feat(simulation): Ran simulation with 10000

Signed-off-by: Tuan-Dat Tran <tuan-dat.tran@tudattr.dev>

aoi_cache_simulation.py

@@ -0,0 +1,306 @@
+import simpy
+import random
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from enum import Enum
+
+
+# Types of cache
+class CacheType(Enum):
+    LRU = 1
+    RANDOM_EVICTION = 2
+
+
+# Constants
+SEED = 42
+DATABASE_OBJECTS = 100  # Number of objects in the database
+ACCESS_COUNT_LIMIT = 10  # Total time to run the simulation
+EXPORT_NAME = "./logs/export.csv"
+
+ZIPF_CONSTANT = (
+    2  # Shape parameter for the Zipf distribution (controls skewness) Needs to be: 1<
+)
+
+# Set random seeds
+random.seed(SEED)
+np.random.seed(SEED)
+
+# Initialize simulation environment
+env = simpy.Environment()
+
+
+CACHE_CAPACITY = DATABASE_OBJECTS  # Maximum number of objects the cache can hold
+
+# MAX_REFRESH_RATE is used as the maximum for a uniform
+# distribution for mu.
+# If MAX_REFRESH_RATE is 0, we do not do any refreshes.
+MAX_REFRESH_RATE = 10
+
+cache_type = CacheType.LRU
+
+# CACHE_TTL is used to determin which TTL to set when an
+# object is pulled into the cache
+# If CACHE_TTL is set to 0, the TTL is infinite
+CACHE_TTL = 5
+
+
+configurations = {
+    "default": (DATABASE_OBJECTS, 10, CacheType.LRU, 5),
+    "No Refresh": (DATABASE_OBJECTS, 0, CacheType.LRU, 5),
+    "Infinite TTL": (int(DATABASE_OBJECTS / 2), 0, CacheType.LRU, 0),
+    "Random Eviction": (int(DATABASE_OBJECTS / 2), 10, CacheType.RANDOM_EVICTION, 5),
+    "RE without Refresh": (int(DATABASE_OBJECTS / 2), 0, CacheType.RANDOM_EVICTION, 5),
+}
+
+config = configurations["default"]
+
+CACHE_CAPACITY = config[0]
+MAX_REFRESH_RATE = config[1]
+cache_type = config[2]
+CACHE_TTL = config[3]
+
+
+class Database:
+    def __init__(self):
+        # Each object now has a specific refresh rate 'mu'
+        self.data = {i: f"Object {i}" for i in range(1, DATABASE_OBJECTS + 1)}
+        self.lambda_values = {
+            i: np.random.zipf(ZIPF_CONSTANT) for i in range(1, DATABASE_OBJECTS + 1)
+        }  # Request rate 'lambda' for each object
+        # Refresh rate 'mu' for each object
+        if MAX_REFRESH_RATE == 0:
+            self.mu_values = {i: 0 for i in range(1, DATABASE_OBJECTS + 1)}
+        else:
+            self.mu_values = {
+                i: np.random.uniform(1, MAX_REFRESH_RATE)
+                for i in range(1, DATABASE_OBJECTS + 1)
+            }
+        self.next_request = {
+            i: np.random.exponential(self.lambda_values[i])
+            for i in range(1, DATABASE_OBJECTS + 1)
+        }
+
+    def get_object(self, obj_id):
+        # print(f"[{env.now:.2f}] Database: Fetched {self.data.get(obj_id, 'Unknown')} for ID {obj_id}")
+        return self.data.get(obj_id, None)
+
+
+class Cache:
+    def __init__(self, env, db, cache_type):
+        self.cache_type = cache_type
+        self.env = env
+        self.db = db
+        self.storage = {}  # Dictionary to store cached objects
+        self.ttl = {}  # Dictionary to store TTLs
+        self.age = {}  # Dictionary to store age of each object
+        self.cache_size_over_time = []  # To record cache state at each interval
+        self.cache_next_request_over_time = []
+        self.request_log = {i: [] for i in range(1, DATABASE_OBJECTS + 1)}
+        self.hits = {
+            i: 0 for i in range(1, DATABASE_OBJECTS + 1)
+        }  # Track hits per object
+        self.misses = {
+            i: 0 for i in range(1, DATABASE_OBJECTS + 1)
+        }  # Track misses per object
+        self.cumulative_age = {
+            i: 0 for i in range(1, DATABASE_OBJECTS + 1)
+        }  # Track cumulative age per object
+        self.access_count = {
+            i: 0 for i in range(1, DATABASE_OBJECTS + 1)
+        }  # Track access count per object
+        self.next_refresh = {}  # Track the next refresh time for each cached object
+
+    def get(self, obj_id):
+        if obj_id in self.storage and (self.ttl[obj_id] > env.now or CACHE_TTL == 0):
+            # Cache hit: increment hit count and update cumulative age
+            self.hits[obj_id] += 1
+            self.cumulative_age[obj_id] += self.age[obj_id]
+            self.access_count[obj_id] += 1
+        else:
+            # Cache miss: increment miss count
+            self.misses[obj_id] += 1
+            self.access_count[obj_id] += 1
+
+            # Fetch the object from the database if it’s not in cache
+            obj = self.db.get_object(obj_id)
+
+            # If the cache is full, evict the oldest object
+            if len(self.storage) >= CACHE_CAPACITY:
+                if self.cache_type == CacheType.LRU:
+                    self.evict_oldest()
+                elif self.cache_type == CacheType.RANDOM_EVICTION:
+                    self.evict_random()
+
+            # Add the object to cache, set TTL, reset age, and schedule next refresh
+            self.storage[obj_id] = obj
+            if CACHE_TTL != 0:
+                self.ttl[obj_id] = env.now + CACHE_TTL
+            else:
+                self.ttl[obj_id] = 0
+            self.age[obj_id] = 0
+            if MAX_REFRESH_RATE != 0:
+                self.next_refresh[obj_id] = env.now + np.random.exponential(
+                    self.db.mu_values[obj_id]
+                )  # Schedule refresh
+
+    def evict_oldest(self):
+        """Remove the oldest item from the cache to make space."""
+        oldest_id = max(self.age, key=self.age.get)  # Find the oldest item by age
+        print(
+            f"[{env.now:.2f}] Cache: Evicting oldest object {oldest_id} to make space at {self.ttl[oldest_id]:.2f}"
+        )
+        del self.storage[oldest_id]
+        del self.ttl[oldest_id]
+        del self.age[oldest_id]
+
+    def evict_random(self):
+        """Remove a random item from the cache to make space."""
+        random_id = np.random.choice(
+            list(self.storage.keys())
+        )  # Select a random key from the cache
+        print(
+            f"[{env.now:.2f}] Cache: Evicting random object {random_id} to make space at {self.ttl[random_id]:.2f}"
+        )
+        del self.storage[random_id]
+        del self.ttl[random_id]
+        del self.age[random_id]
+
+    def refresh_object(self, obj_id):
+        """Refresh the object from the database to keep it up-to-date. TTL is increased on refresh."""
+        obj = self.db.get_object(obj_id)
+        self.storage[obj_id] = obj
+        if CACHE_TTL != 0:
+            self.ttl[obj_id] = env.now + CACHE_TTL
+        else:
+            self.ttl[obj_id] = 0
+        self.age[obj_id] = 0
+        # print(f"[{env.now:.2f}] Cache: Refreshed object {obj_id}")
+
+    def age_objects(self):
+        """Increment age of each cached object."""
+        for obj_id in list(self.age.keys()):
+            if CACHE_TTL != 0:
+                if self.ttl[obj_id] > env.now:
+                    self.age[obj_id] += 1
+                    # print(f"[{env.now:.2f}] Cache: Object {obj_id} aged to {self.age[obj_id]}")
+                else:
+                    # Remove object if its TTL expired
+                    # print(f"[{env.now:.2f}] Cache: Object {obj_id} expired")
+                    del self.storage[obj_id]
+                    del self.ttl[obj_id]
+                    del self.age[obj_id]
+            else:
+                self.age[obj_id] += 1
+
+    def record_cache_state(self):
+        """Record the current cache state (number of objects in cache) over time."""
+        self.cache_size_over_time.append((env.now, len(self.storage)))
+        self.cache_next_request_over_time.append((env.now, self.db.next_request.copy()))
+
+
+def age_cache_process(env, cache):
+    """Process that ages cache objects over time, removes expired items, and refreshes based on object-specific intervals."""
+    while True:
+        cache.age_objects()  # Age objects and remove expired ones
+
+        if MAX_REFRESH_RATE != 0:
+            # Refresh objects based on their individual refresh intervals
+            for obj_id in list(cache.storage.keys()):
+                # Check if it's time to refresh this object based on next_refresh
+                if env.now >= cache.next_refresh[obj_id]:
+                    cache.refresh_object(obj_id)
+                    # Schedule the next refresh based on the object's mu
+                    cache.next_refresh[obj_id] = env.now + np.random.exponential(
+                        cache.db.mu_values[obj_id]
+                    )
+
+        cache.record_cache_state()  # Record cache state at each time step
+        yield env.timeout(1)  # Run every second
+
+
+def client_request_process(env, cache, event):
+    """Client process that makes requests for objects from the cache."""
+    lowest_lambda_object = max(cache.db.lambda_values.items(), key=lambda x: x[1])
+    lowest_lambda_object = (
+        [lowest_lambda_object]
+        if isinstance(lowest_lambda_object, int)
+        else lowest_lambda_object
+    )
+    while True:
+        obj_id, next_request = min(cache.db.next_request.items(), key=lambda x: x[1])
+        yield env.timeout(next_request - env.now)
+        if env.now >= next_request:
+            # print(f"[{env.now:.2f}] Client: Requesting object {obj_id}")
+            cache.get(obj_id)
+
+            # print(f"[{env.now:.2f}] Client: Schedule next request for {obj_id}")
+            next_request = env.now + np.random.exponential(
+                cache.db.lambda_values[obj_id]
+            )
+            cache.request_log[obj_id].append(next_request)
+            cache.db.next_request[obj_id] = next_request
+        if all(
+            cache.access_count[obj] >= ACCESS_COUNT_LIMIT
+            for obj in lowest_lambda_object
+        ):
+            event.succeed()
+
+
+# Instantiate components
+db = Database()
+cache = Cache(env, db, cache_type)
+stop_event = env.event()
+
+# Start processes
+env.process(age_cache_process(env, cache))
+env.process(client_request_process(env, cache, stop_event))
+
+# Run the simulation
+env.run(until=stop_event)
+
+# Calculate and print hit rate and average age for each object
+for obj_id in range(1, DATABASE_OBJECTS + 1):
+    if cache.access_count[obj_id] != 0:
+        hit_rate = cache.hits[obj_id] / max(
+            1, cache.access_count[obj_id]
+        )  # Avoid division by zero
+        avg_age = cache.cumulative_age[obj_id] / max(
+            1, cache.hits[obj_id]
+        )  # Only average over hits
+        print(
+            f"Object {obj_id}: Hit Rate = {hit_rate:.2f}, Average Age = {avg_age:.2f}"
+        )
+
+# Extract recorded data for plotting
+times, cache_sizes = zip(*cache.cache_size_over_time)
+
+# Plot the cache size over time
+plt.figure(figsize=(30, 5))
+plt.plot(times, cache_sizes, label="Objects in Cache")
+plt.xlabel("Time (s)")
+plt.ylabel("Number of Cached Objects")
+plt.title("Number of Objects in Cache Over Time")
+plt.legend()
+plt.grid(True)
+plt.savefig("./graphs/objects_in_cache_over_time.pdf")
+
+access_count = pd.DataFrame.from_dict(
+    cache.access_count, orient="index", columns=["access_count"]
+)
+hits = pd.DataFrame.from_dict(cache.hits, orient="index", columns=["hits"])
+misses = pd.DataFrame.from_dict(cache.misses, orient="index", columns=["misses"])
+mu = pd.DataFrame.from_dict(db.mu_values, orient="index", columns=["mu"])
+lmbda = pd.DataFrame.from_dict(db.lambda_values, orient="index", columns=["lambda"])
+hit_rate = pd.DataFrame(
+    np.round((hits.to_numpy() / access_count.to_numpy()) * 100, 2), columns=["hit_rate"]
+)
+
+merged = (
+    access_count.merge(hits, left_index=True, right_index=True)
+    .merge(misses, left_index=True, right_index=True)
+    .merge(mu, left_index=True, right_index=True)
+    .merge(lmbda, left_index=True, right_index=True)
+    .merge(hit_rate, left_index=True, right_index=True)
+)
+merged.to_csv(EXPORT_NAME)

experiments/No_Refresh_0_5s_ttl/details.csv

@@ -0,0 +1,101 @@
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experiments/No_Refresh_0_5s_ttl/hit_age.csv

@@ -0,0 +1,101 @@
+obj_id,hit_rate,avg_age
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experiments/No_Refresh_1_0s_ttl/hit_age.csv

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experiments/No_Refresh_3_0s_ttl/hit_age.csv

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experiments/No_Refresh_3_0s_ttl/overall_hit_age.csv

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experiments/No_Refresh_4_0s_ttl/details.csv

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experiments/No_Refresh_4_0s_ttl/overall_hit_age.csv

@@ -0,0 +1,9 @@
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experiments/No_Refresh_5_0s_ttl/details.csv

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experiments/No_Refresh_5_0s_ttl/hit_age.csv

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experiments/No_Refresh_5_0s_ttl/overall_hit_age.csv

@@ -0,0 +1,9 @@
+,hit_rate,avg_age
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experiments/README.md

@@ -0,0 +1,17 @@
+# Experiments: No Refresh with variable TTL
+
+Explanation for files in each experiment:
+
+- `details.csv`: Access Count, Hit, Miss, Mu, Lambda and Hit Rate for each object
+- `hit_age.csv`: Shows hit rate/average age at time of request for each object.
+- `lambda_distribution.pdf`: Lambda Distribution across all objects/discrete
+  values of the Zipf distribution
+- `lambda_vs_access_count.pdf`: Displays the access count against lambda,
+  expecting a higher lambda to result in a higher access count.
+- `objects_in_cache_over_time.pdf`: Amount of cache entries at given time.
+- `overall_hit_age.csv`: Cumulative description of `hit_age.csv`
+
+Length of simulation doesn't change much since we're not touching the request
+rate across the simulations.
+Break condition for the simulation is when the each database object has been
+requested at least `ACCESS_COUNT_LIMIT` (i.e. 10) times.

experiments/avg_ages.md

@@ -0,0 +1,8 @@
+|      |   avg_ages |
+|:-----|-----------:|
+| 0.5s |   0.248882 |
+| 1.0s |   0.499741 |
+| 2.0s |   1.00137  |
+| 3.0s |   1.50042  |
+| 4.0s |   2.00102  |
+| 5.0s |   2.49973  |

experiments/hit_rates.md

@@ -0,0 +1,8 @@
+|      |   hit_rates |
+|:-----|------------:|
+| 0.5s |    0.460409 |
+| 1.0s |    0.633097 |
+| 2.0s |    0.733979 |
+| 3.0s |    0.80848  |
+| 4.0s |    0.851056 |
+| 5.0s |    0.878597 |

export.csv

@@ -1,100 +0,0 @@
-,access_count,hits,misses,mu,lambda,hit_rate
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-99,338235,324673,13562,1.6556673379589992,1,52.28

multi_aoi_cache_simulation.ipynb

@@ -0,0 +1,511 @@
+{
+ "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",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# Types of cache\n",
+    "class CacheType(Enum):\n",
+    "    LRU = 1\n",
+    "    RANDOM_EVICTION = 2\n",
+    "\n",
+    "# Constants\n",
+    "SEED = 42\n",
+    "DATABASE_OBJECTS = 100 # Number of objects in the database\n",
+    "ACCESS_COUNT_LIMIT = 10000   # 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",
+    "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": "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(1/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": 4,
+   "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.cumulative_age[obj_id] += 0\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(1/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": 5,
+   "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(1/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": 6,
+   "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",
+    "    last_print = 0\n",
+    "    with tqdm(total=ACCESS_COUNT_LIMIT, desc=\"Progress\", leave=True) as pbar:\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 (int(env.now) % 1) == 0 and int(env.now) != last_print:\n",
+    "                last_print = int(env.now)\n",
+    "                pbar.n = min(cache.access_count.values())\n",
+    "                pbar.refresh()\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(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",
+    "            if all(access_count >= ACCESS_COUNT_LIMIT for access_count in cache.access_count.values()):\n",
+    "                event.succeed()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "342d1932-94b5-4e0b-89b5-d4edaaccc59c",
+   "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",
+    "experiments = configurations.keys()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6b9680ed-19bc-4f5b-8f22-95ffb319f068",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['No Refresh (0.5s ttl)',\n",
+       " 'No Refresh (1.0s ttl)',\n",
+       " 'No Refresh (2.0s ttl)',\n",
+       " 'No Refresh (3.0s ttl)',\n",
+       " 'No Refresh (4.0s ttl)',\n",
+       " 'No Refresh (5.0s ttl)']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "experiments = [e for e in experiments if \"ttl)\" in e]\n",
+    "experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ff299ca-ec65-453b-b167-9a0f7728a207",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Progress:  69%|███████████████████████████████████                | 6876/10000 [00:40<00:18, 169.24it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "for experiment_name in experiments:\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",
+    "\n",
+    "    # 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()\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",
+    "\n",
+    "    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",
+    "            statistics.append({\"obj_id\": obj_id,\"hit_rate\": hit_rate,\"avg_age\": avg_age})\n",
+    "\n",
+    "    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\")\n",
+    "\n",
+    "    avg_age = {\n",
+    "        obj_id: cache.cumulative_age[obj_id] / max(1, cache.access_count[obj_id]) \n",
+    "        for obj_id in range(1, DATABASE_OBJECTS + 1)\n",
+    "    }\n",
+    "\n",
+    "    hit_rate = {\n",
+    "        obj_id: np.round((cache.hits[obj_id] / max(1, cache.access_count[obj_id]))*100,2) \n",
+    "        for obj_id in range(1, DATABASE_OBJECTS + 1)\n",
+    "    }\n",
+    "    \n",
+    "    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.from_dict(hit_rate, orient='index', columns=['hit_rate'])\n",
+    "    avg_age = pd.DataFrame.from_dict(avg_age, orient='index', columns=['avg_age'])\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(avg_age, left_index=True, right_index=True)\n",
+    "    merged.to_csv(f\"{TEMP_BASE_DIR}/details.csv\", index_label=\"obj_id\")\n",
+    "\n",
+    "    # 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()\n",
+    "\n",
+    "    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()\n",
+    "\n",
+    "    # 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()\n",
+    "\n",
+    "    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()\n",
+    "\n",
+    "    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",
+    "\n",
+    "    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)\n",
+    "\n",
+    "    del env\n",
+    "    del cache\n",
+    "    del db"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0827d71-e5bd-4303-b806-a19e17b28855",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}

note.md

@@ -42,38 +42,45 @@ miss requests and cache updates should not go over the bandwidth
 
 ### Two versions
 
-1.
+- [x] Default
 
-- Do Refresh
+  - Do Refresh
-- Do Request
+  - Do Request
 
-2.
+- [x] No Refresh
 
-- Just Request
+  - Just Request
-- No Refresh
+  - No Refresh
 
-3.
+- [x] Infinite TTL
 
-- LRU
+  - LRU
-- Infinite TTL
+  - Infinite TTL
-- No Refresh
+  - No Refresh
 
-4.
+- [x] Random Eviction
 
-- Random eviction
+  - Random eviction
-- Regular TTL
+  - Regular TTL
-- With Refresh
+  - With Refresh
-
-5.
-
-- Random eviction
-- Regular TTL
-- Without Refresh
 
+- [x] Random Eviction w/o Refresh
+  - Random eviction
+  - Regular TTL
+  - No Refresh
 
+| Name                 | Cache Capacity       | MAX_REFRESH_RATE | cache_type                | CACHE_TTL |
+| -------------------- | -------------------- | ---------------- | ------------------------- | --------- |
+| Default              | DATABASE_OBJECTS     | 1<               | CacheType.LRU             | 5         |
+| No Refresh           | DATABASE_OBJECTS     | 0                | CacheType.LRU             | 5         |
+| Infinite TTL         | DATABASE_OBJECTS / 2 | 0                | CacheType.LRU             | 0         |
+| Random Eviction (RE) | DATABASE_OBJECTS / 2 | 1<               | CacheType.RANDOM_EVICTION | 5         |
+| RE without Refresh   | DATABASE_OBJECTS / 2 | 0                | CacheType.RANDOM_EVICTION | 5         |
 
 ### Runtime
 
 CPU times: user 3min 46s, sys: 43 s, total: 4min 29s
 Wall time: 4min 29s
-for ACCESS_COUNT_LIMIT = 10_000   # Total time to run the simulation
+for ACCESS_COUNT_LIMIT = 10_000 # Total time to run the simulation
+
+## Notes 11/27/2024