feat(simulation): Added time spent in cache log for each object

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

01_nb_cncf_optimization/02-objective_multi-core_gridsearch.ipynb

@@ -237,16 +237,16 @@
       "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
       "[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    0.7s\n",
       "[Parallel(n_jobs=-1)]: Done 1420 tasks      | elapsed:    1.3s\n",
-      "[Parallel(n_jobs=-1)]: Done 54501 tasks      | elapsed:  1.1min\n",
-      "[Parallel(n_jobs=-1)]: Done 64152 out of 64152 | elapsed:  2.5min finished\n"
+      "[Parallel(n_jobs=-1)]: Done 55524 tasks      | elapsed:  1.2min\n",
+      "[Parallel(n_jobs=-1)]: Done 64152 out of 64152 | elapsed:  2.6min finished\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 54.8 s, sys: 1.33 s, total: 56.1 s\n",
-      "Wall time: 3min 23s\n"
+      "CPU times: user 53 s, sys: 1.26 s, total: 54.3 s\n",
+      "Wall time: 3min 24s\n"
      ]
     }
    ],
@@ -269,6 +269,44 @@
     "else:\n",
     "    results_df = pd.read_csv(objective_result_file)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e79e6ed1-d6a5-4b04-a2b2-b3f0984e0fbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Result:\n",
+      "Objective                                                              0.05\n",
+      "Optimal TTL               [inf, inf, inf, inf, inf, inf, inf, inf, inf, ...\n",
+      "db_object_count                                                          10\n",
+      "cache_size                                                             10.0\n",
+      "c_f (Miss Cost)                                                         1.5\n",
+      "c_delta (Refresh Cost)                                                 0.01\n",
+      "Name: 2448, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# After performing the grid search and obtaining the DataFrame 'results_df'\n",
+    "best_row = results_df.loc[results_df['Objective'].idxmin()]\n",
+    "\n",
+    "# Display the best row\n",
+    "print(\"Best Result:\")\n",
+    "print(best_row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "924fd9c4-6670-4cf4-93a7-b1484111b333",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {