From 032251dd78415936ae0dd8aab9c899524d9d27e5 Mon Sep 17 00:00:00 2001
From: Tuan-Dat Tran <tuan-dat.tran@tudattr.dev>
Date: Thu, 5 Dec 2024 13:48:20 +0100
Subject: [PATCH] refactor/fix(simulation): Added expected hitrate for each
 objct and refactored Cache to handle TTL/non-TTL Cache-Types

Signed-off-by: Tuan-Dat Tran <tuan-dat.tran@tudattr.dev>
---
 .../.aoi_cache/lambda_distribution.pdf        |  Bin 11740 -> 11740 bytes
 .../.aoi_cache/lambda_vs_access_count.pdf     |  Bin 12996 -> 12443 bytes
 .../.aoi_cache/objects_in_cache_over_time.pdf |  Bin 114269 -> 22233 bytes
 .../aoi_cache_simulation.ipynb                | 1113 +++++------------
 4 files changed, 336 insertions(+), 777 deletions(-)

diff --git a/00_aoi_caching_simulation/.aoi_cache/lambda_distribution.pdf b/00_aoi_caching_simulation/.aoi_cache/lambda_distribution.pdf
index 8e8500db35f8a7732d58a69437bf9e84d3319973..36c3e49047ec90816777d92cc2b91833245b0f36 100644
GIT binary patch
delta 19
acmcZ;eJ6SYlNOt)p|Odv;bso4E6e~#-UdDZ

delta 19
acmcZ;eJ6SYlNOtap}C=f@n#OKE6e~#)&@NQ

diff --git a/00_aoi_caching_simulation/.aoi_cache/lambda_vs_access_count.pdf b/00_aoi_caching_simulation/.aoi_cache/lambda_vs_access_count.pdf
index 4a725ab1ec559e55c520532309d3ee04c866846b..824b4bd8030fb9a08d6fc6b1963f5a0b630b3886 100644
GIT binary patch
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diff --git a/00_aoi_caching_simulation/.aoi_cache/objects_in_cache_over_time.pdf b/00_aoi_caching_simulation/.aoi_cache/objects_in_cache_over_time.pdf
index 1fc00702e4b88d0b7b44b9f7e59e5cee23679a9a..931a5142a37272568e1b4449c62c339f0444aed2 100644
GIT binary patch
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diff --git a/00_aoi_caching_simulation/aoi_cache_simulation.ipynb b/00_aoi_caching_simulation/aoi_cache_simulation.ipynb
index c2f0fd8..6d34cb2 100644
--- a/00_aoi_caching_simulation/aoi_cache_simulation.ipynb
+++ b/00_aoi_caching_simulation/aoi_cache_simulation.ipynb
@@ -16,16 +16,18 @@
     "import os\n",
     "import shutil\n",
     "from tqdm import tqdm\n",
+    "import math\n",
     "\n",
     "# Types of cache\n",
     "class CacheType(Enum):\n",
     "    LRU = 1\n",
     "    RANDOM_EVICTION = 2\n",
+    "    TTL = 3\n",
     "\n",
     "# Constants\n",
     "SEED = 42\n",
     "DATABASE_OBJECTS = 100 # Number of objects in the database\n",
-    "ACCESS_COUNT_LIMIT = 2000   # Total time to run the simulation\n",
+    "ACCESS_COUNT_LIMIT = 1000   # Total time to run the simulation\n",
     "EXPERIMENT_BASE_DIR = \"./experiments/\"\n",
     "TEMP_BASE_DIR = \"./.aoi_cache/\"\n",
     "\n",
@@ -93,12 +95,12 @@
     "    \"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",
+    "    \"No Refresh (0.5s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 0.5),\n",
+    "    \"No Refresh (1.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 1),\n",
+    "    \"No Refresh (2.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 2),\n",
+    "    \"No Refresh (3.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 3),\n",
+    "    \"No Refresh (4.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 4),\n",
+    "    \"No Refresh (5.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 5),\n",
     "}\n",
     "\n",
     "experiment_name = \"No Refresh (5.0s ttl)\"\n",
@@ -107,7 +109,11 @@
     "CACHE_CAPACITY = config[0]\n",
     "MAX_REFRESH_RATE = config[1]\n",
     "cache_type = config[2]\n",
-    "CACHE_TTL = config[3]\n"
+    "CACHE_TTL = config[3]\n",
+    "\n",
+    "if cache_type == CacheType.TTL:\n",
+    "    assert CACHE_TTL > 0, \"Needs CACHE_TTL to be greater than 0 when using TTL-Cache.\"\n",
+    "    assert CACHE_CAPACITY >= DATABASE_OBJECTS, \"Cache Size needs to be greater or equal to the amount of Database Objects.\""
    ]
   },
   {
@@ -162,79 +168,90 @@
     "        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",
     "        \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",
+    "        if obj_id in self.storage:\n",
+    "            # Cache hit: Refresh TTL if TTL-Cache\n",
+    "            if self.cache_type == CacheType.TTL:\n",
+    "                if self.ttl[obj_id] > env.now:\n",
+    "                    self.ttl[obj_id] = env.now + CACHE_TTL\n",
+    "                    \n",
     "            # Cache hit: increment hit count and update cumulative age\n",
     "            self.hits[obj_id] += 1\n",
-    "            self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n",
     "            self.access_count[obj_id] += 1\n",
+    "            age = (env.now - self.initial_fetch[obj_id])\n",
+    "            self.cumulative_age[obj_id] += age\n",
+    "\n",
+    "            # Cache hit: Refresh database object\n",
+    "            # self.initial_fetch[obj_id] = env.now\n",
     "        else:\n",
+    "            assert obj_id not in self.storage.keys(), \"Found object in cache on miss.\"\n",
+    "            assert obj_id not in self.initial_fetch.keys(), \"Found age timer on miss.\"\n",
+    "            assert obj_id not in self.object_start_time.keys(), \"Found cache time ratio timer on miss.\"\n",
+    "            # Cache miss: Add TTL if TTL-Cache\n",
+    "            # When full cache: If non-TTL-Cache: Evict. If TTL-Cache: Don't add to Cache.\n",
+    "            if self.cache_type == CacheType.TTL:\n",
+    "                assert obj_id not in self.ttl.keys(), \"Found cache time ratio timer on miss.\"\n",
+    "                self.ttl[obj_id] = env.now + CACHE_TTL\n",
+    "            else:\n",
+    "                if len(self.storage) == DATABASE_OBJECTS:\n",
+    "                    if self.cache_type == CacheType.LRU:\n",
+    "                        self.evict_oldest()\n",
+    "                    elif self.cache_type == CacheType.RANDOM_EVICTION:\n",
+    "                        self.evict_random()\n",
+    "                    elif self.cache-type == CacheType.TTL:\n",
+    "                        return\n",
+    "            \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",
+    "            self.cumulative_age[obj_id] += 0\n",
+    "            \n",
+    "            # Cache miss: Fetch the object from the database\n",
+    "            self.storage[obj_id] = self.db.get_object(obj_id)\n",
     "            self.initial_fetch[obj_id] = env.now\n",
     "            self.object_start_time[obj_id] = env.now\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",
     "            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 = min(self.initial_fetch, key=self.initial_fetch.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",
+    "        self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
     "        del self.storage[oldest_id]\n",
-    "        del self.ttl[oldest_id]\n",
     "        del self.initial_fetch[oldest_id]\n",
+    "        del self.object_start_time[obj_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",
+    "        self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
     "        del self.storage[random_id]\n",
-    "        del self.ttl[random_id]\n",
     "        del self.initial_fetch[random_id]\n",
+    "        del self.object_start_time[obj_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",
+    "        if self.cache_type == CacheType.TTL:\n",
     "            self.ttl[obj_id] = env.now + CACHE_TTL\n",
-    "        else:\n",
-    "            self.ttl[obj_id] = 0\n",
-    "        self.initial_fetch[obj_id] = env.now\n",
+    "        self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
     "        # print(f\"[{env.now:.2f}] Cache: Refreshed object {obj_id}\")\n",
     "        \n",
     "    def check_expired(self):\n",
     "        \"\"\"Increment age of each cached object.\"\"\"\n",
-    "        for obj_id in list(self.ttl.keys()):\n",
-    "            # print(f\"[{env.now:.2f}] Cache: Object {obj_id} aged to {env.now-self.initial_fetch[obj_id]}\")\n",
-    "            if CACHE_TTL != 0 and self.ttl[obj_id] <= env.now:\n",
-    "                # Remove object if its TTL expired\n",
-    "                # print(f\"[{env.now:.2f}] Cache: Object {obj_id} expired\")\n",
-    "                self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
-    "                del self.storage[obj_id]\n",
-    "                del self.ttl[obj_id]\n",
-    "                del self.initial_fetch[obj_id]\n",
-    "                del self.object_start_time[obj_id]\n",
+    "        if self.cache_type == CacheType.TTL:\n",
+    "            for obj_id in list(self.ttl.keys()):\n",
+    "                if self.ttl[obj_id] <= env.now:\n",
+    "                    # Remove object if its TTL expired\n",
+    "                    # print(f\"[{env.now:.2f}] Cache: Object {obj_id} expired\")\n",
+    "                    self.cumulative_cache_time[obj_id] += (env.now - self.object_start_time[obj_id])\n",
+    "                    del self.storage[obj_id]\n",
+    "                    del self.ttl[obj_id]\n",
+    "                    del self.initial_fetch[obj_id]\n",
+    "                    del self.object_start_time[obj_id]\n",
     "\n",
     "                \n",
     "    def record_cache_state(self):\n",
@@ -254,7 +271,7 @@
     "    \"\"\"Process that ages cache objects over time, removes expired items, and refreshes based on object-specific intervals.\"\"\"\n",
     "    last_full_second = 0\n",
     "    while True:\n",
-    "        cache.check_expired()  # Age objects and remove expired ones\n",
+    "        cache.check_expired()  # Remove expired objects\n",
     "\n",
     "        if MAX_REFRESH_RATE != 0:\n",
     "            # Refresh objects based on their individual refresh intervals\n",
@@ -283,10 +300,13 @@
     "        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",
+    "\n",
+    "            # For progress bar\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",
+    "                \n",
     "            if env.now >= next_request:\n",
     "                # print(f\"[{env.now:.2f}] Client: Requesting object {obj_id}\")\n",
     "                cache.get(obj_id)\n",
@@ -295,8 +315,12 @@
     "                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(env.now)\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()"
    ]
   },
@@ -330,16 +354,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Progress: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉| 1999/2000 [00:10<00:00, 183.69it/s]"
+      "Progress: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:05<00:00, 182.11it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2114.009548152859\n",
-      "CPU times: user 9.85 s, sys: 1.31 s, total: 11.2 s\n",
-      "Wall time: 10.9 s\n"
+      "Simulation ended after 1056.5430396768973 seconds.\n",
+      "CPU times: user 5.01 s, sys: 614 ms, total: 5.63 s\n",
+      "Wall time: 5.5 s\n"
      ]
     }
    ],
@@ -365,106 +389,106 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Object 1: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.07, Exprected Age = 0.35\n",
-      "Object 2: Hit Rate = 0.94, Average Time spend in Cache: 0.87,Average Age = 2.32, Exprected Age = 0.44\n",
-      "Object 3: Hit Rate = 0.84, Average Time spend in Cache: 0.82,Average Age = 2.08, Exprected Age = 0.35\n",
-      "Object 4: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.10, Exprected Age = 0.35\n",
-      "Object 5: Hit Rate = 0.91, Average Time spend in Cache: 0.87,Average Age = 2.26, Exprected Age = 0.41\n",
-      "Object 6: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.09, Exprected Age = 0.35\n",
-      "Object 7: Hit Rate = 0.96, Average Time spend in Cache: 0.87,Average Age = 2.40, Exprected Age = 0.46\n",
-      "Object 8: Hit Rate = 0.84, Average Time spend in Cache: 0.82,Average Age = 2.07, Exprected Age = 0.35\n",
-      "Object 9: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.03, Exprected Age = 0.35\n",
-      "Object 10: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.04, Exprected Age = 0.35\n",
-      "Object 11: Hit Rate = 0.91, Average Time spend in Cache: 0.89,Average Age = 2.26, Exprected Age = 0.41\n",
-      "Object 12: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.04, Exprected Age = 0.35\n",
-      "Object 13: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.10, Exprected Age = 0.35\n",
-      "Object 14: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.07, Exprected Age = 0.34\n",
-      "Object 15: Hit Rate = 0.91, Average Time spend in Cache: 0.86,Average Age = 2.25, Exprected Age = 0.41\n",
-      "Object 16: Hit Rate = 0.91, Average Time spend in Cache: 0.87,Average Age = 2.28, Exprected Age = 0.42\n",
-      "Object 17: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.01, Exprected Age = 0.35\n",
-      "Object 18: Hit Rate = 0.83, Average Time spend in Cache: 0.80,Average Age = 2.13, Exprected Age = 0.35\n",
-      "Object 19: Hit Rate = 0.94, Average Time spend in Cache: 0.87,Average Age = 2.32, Exprected Age = 0.44\n",
-      "Object 20: Hit Rate = 0.83, Average Time spend in Cache: 0.80,Average Age = 2.04, Exprected Age = 0.35\n",
-      "Object 21: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.07, Exprected Age = 0.35\n",
-      "Object 22: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.09, Exprected Age = 0.35\n",
-      "Object 23: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.08, Exprected Age = 0.35\n",
-      "Object 24: Hit Rate = 0.91, Average Time spend in Cache: 0.88,Average Age = 2.28, Exprected Age = 0.41\n",
-      "Object 25: Hit Rate = 0.84, Average Time spend in Cache: 0.80,Average Age = 2.08, Exprected Age = 0.35\n",
-      "Object 26: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.07, Exprected Age = 0.35\n",
-      "Object 27: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.15, Exprected Age = 0.35\n",
-      "Object 28: Hit Rate = 0.96, Average Time spend in Cache: 0.86,Average Age = 2.41, Exprected Age = 0.46\n",
-      "Object 29: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.08, Exprected Age = 0.35\n",
-      "Object 30: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.07, Exprected Age = 0.35\n",
-      "Object 31: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.06, Exprected Age = 0.34\n",
-      "Object 32: Hit Rate = 0.95, Average Time spend in Cache: 0.86,Average Age = 2.36, Exprected Age = 0.45\n",
-      "Object 33: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.12, Exprected Age = 0.35\n",
-      "Object 34: Hit Rate = 0.95, Average Time spend in Cache: 0.87,Average Age = 2.37, Exprected Age = 0.45\n",
-      "Object 35: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.05, Exprected Age = 0.35\n",
-      "Object 36: Hit Rate = 0.83, Average Time spend in Cache: 0.83,Average Age = 2.02, Exprected Age = 0.34\n",
-      "Object 37: Hit Rate = 0.84, Average Time spend in Cache: 0.82,Average Age = 2.05, Exprected Age = 0.35\n",
-      "Object 38: Hit Rate = 0.94, Average Time spend in Cache: 0.87,Average Age = 2.36, Exprected Age = 0.44\n",
-      "Object 39: Hit Rate = 0.98, Average Time spend in Cache: 0.76,Average Age = 2.44, Exprected Age = 0.48\n",
-      "Object 40: Hit Rate = 0.83, Average Time spend in Cache: 0.79,Average Age = 2.07, Exprected Age = 0.34\n",
-      "Object 41: Hit Rate = 0.95, Average Time spend in Cache: 0.85,Average Age = 2.38, Exprected Age = 0.45\n",
-      "Object 42: Hit Rate = 0.95, Average Time spend in Cache: 0.86,Average Age = 2.37, Exprected Age = 0.45\n",
-      "Object 43: Hit Rate = 0.91, Average Time spend in Cache: 0.88,Average Age = 2.24, Exprected Age = 0.41\n",
-      "Object 44: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.04, Exprected Age = 0.34\n",
-      "Object 45: Hit Rate = 0.83, Average Time spend in Cache: 0.83,Average Age = 2.11, Exprected Age = 0.35\n",
-      "Object 46: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.12, Exprected Age = 0.35\n",
-      "Object 47: Hit Rate = 0.98, Average Time spend in Cache: 0.78,Average Age = 2.45, Exprected Age = 0.48\n",
-      "Object 48: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.14, Exprected Age = 0.35\n",
-      "Object 49: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.12, Exprected Age = 0.35\n",
-      "Object 50: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.06, Exprected Age = 0.35\n",
-      "Object 51: Hit Rate = 0.96, Average Time spend in Cache: 0.87,Average Age = 2.41, Exprected Age = 0.46\n",
-      "Object 52: Hit Rate = 0.98, Average Time spend in Cache: 0.81,Average Age = 2.46, Exprected Age = 0.48\n",
-      "Object 53: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.06, Exprected Age = 0.35\n",
-      "Object 54: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.05, Exprected Age = 0.34\n",
-      "Object 55: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.09, Exprected Age = 0.34\n",
-      "Object 56: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.07, Exprected Age = 0.34\n",
-      "Object 57: Hit Rate = 0.84, Average Time spend in Cache: 0.83,Average Age = 2.07, Exprected Age = 0.35\n",
-      "Object 58: Hit Rate = 0.99, Average Time spend in Cache: 0.68,Average Age = 2.46, Exprected Age = 0.49\n",
-      "Object 59: Hit Rate = 0.91, Average Time spend in Cache: 0.87,Average Age = 2.23, Exprected Age = 0.41\n",
-      "Object 60: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.07, Exprected Age = 0.35\n",
-      "Object 61: Hit Rate = 0.99, Average Time spend in Cache: 0.57,Average Age = 2.47, Exprected Age = 0.49\n",
-      "Object 62: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.07, Exprected Age = 0.34\n",
-      "Object 63: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.08, Exprected Age = 0.35\n",
-      "Object 64: Hit Rate = 0.91, Average Time spend in Cache: 0.87,Average Age = 2.27, Exprected Age = 0.41\n",
-      "Object 65: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.06, Exprected Age = 0.35\n",
-      "Object 66: Hit Rate = 0.98, Average Time spend in Cache: 0.78,Average Age = 2.46, Exprected Age = 0.48\n",
-      "Object 67: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.05, Exprected Age = 0.35\n",
-      "Object 68: Hit Rate = 1.00, Average Time spend in Cache: 0.29,Average Age = 2.49, Exprected Age = 0.50\n",
-      "Object 69: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.04, Exprected Age = 0.34\n",
-      "Object 70: Hit Rate = 0.83, Average Time spend in Cache: 0.83,Average Age = 2.06, Exprected Age = 0.35\n",
-      "Object 71: Hit Rate = 0.91, Average Time spend in Cache: 0.84,Average Age = 2.25, Exprected Age = 0.41\n",
-      "Object 72: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.09, Exprected Age = 0.35\n",
-      "Object 73: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.10, Exprected Age = 0.35\n",
-      "Object 74: Hit Rate = 0.84, Average Time spend in Cache: 0.82,Average Age = 2.06, Exprected Age = 0.35\n",
-      "Object 75: Hit Rate = 0.94, Average Time spend in Cache: 0.88,Average Age = 2.31, Exprected Age = 0.44\n",
-      "Object 76: Hit Rate = 0.91, Average Time spend in Cache: 0.86,Average Age = 2.28, Exprected Age = 0.41\n",
-      "Object 77: Hit Rate = 0.91, Average Time spend in Cache: 0.87,Average Age = 2.25, Exprected Age = 0.42\n",
-      "Object 78: Hit Rate = 0.94, Average Time spend in Cache: 0.88,Average Age = 2.32, Exprected Age = 0.44\n",
-      "Object 79: Hit Rate = 0.99, Average Time spend in Cache: 0.71,Average Age = 2.46, Exprected Age = 0.49\n",
-      "Object 80: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.10, Exprected Age = 0.35\n",
-      "Object 81: Hit Rate = 0.83, Average Time spend in Cache: 0.80,Average Age = 2.06, Exprected Age = 0.34\n",
-      "Object 82: Hit Rate = 0.96, Average Time spend in Cache: 0.85,Average Age = 2.41, Exprected Age = 0.46\n",
-      "Object 83: Hit Rate = 0.91, Average Time spend in Cache: 0.87,Average Age = 2.32, Exprected Age = 0.41\n",
-      "Object 84: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.05, Exprected Age = 0.35\n",
-      "Object 85: Hit Rate = 0.83, Average Time spend in Cache: 0.82,Average Age = 2.06, Exprected Age = 0.35\n",
-      "Object 86: Hit Rate = 0.91, Average Time spend in Cache: 0.86,Average Age = 2.28, Exprected Age = 0.41\n",
-      "Object 87: Hit Rate = 0.84, Average Time spend in Cache: 0.82,Average Age = 2.09, Exprected Age = 0.35\n",
-      "Object 88: Hit Rate = 0.91, Average Time spend in Cache: 0.86,Average Age = 2.24, Exprected Age = 0.41\n",
-      "Object 89: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.04, Exprected Age = 0.35\n",
-      "Object 90: Hit Rate = 0.84, Average Time spend in Cache: 0.81,Average Age = 2.08, Exprected Age = 0.35\n",
-      "Object 91: Hit Rate = 0.91, Average Time spend in Cache: 0.88,Average Age = 2.26, Exprected Age = 0.41\n",
-      "Object 92: Hit Rate = 0.91, Average Time spend in Cache: 0.84,Average Age = 2.28, Exprected Age = 0.41\n",
-      "Object 93: Hit Rate = 0.94, Average Time spend in Cache: 0.89,Average Age = 2.38, Exprected Age = 0.44\n",
-      "Object 94: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.09, Exprected Age = 0.34\n",
-      "Object 95: Hit Rate = 0.91, Average Time spend in Cache: 0.87,Average Age = 2.25, Exprected Age = 0.41\n",
-      "Object 96: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.04, Exprected Age = 0.35\n",
-      "Object 97: Hit Rate = 0.83, Average Time spend in Cache: 0.81,Average Age = 2.08, Exprected Age = 0.35\n",
-      "Object 98: Hit Rate = 0.99, Average Time spend in Cache: 0.47,Average Age = 2.48, Exprected Age = 0.49\n",
-      "Object 99: Hit Rate = 0.95, Average Time spend in Cache: 0.86,Average Age = 2.38, Exprected Age = 0.45\n",
-      "Object 100: Hit Rate = 0.91, Average Time spend in Cache: 0.87,Average Age = 2.26, Exprected Age = 0.41\n"
+      "Object 1: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 86.27, Expected Age = 0.49\n",
+      "Object 2: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 518.48, Expected Age = 0.50\n",
+      "Object 3: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 130.46, Expected Age = 0.49\n",
+      "Object 4: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 65.80, Expected Age = 0.49\n",
+      "Object 5: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 516.46, Expected Age = 0.50\n",
+      "Object 6: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 88.47, Expected Age = 0.49\n",
+      "Object 7: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 525.33, Expected Age = 0.50\n",
+      "Object 8: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 144.73, Expected Age = 0.49\n",
+      "Object 9: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 113.08, Expected Age = 0.49\n",
+      "Object 10: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 69.91, Expected Age = 0.49\n",
+      "Object 11: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 545.38, Expected Age = 0.50\n",
+      "Object 12: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 85.16, Expected Age = 0.49\n",
+      "Object 13: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 86.42, Expected Age = 0.49\n",
+      "Object 14: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 89.83, Expected Age = 0.49\n",
+      "Object 15: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 518.96, Expected Age = 0.50\n",
+      "Object 16: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 540.84, Expected Age = 0.50\n",
+      "Object 17: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 122.01, Expected Age = 0.49\n",
+      "Object 18: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 134.20, Expected Age = 0.49\n",
+      "Object 19: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 526.43, Expected Age = 0.50\n",
+      "Object 20: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 75.73, Expected Age = 0.49\n",
+      "Object 21: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 95.90, Expected Age = 0.49\n",
+      "Object 22: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 63.96, Expected Age = 0.49\n",
+      "Object 23: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 139.96, Expected Age = 0.49\n",
+      "Object 24: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 528.02, Expected Age = 0.50\n",
+      "Object 25: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 122.85, Expected Age = 0.49\n",
+      "Object 26: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 96.64, Expected Age = 0.49\n",
+      "Object 27: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 79.15, Expected Age = 0.49\n",
+      "Object 28: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 532.88, Expected Age = 0.50\n",
+      "Object 29: Hit Rate = 1.00, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 203.43, Expected Age = 0.50\n",
+      "Object 30: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 128.23, Expected Age = 0.49\n",
+      "Object 31: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 145.19, Expected Age = 0.49\n",
+      "Object 32: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 533.60, Expected Age = 0.50\n",
+      "Object 33: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 134.02, Expected Age = 0.49\n",
+      "Object 34: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 524.09, Expected Age = 0.50\n",
+      "Object 35: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 116.60, Expected Age = 0.49\n",
+      "Object 36: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 134.98, Expected Age = 0.49\n",
+      "Object 37: Hit Rate = 1.00, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 174.10, Expected Age = 0.50\n",
+      "Object 38: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 531.44, Expected Age = 0.50\n",
+      "Object 39: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 529.81, Expected Age = 0.50\n",
+      "Object 40: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 79.26, Expected Age = 0.49\n",
+      "Object 41: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 529.68, Expected Age = 0.50\n",
+      "Object 42: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 525.51, Expected Age = 0.50\n",
+      "Object 43: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 534.66, Expected Age = 0.50\n",
+      "Object 44: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 222.32, Expected Age = 0.49\n",
+      "Object 45: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 71.63, Expected Age = 0.49\n",
+      "Object 46: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 95.27, Expected Age = 0.49\n",
+      "Object 47: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 530.73, Expected Age = 0.50\n",
+      "Object 48: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 142.18, Expected Age = 0.49\n",
+      "Object 49: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 70.83, Expected Age = 0.49\n",
+      "Object 50: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 83.70, Expected Age = 0.49\n",
+      "Object 51: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 528.00, Expected Age = 0.50\n",
+      "Object 52: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 527.27, Expected Age = 0.50\n",
+      "Object 53: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 150.62, Expected Age = 0.49\n",
+      "Object 54: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 95.04, Expected Age = 0.49\n",
+      "Object 55: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 120.14, Expected Age = 0.49\n",
+      "Object 56: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 106.77, Expected Age = 0.49\n",
+      "Object 57: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 144.58, Expected Age = 0.49\n",
+      "Object 58: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 525.37, Expected Age = 0.50\n",
+      "Object 59: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 530.54, Expected Age = 0.50\n",
+      "Object 60: Hit Rate = 1.00, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 132.33, Expected Age = 0.50\n",
+      "Object 61: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 530.68, Expected Age = 0.50\n",
+      "Object 62: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 63.55, Expected Age = 0.49\n",
+      "Object 63: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 81.29, Expected Age = 0.49\n",
+      "Object 64: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 522.91, Expected Age = 0.50\n",
+      "Object 65: Hit Rate = 1.00, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 159.42, Expected Age = 0.50\n",
+      "Object 66: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 529.66, Expected Age = 0.50\n",
+      "Object 67: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 74.62, Expected Age = 0.49\n",
+      "Object 68: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 529.53, Expected Age = 0.50\n",
+      "Object 69: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 136.06, Expected Age = 0.49\n",
+      "Object 70: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 98.80, Expected Age = 0.49\n",
+      "Object 71: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 532.41, Expected Age = 0.50\n",
+      "Object 72: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 140.26, Expected Age = 0.49\n",
+      "Object 73: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 86.30, Expected Age = 0.49\n",
+      "Object 74: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 140.32, Expected Age = 0.49\n",
+      "Object 75: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 527.98, Expected Age = 0.50\n",
+      "Object 76: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 530.24, Expected Age = 0.50\n",
+      "Object 77: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 259.58, Expected Age = 0.50\n",
+      "Object 78: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 528.38, Expected Age = 0.50\n",
+      "Object 79: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 525.00, Expected Age = 0.50\n",
+      "Object 80: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 70.65, Expected Age = 0.49\n",
+      "Object 81: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 142.99, Expected Age = 0.49\n",
+      "Object 82: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 528.39, Expected Age = 0.50\n",
+      "Object 83: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 527.83, Expected Age = 0.50\n",
+      "Object 84: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 127.04, Expected Age = 0.49\n",
+      "Object 85: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 133.20, Expected Age = 0.49\n",
+      "Object 86: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 538.40, Expected Age = 0.50\n",
+      "Object 87: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 94.57, Expected Age = 0.49\n",
+      "Object 88: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 530.51, Expected Age = 0.50\n",
+      "Object 89: Hit Rate = 1.00, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 131.36, Expected Age = 0.50\n",
+      "Object 90: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 104.07, Expected Age = 0.49\n",
+      "Object 91: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 525.81, Expected Age = 0.50\n",
+      "Object 92: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 530.95, Expected Age = 0.50\n",
+      "Object 93: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 524.21, Expected Age = 0.50\n",
+      "Object 94: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 1.00, Average Age = 137.10, Expected Age = 0.49\n",
+      "Object 95: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 533.23, Expected Age = 0.50\n",
+      "Object 96: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.98, Average Age = 82.53, Expected Age = 0.49\n",
+      "Object 97: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.98, Average Age = 40.14, Expected Age = 0.49\n",
+      "Object 98: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 527.31, Expected Age = 0.50\n",
+      "Object 99: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 524.64, Expected Age = 0.50\n",
+      "Object 100: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 526.64, Expected Age = 0.50\n"
      ]
     }
    ],
@@ -474,538 +498,17 @@
     "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.access_count[obj_id])\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",
-    "        expected_age = (0.5*pow(hit_rate,2))\n",
-    "        print(f\"Object {obj_id}: Hit Rate = {hit_rate:.2f}, Average Time spend in Cache: {avg_cache_time:.2f},Average Age = {avg_age:.2f}, Exprected Age = {expected_age:.2f}\")\n",
-    "        statistics.append({\"obj_id\": obj_id,\"hit_rate\": hit_rate, \"avg_cache_time\":avg_cache_time, \"avg_age\": avg_age, \"expected_age\": expected_age})"
+    "        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_hitrate\": expected_hit_rate, \"avg_cache_time\":avg_cache_time, \"avg_age\": avg_age, \"expected_age\": expected_age})"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "3f9f5442-dee5-4545-b7b0-6a716e9d943b",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'obj_id': 1,\n",
-       "  'hit_rate': 0.8343359555761222,\n",
-       "  'avg_cache_time': {0.810432254071845},\n",
-       "  'avg_age': 2.068923674198366,\n",
-       "  'expected_age': 0.34805824338356045},\n",
-       " {'obj_id': 2,\n",
-       "  'hit_rate': 0.9368521766863339,\n",
-       "  'avg_cache_time': {0.8697853571068594},\n",
-       "  'avg_age': 2.3207402902916767,\n",
-       "  'expected_age': 0.4388460004809609},\n",
-       " {'obj_id': 3,\n",
-       "  'hit_rate': 0.8387391502969392,\n",
-       "  'avg_cache_time': {0.8200673629152411},\n",
-       "  'avg_age': 2.083112933481689,\n",
-       "  'expected_age': 0.3517416811204158},\n",
-       " {'obj_id': 4,\n",
-       "  'hit_rate': 0.8345864661654135,\n",
-       "  'avg_cache_time': {0.817667207716411},\n",
-       "  'avg_age': 2.098241838496266,\n",
-       "  'expected_age': 0.3482672847532365},\n",
-       " {'obj_id': 5,\n",
-       "  'hit_rate': 0.9079009995240361,\n",
-       "  'avg_cache_time': {0.8745472033792867},\n",
-       "  'avg_age': 2.2637599890084745,\n",
-       "  'expected_age': 0.41214211246837196},\n",
-       " {'obj_id': 6,\n",
-       "  'hit_rate': 0.8327790973871734,\n",
-       "  'avg_cache_time': {0.8080445983334636},\n",
-       "  'avg_age': 2.0885926647923605,\n",
-       "  'expected_age': 0.3467605125224976},\n",
-       " {'obj_id': 7,\n",
-       "  'hit_rate': 0.9611706197398623,\n",
-       "  'avg_cache_time': {0.8721183854707718},\n",
-       "  'avg_age': 2.396789243995202,\n",
-       "  'expected_age': 0.4619244801255555},\n",
-       " {'obj_id': 8,\n",
-       "  'hit_rate': 0.8350230414746543,\n",
-       "  'avg_cache_time': {0.8224079743937736},\n",
-       "  'avg_age': 2.0748384445301578,\n",
-       "  'expected_age': 0.34863173989679114},\n",
-       " {'obj_id': 9,\n",
-       "  'hit_rate': 0.8310523831996225,\n",
-       "  'avg_cache_time': {0.808204836664193},\n",
-       "  'avg_age': 2.030852029418002,\n",
-       "  'expected_age': 0.3453240318108861},\n",
-       " {'obj_id': 10,\n",
-       "  'hit_rate': 0.8308288899660689,\n",
-       "  'avg_cache_time': {0.8177615771744638},\n",
-       "  'avg_age': 2.0440294768985754,\n",
-       "  'expected_age': 0.34513832220112506},\n",
-       " {'obj_id': 11,\n",
-       "  'hit_rate': 0.9088757396449704,\n",
-       "  'avg_cache_time': {0.891248201160126},\n",
-       "  'avg_age': 2.2625636303653898,\n",
-       "  'expected_age': 0.413027555057596},\n",
-       " {'obj_id': 12,\n",
-       "  'hit_rate': 0.8373071528751753,\n",
-       "  'avg_cache_time': {0.8102551278956152},\n",
-       "  'avg_age': 2.0383876738470725,\n",
-       "  'expected_age': 0.35054163412796613},\n",
-       " {'obj_id': 13,\n",
-       "  'hit_rate': 0.8361344537815126,\n",
-       "  'avg_cache_time': {0.8105750271598551},\n",
-       "  'avg_age': 2.0954043404028435,\n",
-       "  'expected_age': 0.34956041240025426},\n",
-       " {'obj_id': 14,\n",
-       "  'hit_rate': 0.8290556900726392,\n",
-       "  'avg_cache_time': {0.8201096214390584},\n",
-       "  'avg_age': 2.0706796740450875,\n",
-       "  'expected_age': 0.34366666862091},\n",
-       " {'obj_id': 15,\n",
-       "  'hit_rate': 0.908745247148289,\n",
-       "  'avg_cache_time': {0.8605605035087328},\n",
-       "  'avg_age': 2.2466123908950646,\n",
-       "  'expected_age': 0.4129089621073024},\n",
-       " {'obj_id': 16,\n",
-       "  'hit_rate': 0.9110956360259982,\n",
-       "  'avg_cache_time': {0.8747680644435698},\n",
-       "  'avg_age': 2.2834548671334542,\n",
-       "  'expected_age': 0.41504762899280906},\n",
-       " {'obj_id': 17,\n",
-       "  'hit_rate': 0.8320683111954459,\n",
-       "  'avg_cache_time': {0.8150399870989544},\n",
-       "  'avg_age': 2.0097170455667013,\n",
-       "  'expected_age': 0.3461688372478207},\n",
-       " {'obj_id': 18,\n",
-       "  'hit_rate': 0.8342067651262506,\n",
-       "  'avg_cache_time': {0.8032009912464316},\n",
-       "  'avg_age': 2.1273968056684316,\n",
-       "  'expected_age': 0.3479504634912017},\n",
-       " {'obj_id': 19,\n",
-       "  'hit_rate': 0.9354469684588985,\n",
-       "  'avg_cache_time': {0.8651012574814502},\n",
-       "  'avg_age': 2.3206810673715705,\n",
-       "  'expected_age': 0.43753051539947174},\n",
-       " {'obj_id': 20,\n",
-       "  'hit_rate': 0.833976833976834,\n",
-       "  'avg_cache_time': {0.7986061563297104},\n",
-       "  'avg_age': 2.0432248271474878,\n",
-       "  'expected_age': 0.34775867980501185},\n",
-       " {'obj_id': 21,\n",
-       "  'hit_rate': 0.8348837209302326,\n",
-       "  'avg_cache_time': {0.8152281904411368},\n",
-       "  'avg_age': 2.0677338592418755,\n",
-       "  'expected_age': 0.3485154137371552},\n",
-       " {'obj_id': 22,\n",
-       "  'hit_rate': 0.8338068181818182,\n",
-       "  'avg_cache_time': {0.8174427840452981},\n",
-       "  'avg_age': 2.086382182099578,\n",
-       "  'expected_age': 0.3476169050232438},\n",
-       " {'obj_id': 23,\n",
-       "  'hit_rate': 0.8357510528778662,\n",
-       "  'avg_cache_time': {0.8057637380263669},\n",
-       "  'avg_age': 2.0794074474530007,\n",
-       "  'expected_age': 0.34923991119323095},\n",
-       " {'obj_id': 24,\n",
-       "  'hit_rate': 0.9097920074783828,\n",
-       "  'avg_cache_time': {0.8768806713137},\n",
-       "  'avg_age': 2.277444995774618,\n",
-       "  'expected_age': 0.4138607484357729},\n",
-       " {'obj_id': 25,\n",
-       "  'hit_rate': 0.8363039399624765,\n",
-       "  'avg_cache_time': {0.8033370429558485},\n",
-       "  'avg_age': 2.0831638192632096,\n",
-       "  'expected_age': 0.3497021399983808},\n",
-       " {'obj_id': 26,\n",
-       "  'hit_rate': 0.8382218148487626,\n",
-       "  'avg_cache_time': {0.8105553974196054},\n",
-       "  'avg_age': 2.0739601366722598,\n",
-       "  'expected_age': 0.3513079054441766},\n",
-       " {'obj_id': 27,\n",
-       "  'hit_rate': 0.8346604215456674,\n",
-       "  'avg_cache_time': {0.8174373373929206},\n",
-       "  'avg_age': 2.1512142438129023,\n",
-       "  'expected_age': 0.34832900964739566},\n",
-       " {'obj_id': 28,\n",
-       "  'hit_rate': 0.960876670191291,\n",
-       "  'avg_cache_time': {0.8625135261787757},\n",
-       "  'avg_age': 2.4084789355150056,\n",
-       "  'expected_age': 0.4616419876589515},\n",
-       " {'obj_id': 29,\n",
-       "  'hit_rate': 0.8324705882352941,\n",
-       "  'avg_cache_time': {0.8104802301710875},\n",
-       "  'avg_age': 2.0824021086384605,\n",
-       "  'expected_age': 0.3465036401384083},\n",
-       " {'obj_id': 30,\n",
-       "  'hit_rate': 0.8315191670610507,\n",
-       "  'avg_cache_time': {0.8174875104134197},\n",
-       "  'avg_age': 2.072225068112276,\n",
-       "  'expected_age': 0.34571206259495174},\n",
-       " {'obj_id': 31,\n",
-       "  'hit_rate': 0.8260656540911318,\n",
-       "  'avg_cache_time': {0.8153992880564279},\n",
-       "  'avg_age': 2.061721077642618,\n",
-       "  'expected_age': 0.3411922324345047},\n",
-       " {'obj_id': 32,\n",
-       "  'hit_rate': 0.952234570820525,\n",
-       "  'avg_cache_time': {0.8556777555326739},\n",
-       "  'avg_age': 2.3617840905668293,\n",
-       "  'expected_age': 0.4533753389328747},\n",
-       " {'obj_id': 33,\n",
-       "  'hit_rate': 0.8347378277153558,\n",
-       "  'avg_cache_time': {0.8177898808005426},\n",
-       "  'avg_age': 2.1157873141297703,\n",
-       "  'expected_age': 0.3483936205094755},\n",
-       " {'obj_id': 34,\n",
-       "  'hit_rate': 0.953300196509074,\n",
-       "  'avg_cache_time': {0.8723919138738911},\n",
-       "  'avg_age': 2.37152246660409,\n",
-       "  'expected_age': 0.4543906323321196},\n",
-       " {'obj_id': 35,\n",
-       "  'hit_rate': 0.8371985157699443,\n",
-       "  'avg_cache_time': {0.8056650949556616},\n",
-       "  'avg_age': 2.0546440283833953,\n",
-       "  'expected_age': 0.35045067740369884},\n",
-       " {'obj_id': 36,\n",
-       "  'hit_rate': 0.8293269230769231,\n",
-       "  'avg_cache_time': {0.8321331411343728},\n",
-       "  'avg_age': 2.019798753122858,\n",
-       "  'expected_age': 0.3438915726701184},\n",
-       " {'obj_id': 37,\n",
-       "  'hit_rate': 0.8385178408051235,\n",
-       "  'avg_cache_time': {0.820059027970993},\n",
-       "  'avg_age': 2.0459185671323534,\n",
-       "  'expected_age': 0.3515560846742432},\n",
-       " {'obj_id': 38,\n",
-       "  'hit_rate': 0.9372218690400509,\n",
-       "  'avg_cache_time': {0.8698808303188571},\n",
-       "  'avg_age': 2.3594967138941594,\n",
-       "  'expected_age': 0.43919241590346314},\n",
-       " {'obj_id': 39,\n",
-       "  'hit_rate': 0.9754142169962586,\n",
-       "  'avg_cache_time': {0.7626087843902165},\n",
-       "  'avg_age': 2.4388237909562465,\n",
-       "  'expected_age': 0.47571644735921215},\n",
-       " {'obj_id': 40,\n",
-       "  'hit_rate': 0.8275,\n",
-       "  'avg_cache_time': {0.794355906656446},\n",
-       "  'avg_age': 2.0686563554852295,\n",
-       "  'expected_age': 0.34237812500000003},\n",
-       " {'obj_id': 41,\n",
-       "  'hit_rate': 0.9524259237398182,\n",
-       "  'avg_cache_time': {0.845923081620667},\n",
-       "  'avg_age': 2.3773225761537065,\n",
-       "  'expected_age': 0.453557570105823},\n",
-       " {'obj_id': 42,\n",
-       "  'hit_rate': 0.9532319832888476,\n",
-       "  'avg_cache_time': {0.8603717658690662},\n",
-       "  'avg_age': 2.368518600216613,\n",
-       "  'expected_age': 0.45432560698239494},\n",
-       " {'obj_id': 43,\n",
-       "  'hit_rate': 0.9076295585412668,\n",
-       "  'avg_cache_time': {0.876926816727247},\n",
-       "  'avg_age': 2.244802766787443,\n",
-       "  'expected_age': 0.41189570776890744},\n",
-       " {'obj_id': 44,\n",
-       "  'hit_rate': 0.82810236600676,\n",
-       "  'avg_cache_time': {0.8224668160130034},\n",
-       "  'avg_age': 2.0368413613592855,\n",
-       "  'expected_age': 0.34287676429299696},\n",
-       " {'obj_id': 45,\n",
-       "  'hit_rate': 0.8323863636363636,\n",
-       "  'avg_cache_time': {0.8319088220634105},\n",
-       "  'avg_age': 2.1104292351117517,\n",
-       "  'expected_age': 0.3464335291838843},\n",
-       " {'obj_id': 46,\n",
-       "  'hit_rate': 0.8343443770415305,\n",
-       "  'avg_cache_time': {0.8225991984196005},\n",
-       "  'avg_age': 2.1195374144133803,\n",
-       "  'expected_age': 0.3480652697504098},\n",
-       " {'obj_id': 47,\n",
-       "  'hit_rate': 0.980509111403344,\n",
-       "  'avg_cache_time': {0.7793000913510133},\n",
-       "  'avg_age': 2.4490817341548503,\n",
-       "  'expected_age': 0.4806990587724876},\n",
-       " {'obj_id': 48,\n",
-       "  'hit_rate': 0.8343675417661098,\n",
-       "  'avg_cache_time': {0.8080294162265821},\n",
-       "  'avg_age': 2.136948776052968,\n",
-       "  'expected_age': 0.3480845973764105},\n",
-       " {'obj_id': 49,\n",
-       "  'hit_rate': 0.8318116290245074,\n",
-       "  'avg_cache_time': {0.8130078566363472},\n",
-       "  'avg_age': 2.1232360998461477,\n",
-       "  'expected_age': 0.3459552930902024},\n",
-       " {'obj_id': 50,\n",
-       "  'hit_rate': 0.8326159732185557,\n",
-       "  'avg_cache_time': {0.817458050161407},\n",
-       "  'avg_age': 2.055003674129556,\n",
-       "  'expected_age': 0.3466246794293414},\n",
-       " {'obj_id': 51,\n",
-       "  'hit_rate': 0.9612454770519901,\n",
-       "  'avg_cache_time': {0.8652760189728331},\n",
-       "  'avg_age': 2.4115942850497527,\n",
-       "  'expected_age': 0.46199643357645404},\n",
-       " {'obj_id': 52,\n",
-       "  'hit_rate': 0.9782768777614138,\n",
-       "  'avg_cache_time': {0.8078795902330617},\n",
-       "  'avg_age': 2.457015718981127,\n",
-       "  'expected_age': 0.4785128247813101},\n",
-       " {'obj_id': 53,\n",
-       "  'hit_rate': 0.8317399617590823,\n",
-       "  'avg_cache_time': {0.8224509800875742},\n",
-       "  'avg_age': 2.0588664125261387,\n",
-       "  'expected_age': 0.3458956819934998},\n",
-       " {'obj_id': 54,\n",
-       "  'hit_rate': 0.8293036750483559,\n",
-       "  'avg_cache_time': {0.8081452701769272},\n",
-       "  'avg_age': 2.053967188615532,\n",
-       "  'expected_age': 0.34387229272435454},\n",
-       " {'obj_id': 55,\n",
-       "  'hit_rate': 0.828125,\n",
-       "  'avg_cache_time': {0.8176938747650575},\n",
-       "  'avg_age': 2.085346884674153,\n",
-       "  'expected_age': 0.3428955078125},\n",
-       " {'obj_id': 56,\n",
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-       " {'obj_id': 97,\n",
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-       " {'obj_id': 98,\n",
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-       "  'avg_cache_time': {0.4651534320375863},\n",
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-       " {'obj_id': 99,\n",
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-       "  'avg_cache_time': {0.8627920808474391},\n",
-       "  'avg_age': 2.375694850086415,\n",
-       "  'expected_age': 0.4539553063119159},\n",
-       " {'obj_id': 100,\n",
-       "  'hit_rate': 0.9087917254348848,\n",
-       "  'avg_cache_time': {0.8674273959799007},\n",
-       "  'avg_age': 2.2584215419035614,\n",
-       "  'expected_age': 0.4129512001094576}]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "statistics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
    "id": "b2d18372-cdba-4151-ae32-5bf45466bf94",
    "metadata": {},
    "outputs": [],
@@ -1017,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "80971714-44f1-47db-9e89-85be7c885bde",
    "metadata": {},
    "outputs": [
@@ -1048,6 +551,8 @@
        "      <th>mu</th>\n",
        "      <th>lambda</th>\n",
        "      <th>hit_rate</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",
@@ -1056,63 +561,73 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2161</td>\n",
-       "      <td>1803</td>\n",
-       "      <td>358</td>\n",
+       "      <td>1060</td>\n",
+       "      <td>1048</td>\n",
+       "      <td>12</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.834336</td>\n",
-       "      <td>2.068924</td>\n",
-       "      <td>0.348058</td>\n",
-       "      <td>1.720865</td>\n",
+       "      <td>0.988679</td>\n",
+       "      <td>0.986652</td>\n",
+       "      <td>0.002027</td>\n",
+       "      <td>86.266415</td>\n",
+       "      <td>0.488743</td>\n",
+       "      <td>85.777672</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>6271</td>\n",
-       "      <td>5875</td>\n",
-       "      <td>396</td>\n",
+       "      <td>3141</td>\n",
+       "      <td>3140</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
-       "      <td>0.936852</td>\n",
-       "      <td>2.320740</td>\n",
-       "      <td>0.438846</td>\n",
-       "      <td>1.881894</td>\n",
+       "      <td>0.999682</td>\n",
+       "      <td>0.999596</td>\n",
+       "      <td>0.000086</td>\n",
+       "      <td>518.478962</td>\n",
+       "      <td>0.499682</td>\n",
+       "      <td>517.979280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>2189</td>\n",
-       "      <td>1836</td>\n",
-       "      <td>353</td>\n",
+       "      <td>1060</td>\n",
+       "      <td>1054</td>\n",
+       "      <td>6</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.838739</td>\n",
-       "      <td>2.083113</td>\n",
-       "      <td>0.351742</td>\n",
-       "      <td>1.731371</td>\n",
+       "      <td>0.994340</td>\n",
+       "      <td>0.992451</td>\n",
+       "      <td>0.001888</td>\n",
+       "      <td>130.457732</td>\n",
+       "      <td>0.494356</td>\n",
+       "      <td>129.963377</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2128</td>\n",
-       "      <td>1776</td>\n",
-       "      <td>352</td>\n",
+       "      <td>1053</td>\n",
+       "      <td>1041</td>\n",
+       "      <td>12</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.834586</td>\n",
-       "      <td>2.098242</td>\n",
-       "      <td>0.348267</td>\n",
-       "      <td>1.749975</td>\n",
+       "      <td>0.988604</td>\n",
+       "      <td>0.995039</td>\n",
+       "      <td>-0.006435</td>\n",
+       "      <td>65.802517</td>\n",
+       "      <td>0.488669</td>\n",
+       "      <td>65.313848</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>4202</td>\n",
-       "      <td>3815</td>\n",
-       "      <td>387</td>\n",
+       "      <td>2073</td>\n",
+       "      <td>2072</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
-       "      <td>0.907901</td>\n",
-       "      <td>2.263760</td>\n",
-       "      <td>0.412142</td>\n",
-       "      <td>1.851618</td>\n",
+       "      <td>0.999518</td>\n",
+       "      <td>0.999509</td>\n",
+       "      <td>0.000008</td>\n",
+       "      <td>516.463009</td>\n",
+       "      <td>0.499518</td>\n",
+       "      <td>515.963491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1125,103 +640,115 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96</th>\n",
-       "      <td>2101</td>\n",
-       "      <td>1750</td>\n",
-       "      <td>351</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>1015</td>\n",
+       "      <td>11</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.832937</td>\n",
-       "      <td>2.038627</td>\n",
-       "      <td>0.346892</td>\n",
-       "      <td>1.691735</td>\n",
+       "      <td>0.989279</td>\n",
+       "      <td>0.983446</td>\n",
+       "      <td>0.005833</td>\n",
+       "      <td>82.531337</td>\n",
+       "      <td>0.489336</td>\n",
+       "      <td>82.042001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97</th>\n",
-       "      <td>2057</td>\n",
-       "      <td>1709</td>\n",
-       "      <td>348</td>\n",
+       "      <td>1015</td>\n",
+       "      <td>1001</td>\n",
+       "      <td>14</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.830822</td>\n",
-       "      <td>2.078611</td>\n",
-       "      <td>0.345132</td>\n",
-       "      <td>1.733479</td>\n",
+       "      <td>0.986207</td>\n",
+       "      <td>0.984133</td>\n",
+       "      <td>0.002074</td>\n",
+       "      <td>40.135630</td>\n",
+       "      <td>0.486302</td>\n",
+       "      <td>39.649328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>98</th>\n",
-       "      <td>78225</td>\n",
-       "      <td>77804</td>\n",
-       "      <td>421</td>\n",
+       "      <td>39278</td>\n",
+       "      <td>39277</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>37</td>\n",
-       "      <td>0.994618</td>\n",
-       "      <td>2.481104</td>\n",
-       "      <td>0.494633</td>\n",
-       "      <td>1.986472</td>\n",
+       "      <td>0.999975</td>\n",
+       "      <td>0.999964</td>\n",
+       "      <td>0.000011</td>\n",
+       "      <td>527.312192</td>\n",
+       "      <td>0.499975</td>\n",
+       "      <td>526.812217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99</th>\n",
-       "      <td>8546</td>\n",
-       "      <td>8143</td>\n",
-       "      <td>403</td>\n",
+       "      <td>4158</td>\n",
+       "      <td>4157</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>4</td>\n",
-       "      <td>0.952843</td>\n",
-       "      <td>2.375695</td>\n",
-       "      <td>0.453955</td>\n",
-       "      <td>1.921740</td>\n",
+       "      <td>0.999759</td>\n",
+       "      <td>0.999997</td>\n",
+       "      <td>-0.000237</td>\n",
+       "      <td>524.637562</td>\n",
+       "      <td>0.499760</td>\n",
+       "      <td>524.137803</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100</th>\n",
-       "      <td>4254</td>\n",
-       "      <td>3866</td>\n",
-       "      <td>388</td>\n",
+       "      <td>2084</td>\n",
+       "      <td>2083</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
-       "      <td>0.908792</td>\n",
-       "      <td>2.258422</td>\n",
-       "      <td>0.412951</td>\n",
-       "      <td>1.845470</td>\n",
+       "      <td>0.999520</td>\n",
+       "      <td>0.999942</td>\n",
+       "      <td>-0.000421</td>\n",
+       "      <td>526.641909</td>\n",
+       "      <td>0.499520</td>\n",
+       "      <td>526.142389</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>100 rows × 9 columns</p>\n",
+       "<p>100 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     access_count   hits  misses  mu  lambda  hit_rate   avg_age  \\\n",
-       "1            2161   1803     358   0       1  0.834336  2.068924   \n",
-       "2            6271   5875     396   0       3  0.936852  2.320740   \n",
-       "3            2189   1836     353   0       1  0.838739  2.083113   \n",
-       "4            2128   1776     352   0       1  0.834586  2.098242   \n",
-       "5            4202   3815     387   0       2  0.907901  2.263760   \n",
-       "..            ...    ...     ...  ..     ...       ...       ...   \n",
-       "96           2101   1750     351   0       1  0.832937  2.038627   \n",
-       "97           2057   1709     348   0       1  0.830822  2.078611   \n",
-       "98          78225  77804     421   0      37  0.994618  2.481104   \n",
-       "99           8546   8143     403   0       4  0.952843  2.375695   \n",
-       "100          4254   3866     388   0       2  0.908792  2.258422   \n",
+       "     access_count   hits  misses  mu  lambda  hit_rate  avg_cache_time  \\\n",
+       "1            1060   1048      12   0       1  0.988679        0.986652   \n",
+       "2            3141   3140       1   0       3  0.999682        0.999596   \n",
+       "3            1060   1054       6   0       1  0.994340        0.992451   \n",
+       "4            1053   1041      12   0       1  0.988604        0.995039   \n",
+       "5            2073   2072       1   0       2  0.999518        0.999509   \n",
+       "..            ...    ...     ...  ..     ...       ...             ...   \n",
+       "96           1026   1015      11   0       1  0.989279        0.983446   \n",
+       "97           1015   1001      14   0       1  0.986207        0.984133   \n",
+       "98          39278  39277       1   0      37  0.999975        0.999964   \n",
+       "99           4158   4157       1   0       4  0.999759        0.999997   \n",
+       "100          2084   2083       1   0       2  0.999520        0.999942   \n",
        "\n",
-       "     expected_age  age_delta  \n",
-       "1        0.348058   1.720865  \n",
-       "2        0.438846   1.881894  \n",
-       "3        0.351742   1.731371  \n",
-       "4        0.348267   1.749975  \n",
-       "5        0.412142   1.851618  \n",
-       "..            ...        ...  \n",
-       "96       0.346892   1.691735  \n",
-       "97       0.345132   1.733479  \n",
-       "98       0.494633   1.986472  \n",
-       "99       0.453955   1.921740  \n",
-       "100      0.412951   1.845470  \n",
+       "     cache_time_delta     avg_age  expected_age   age_delta  \n",
+       "1            0.002027   86.266415      0.488743   85.777672  \n",
+       "2            0.000086  518.478962      0.499682  517.979280  \n",
+       "3            0.001888  130.457732      0.494356  129.963377  \n",
+       "4           -0.006435   65.802517      0.488669   65.313848  \n",
+       "5            0.000008  516.463009      0.499518  515.963491  \n",
+       "..                ...         ...           ...         ...  \n",
+       "96           0.005833   82.531337      0.489336   82.042001  \n",
+       "97           0.002074   40.135630      0.486302   39.649328  \n",
+       "98           0.000011  527.312192      0.499975  526.812217  \n",
+       "99          -0.000237  524.637562      0.499760  524.137803  \n",
+       "100         -0.000421  526.641909      0.499520  526.142389  \n",
        "\n",
-       "[100 rows x 9 columns]"
+       "[100 rows x 11 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1252,6 +779,38 @@
     "merged"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8630b3e8-50d1-4590-833d-27651d84a366",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "access_count        1026.000000\n",
+       "hits                1015.000000\n",
+       "misses                11.000000\n",
+       "mu                     0.000000\n",
+       "lambda                 1.000000\n",
+       "hit_rate               0.989279\n",
+       "avg_cache_time         0.983446\n",
+       "cache_time_delta       0.005833\n",
+       "avg_age               82.531337\n",
+       "expected_age           0.489336\n",
+       "age_delta             82.042001\n",
+       "Name: 96, dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged.iloc[merged['cache_time_delta'].argmax()]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -1260,7 +819,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 3000x500 with 1 Axes>"
       ]
@@ -1334,7 +893,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]