diff --git a/00_aoi_caching_simulation/.aoi_cache/lambda_distribution.pdf b/00_aoi_caching_simulation/.aoi_cache/lambda_distribution.pdf
deleted file mode 100644
index 36c3e49..0000000
Binary files a/00_aoi_caching_simulation/.aoi_cache/lambda_distribution.pdf and /dev/null differ
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
deleted file mode 100644
index 824b4bd..0000000
Binary files a/00_aoi_caching_simulation/.aoi_cache/lambda_vs_access_count.pdf and /dev/null differ
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
deleted file mode 100644
index 931a514..0000000
Binary files a/00_aoi_caching_simulation/.aoi_cache/objects_in_cache_over_time.pdf and /dev/null differ
diff --git a/00_aoi_caching_simulation/.gitignore b/00_aoi_caching_simulation/.gitignore
new file mode 100644
index 0000000..f85dc1c
--- /dev/null
+++ b/00_aoi_caching_simulation/.gitignore
@@ -0,0 +1 @@
+.aoi_cache/
diff --git a/00_aoi_caching_simulation/aoi_cache_simulation.ipynb b/00_aoi_caching_simulation/aoi_cache_simulation.ipynb
index 6d34cb2..4dbbd5d 100644
--- a/00_aoi_caching_simulation/aoi_cache_simulation.ipynb
+++ b/00_aoi_caching_simulation/aoi_cache_simulation.ipynb
@@ -27,7 +27,7 @@
     "# Constants\n",
     "SEED = 42\n",
     "DATABASE_OBJECTS = 100 # Number of objects in the database\n",
-    "ACCESS_COUNT_LIMIT = 1000   # Total time to run the simulation\n",
+    "ACCESS_COUNT_LIMIT = 100   # Total time to run the simulation\n",
     "EXPERIMENT_BASE_DIR = \"./experiments/\"\n",
     "TEMP_BASE_DIR = \"./.aoi_cache/\"\n",
     "\n",
@@ -103,7 +103,7 @@
     "    \"No Refresh (5.0s ttl)\": (DATABASE_OBJECTS, 0, CacheType.TTL, 5),\n",
     "}\n",
     "\n",
-    "experiment_name = \"No Refresh (5.0s ttl)\"\n",
+    "experiment_name = \"No Refresh (0.5s ttl)\"\n",
     "config = configurations[experiment_name]\n",
     "\n",
     "CACHE_CAPACITY = config[0]\n",
@@ -177,10 +177,10 @@
     "            # Cache hit: increment hit count and update cumulative age\n",
     "            self.hits[obj_id] += 1\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",
+    "            self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n",
     "\n",
-    "            # Cache hit: Refresh database object\n",
+    "            # Cache hit: Refresh database object on hit\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",
@@ -203,13 +203,14 @@
     "            # Cache miss: increment miss count\n",
     "            self.misses[obj_id] += 1\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",
+    "            self.initial_fetch[obj_id] = env.now\n",
+    "            self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n",
+    "            \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",
@@ -269,9 +270,9 @@
    "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",
-    "    last_full_second = 0\n",
     "    while True:\n",
-    "        cache.check_expired()  # Remove expired objects\n",
+    "        if cache.cache_type == CacheType.TTL:\n",
+    "            cache.check_expired()  # Remove expired objects\n",
     "\n",
     "        if MAX_REFRESH_RATE != 0:\n",
     "            # Refresh objects based on their individual refresh intervals\n",
@@ -354,16 +355,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Progress: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:05<00:00, 182.11it/s]"
+      "Progress:  99%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋  | 99/100 [00:00<00:00, 158.42it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "Simulation ended after 123.41758277479552 seconds.\n",
+      "CPU times: user 550 ms, sys: 98.6 ms, total: 648 ms\n",
+      "Wall time: 632 ms\n"
      ]
     }
    ],
@@ -389,106 +390,106 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "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"
+      "Object 1: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.08\n",
+      "Object 2: Hit Rate = 0.80, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.80, Average Age = 0.73, Expected Age = 0.32\n",
+      "Object 3: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.16, Expected Age = 0.07\n",
+      "Object 4: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.15, Expected Age = 0.07\n",
+      "Object 5: Hit Rate = 0.60, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.29, Expected Age = 0.18\n",
+      "Object 6: Hit Rate = 0.38, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.12, Expected Age = 0.07\n",
+      "Object 7: Hit Rate = 0.93, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.94, Average Age = 2.35, Expected Age = 0.43\n",
+      "Object 8: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.19, Expected Age = 0.09\n",
+      "Object 9: Hit Rate = 0.36, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.10, Expected Age = 0.06\n",
+      "Object 10: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.14, Expected Age = 0.07\n",
+      "Object 11: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.33, Expected Age = 0.21\n",
+      "Object 12: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.17, Expected Age = 0.10\n",
+      "Object 13: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.19, Expected Age = 0.09\n",
+      "Object 14: Hit Rate = 0.33, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.10, Expected Age = 0.06\n",
+      "Object 15: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.66, Average Age = 0.36, Expected Age = 0.21\n",
+      "Object 16: Hit Rate = 0.62, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.62, Average Age = 0.38, Expected Age = 0.19\n",
+      "Object 17: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.13, Expected Age = 0.07\n",
+      "Object 18: Hit Rate = 0.38, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.14, Expected Age = 0.07\n",
+      "Object 19: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.76, Average Age = 0.60, Expected Age = 0.30\n",
+      "Object 20: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.18, Expected Age = 0.08\n",
+      "Object 21: Hit Rate = 0.46, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.19, Expected Age = 0.11\n",
+      "Object 22: Hit Rate = 0.46, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.19, Expected Age = 0.10\n",
+      "Object 23: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.45, Average Age = 0.15, Expected Age = 0.09\n",
+      "Object 24: Hit Rate = 0.67, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.65, Average Age = 0.39, Expected Age = 0.23\n",
+      "Object 25: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.11, Expected Age = 0.07\n",
+      "Object 26: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.15, Expected Age = 0.09\n",
+      "Object 27: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.16, Expected Age = 0.09\n",
+      "Object 28: Hit Rate = 0.91, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.91, Average Age = 1.78, Expected Age = 0.42\n",
+      "Object 29: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.17, Expected Age = 0.08\n",
+      "Object 30: Hit Rate = 0.46, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.20, Expected Age = 0.10\n",
+      "Object 31: Hit Rate = 0.35, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.06\n",
+      "Object 32: Hit Rate = 0.85, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.86, Average Age = 1.01, Expected Age = 0.37\n",
+      "Object 33: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.15, Expected Age = 0.07\n",
+      "Object 34: Hit Rate = 0.86, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.86, Average Age = 0.91, Expected Age = 0.37\n",
+      "Object 35: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.17, Expected Age = 0.08\n",
+      "Object 36: Hit Rate = 0.32, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.34, Average Age = 0.09, Expected Age = 0.05\n",
+      "Object 37: Hit Rate = 0.48, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.23, Expected Age = 0.11\n",
+      "Object 38: Hit Rate = 0.75, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.79, Average Age = 0.59, Expected Age = 0.28\n",
+      "Object 39: Hit Rate = 0.98, Expected Hit Rate = 0.98, Average Time spend in Cache: 0.98, Average Age = 6.02, Expected Age = 0.48\n",
+      "Object 40: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.18, Expected Age = 0.10\n",
+      "Object 41: Hit Rate = 0.85, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 0.97, Expected Age = 0.36\n",
+      "Object 42: Hit Rate = 0.88, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 1.44, Expected Age = 0.39\n",
+      "Object 43: Hit Rate = 0.62, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.56, Average Age = 0.34, Expected Age = 0.19\n",
+      "Object 44: Hit Rate = 0.31, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.08, Expected Age = 0.05\n",
+      "Object 45: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.17, Expected Age = 0.08\n",
+      "Object 46: Hit Rate = 0.47, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.20, Expected Age = 0.11\n",
+      "Object 47: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 15.52, Expected Age = 0.49\n",
+      "Object 48: Hit Rate = 0.49, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.29, Expected Age = 0.12\n",
+      "Object 49: Hit Rate = 0.51, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.23, Expected Age = 0.13\n",
+      "Object 50: Hit Rate = 0.41, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.14, Expected Age = 0.08\n",
+      "Object 51: Hit Rate = 0.91, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.90, Average Age = 1.40, Expected Age = 0.41\n",
+      "Object 52: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 8.54, Expected Age = 0.49\n",
+      "Object 53: Hit Rate = 0.34, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.13, Expected Age = 0.06\n",
+      "Object 54: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.14, Expected Age = 0.07\n",
+      "Object 55: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.26, Expected Age = 0.09\n",
+      "Object 56: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.13, Expected Age = 0.09\n",
+      "Object 57: Hit Rate = 0.35, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.09, Expected Age = 0.06\n",
+      "Object 58: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 25.12, Expected Age = 0.50\n",
+      "Object 59: Hit Rate = 0.59, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.57, Average Age = 0.28, Expected Age = 0.17\n",
+      "Object 60: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.21, Expected Age = 0.09\n",
+      "Object 61: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 62.77, Expected Age = 0.50\n",
+      "Object 62: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.44, Average Age = 0.18, Expected Age = 0.10\n",
+      "Object 63: Hit Rate = 0.38, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.15, Expected Age = 0.07\n",
+      "Object 64: Hit Rate = 0.67, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.67, Average Age = 0.35, Expected Age = 0.22\n",
+      "Object 65: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.17, Expected Age = 0.10\n",
+      "Object 66: Hit Rate = 0.99, Expected Hit Rate = 0.99, Average Time spend in Cache: 0.99, Average Age = 15.37, Expected Age = 0.49\n",
+      "Object 67: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.19, Expected Age = 0.10\n",
+      "Object 68: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 61.45, Expected Age = 0.50\n",
+      "Object 69: Hit Rate = 0.37, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.38, Average Age = 0.12, Expected Age = 0.07\n",
+      "Object 70: Hit Rate = 0.39, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.16, Expected Age = 0.08\n",
+      "Object 71: Hit Rate = 0.66, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.62, Average Age = 0.47, Expected Age = 0.22\n",
+      "Object 72: Hit Rate = 0.36, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.15, Expected Age = 0.06\n",
+      "Object 73: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.18, Expected Age = 0.09\n",
+      "Object 74: Hit Rate = 0.44, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.45, Average Age = 0.20, Expected Age = 0.10\n",
+      "Object 75: Hit Rate = 0.78, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.75, Average Age = 0.68, Expected Age = 0.30\n",
+      "Object 76: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.65, Average Age = 0.40, Expected Age = 0.21\n",
+      "Object 77: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.36, Expected Age = 0.21\n",
+      "Object 78: Hit Rate = 0.80, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.81, Average Age = 0.83, Expected Age = 0.32\n",
+      "Object 79: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 32.55, Expected Age = 0.50\n",
+      "Object 80: Hit Rate = 0.33, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.40, Average Age = 0.11, Expected Age = 0.06\n",
+      "Object 81: Hit Rate = 0.42, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.39, Average Age = 0.16, Expected Age = 0.09\n",
+      "Object 82: Hit Rate = 0.92, Expected Hit Rate = 0.92, Average Time spend in Cache: 0.92, Average Age = 1.87, Expected Age = 0.42\n",
+      "Object 83: Hit Rate = 0.65, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.65, Average Age = 0.36, Expected Age = 0.21\n",
+      "Object 84: Hit Rate = 0.45, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.46, Average Age = 0.19, Expected Age = 0.10\n",
+      "Object 85: Hit Rate = 0.35, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.36, Average Age = 0.11, Expected Age = 0.06\n",
+      "Object 86: Hit Rate = 0.55, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.58, Average Age = 0.29, Expected Age = 0.15\n",
+      "Object 87: Hit Rate = 0.50, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.26, Expected Age = 0.12\n",
+      "Object 88: Hit Rate = 0.59, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.61, Average Age = 0.28, Expected Age = 0.18\n",
+      "Object 89: Hit Rate = 0.44, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.18, Expected Age = 0.10\n",
+      "Object 90: Hit Rate = 0.35, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.42, Average Age = 0.13, Expected Age = 0.06\n",
+      "Object 91: Hit Rate = 0.63, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.63, Average Age = 0.31, Expected Age = 0.20\n",
+      "Object 92: Hit Rate = 0.62, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.61, Average Age = 0.35, Expected Age = 0.19\n",
+      "Object 93: Hit Rate = 0.80, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.80, Average Age = 0.86, Expected Age = 0.32\n",
+      "Object 94: Hit Rate = 0.38, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.15, Expected Age = 0.07\n",
+      "Object 95: Hit Rate = 0.66, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.62, Average Age = 0.32, Expected Age = 0.22\n",
+      "Object 96: Hit Rate = 0.40, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.43, Average Age = 0.13, Expected Age = 0.08\n",
+      "Object 97: Hit Rate = 0.43, Expected Hit Rate = 0.39, Average Time spend in Cache: 0.41, Average Age = 0.19, Expected Age = 0.09\n",
+      "Object 98: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 62.04, Expected Age = 0.50\n",
+      "Object 99: Hit Rate = 0.86, Expected Hit Rate = 0.86, Average Time spend in Cache: 0.87, Average Age = 0.96, Expected Age = 0.37\n",
+      "Object 100: Hit Rate = 0.64, Expected Hit Rate = 0.63, Average Time spend in Cache: 0.61, Average Age = 0.31, Expected Age = 0.21\n"
      ]
     }
    ],
@@ -503,7 +504,7 @@
     "        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})"
+    "        statistics.append({\"obj_id\": obj_id,\"hit_rate\": hit_rate, \"expected_hit_rate\": expected_hit_rate, \"avg_cache_time\":avg_cache_time, \"avg_age\": avg_age, \"expected_age\": expected_age})"
    ]
   },
   {
@@ -551,6 +552,8 @@
        "      <th>mu</th>\n",
        "      <th>lambda</th>\n",
        "      <th>hit_rate</th>\n",
+       "      <th>expected_hit_rate</th>\n",
+       "      <th>expected_hit_rate_delta</th>\n",
        "      <th>avg_cache_time</th>\n",
        "      <th>cache_time_delta</th>\n",
        "      <th>avg_age</th>\n",
@@ -561,73 +564,83 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1060</td>\n",
-       "      <td>1048</td>\n",
-       "      <td>12</td>\n",
+       "      <td>122</td>\n",
+       "      <td>48</td>\n",
+       "      <td>74</td>\n",
        "      <td>0</td>\n",
        "      <td>1</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",
+       "      <td>0.393443</td>\n",
+       "      <td>0.393469</td>\n",
+       "      <td>-0.000027</td>\n",
+       "      <td>0.403492</td>\n",
+       "      <td>-0.010049</td>\n",
+       "      <td>0.152056</td>\n",
+       "      <td>0.077399</td>\n",
+       "      <td>0.074657</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>3141</td>\n",
-       "      <td>3140</td>\n",
-       "      <td>1</td>\n",
+       "      <td>382</td>\n",
+       "      <td>304</td>\n",
+       "      <td>78</td>\n",
        "      <td>0</td>\n",
        "      <td>3</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",
+       "      <td>0.795812</td>\n",
+       "      <td>0.776870</td>\n",
+       "      <td>0.018942</td>\n",
+       "      <td>0.796822</td>\n",
+       "      <td>-0.001011</td>\n",
+       "      <td>0.725838</td>\n",
+       "      <td>0.316658</td>\n",
+       "      <td>0.409180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1060</td>\n",
-       "      <td>1054</td>\n",
-       "      <td>6</td>\n",
+       "      <td>127</td>\n",
+       "      <td>47</td>\n",
+       "      <td>80</td>\n",
        "      <td>0</td>\n",
        "      <td>1</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",
+       "      <td>0.370079</td>\n",
+       "      <td>0.393469</td>\n",
+       "      <td>-0.023391</td>\n",
+       "      <td>0.439155</td>\n",
+       "      <td>-0.069076</td>\n",
+       "      <td>0.161354</td>\n",
+       "      <td>0.068479</td>\n",
+       "      <td>0.092875</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1053</td>\n",
-       "      <td>1041</td>\n",
-       "      <td>12</td>\n",
+       "      <td>113</td>\n",
+       "      <td>42</td>\n",
+       "      <td>71</td>\n",
        "      <td>0</td>\n",
        "      <td>1</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",
+       "      <td>0.371681</td>\n",
+       "      <td>0.393469</td>\n",
+       "      <td>-0.021788</td>\n",
+       "      <td>0.386356</td>\n",
+       "      <td>-0.014674</td>\n",
+       "      <td>0.146023</td>\n",
+       "      <td>0.069074</td>\n",
+       "      <td>0.076949</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>2073</td>\n",
-       "      <td>2072</td>\n",
-       "      <td>1</td>\n",
+       "      <td>244</td>\n",
+       "      <td>147</td>\n",
+       "      <td>97</td>\n",
        "      <td>0</td>\n",
        "      <td>2</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",
+       "      <td>0.602459</td>\n",
+       "      <td>0.632121</td>\n",
+       "      <td>-0.029662</td>\n",
+       "      <td>0.628468</td>\n",
+       "      <td>-0.026009</td>\n",
+       "      <td>0.287673</td>\n",
+       "      <td>0.181478</td>\n",
+       "      <td>0.106195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -642,110 +655,135 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96</th>\n",
-       "      <td>1026</td>\n",
-       "      <td>1015</td>\n",
-       "      <td>11</td>\n",
+       "      <td>139</td>\n",
+       "      <td>56</td>\n",
+       "      <td>83</td>\n",
        "      <td>0</td>\n",
        "      <td>1</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",
+       "      <td>0.402878</td>\n",
+       "      <td>0.393469</td>\n",
+       "      <td>0.009408</td>\n",
+       "      <td>0.432752</td>\n",
+       "      <td>-0.029874</td>\n",
+       "      <td>0.126856</td>\n",
+       "      <td>0.081155</td>\n",
+       "      <td>0.045700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97</th>\n",
-       "      <td>1015</td>\n",
-       "      <td>1001</td>\n",
-       "      <td>14</td>\n",
+       "      <td>127</td>\n",
+       "      <td>55</td>\n",
+       "      <td>72</td>\n",
        "      <td>0</td>\n",
        "      <td>1</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",
+       "      <td>0.433071</td>\n",
+       "      <td>0.393469</td>\n",
+       "      <td>0.039602</td>\n",
+       "      <td>0.413172</td>\n",
+       "      <td>0.019899</td>\n",
+       "      <td>0.191692</td>\n",
+       "      <td>0.093775</td>\n",
+       "      <td>0.097917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>98</th>\n",
-       "      <td>39278</td>\n",
-       "      <td>39277</td>\n",
+       "      <td>4578</td>\n",
+       "      <td>4577</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>37</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",
+       "      <td>0.999782</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.000218</td>\n",
+       "      <td>0.999689</td>\n",
+       "      <td>0.000092</td>\n",
+       "      <td>62.040629</td>\n",
+       "      <td>0.499782</td>\n",
+       "      <td>61.540847</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99</th>\n",
-       "      <td>4158</td>\n",
-       "      <td>4157</td>\n",
-       "      <td>1</td>\n",
+       "      <td>482</td>\n",
+       "      <td>415</td>\n",
+       "      <td>67</td>\n",
        "      <td>0</td>\n",
        "      <td>4</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",
+       "      <td>0.860996</td>\n",
+       "      <td>0.864665</td>\n",
+       "      <td>-0.003669</td>\n",
+       "      <td>0.866371</td>\n",
+       "      <td>-0.005375</td>\n",
+       "      <td>0.959311</td>\n",
+       "      <td>0.370657</td>\n",
+       "      <td>0.588654</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100</th>\n",
-       "      <td>2084</td>\n",
-       "      <td>2083</td>\n",
-       "      <td>1</td>\n",
+       "      <td>249</td>\n",
+       "      <td>160</td>\n",
+       "      <td>89</td>\n",
        "      <td>0</td>\n",
        "      <td>2</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",
+       "      <td>0.642570</td>\n",
+       "      <td>0.632121</td>\n",
+       "      <td>0.010450</td>\n",
+       "      <td>0.610462</td>\n",
+       "      <td>0.032108</td>\n",
+       "      <td>0.305314</td>\n",
+       "      <td>0.206448</td>\n",
+       "      <td>0.098866</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>100 rows × 11 columns</p>\n",
+       "<p>100 rows × 13 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     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",
+       "     access_count  hits  misses  mu  lambda  hit_rate  expected_hit_rate  \\\n",
+       "1             122    48      74   0       1  0.393443           0.393469   \n",
+       "2             382   304      78   0       3  0.795812           0.776870   \n",
+       "3             127    47      80   0       1  0.370079           0.393469   \n",
+       "4             113    42      71   0       1  0.371681           0.393469   \n",
+       "5             244   147      97   0       2  0.602459           0.632121   \n",
+       "..            ...   ...     ...  ..     ...       ...                ...   \n",
+       "96            139    56      83   0       1  0.402878           0.393469   \n",
+       "97            127    55      72   0       1  0.433071           0.393469   \n",
+       "98           4578  4577       1   0      37  0.999782           1.000000   \n",
+       "99            482   415      67   0       4  0.860996           0.864665   \n",
+       "100           249   160      89   0       2  0.642570           0.632121   \n",
        "\n",
-       "     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",
+       "     expected_hit_rate_delta  avg_cache_time  cache_time_delta    avg_age  \\\n",
+       "1                  -0.000027        0.403492         -0.010049   0.152056   \n",
+       "2                   0.018942        0.796822         -0.001011   0.725838   \n",
+       "3                  -0.023391        0.439155         -0.069076   0.161354   \n",
+       "4                  -0.021788        0.386356         -0.014674   0.146023   \n",
+       "5                  -0.029662        0.628468         -0.026009   0.287673   \n",
+       "..                       ...             ...               ...        ...   \n",
+       "96                  0.009408        0.432752         -0.029874   0.126856   \n",
+       "97                  0.039602        0.413172          0.019899   0.191692   \n",
+       "98                 -0.000218        0.999689          0.000092  62.040629   \n",
+       "99                 -0.003669        0.866371         -0.005375   0.959311   \n",
+       "100                 0.010450        0.610462          0.032108   0.305314   \n",
        "\n",
-       "[100 rows x 11 columns]"
+       "     expected_age  age_delta  \n",
+       "1        0.077399   0.074657  \n",
+       "2        0.316658   0.409180  \n",
+       "3        0.068479   0.092875  \n",
+       "4        0.069074   0.076949  \n",
+       "5        0.181478   0.106195  \n",
+       "..            ...        ...  \n",
+       "96       0.081155   0.045700  \n",
+       "97       0.093775   0.097917  \n",
+       "98       0.499782  61.540847  \n",
+       "99       0.370657   0.588654  \n",
+       "100      0.206448   0.098866  \n",
+       "\n",
+       "[100 rows x 13 columns]"
       ]
      },
      "execution_count": 12,
@@ -759,12 +797,18 @@
     "misses = pd.DataFrame.from_dict(cache.misses, orient='index', columns=['misses'])\n",
     "mu = pd.DataFrame.from_dict(db.mu_values, orient='index', columns=['mu'])\n",
     "lmbda = pd.DataFrame.from_dict(db.lambda_values, orient='index', columns=['lambda'])\n",
+    "\n",
     "hit_rate = pd.DataFrame(stats['hit_rate'])\n",
     "hit_rate.index = range(1,DATABASE_OBJECTS + 1)\n",
+    "expected_hit_rate = pd.DataFrame(stats['expected_hit_rate'])\n",
+    "expected_hit_rate.index = range(1,DATABASE_OBJECTS + 1)\n",
+    "expected_hit_rate_delta = pd.DataFrame((hit_rate.to_numpy()-expected_hit_rate.to_numpy()), columns=['expected_hit_rate_delta'])\n",
+    "expected_hit_rate_delta.index = range(1,DATABASE_OBJECTS + 1)\n",
     "avg_cache_time = pd.DataFrame(stats['avg_cache_time'])\n",
     "avg_cache_time.index = range(1,DATABASE_OBJECTS + 1)\n",
     "cache_time_delta = pd.DataFrame((hit_rate.to_numpy()-avg_cache_time.to_numpy()), columns=['cache_time_delta'])\n",
     "cache_time_delta.index = range(1,DATABASE_OBJECTS + 1)\n",
+    "\n",
     "avg_age = pd.DataFrame(stats['avg_age'])\n",
     "avg_age.index = range(1,DATABASE_OBJECTS + 1)\n",
     "expected_age = (0.5*pow(hit_rate,2)).rename(columns={'hit_rate': \"expected_age\"})\n",
@@ -773,7 +817,8 @@
     "\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_cache_time, left_index=True, right_index=True).merge(cache_time_delta, left_index=True, right_index=True) \\\n",
+    "    .merge(hit_rate, left_index=True, right_index=True).merge(expected_hit_rate, left_index=True, right_index=True).merge(expected_hit_rate_delta, left_index=True, right_index=True) \\\n",
+    "    .merge(avg_cache_time, left_index=True, right_index=True).merge(cache_time_delta, left_index=True, right_index=True) \\\n",
     "    .merge(avg_age, left_index=True, right_index=True).merge(expected_age, left_index=True, right_index=True).merge(age_delta, left_index=True, right_index=True)\n",
     "merged.to_csv(f\"{TEMP_BASE_DIR}/details.csv\", index_label=\"obj_id\")\n",
     "merged"
@@ -782,44 +827,12 @@
   {
    "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,
    "id": "01f8f9ee-c278-4a22-8562-ba02e77f5ddd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 3000x500 with 1 Axes>"
       ]
@@ -847,7 +860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "f30a0497-9b2e-4ea9-8ebf-6687de19aaa9",
    "metadata": {},
    "outputs": [
@@ -887,13 +900,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "c192564b-d3c6-40e1-a614-f7a5ee787c4e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -918,7 +931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "00a12eea-c805-4209-9143-48fa65619873",
    "metadata": {},
    "outputs": [
@@ -957,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "adbfeb40-76bd-4224-ac45-65c7b2b2cb7b",
    "metadata": {},
    "outputs": [],
@@ -972,7 +985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "1f550686-3463-4e50-be83-ceafb27512b0",
    "metadata": {},
    "outputs": [],
@@ -992,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "b47990b1-0231-43ac-8bc5-8340abe4a8b3",
    "metadata": {},
    "outputs": [],
@@ -1005,7 +1018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "db83cad4-7cc6-4702-ae3a-d1af30a561d2",
    "metadata": {},
    "outputs": [],
diff --git a/00_aoi_caching_simulation/multi_aoi_cache_simulation.ipynb b/00_aoi_caching_simulation/multi_aoi_cache_simulation.ipynb
index 2a85969..6056cb6 100644
--- a/00_aoi_caching_simulation/multi_aoi_cache_simulation.ipynb
+++ b/00_aoi_caching_simulation/multi_aoi_cache_simulation.ipynb
@@ -121,7 +121,7 @@
     "        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.initial_fetch = {}      # Dictionary to store when an object was fetched from the databse to determine the age\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",
@@ -134,80 +134,91 @@
     "        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] += self.age[obj_id]\n",
     "            self.access_count[obj_id] += 1\n",
+    "            \n",
+    "            self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n",
+    "\n",
+    "            # Cache hit: Refresh database object on hit\n",
+    "            # self.initial_fetch[obj_id] = env.now\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",
-    "            self.age[obj_id] = 0\n",
-    "            \n",
-    "            # Fetch the object from the database if it’s not in cache\n",
-    "            obj = self.db.get_object(obj_id)\n",
-    "            self.object_start_time[obj_id] = env.now\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",
+    "            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",
-    "                self.ttl[obj_id] = 0\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.access_count[obj_id] += 1\n",
+    "            \n",
+    "            # Cache miss: Fetch the object from the database\n",
+    "            self.storage[obj_id] = self.db.get_object(obj_id)\n",
+    "            self.object_start_time[obj_id] = env.now\n",
+    "            \n",
+    "            self.initial_fetch[obj_id] = env.now\n",
+    "            self.cumulative_age[obj_id] += (env.now - self.initial_fetch[obj_id])\n",
+    "            \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",
+    "        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.age[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.age[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.age[obj_id] = 0\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 age_objects(self):\n",
+    "    def check_expired(self):\n",
     "        \"\"\"Increment age of each cached object.\"\"\"\n",
-    "        for obj_id in list(self.age.keys()):\n",
-    "            self.age[obj_id] += 1\n",
-    "            # print(f\"[{env.now:.2f}] Cache: Object {obj_id} aged to {self.age[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.age[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",
@@ -226,8 +237,8 @@
     "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",
+    "        if cache.cache_type == CacheType.TTL:\n",
+    "            cache.check_expired()  # Remove expired objects\n",
     "\n",
     "        if MAX_REFRESH_RATE != 0:\n",
     "            # Refresh objects based on their individual refresh intervals\n",
@@ -239,7 +250,7 @@
     "                    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"
+    "        yield env.timeout(0.05)  # Run every second"
    ]
   },
   {
@@ -256,10 +267,13 @@
     "        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",
@@ -268,7 +282,12 @@
     "                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(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()"
    ]
   },
@@ -281,18 +300,19 @@
    "source": [
     "configurations = {\n",
     "    \"default\": (DATABASE_OBJECTS, 10, CacheType.LRU, 5),\n",
-    "    \"No Refresh\": (DATABASE_OBJECTS, 0, CacheType.LRU, 5),\n",
+    "    \"No Refresh\": (DATABASE_OBJECTS, 0, CacheType.TTL, 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",
+    "    \"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",
-    "experiments = configurations.keys()\n"
+    "\n",
+    "experiments = configurations.keys()"
    ]
   },
   {
@@ -355,6 +375,10 @@
     "    cache_type = config[2]\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.\"\n",
+    "    \n",
     "    # Initialize simulation environment\n",
     "    env = simpy.Environment()\n",
     "    \n",
@@ -376,12 +400,13 @@
     "    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})\n",
-    "\n",
+    "            avg_age = cache.cumulative_age[obj_id] / max(1, cache.access_count[obj_id])\n",
+    "            expected_age = pow(hit_rate,2) / 2\n",
+    "            # print(f\"Object {obj_id}: Hit Rate = {hit_rate:.2f}, Expected Hit Rate = {expected_hit_rate:.2f}, Average Time spend in Cache: {avg_cache_time:.2f}, Average Age = {avg_age:.2f}, Expected Age = {expected_age:.2f}\")\n",
+    "            statistics.append({\"obj_id\": obj_id,\"hit_rate\": hit_rate, \"expected_hit_rate\": expected_hit_rate, \"avg_cache_time\":avg_cache_time, \"avg_age\": avg_age, \"expected_age\": expected_age})\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",
@@ -391,12 +416,18 @@
     "    misses = pd.DataFrame.from_dict(cache.misses, orient='index', columns=['misses'])\n",
     "    mu = pd.DataFrame.from_dict(db.mu_values, orient='index', columns=['mu'])\n",
     "    lmbda = pd.DataFrame.from_dict(db.lambda_values, orient='index', columns=['lambda'])\n",
+    "    \n",
     "    hit_rate = pd.DataFrame(stats['hit_rate'])\n",
     "    hit_rate.index = range(1,DATABASE_OBJECTS + 1)\n",
+    "    expected_hit_rate = pd.DataFrame(stats['expected_hit_rate'])\n",
+    "    expected_hit_rate.index = range(1,DATABASE_OBJECTS + 1)\n",
+    "    expected_hit_rate_delta = pd.DataFrame((hit_rate.to_numpy()-expected_hit_rate.to_numpy()), columns=['expected_hit_rate_delta'])\n",
+    "    expected_hit_rate_delta.index = range(1,DATABASE_OBJECTS + 1)\n",
     "    avg_cache_time = pd.DataFrame(stats['avg_cache_time'])\n",
     "    avg_cache_time.index = range(1,DATABASE_OBJECTS + 1)\n",
     "    cache_time_delta = pd.DataFrame((hit_rate.to_numpy()-avg_cache_time.to_numpy()), columns=['cache_time_delta'])\n",
     "    cache_time_delta.index = range(1,DATABASE_OBJECTS + 1)\n",
+    "    \n",
     "    avg_age = pd.DataFrame(stats['avg_age'])\n",
     "    avg_age.index = range(1,DATABASE_OBJECTS + 1)\n",
     "    expected_age = (0.5*pow(hit_rate,2)).rename(columns={'hit_rate': \"expected_age\"})\n",
@@ -405,7 +436,8 @@
     "    \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_cache_time, left_index=True, right_index=True).merge(cache_time_delta, left_index=True, right_index=True) \\\n",
+    "        .merge(hit_rate, left_index=True, right_index=True).merge(expected_hit_rate, left_index=True, right_index=True).merge(expected_hit_rate_delta, left_index=True, right_index=True) \\\n",
+    "        .merge(avg_cache_time, left_index=True, right_index=True).merge(cache_time_delta, left_index=True, right_index=True) \\\n",
     "        .merge(avg_age, left_index=True, right_index=True).merge(expected_age, left_index=True, right_index=True).merge(age_delta, left_index=True, right_index=True)\n",
     "    merged.to_csv(f\"{TEMP_BASE_DIR}/details.csv\", index_label=\"obj_id\")\n",
     "\n",