||
"Object 6: Hit Rate = 0.38, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.12, Expected Age = 0.07\n",
|
||
"Object 7: Hit Rate = 0.93, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.94, Average Age = 2.35, Expected Age = 0.43\n",
|
||
"Object 8: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.19, Expected Age = 0.09\n",
|
||
"Object 9: Hit Rate = 0.36, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.10, Expected Age = 0.06\n",
|
||
"Object 10: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.14, Expected Age = 0.07\n",
|
||
"Object 11: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.33, Expected Age = 0.21\n",
|
||
"Object 12: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.17, Expected Age = 0.10\n",
|
||
"Object 13: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.19, Expected Age = 0.09\n",
|
||
"Object 14: Hit Rate = 0.33, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.10, Expected Age = 0.06\n",
|
||
"Object 15: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.66, Average Age = 0.36, Expected Age = 0.21\n",
|
||
"Object 16: Hit Rate = 0.62, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.62, Average Age = 0.38, Expected Age = 0.19\n",
|
||
"Object 17: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.13, Expected Age = 0.07\n",
|
||
"Object 18: Hit Rate = 0.38, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.14, Expected Age = 0.07\n",
|
||
"Object 19: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.76, Average Age = 0.60, Expected Age = 0.30\n",
|
||
"Object 20: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.18, Expected Age = 0.08\n",
|
||
"Object 21: Hit Rate = 0.46, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.19, Expected Age = 0.11\n",
|
||
"Object 22: Hit Rate = 0.46, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.19, Expected Age = 0.10\n",
|
||
"Object 23: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.45, Average Age = 0.15, Expected Age = 0.09\n",
|
||
"Object 24: Hit Rate = 0.67, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.65, Average Age = 0.39, Expected Age = 0.23\n",
|
||
"Object 25: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.11, Expected Age = 0.07\n",
|
||
"Object 26: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.15, Expected Age = 0.09\n",
|
||
"Object 27: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.09\n",
|
||
"Object 28: Hit Rate = 0.91, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.91, Average Age = 1.78, Expected Age = 0.42\n",
|
||
"Object 29: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.17, Expected Age = 0.08\n",
|
||
"Object 30: Hit Rate = 0.46, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.20, Expected Age = 0.10\n",
|
||
"Object 31: Hit Rate = 0.35, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.06\n",
|
||
"Object 32: Hit Rate = 0.85, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.86, Average Age = 1.01, Expected Age = 0.37\n",
|
||
"Object 33: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.15, Expected Age = 0.07\n",
|
||
"Object 34: Hit Rate = 0.86, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.86, Average Age = 0.91, Expected Age = 0.37\n",
|
||
"Object 35: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.17, Expected Age = 0.08\n",
|
||
"Object 36: Hit Rate = 0.32, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.34, Average Age = 0.09, Expected Age = 0.05\n",
|
||
"Object 37: Hit Rate = 0.48, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.23, Expected Age = 0.11\n",
|
||
"Object 38: Hit Rate = 0.75, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.79, Average Age = 0.59, Expected Age = 0.28\n",
|
||
"Object 39: Hit Rate = 0.98, Expected Hit Rate = 0.98, Average Time spend in Cache: 0.98, Average Age = 6.02, Expected Age = 0.48\n",
|
||
"Object 40: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.18, Expected Age = 0.10\n",
|
||
"Object 41: Hit Rate = 0.85, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 0.97, Expected Age = 0.36\n",
|
||
"Object 42: Hit Rate = 0.88, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 1.44, Expected Age = 0.39\n",
|
||
"Object 43: Hit Rate = 0.62, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.56, Average Age = 0.34, Expected Age = 0.19\n",
|
||
"Object 44: Hit Rate = 0.31, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.08, Expected Age = 0.05\n",
|
||
"Object 45: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.17, Expected Age = 0.08\n",
|
||
"Object 46: Hit Rate = 0.47, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.20, Expected Age = 0.11\n",
|
||
"Object 47: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 15.52, Expected Age = 0.49\n",
|
||
"Object 48: Hit Rate = 0.49, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.29, Expected Age = 0.12\n",
|
||
"Object 49: Hit Rate = 0.51, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.23, Expected Age = 0.13\n",
|
||
"Object 50: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.14, Expected Age = 0.08\n",
|
||
"Object 51: Hit Rate = 0.91, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.90, Average Age = 1.40, Expected Age = 0.41\n",
|
||
"Object 52: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 8.54, Expected Age = 0.49\n",
|
||
"Object 53: Hit Rate = 0.34, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.13, Expected Age = 0.06\n",
|
||
"Object 54: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.14, Expected Age = 0.07\n",
|
||
"Object 55: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.26, Expected Age = 0.09\n",
|
||
"Object 56: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.13, Expected Age = 0.09\n",
|
||
"Object 57: Hit Rate = 0.35, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.09, Expected Age = 0.06\n",
|
||
"Object 58: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 25.12, Expected Age = 0.50\n",
|
||
"Object 59: Hit Rate = 0.59, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.57, Average Age = 0.28, Expected Age = 0.17\n",
|
||
"Object 60: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.21, Expected Age = 0.09\n",
|
||
"Object 61: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 62.77, Expected Age = 0.50\n",
|
||
"Object 62: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.18, Expected Age = 0.10\n",
|
||
"Object 63: Hit Rate = 0.38, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.15, Expected Age = 0.07\n",
|
||
"Object 64: Hit Rate = 0.67, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.67, Average Age = 0.35, Expected Age = 0.22\n",
|
||
"Object 65: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.17, Expected Age = 0.10\n",
|
||
"Object 66: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 15.37, Expected Age = 0.49\n",
|
||
"Object 67: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.19, Expected Age = 0.10\n",
|
||
"Object 68: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 61.45, Expected Age = 0.50\n",
|
||
"Object 69: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.12, Expected Age = 0.07\n",
|
||
"Object 70: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.08\n",
|
||
"Object 71: Hit Rate = 0.66, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.62, Average Age = 0.47, Expected Age = 0.22\n",
|
||
"Object 72: Hit Rate = 0.36, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.06\n",
|
||
"Object 73: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.18, Expected Age = 0.09\n",
|
||
"Object 74: Hit Rate = 0.44, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.45, Average Age = 0.20, Expected Age = 0.10\n",
|
||
"Object 75: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.75, Average Age = 0.68, Expected Age = 0.30\n",
|
||
"Object 76: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.65, Average Age = 0.40, Expected Age = 0.21\n",
|
||
"Object 77: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.36, Expected Age = 0.21\n",
|
||
"Object 78: Hit Rate = 0.80, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.81, Average Age = 0.83, Expected Age = 0.32\n",
|
||
"Object 79: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 32.55, Expected Age = 0.50\n",
|
||
"Object 80: Hit Rate = 0.33, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.11, Expected Age = 0.06\n",
|
||
"Object 81: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.16, Expected Age = 0.09\n",
|
||
"Object 82: Hit Rate = 0.92, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.92, Average Age = 1.87, Expected Age = 0.42\n",
|
||
"Object 83: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.65, Average Age = 0.36, Expected Age = 0.21\n",
|
||
"Object 84: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.19, Expected Age = 0.10\n",
|
||
"Object 85: Hit Rate = 0.35, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.36, Average Age = 0.11, Expected Age = 0.06\n",
|
||
"Object 86: Hit Rate = 0.55, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.58, Average Age = 0.29, Expected Age = 0.15\n",
|
||
"Object 87: Hit Rate = 0.50, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.26, Expected Age = 0.12\n",
|
||
"Object 88: Hit Rate = 0.59, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.61, Average Age = 0.28, Expected Age = 0.18\n",
|
||
"Object 89: Hit Rate = 0.44, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.18, Expected Age = 0.10\n",
|
||
"Object 90: Hit Rate = 0.35, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.13, Expected Age = 0.06\n",
|
||
"Object 91: Hit Rate = 0.63, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.31, Expected Age = 0.20\n",
|
||
"Object 92: Hit Rate = 0.62, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.61, Average Age = 0.35, Expected Age = 0.19\n",
|
||
"Object 93: Hit Rate = 0.80, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.80, Average Age = 0.86, Expected Age = 0.32\n",
|
||
"Object 94: Hit Rate = 0.38, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.15, Expected Age = 0.07\n",
|
||
"Object 95: Hit Rate = 0.66, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.62, Average Age = 0.32, Expected Age = 0.22\n",
|
||
"Object 96: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.13, Expected Age = 0.08\n",
|
||
"Object 97: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.19, Expected Age = 0.09\n",
|
||
"Object 98: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 62.04, Expected Age = 0.50\n",
|
||
"Object 99: Hit Rate = 0.86, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 0.96, Expected Age = 0.37\n",
|
||
"Object 100: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.61, Average Age = 0.31, Expected Age = 0.21\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",
|
||
" expected_hit_rate = 1-math.exp(-db.lambda_values[obj_id]*CACHE_TTL)\n",
|
||
" avg_cache_time = cache.cumulative_cache_time[obj_id] / max(1, simulation_end_time) # Only average over hits\n",
|
||
" avg_age = cache.cumulative_age[obj_id] / max(1, cache.access_count[obj_id])\n",
|
||
" expected_age = pow(hit_rate,2) / 2\n",
|
||
" print(f\"Object {obj_id}: Hit Rate = {hit_rate:.2f}, Expected Hit Rate = {expected_hit_rate:.2f}, Average Time spend in Cache: {avg_cache_time:.2f}, Average Age = {avg_age:.2f}, Expected Age = {expected_age:.2f}\")\n",
|
||
" statistics.append({\"obj_id\": obj_id,\"hit_rate\": hit_rate, \"expected_hit_rate\": expected_hit_rate, \"avg_cache_time\":avg_cache_time, \"avg_age\": avg_age, \"expected_age\": expected_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",
|
||
" <th>expected_hit_rate</th>\n",
|
||
" <th>expected_hit_rate_delta</th>\n",
|
||
" <th>avg_cache_time</th>\n",
|
||
" <th>cache_time_delta</th>\n",
|
||
" <th>avg_age</th>\n",
|
||
" <th>expected_age</th>\n",
|
||
" <th>age_delta</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>122</td>\n",
|
||
" <td>48</td>\n",
|
||
" <td>74</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.393443</td>\n",
|
||
" <td>0.393469</td>\n",
|
||
" <td>-0.000027</td>\n",
|
||
" <td>0.403492</td>\n",
|
||
" <td>-0.010049</td>\n",
|
||
" <td>0.152056</td>\n",
|
||
" <td>0.077399</td>\n",
|
||
" <td>0.074657</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>382</td>\n",
|
||
" <td>304</td>\n",
|
||
" <td>78</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.795812</td>\n",
|
||
" <td>0.776870</td>\n",
|
||
" <td>0.018942</td>\n",
|
||
" <td>0.796822</td>\n",
|
||
" <td>-0.001011</td>\n",
|
||
" <td>0.725838</td>\n",
|
||
" <td>0.316658</td>\n",
|
||
" <td>0.409180</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>127</td>\n",
|
||
" <td>47</td>\n",
|
||
" <td>80</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.370079</td>\n",
|
||
" <td>0.393469</td>\n",
|
||
" <td>-0.023391</td>\n",
|
||
" <td>0.439155</td>\n",
|
||
" <td>-0.069076</td>\n",
|
||
" <td>0.161354</td>\n",
|
||
" <td>0.068479</td>\n",
|
||
" <td>0.092875</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>113</td>\n",
|
||
" <td>42</td>\n",
|
||
" <td>71</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.371681</td>\n",
|
||
" <td>0.393469</td>\n",
|
||
" <td>-0.021788</td>\n",
|
||
" <td>0.386356</td>\n",
|
||
" <td>-0.014674</td>\n",
|
||
" <td>0.146023</td>\n",
|
||
" <td>0.069074</td>\n",
|
||
" <td>0.076949</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>244</td>\n",
|
||
" <td>147</td>\n",
|
||
" <td>97</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.602459</td>\n",
|
||
" <td>0.632121</td>\n",
|
||
" <td>-0.029662</td>\n",
|
||
" <td>0.628468</td>\n",
|
||
" <td>-0.026009</td>\n",
|
||
" <td>0.287673</td>\n",
|
||
" <td>0.181478</td>\n",
|
||
" <td>0.106195</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",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>96</th>\n",
|
||
" <td>139</td>\n",
|
||
" <td>56</td>\n",
|
||
" <td>83</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.402878</td>\n",
|
||
" <td>0.393469</td>\n",
|
||
" <td>0.009408</td>\n",
|
||
" <td>0.432752</td>\n",
|
||
" <td>-0.029874</td>\n",
|
||
" <td>0.126856</td>\n",
|
||
" <td>0.081155</td>\n",
|
||
" <td>0.045700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>97</th>\n",
|
||
" <td>127</td>\n",
|
||
" <td>55</td>\n",
|
||
" <td>72</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.433071</td>\n",
|
||
" <td>0.393469</td>\n",
|
||
" <td>0.039602</td>\n",
|
||
" <td>0.413172</td>\n",
|
||
" <td>0.019899</td>\n",
|
||
" <td>0.191692</td>\n",
|
||
" <td>0.093775</td>\n",
|
||
" <td>0.097917</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>98</th>\n",
|
||
" <td>4578</td>\n",
|
||
" <td>4577</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>37</td>\n",
|
||
" <td>0.999782</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>-0.000218</td>\n",
|
||
" <td>0.999689</td>\n",
|
||
" <td>0.000092</td>\n",
|
||
" <td>62.040629</td>\n",
|
||
" <td>0.499782</td>\n",
|
||
" <td>61.540847</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>99</th>\n",
|
||
" <td>482</td>\n",
|
||
" <td>415</td>\n",
|
||
" <td>67</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>0.860996</td>\n",
|
||
" <td>0.864665</td>\n",
|
||
" <td>-0.003669</td>\n",
|
||
" <td>0.866371</td>\n",
|
||
" <td>-0.005375</td>\n",
|
||
" <td>0.959311</td>\n",
|
||
" <td>0.370657</td>\n",
|
||
" <td>0.588654</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>100</th>\n",
|
||
" <td>249</td>\n",
|
||
" <td>160</td>\n",
|
||
" <td>89</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.642570</td>\n",
|
||
" <td>0.632121</td>\n",
|
||
" <td>0.010450</td>\n",
|
||
" <td>0.610462</td>\n",
|
||
" <td>0.032108</td>\n",
|
||
" <td>0.305314</td>\n",
|
||
" <td>0.206448</td>\n",
|
||
" <td>0.098866</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>100 rows × 13 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" access_count hits misses mu lambda hit_rate expected_hit_rate \\\n",
|
||
"1 122 48 74 0 1 0.393443 0.393469 \n",
|
||
"2 382 304 78 0 3 0.795812 0.776870 \n",
|
||
"3 127 47 80 0 1 0.370079 0.393469 \n",
|
||
"4 113 42 71 0 1 0.371681 0.393469 \n",
|
||
"5 244 147 97 0 2 0.602459 0.632121 \n",
|
||
".. ... ... ... .. ... ... ... \n",
|
||
"96 139 56 83 0 1 0.402878 0.393469 \n",
|
||
"97 127 55 72 0 1 0.433071 0.393469 \n",
|
||
"98 4578 4577 1 0 37 0.999782 1.000000 \n",
|
||
"99 482 415 67 0 4 0.860996 0.864665 \n",
|
||
"100 249 160 89 0 2 0.642570 0.632121 \n",
|
||
"\n",
|
||
" expected_hit_rate_delta avg_cache_time cache_time_delta avg_age \\\n",
|
||
"1 -0.000027 0.403492 -0.010049 0.152056 \n",
|
||
"2 0.018942 0.796822 -0.001011 0.725838 \n",
|
||
"3 -0.023391 0.439155 -0.069076 0.161354 \n",
|
||
"4 -0.021788 0.386356 -0.014674 0.146023 \n",
|
||
"5 -0.029662 0.628468 -0.026009 0.287673 \n",
|
||
".. ... ... ... ... \n",
|
||
"96 0.009408 0.432752 -0.029874 0.126856 \n",
|
||
"97 0.039602 0.413172 0.019899 0.191692 \n",
|
||
"98 -0.000218 0.999689 0.000092 62.040629 \n",
|
||
"99 -0.003669 0.866371 -0.005375 0.959311 \n",
|
||
"100 0.010450 0.610462 0.032108 0.305314 \n",
|
||
"\n",
|
||
" expected_age age_delta \n",
|
||
"1 0.077399 0.074657 \n",
|
||
"2 0.316658 0.409180 \n",
|
||
"3 0.068479 0.092875 \n",
|
||
"4 0.069074 0.076949 \n",
|
||
"5 0.181478 0.106195 \n",
|
||
".. ... ... \n",
|
||
"96 0.081155 0.045700 \n",
|
||
"97 0.093775 0.097917 \n",
|
||
"98 0.499782 61.540847 \n",
|
||
"99 0.370657 0.588654 \n",
|
||
"100 0.206448 0.098866 \n",
|
||
"\n",
|
||
"[100 rows x 13 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",
|
||
"\n",
|
||
"hit_rate = pd.DataFrame(stats['hit_rate'])\n",
|
||
"hit_rate.index = range(1,DATABASE_OBJECTS + 1)\n",
|
||
"expected_hit_rate = pd.DataFrame(stats['expected_hit_rate'])\n",
|
||
"expected_hit_rate.index = range(1,DATABASE_OBJECTS + 1)\n",
|
||
"expected_hit_rate_delta = pd.DataFrame((hit_rate.to_numpy()-expected_hit_rate.to_numpy()), columns=['expected_hit_rate_delta'])\n",
|
||
"expected_hit_rate_delta.index = range(1,DATABASE_OBJECTS + 1)\n",
|
||
"avg_cache_time = pd.DataFrame(stats['avg_cache_time'])\n",
|
||
"avg_cache_time.index = range(1,DATABASE_OBJECTS + 1)\n",
|
||
"cache_time_delta = pd.DataFrame((hit_rate.to_numpy()-avg_cache_time.to_numpy()), columns=['cache_time_delta'])\n",
|
||
"cache_time_delta.index = range(1,DATABASE_OBJECTS + 1)\n",
|
||
"\n",
|
||
"avg_age = pd.DataFrame(stats['avg_age'])\n",
|
||
"avg_age.index = range(1,DATABASE_OBJECTS + 1)\n",
|
||
"expected_age = (0.5*pow(hit_rate,2)).rename(columns={'hit_rate': \"expected_age\"})\n",
|
||
"age_delta = pd.DataFrame((avg_age.to_numpy()-expected_age.to_numpy()), columns=['age_delta'])\n",
|
||
"age_delta.index = range(1,DATABASE_OBJECTS + 1)\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).merge(expected_hit_rate, left_index=True, right_index=True).merge(expected_hit_rate_delta, left_index=True, right_index=True) \\\n",
|
||
" .merge(avg_cache_time, left_index=True, right_index=True).merge(cache_time_delta, left_index=True, right_index=True) \\\n",
|
||
" .merge(avg_age, left_index=True, right_index=True).merge(expected_age, left_index=True, right_index=True).merge(age_delta, left_index=True, right_index=True)\n",
|
||
"merged.to_csv(f\"{TEMP_BASE_DIR}/details.csv\", index_label=\"obj_id\")\n",
|
||
"merged"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "01f8f9ee-c278-4a22-8562-ba02e77f5ddd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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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": "c192564b-d3c6-40e1-a614-f7a5ee787c4e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Plotting lambda against access_count.\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8, 6))\n",
|
||
"plt.scatter(merged['lambda'], merged['access_count'], alpha=0.7, edgecolor='k')\n",
|
||
"plt.title('Lambda vs Access Count', fontsize=14)\n",
|
||
"plt.xlabel('Lambda', fontsize=12)\n",
|
||
"plt.ylabel('Access Count', fontsize=12)\n",
|
||
"plt.grid(alpha=0.3)\n",
|
||
"\n",
|
||
"plt.savefig(f\"{TEMP_BASE_DIR}/lambda_vs_access_count.pdf\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"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": 17,
|
||
"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": 18,
|
||
"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": 19,
|
||
"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": 20,
|
||
"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
|
||
}
|