diff --git a/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb b/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb
index f129f27..9732abb 100644
--- a/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb
+++ b/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb
@@ -22,7 +22,7 @@
     "\n",
     "# Constants\n",
     "SEED = 42\n",
-    "ACCESS_COUNT_LIMIT = 10   # 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",
     "BASE_FILE = pd.read_csv(\"../calculated.csv\")\n",
@@ -283,7 +283,7 @@
     "                                      )\n",
     "            self.storage[obj_id] = cache_object\n",
     "            \n",
-    "            assert len(self.cumulative_age[obj_id]) == self.access_count[obj_id], f\"[{self.env.now:.2f}] Age values collected and object access count do not match.\"rsa diagram\n",
+    "            assert len(self.cumulative_age[obj_id]) == self.access_count[obj_id], f\"[{self.env.now:.2f}] Age values collected and object access count do not match.\"\n",
     "            # print(f\"[{env.now:.2f}] {obj_id} Miss: Average Age {sum(self.cumulative_age[obj_id])/len(self.cumulative_age[obj_id]):.2f} \")\n",
     "            return self.storage[obj_id]\n",
     "\n",
@@ -496,16 +496,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Progress:  90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                | 9/10 [00:01<00:00,  6.32it/s]"
+      "Progress: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:53<00:00, 18.64it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Simulation ended after 682.3621311693207 seconds.\n",
-      "CPU times: user 1.34 s, sys: 244 ms, total: 1.59 s\n",
-      "Wall time: 1.44 s\n"
+      "Simulation ended after 41546.30037303802 seconds.\n",
+      "CPU times: user 50.4 s, sys: 8.37 s, total: 58.7 s\n",
+      "Wall time: 53.6 s\n"
      ]
     }
    ],
@@ -529,27 +529,6 @@
     "database_object_count = len(db.data)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "f9f46f00-26f8-48c5-8daa-4bf5408c2595",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[o.next_expiry for o in cache.storage.values()]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -652,16 +631,16 @@
       "Object 87: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n",
       "Object 88: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n",
       "Object 89: Hit Rate = 0.00, Expected Hit Rate = 0.00, Average Time spend in Cache: 0.00, Average Age = 0.00, Expected Age = 0.00\n",
-      "Object 90: Hit Rate = 0.99, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.53, Expected Age = 376.97\n",
-      "Object 91: Hit Rate = 0.99, Expected Hit Rate = 1.00, Average Time spend in Cache: 0.99, Average Age = 0.50, Expected Age = 352.37\n",
-      "Object 92: Hit Rate = 0.99, Expected Hit Rate = 1.00, Average Time spend in Cache: 0.99, Average Age = 0.50, Expected Age = 312.66\n",
-      "Object 93: Hit Rate = 0.99, Expected Hit Rate = 1.00, Average Time spend in Cache: 0.99, Average Age = 0.51, Expected Age = 319.02\n",
-      "Object 94: Hit Rate = 0.99, Expected Hit Rate = 1.00, Average Time spend in Cache: 0.99, Average Age = 0.49, Expected Age = 378.40\n",
-      "Object 95: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.40, Expected Age = 368.79\n",
-      "Object 96: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.36, Expected Age = 332.67\n",
-      "Object 97: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.33, Expected Age = 345.01\n",
-      "Object 98: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.28, Expected Age = 367.84\n",
-      "Object 99: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.23, Expected Age = 349.75\n"
+      "Object 90: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.55, Expected Age = 20367.51\n",
+      "Object 91: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.54, Expected Age = 20926.62\n",
+      "Object 92: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.51, Expected Age = 20502.64\n",
+      "Object 93: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.49, Expected Age = 20425.76\n",
+      "Object 94: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.46, Expected Age = 20902.52\n",
+      "Object 95: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.43, Expected Age = 20666.00\n",
+      "Object 96: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.39, Expected Age = 20772.20\n",
+      "Object 97: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.35, Expected Age = 20708.69\n",
+      "Object 98: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.30, Expected Age = 20858.00\n",
+      "Object 99: Hit Rate = 1.00, Expected Hit Rate = 1.00, Average Time spend in Cache: 1.00, Average Age = 0.22, Expected Age = 20546.25\n"
      ]
     }
    ],
@@ -670,13 +649,37 @@
     "# Calculate and print hit rate and average age for each object\n",
     "for obj_id in range(database_object_count):\n",
     "    if cache.access_count[obj_id] != 0:\n",
+    "        output = \"\"\n",
+    "        expected_hit_rate = None\n",
     "        hit_rate = cache.hits[obj_id] / max(1, cache.access_count[obj_id])\n",
-    "        expected_hit_rate = 1-math.exp(-db.data[obj_id].lambda_value*(db.data[obj_id].ttl if db.data[obj_id].ttl is not None else 1))\n",
+    "        output += f\"Object {obj_id}: Hit Rate = {hit_rate:.2f}, \"\n",
+    "        if db.data[obj_id].ttl is not None:\n",
+    "            expected_hit_rate = 1-math.exp(-db.data[obj_id].lambda_value*(db.data[obj_id].ttl))\n",
+    "            output += f\"Expected Hit Rate = {expected_hit_rate:.2f}, \"\n",
     "        avg_cache_time = sum(cache.cumulative_cache_time[obj_id]) / max(1, simulation_end_time) \n",
+    "        output += f\"Average Time spend in Cache: {avg_cache_time:.2f}, \"\n",
     "        avg_age = sum(cache.cumulative_age[obj_id]) / max(len(cache.cumulative_age[obj_id]), 1)\n",
+    "        output += f\"Average Age = {avg_age:.2f}, \"\n",
     "        expected_age = hit_rate / (db.data[obj_id].lambda_value * (1 - pow(hit_rate,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})"
+    "        output += f\"Expected Age = {expected_age:.2f}\"\n",
+    "        print(output)\n",
+    "        if db.data[obj_id].ttl is not None:\n",
+    "            statistics.append({\n",
+    "                \"obj_id\": obj_id,\n",
+    "                \"hit_rate\": hit_rate, \n",
+    "                \"expected_hit_rate\": expected_hit_rate, \n",
+    "                \"avg_cache_time\":avg_cache_time, \n",
+    "                \"avg_age\": avg_age, \n",
+    "                \"expected_age\": expected_age\n",
+    "                })\n",
+    "        else:\n",
+    "            statistics.append({\n",
+    "                \"obj_id\": obj_id,\n",
+    "                \"hit_rate\": hit_rate, \n",
+    "                \"avg_cache_time\":avg_cache_time, \n",
+    "                \"avg_age\": avg_age, \n",
+    "                \"expected_age\": expected_age\n",
+    "                })"
    ]
   },
   {
@@ -694,17 +697,20 @@
   {
    "cell_type": "code",
    "execution_count": 18,
+   "id": "be7e67e7-4533-438a-ab65-ca813f48052a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expected_hit_rate = None\n",
+    "expected_hit_rate_delta = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "id": "80971714-44f1-47db-9e89-85be7c885bde",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_16109/3693436280.py:26: RuntimeWarning: invalid value encountered in divide\n",
-      "  age_delta_p = pd.DataFrame(np.where(expected_age.to_numpy().T[0] != 0, age_delta.to_numpy().T[0] / expected_age.to_numpy().T[0], 0), columns=['age_delta in %'])\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -732,107 +738,89 @@
        "      <th>mu</th>\n",
        "      <th>lambda</th>\n",
        "      <th>hit_rate</th>\n",
-       "      <th>optimal_hitrates</th>\n",
        "      <th>expected_hit_rate</th>\n",
        "      <th>expected_hit_rate_delta</th>\n",
        "      <th>avg_cache_time</th>\n",
        "      <th>cache_time_delta</th>\n",
        "      <th>avg_age</th>\n",
-       "      <th>expected_age</th>\n",
-       "      <th>age_delta</th>\n",
-       "      <th>age_delta in %</th>\n",
+       "      <th>ages</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>22</td>\n",
+       "      <td>1062</td>\n",
        "      <td>0</td>\n",
-       "      <td>22</td>\n",
+       "      <td>1062</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.0251</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0513</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>13</td>\n",
+       "      <td>1088</td>\n",
        "      <td>0</td>\n",
-       "      <td>13</td>\n",
+       "      <td>1088</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.0253</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0513</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>22</td>\n",
+       "      <td>1000</td>\n",
        "      <td>0</td>\n",
-       "      <td>22</td>\n",
+       "      <td>1000</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.0255</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.4000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>20</td>\n",
+       "      <td>1044</td>\n",
        "      <td>0</td>\n",
-       "      <td>20</td>\n",
+       "      <td>1044</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.0257</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.2254</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>23</td>\n",
+       "      <td>1129</td>\n",
        "      <td>0</td>\n",
-       "      <td>23</td>\n",
+       "      <td>1129</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.0260</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.0000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -848,118 +836,100 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>95</th>\n",
-       "      <td>204</td>\n",
-       "      <td>203</td>\n",
+       "      <td>11404</td>\n",
+       "      <td>11403</td>\n",
        "      <td>1</td>\n",
        "      <td>2.344666</td>\n",
        "      <td>0.2759</td>\n",
-       "      <td>0.995098</td>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>0.999912</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>-0.004902</td>\n",
-       "      <td>0.996384</td>\n",
-       "      <td>-0.001286</td>\n",
-       "      <td>0.396097</td>\n",
-       "      <td>368.790815</td>\n",
-       "      <td>-368.394718</td>\n",
-       "      <td>-0.998926</td>\n",
+       "      <td>-0.000088</td>\n",
+       "      <td>0.999941</td>\n",
+       "      <td>-0.000028</td>\n",
+       "      <td>0.428322</td>\n",
+       "      <td>[0, 0.0891512597998787, 0.06415110059353868, 0...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96</th>\n",
-       "      <td>220</td>\n",
-       "      <td>219</td>\n",
+       "      <td>13706</td>\n",
+       "      <td>13705</td>\n",
        "      <td>1</td>\n",
        "      <td>2.563445</td>\n",
        "      <td>0.3299</td>\n",
-       "      <td>0.995455</td>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>0.999927</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>-0.004545</td>\n",
-       "      <td>0.996714</td>\n",
-       "      <td>-0.001260</td>\n",
-       "      <td>0.361816</td>\n",
-       "      <td>332.674842</td>\n",
-       "      <td>-332.313026</td>\n",
-       "      <td>-0.998912</td>\n",
+       "      <td>-0.000073</td>\n",
+       "      <td>0.999946</td>\n",
+       "      <td>-0.000019</td>\n",
+       "      <td>0.386432</td>\n",
+       "      <td>[0, 0.31426861097273395, 0.07673416342145734, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97</th>\n",
-       "      <td>287</td>\n",
-       "      <td>286</td>\n",
+       "      <td>17197</td>\n",
+       "      <td>17196</td>\n",
        "      <td>1</td>\n",
        "      <td>2.876043</td>\n",
        "      <td>0.4152</td>\n",
-       "      <td>0.996516</td>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>0.999942</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>-0.003484</td>\n",
-       "      <td>0.998031</td>\n",
-       "      <td>-0.001515</td>\n",
-       "      <td>0.334248</td>\n",
-       "      <td>345.013400</td>\n",
-       "      <td>-344.679152</td>\n",
-       "      <td>-0.999031</td>\n",
+       "      <td>-0.000058</td>\n",
+       "      <td>0.999968</td>\n",
+       "      <td>-0.000026</td>\n",
+       "      <td>0.349340</td>\n",
+       "      <td>[0, 0.9920726875299433, 0.11549947514999914, 0...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>98</th>\n",
-       "      <td>423</td>\n",
-       "      <td>422</td>\n",
+       "      <td>23958</td>\n",
+       "      <td>23957</td>\n",
        "      <td>1</td>\n",
        "      <td>3.381806</td>\n",
        "      <td>0.5743</td>\n",
-       "      <td>0.997636</td>\n",
-       "      <td>0.5528</td>\n",
+       "      <td>0.999958</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>-0.002364</td>\n",
-       "      <td>0.999934</td>\n",
-       "      <td>-0.002298</td>\n",
-       "      <td>0.283229</td>\n",
-       "      <td>367.838593</td>\n",
-       "      <td>-367.555365</td>\n",
-       "      <td>-0.999230</td>\n",
+       "      <td>-0.000042</td>\n",
+       "      <td>0.999999</td>\n",
+       "      <td>-0.000041</td>\n",
+       "      <td>0.297864</td>\n",
+       "      <td>[0, 0.055604529502096156, 0.05810636385222967,...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99</th>\n",
-       "      <td>700</td>\n",
-       "      <td>699</td>\n",
+       "      <td>41093</td>\n",
+       "      <td>41092</td>\n",
        "      <td>1</td>\n",
        "      <td>4.462294</td>\n",
        "      <td>1.0000</td>\n",
-       "      <td>0.998571</td>\n",
-       "      <td>0.0000</td>\n",
+       "      <td>0.999976</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>-0.001429</td>\n",
-       "      <td>0.999833</td>\n",
-       "      <td>-0.001261</td>\n",
-       "      <td>0.227276</td>\n",
-       "      <td>349.749821</td>\n",
-       "      <td>-349.522545</td>\n",
-       "      <td>-0.999350</td>\n",
+       "      <td>-0.000024</td>\n",
+       "      <td>0.999997</td>\n",
+       "      <td>-0.000022</td>\n",
+       "      <td>0.223745</td>\n",
+       "      <td>[0, 0.1683508324120914, 0.24845511096226777, 0...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>100 rows × 15 columns</p>\n",
+       "<p>100 rows × 12 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    access_count  hits  misses        mu  lambda  hit_rate  optimal_hitrates  \\\n",
-       "0             22     0      22  1.000000  0.0251  0.000000            0.0513   \n",
-       "1             13     0      13  1.000000  0.0253  0.000000            0.0513   \n",
-       "2             22     0      22  1.000000  0.0255  0.000000            0.4000   \n",
-       "3             20     0      20  1.000000  0.0257  0.000000            0.2254   \n",
-       "4             23     0      23  1.000000  0.0260  0.000000            0.0000   \n",
-       "..           ...   ...     ...       ...     ...       ...               ...   \n",
-       "95           204   203       1  2.344666  0.2759  0.995098            0.0000   \n",
-       "96           220   219       1  2.563445  0.3299  0.995455            0.0000   \n",
-       "97           287   286       1  2.876043  0.4152  0.996516            0.0000   \n",
-       "98           423   422       1  3.381806  0.5743  0.997636            0.5528   \n",
-       "99           700   699       1  4.462294  1.0000  0.998571            0.0000   \n",
+       "    access_count   hits  misses        mu  lambda  hit_rate  \\\n",
+       "0           1062      0    1062  1.000000  0.0251  0.000000   \n",
+       "1           1088      0    1088  1.000000  0.0253  0.000000   \n",
+       "2           1000      0    1000  1.000000  0.0255  0.000000   \n",
+       "3           1044      0    1044  1.000000  0.0257  0.000000   \n",
+       "4           1129      0    1129  1.000000  0.0260  0.000000   \n",
+       "..           ...    ...     ...       ...     ...       ...   \n",
+       "95         11404  11403       1  2.344666  0.2759  0.999912   \n",
+       "96         13706  13705       1  2.563445  0.3299  0.999927   \n",
+       "97         17197  17196       1  2.876043  0.4152  0.999942   \n",
+       "98         23958  23957       1  3.381806  0.5743  0.999958   \n",
+       "99         41093  41092       1  4.462294  1.0000  0.999976   \n",
        "\n",
        "    expected_hit_rate  expected_hit_rate_delta  avg_cache_time  \\\n",
        "0                 0.0                 0.000000        0.000000   \n",
@@ -968,29 +938,42 @@
        "3                 0.0                 0.000000        0.000000   \n",
        "4                 0.0                 0.000000        0.000000   \n",
        "..                ...                      ...             ...   \n",
-       "95                1.0                -0.004902        0.996384   \n",
-       "96                1.0                -0.004545        0.996714   \n",
-       "97                1.0                -0.003484        0.998031   \n",
-       "98                1.0                -0.002364        0.999934   \n",
-       "99                1.0                -0.001429        0.999833   \n",
+       "95                1.0                -0.000088        0.999941   \n",
+       "96                1.0                -0.000073        0.999946   \n",
+       "97                1.0                -0.000058        0.999968   \n",
+       "98                1.0                -0.000042        0.999999   \n",
+       "99                1.0                -0.000024        0.999997   \n",
        "\n",
-       "    cache_time_delta   avg_age  expected_age   age_delta  age_delta in %  \n",
-       "0           0.000000  0.000000      0.000000    0.000000        0.000000  \n",
-       "1           0.000000  0.000000      0.000000    0.000000        0.000000  \n",
-       "2           0.000000  0.000000      0.000000    0.000000        0.000000  \n",
-       "3           0.000000  0.000000      0.000000    0.000000        0.000000  \n",
-       "4           0.000000  0.000000      0.000000    0.000000        0.000000  \n",
-       "..               ...       ...           ...         ...             ...  \n",
-       "95         -0.001286  0.396097    368.790815 -368.394718       -0.998926  \n",
-       "96         -0.001260  0.361816    332.674842 -332.313026       -0.998912  \n",
-       "97         -0.001515  0.334248    345.013400 -344.679152       -0.999031  \n",
-       "98         -0.002298  0.283229    367.838593 -367.555365       -0.999230  \n",
-       "99         -0.001261  0.227276    349.749821 -349.522545       -0.999350  \n",
+       "    cache_time_delta   avg_age  \\\n",
+       "0           0.000000  0.000000   \n",
+       "1           0.000000  0.000000   \n",
+       "2           0.000000  0.000000   \n",
+       "3           0.000000  0.000000   \n",
+       "4           0.000000  0.000000   \n",
+       "..               ...       ...   \n",
+       "95         -0.000028  0.428322   \n",
+       "96         -0.000019  0.386432   \n",
+       "97         -0.000026  0.349340   \n",
+       "98         -0.000041  0.297864   \n",
+       "99         -0.000022  0.223745   \n",
        "\n",
-       "[100 rows x 15 columns]"
+       "                                                 ages  \n",
+       "0   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+       "1   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+       "2   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+       "3   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+       "4   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
+       "..                                                ...  \n",
+       "95  [0, 0.0891512597998787, 0.06415110059353868, 0...  \n",
+       "96  [0, 0.31426861097273395, 0.07673416342145734, ...  \n",
+       "97  [0, 0.9920726875299433, 0.11549947514999914, 0...  \n",
+       "98  [0, 0.055604529502096156, 0.05810636385222967,...  \n",
+       "99  [0, 0.1683508324120914, 0.24845511096226777, 0...  \n",
+       "\n",
+       "[100 rows x 12 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1004,12 +987,11 @@
     "\n",
     "hit_rate = pd.DataFrame(stats['hit_rate'])\n",
     "hit_rate.index = range(database_object_count)\n",
-    "optimal_hitrate = pd.DataFrame(BASE_FILE['optimal_hitrates'])\n",
-    "optimal_hitrate.index = range(database_object_count)\n",
-    "expected_hit_rate = pd.DataFrame(stats['expected_hit_rate'])\n",
-    "expected_hit_rate.index = range(database_object_count)\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(database_object_count)\n",
+    "if 'expected_hit_rate' in stats:\n",
+    "    expected_hit_rate = pd.DataFrame(stats['expected_hit_rate'])\n",
+    "    expected_hit_rate.index = range(database_object_count)\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(database_object_count)\n",
     "avg_cache_time = pd.DataFrame(stats['avg_cache_time'])\n",
     "avg_cache_time.index = range(database_object_count)\n",
     "cache_time_delta = pd.DataFrame((hit_rate.to_numpy()-avg_cache_time.to_numpy()), columns=['cache_time_delta'])\n",
@@ -1017,31 +999,30 @@
     "\n",
     "avg_age = pd.DataFrame(stats['avg_age'])\n",
     "avg_age.index = range(database_object_count)\n",
-    "expected_age = pd.DataFrame(stats['expected_age'])\n",
-    "expected_age.index = range(database_object_count)\n",
-    "age_delta = pd.DataFrame((avg_age.to_numpy()-expected_age.to_numpy()), columns=['age_delta'])\n",
-    "age_delta.index = range(database_object_count)\n",
-    "age_delta_p = pd.DataFrame(np.where(expected_age.to_numpy().T[0] != 0, age_delta.to_numpy().T[0] / expected_age.to_numpy().T[0], 0), columns=['age_delta in %'])\n",
-    "age_delta_p.index = range(database_object_count)\n",
+    "\n",
+    "ages = {k: str(v) for k,v in cache.cumulative_age.items()}\n",
+    "ages = pd.DataFrame.from_dict(ages, orient='index', columns=['ages'])\n",
     "\n",
     "merged = access_count.merge(hits, left_index=True, right_index=True).merge(misses, left_index=True, right_index=True) \\\n",
     "    .merge(mu, left_index=True, right_index=True).merge(lmbda, left_index=True, right_index=True) \\\n",
-    "    .merge(hit_rate, left_index=True, right_index=True).merge(optimal_hitrate, 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).merge(age_delta_p, left_index=True, right_index=True)\n",
+    "    .merge(hit_rate, left_index=True, right_index=True)\n",
+    "if 'expected_hit_rate' in stats:\n",
+    "    merged = merged.merge(expected_hit_rate, left_index=True, right_index=True).merge(expected_hit_rate_delta, left_index=True, right_index=True)\n",
+    "merged = merged.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(ages, left_index=True, right_index=True)\n",
     "merged.to_csv(f\"{TEMP_BASE_DIR}/details.csv\", index_label=\"obj_id\")\n",
     "merged"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "01f8f9ee-c278-4a22-8562-ba02e77f5ddd",
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 3000x500 with 1 Axes>"
       ]
@@ -1069,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "f30a0497-9b2e-4ea9-8ebf-6687de19aaa9",
    "metadata": {},
    "outputs": [
@@ -1109,13 +1090,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "c192564b-d3c6-40e1-a614-f7a5ee787c4e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1140,7 +1121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "00a12eea-c805-4209-9143-48fa65619873",
    "metadata": {},
    "outputs": [
@@ -1179,7 +1160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "id": "adbfeb40-76bd-4224-ac45-65c7b2b2cb7b",
    "metadata": {},
    "outputs": [],
@@ -1194,7 +1175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "id": "1f550686-3463-4e50-be83-ceafb27512b0",
    "metadata": {},
    "outputs": [],
@@ -1214,7 +1195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "id": "5b6dac2e-8596-4e7c-97d8-aaf9632e4154",
    "metadata": {},
    "outputs": [
@@ -1232,7 +1213,7 @@
        "Name: lambda, dtype: float64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1243,7 +1224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "id": "29393374-e379-42c8-8333-abfecc18e828",
    "metadata": {},
    "outputs": [
@@ -1261,7 +1242,7 @@
        "Name: lambda, dtype: float64"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1272,7 +1253,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
+   "id": "3f314883-98ec-4a59-ba80-11cf753a423e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.SimulationConfig"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SimulationConfig.mro"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "id": "b47990b1-0231-43ac-8bc5-8340abe4a8b3",
    "metadata": {},
    "outputs": [],
@@ -1285,7 +1287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "id": "db83cad4-7cc6-4702-ae3a-d1af30a561d2",
    "metadata": {},
    "outputs": [],
diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-results/details.csv b/00_aoi_caching_simulation/input/2024-12-13/lru-results/details.csv
new file mode 100644
index 0000000..dd8cdec
--- /dev/null
+++ b/00_aoi_caching_simulation/input/2024-12-13/lru-results/details.csv
@@ -0,0 +1,101 @@
+obj_id,access_count,hits,misses,mu,lambda,hit_rate,avg_cache_time,cache_time_delta,avg_age,ages
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diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-results/hit_age.csv b/00_aoi_caching_simulation/input/2024-12-13/lru-results/hit_age.csv
new file mode 100644
index 0000000..b1d862d
--- /dev/null
+++ b/00_aoi_caching_simulation/input/2024-12-13/lru-results/hit_age.csv
@@ -0,0 +1,101 @@
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diff --git a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/lambda_distribution.pdf b/00_aoi_caching_simulation/input/2024-12-13/lru-results/lambda_distribution.pdf
similarity index 99%
rename from 00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/lambda_distribution.pdf
rename to 00_aoi_caching_simulation/input/2024-12-13/lru-results/lambda_distribution.pdf
index f244cbe..2aefc49 100644
Binary files a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/lambda_distribution.pdf and b/00_aoi_caching_simulation/input/2024-12-13/lru-results/lambda_distribution.pdf differ
diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/lambda_vs_access_count.pdf b/00_aoi_caching_simulation/input/2024-12-13/lru-results/lambda_vs_access_count.pdf
similarity index 70%
rename from 00_aoi_caching_simulation/input/2024-12-13/lru-test-results/lambda_vs_access_count.pdf
rename to 00_aoi_caching_simulation/input/2024-12-13/lru-results/lambda_vs_access_count.pdf
index 65aea0f..d1acc61 100644
Binary files a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/lambda_vs_access_count.pdf and b/00_aoi_caching_simulation/input/2024-12-13/lru-results/lambda_vs_access_count.pdf differ
diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/objects_in_cache_over_time.pdf b/00_aoi_caching_simulation/input/2024-12-13/lru-results/objects_in_cache_over_time.pdf
similarity index 78%
rename from 00_aoi_caching_simulation/input/2024-12-13/lru-test-results/objects_in_cache_over_time.pdf
rename to 00_aoi_caching_simulation/input/2024-12-13/lru-results/objects_in_cache_over_time.pdf
index c1ac971..878bef9 100644
Binary files a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/objects_in_cache_over_time.pdf and b/00_aoi_caching_simulation/input/2024-12-13/lru-results/objects_in_cache_over_time.pdf differ
diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-results/overall_hit_age.csv b/00_aoi_caching_simulation/input/2024-12-13/lru-results/overall_hit_age.csv
new file mode 100644
index 0000000..02c62e1
--- /dev/null
+++ b/00_aoi_caching_simulation/input/2024-12-13/lru-results/overall_hit_age.csv
@@ -0,0 +1,9 @@
+,hit_rate,avg_cache_time,avg_age,expected_age
+count,100.0,100.0,100.0,100.0
+mean,0.09995752379349948,0.09999849133785652,0.10917612001045782,1.3953153806298055
+std,0.11190861822425655,0.11193809346072345,0.22322689014838046,0.16253739044526075
+min,0.023963133640552997,0.03325329929630683,0.018856436349120938,0.9013872909581104
+25%,0.0449174443820356,0.044494347234733556,0.03831518000091836,1.288293167505807
+50%,0.0576988345511409,0.06109940853243645,0.05086532381138921,1.3907398743079913
+75%,0.099559523400583,0.10079195249416623,0.08472739004581,1.4707372295241448
+max,0.7681812899477048,0.7683946264444863,1.9660540242903553,1.87408139626671
diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/details.csv b/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/details.csv
deleted file mode 100644
index 0745686..0000000
--- a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/details.csv
+++ /dev/null
@@ -1,101 +0,0 @@
-obj_id,access_count,hits,misses,mu,lambda,hit_rate,optimal_hitrates,expected_hit_rate,expected_hit_rate_delta,avg_cache_time,cache_time_delta,avg_age,expected_age,age_delta,age_delta in %
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diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/hit_age.csv b/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/hit_age.csv
deleted file mode 100644
index e3f1e51..0000000
--- a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/hit_age.csv
+++ /dev/null
@@ -1,101 +0,0 @@
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diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/overall_hit_age.csv b/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/overall_hit_age.csv
deleted file mode 100644
index 6f4819c..0000000
--- a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/overall_hit_age.csv
+++ /dev/null
@@ -1,9 +0,0 @@
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diff --git a/00_aoi_caching_simulation/input/2024-12-13/refresh-results/details.csv b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/details.csv
new file mode 100644
index 0000000..9baef46
--- /dev/null
+++ b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/details.csv
@@ -0,0 +1,101 @@
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diff --git a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/hit_age.csv b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/hit_age.csv
similarity index 72%
rename from 00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/hit_age.csv
rename to 00_aoi_caching_simulation/input/2024-12-13/refresh-results/hit_age.csv
index 5aaf0f6..5bbfea6 100644
--- a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/hit_age.csv
+++ b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/hit_age.csv
@@ -89,13 +89,13 @@ obj_id,hit_rate,expected_hit_rate,avg_cache_time,avg_age,expected_age
 87,0.0,0.0,0.0,0.0,0.0
 88,0.0,0.0,0.0,0.0,0.0
 89,0.0,0.0,0.0,0.0,0.0
-90,0.9916666666666667,1.0,0.9988223096220052,0.5268028102250545,376.9650093053353
-91,0.9918032786885246,1.0,0.9893817196074243,0.503623826772317,352.37222269962706
-92,0.9915966386554622,1.0,0.9889380632756557,0.5038289436816613,312.659341148705
-93,0.9925925925925926,1.0,0.9942722719769056,0.5056002196440317,319.0183616317368
-94,0.994475138121547,1.0,0.990943471415988,0.4903754529213453,378.4038049443392
-95,0.9950980392156863,1.0,0.9963837176887927,0.3960969365513211,368.79081460451584
-96,0.9954545454545455,1.0,0.9967141634072977,0.36181604037093523,332.67484244898117
-97,0.9965156794425087,1.0,0.9980311288323087,0.33424832187087034,345.0134000477544
-98,0.9976359338061466,1.0,0.9999342968857495,0.2832287257704404,367.8385933171039
-99,0.9985714285714286,1.0,0.999832687908697,0.2272761939902992,349.74982130091973
+90,0.9998451293170203,1.0,0.9999806574518315,0.554997000606214,20367.507764292128
+91,0.9998614190687362,1.0,0.999825603907617,0.5392195314133789,20926.624029447044
+92,0.9998713164328915,1.0,0.9998183172352216,0.5060456649820166,20502.63843753892
+93,0.9998838829540176,1.0,0.9999059270099743,0.4876205066988089,20425.75894442207
+94,0.999899709156554,1.0,0.999851254333356,0.46247837628351507,20902.515670701632
+95,0.9999123114696598,1.0,0.9999406056837161,0.42832186536432143,20666.002134964023
+96,0.999927039252882,1.0,0.9999460329694837,0.38643223504496843,20772.203670443334
+97,0.9999418503227308,1.0,0.9999676629901119,0.3493398533098376,20708.694587514987
+98,0.9999582602888388,1.0,0.9999989208830471,0.2978637772010669,20858.0010356803
+99,0.9999756649551018,1.0,0.9999972520432827,0.22374460354561168,20546.24999693472
diff --git a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/lambda_distribution.pdf b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/lambda_distribution.pdf
similarity index 70%
rename from 00_aoi_caching_simulation/input/2024-12-13/lru-test-results/lambda_distribution.pdf
rename to 00_aoi_caching_simulation/input/2024-12-13/refresh-results/lambda_distribution.pdf
index ceaf084..97f3825 100644
Binary files a/00_aoi_caching_simulation/input/2024-12-13/lru-test-results/lambda_distribution.pdf and b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/lambda_distribution.pdf differ
diff --git a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/lambda_vs_access_count.pdf b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/lambda_vs_access_count.pdf
similarity index 76%
rename from 00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/lambda_vs_access_count.pdf
rename to 00_aoi_caching_simulation/input/2024-12-13/refresh-results/lambda_vs_access_count.pdf
index d0e0c55..8d3af9a 100644
Binary files a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/lambda_vs_access_count.pdf and b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/lambda_vs_access_count.pdf differ
diff --git a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/objects_in_cache_over_time.pdf b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/objects_in_cache_over_time.pdf
similarity index 75%
rename from 00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/objects_in_cache_over_time.pdf
rename to 00_aoi_caching_simulation/input/2024-12-13/refresh-results/objects_in_cache_over_time.pdf
index cb608de..5bbb620 100644
Binary files a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/objects_in_cache_over_time.pdf and b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/objects_in_cache_over_time.pdf differ
diff --git a/00_aoi_caching_simulation/input/2024-12-13/refresh-results/overall_hit_age.csv b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/overall_hit_age.csv
new file mode 100644
index 0000000..d2d6905
--- /dev/null
+++ b/00_aoi_caching_simulation/input/2024-12-13/refresh-results/overall_hit_age.csv
@@ -0,0 +1,9 @@
+,hit_rate,expected_hit_rate,avg_cache_time,avg_age,expected_age
+count,100.0,100.0,100.0,100.0,100.0
+mean,0.09999076583218432,0.1,0.0999923223450764,0.042360634144497394,2066.7619627193917
+std,0.30148350279480357,0.3015113445777637,0.3014881963028628,0.1318359696508086,6231.814881241828
+min,0.0,0.0,0.0,0.0,0.0
+25%,0.0,0.0,0.0,0.0,0.0
+50%,0.0,0.0,0.0,0.0,0.0
+75%,0.0,0.0,0.0,0.0,0.0
+max,0.9999756649551018,1.0,0.9999989208830471,0.554997000606214,20926.624029447044
diff --git a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/details.csv b/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/details.csv
deleted file mode 100644
index 569b87d..0000000
--- a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/details.csv
+++ /dev/null
@@ -1,101 +0,0 @@
-obj_id,access_count,hits,misses,mu,lambda,hit_rate,optimal_hitrates,expected_hit_rate,expected_hit_rate_delta,avg_cache_time,cache_time_delta,avg_age,expected_age,age_delta,age_delta in %
-0,22,0,22,1.0,0.0251,0.0,0.0513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-1,13,0,13,1.0,0.0253,0.0,0.0513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-2,22,0,22,1.0,0.0255,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-3,20,0,20,1.0,0.0257,0.0,0.2254,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-4,23,0,23,1.0,0.026,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-5,17,0,17,1.0,0.0262,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-6,16,0,16,1.0,0.0264,0.0,0.7852,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-7,19,0,19,1.0,0.0266,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-8,19,0,19,1.0,0.0269,0.0,0.2254,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-9,25,0,25,1.0,0.0271,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-10,29,0,29,1.0,0.0273,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-11,14,0,14,1.0,0.0276,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-12,18,0,18,1.0,0.0278,0.0,0.2254,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-13,20,0,20,1.0,0.0281,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-14,18,0,18,1.0,0.0283,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-15,16,0,16,1.0,0.0286,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-16,15,0,15,1.0,0.0289,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-17,20,0,20,1.0,0.0292,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-18,20,0,20,1.0,0.0294,0.0,0.6536,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-19,18,0,18,1.0,0.0297,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-20,15,0,15,1.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-21,15,0,15,1.0,0.0303,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-22,15,0,15,1.0,0.0306,0.0,0.6746,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-23,16,0,16,1.0,0.031,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-24,15,0,15,1.0,0.0313,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-25,23,0,23,1.0,0.0316,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-26,14,0,14,1.0,0.032,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-27,12,0,12,1.0,0.0323,0.0,0.3292,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-28,23,0,23,1.0,0.0327,0.0,0.0513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-29,24,0,24,1.0,0.033,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-30,23,0,23,1.0,0.0334,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-31,23,0,23,1.0,0.0338,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-32,24,0,24,1.0,0.0342,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-33,21,0,21,1.0,0.0346,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-34,28,0,28,1.0,0.035,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-35,23,0,23,1.0,0.0355,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-36,11,0,11,1.0,0.0359,0.0,0.0513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-37,28,0,28,1.0,0.0364,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-38,24,0,24,1.0,0.0368,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-39,25,0,25,1.0,0.0373,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-40,23,0,23,1.0,0.0378,0.0,0.3292,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-41,29,0,29,1.0,0.0383,0.0,0.4523,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-42,29,0,29,1.0,0.0388,0.0,0.4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-43,23,0,23,1.0,0.0394,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-44,35,0,35,1.0,0.0399,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-45,10,0,10,1.0,0.0405,0.0,0.6922,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-46,24,0,24,1.0,0.0411,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-47,29,0,29,1.0,0.0417,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-48,25,0,25,1.0,0.0424,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-49,28,0,28,1.0,0.043,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-50,24,0,24,1.0,0.0437,0.0,0.0513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-51,21,0,21,1.0,0.0444,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-52,34,0,34,1.0,0.0452,0.0,0.2254,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-53,40,0,40,1.0,0.046,0.0,0.0513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-54,40,0,40,1.0,0.0468,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-55,32,0,32,1.0,0.0476,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-56,34,0,34,1.0,0.0484,0.0,0.5757,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-57,34,0,34,1.0,0.0493,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-58,40,0,40,1.0,0.0503,0.0,0.0513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-59,36,0,36,1.0,0.0513,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-60,27,0,27,1.0,0.0523,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-61,42,0,42,1.0,0.0534,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-62,30,0,30,1.0,0.0545,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-63,34,0,34,1.0,0.0556,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-64,29,0,29,1.0,0.0569,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-65,46,0,46,1.0,0.0582,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-66,38,0,38,1.0,0.0595,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-67,46,0,46,1.0,0.061,0.0,0.2254,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-68,36,0,36,1.0,0.0625,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-69,32,0,32,1.0,0.0641,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-70,39,0,39,1.0,0.0658,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-71,32,0,32,1.0,0.0676,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-72,71,0,71,1.0,0.0695,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-73,34,0,34,1.0,0.0716,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-74,48,0,48,1.0,0.0738,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-75,55,0,55,1.0,0.0761,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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-77,64,0,64,1.0,0.0814,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-78,79,0,79,1.0,0.0843,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-79,61,0,61,1.0,0.0875,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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-81,68,0,68,1.0,0.0948,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-82,53,0,53,1.0,0.099,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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-86,80,0,80,1.0,0.1211,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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-90,120,119,1,1.7768301350390905,0.1585,0.9916666666666667,0.0,1.0,-0.008333333333333304,0.9988223096220052,-0.007155642955338548,0.5268028102250545,376.9650093053353,-376.43820649511025,-0.9986025153602563
-91,122,121,1,1.853224610822832,0.1724,0.9918032786885246,0.0,1.0,-0.008196721311475419,0.9893817196074243,0.0024215590811003107,0.503623826772317,352.37222269962706,-351.86859887285476,-0.9985707618412317
-92,119,118,1,1.9425019425019423,0.1895,0.9915966386554622,0.4,1.0,-0.008403361344537785,0.9889380632756557,0.002658575379806516,0.5038289436816613,312.659341148705,-312.15551220502334,-0.9983885690354538
-93,135,134,1,2.0491803278688523,0.2108,0.9925925925925926,0.0,1.0,-0.007407407407407418,0.9942722719769056,-0.0016796793843130153,0.5056002196440317,319.0183616317368,-318.5127614120928,-0.9984151375580455
-94,181,180,1,2.1795989537925022,0.2385,0.994475138121547,0.0,1.0,-0.0055248618784530246,0.990943471415988,0.0035316667055589335,0.4903754529213453,378.4038049443392,-377.91342949141784,-0.9987040948148144
-95,204,203,1,2.344665885111372,0.2759,0.9950980392156863,0.0,1.0,-0.004901960784313708,0.9963837176887927,-0.0012856784731064552,0.3960969365513211,368.79081460451584,-368.3947176679645,-0.9989259576950795
-96,220,219,1,2.563445270443476,0.3299,0.9954545454545455,0.0,1.0,-0.004545454545454519,0.9967141634072977,-0.0012596179527522011,0.36181604037093523,332.67484244898117,-332.31302640861026,-0.9989124033614702
-97,287,286,1,2.876042565429968,0.4152,0.9965156794425087,0.0,1.0,-0.0034843205574912606,0.9980311288323087,-0.0015154493897999721,0.33424832187087034,345.0134000477544,-344.6791517258835,-0.9990312019132457
-98,423,422,1,3.3818058843422385,0.5743,0.9976359338061466,0.5528,1.0,-0.0023640661938534313,0.9999342968857495,-0.002298363079602961,0.2832287257704404,367.8385933171039,-367.5553645913335,-0.9992300190058463
-99,700,699,1,4.462293618920125,1.0,0.9985714285714286,0.0,1.0,-0.0014285714285714457,0.999832687908697,-0.0012612593372683945,0.2272761939902992,349.74982130091973,-349.52254510692944,-0.9993501749532139
diff --git a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/overall_hit_age.csv b/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/overall_hit_age.csv
deleted file mode 100644
index 6de08ea..0000000
--- a/00_aoi_caching_simulation/input/2024-12-13/refresh-test-result/overall_hit_age.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-,hit_rate,expected_hit_rate,avg_cache_time,avg_age,expected_age
-count,100.0,100.0,100.0,100.0,100.0
-mean,0.0994540994121511,0.1,0.09953253830620824,0.04132897471798277,35.03486211449018
-std,0.29986638766852225,0.3015113445777637,0.3001045971683008,0.1287740000734953,105.86756991809764
-min,0.0,0.0,0.0,0.0,0.0
-25%,0.0,0.0,0.0,0.0,0.0
-50%,0.0,0.0,0.0,0.0,0.0
-75%,0.0,0.0,0.0,0.0,0.0
-max,0.9985714285714286,1.0,0.9999342968857495,0.5268028102250545,378.4038049443392