refactor: Restructure Repository to add eta optimization

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

00_aoi_caching_simulation/.gitignore

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+.ipynb_checkpoints/

00_aoi_caching_simulation/aoi_cache_simulation.py

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+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)

00_aoi_caching_simulation/experiments/No_Refresh_0_5s_ttl/details.csv

@@ -0,0 +1,101 @@
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00_aoi_caching_simulation/experiments/No_Refresh_0_5s_ttl/overall_hit_age.csv

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00_aoi_caching_simulation/experiments/No_Refresh_2_0s_ttl/overall_hit_age.csv

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00_aoi_caching_simulation/experiments/No_Refresh_3_0s_ttl/details.csv

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00_aoi_caching_simulation/experiments/No_Refresh_3_0s_ttl/hit_age.csv

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00_aoi_caching_simulation/experiments/No_Refresh_3_0s_ttl/overall_hit_age.csv

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00_aoi_caching_simulation/experiments/No_Refresh_4_0s_ttl/overall_hit_age.csv

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00_aoi_caching_simulation/experiments/No_Refresh_5_0s_ttl/hit_age.csv

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00_aoi_caching_simulation/experiments/No_Refresh_5_0s_ttl/overall_hit_age.csv

@@ -0,0 +1,9 @@
+,hit_rate,avg_age,expected_age
+count,100.0,100.0,100.0
+mean,0.8725120313088975,2.1796613745422433,0.3820956069194319
+std,0.054253138996698945,0.14088494880608413,0.0486454668305639
+min,0.825,1.9614299153339605,0.34031249999999996
+25%,0.8319067910758455,2.0656374896717775,0.34603445452530274
+50%,0.8375154569422085,2.1252243109839615,0.3507162050513173
+75%,0.9093495418588469,2.2777534596124283,0.41345830570351416
+max,0.9978256165047042,2.521853655548409,0.49782798047649657

00_aoi_caching_simulation/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.

00_aoi_caching_simulation/experiments/avg_ages.md

@@ -0,0 +1,8 @@
+|      |   avg_ages |
+|:-----|-----------:|
+| 0.5s |   0.113919 |
+| 1.0s |   0.305873 |
+| 2.0s |   0.725355 |
+| 3.0s |   1.22305  |
+| 4.0s |   1.70255  |
+| 5.0s |   2.17966  |

00_aoi_caching_simulation/experiments/hit_rates.md

@@ -0,0 +1,8 @@
+|      |   hit_rates |
+|:-----|------------:|
+| 0.5s |    0.459811 |
+| 1.0s |    0.610659 |
+| 2.0s |    0.72776  |
+| 3.0s |    0.815904 |
+| 4.0s |    0.852929 |
+| 5.0s |    0.872512 |

00_aoi_caching_simulation/note.md

@@ -0,0 +1,97 @@
+# Age Caching Simulation
+
+Client -> TTL Cache -> Database
+Capacity C (C = n (example: 100))
+
+TTL increases on cache hit
+Age of information / Age of the entry in the cache
+Database has latest object, cache entry may be old (we don't know)
+
+Age of entry should have low age of information
+
+Update function from cache to refresh based on mu (refresh rate)
+
+Loss function based on TTL and age in cache called beta(i)
+
+Event based simulation
+
+lambda(i) is zipf distribution describing the rate the client requests the object "i"
+
+Inter arrival time of each object => exponential
+
+Hit rate and the average age of the object based on TTL
+
+## Notes 11/13/2024
+
+### Limitations of time
+
+Run the simulation not time based but based on when the least ranked object is requested
+at least times for example.
+Least ranked object -> least zipf value
+
+2-3h
+
+### mu
+
+Simulate lambda and mu to see what increases the cost function
+
+### Bandwidth
+
+Finite bandwidth between cache and server
+miss requests and cache updates should not go over the bandwidth
+
+### Two versions
+
+- [x] Default
+
+  - Do Refresh
+  - Do Request
+
+- [x] No Refresh
+
+  - Just Request
+  - No Refresh
+
+- [x] Infinite TTL
+
+  - LRU
+  - Infinite TTL
+  - No Refresh
+
+- [x] Random Eviction
+
+  - Random eviction
+  - Regular TTL
+  - With 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
+
+## Notes 11/29/2024
+
+C_m = cost for cache miss
+C_delta = cost for refresh
+C = Cm + C_delta over all objects summarized
+
+We wanna minimize cost function
+
+N = number of objects
+B is cache size
+
+C_f roughly equals C_m