diff --git a/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb b/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb index 644207d..cd78046 100644 --- a/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb +++ b/00_aoi_caching_simulation/06-multi_aoi_simulation.ipynb @@ -114,7 +114,7 @@ " lambdas = df[lambda_column_name]\n", "\n", " self.db_objects = [\n", - " DatabaseObject(id=i, data=f\"Generated Object {i}\", lambda_value=[i], mu_value=None, ttl=None) \n", + " DatabaseObject(id=i, data=f\"Generated Object {i}\", lambda_value=lambdas[i], mu_value=None, ttl=None) \n", " for i in range(self.db_objects)\n", " ]\n", " \n", @@ -150,11 +150,23 @@ "\n", "@dataclass\n", "class RefreshSimulation(TTLSimulation):\n", + " \n", " def generate_objects(self, fixed_ttl, max_refresh_rate):\n", " if isinstance(self.db_objects, int):\n", " self.db_objects = [\n", " DatabaseObject(id=i, data=f\"Generated Object {i}\", lambda_value=np.random.zipf(ZIPF_CONSTANT), mu_value=np.random.uniform(1, max_refresh_rate), ttl=fixed_ttl) \n", " for i in range(self.db_objects)\n", + " ]\n", + " \n", + " def from_file(self, path: str, lambda_column_name: str, ttl_column_name: str, mu_column_name: str):\n", + " df = pd.read_csv(path)\n", + " lambdas = df[lambda_column_name]\n", + " ttls = df[ttl_column_name]\n", + " mus = df[mu_column_name]\n", + "\n", + " self.db_objects = [\n", + " DatabaseObject(id=i, data=f\"Generated Object {i}\", lambda_value=lambdas[i], mu_value=mus[i], ttl=ttls[i]) \n", + " for i in range(self.db_objects)\n", " ]" ] }, @@ -439,8 +451,8 @@ "source": [ "# Simulate with a Cache that does lru, We'll have 100 Database Objects and a Cache Size of 10\n", "# We'll generate lambdas from a zipf distribution\n", - "# config = LRUSimulation(100, 10)\n", - "# config.generate_objects()" + "config = LRUSimulation(100, 10)\n", + "config.from_file('../test.csv', 'Lambda')" ] }, { @@ -452,8 +464,8 @@ "source": [ "# Simulate with a Cache that does Refreshes with TTL based eviction, We'll have 100 Database Objects and a Cache Size of 10\n", "# We'll generate lambdas from a zipf distribution. Each object will have a fixed ttl of 1 when its pulled into the cache. Mu for the refresh rate is 10\n", - "# config = RefreshSimulation(100, 80)\n", - "# config.generate_objects(1, 10)" + "# config = RefreshSimulation(100, 10)\n", + "# config.from_file('../test.csv', 'Lambda', 'h_opt_2', 'u_opt_2')" ] }, { @@ -465,8 +477,8 @@ "source": [ "# Simulate with a Cache that does TTL based eviction, We'll have 100 Database Objects and a Cache Size of 10\n", "# We'll take lambdas from the \"lambda\" column of the file \"../calculated.csv\" and the TTLs for each object from the \"optimal_TTL\" column of the same file.\n", - "config = TTLSimulation(100, 10)\n", - "config.from_file(\"../calculated.csv\", \"lambda\", \"optimal_TTL\")" + "# config = TTLSimulation(100, 10)\n", + "# config.from_file(\"../calculated.csv\", \"lambda\", \"optimal_TTL\")" ] }, { @@ -481,14 +493,56 @@ "name": "stderr", "output_type": "stream", "text": [ - "Progress: 100%|█████████████████████████████████████████████████████████████████████████████████████████▊| 998/1000 [00:10<00:00, 98.43it/s]" + "IOPub message rate exceeded.██████████████████████████████████████▏ | 318/1000 [00:08<00:18, 36.80it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.████████████████████████████████████████████████████████████████████▊ | 508/1000 [00:11<00:11, 42.68it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.█████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 712/1000 [00:15<00:06, 45.55it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "IOPub message rate exceeded.██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 890/1000 [00:18<00:02, 47.26it/s]\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n", + "Progress: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊| 999/1000 [00:20<00:00, 48.26it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Simulation ended after 1423.771050302509 seconds.\n" + "Simulation ended after 19747.84052390687 seconds.\n", + "CPU times: user 19.6 s, sys: 3.65 s, total: 23.2 s\n", + "Wall time: 20.7 s\n" ] } ], @@ -524,106 +578,106 @@ "name": "stdout", "output_type": "stream", "text": [ - "Object 0: Hit Rate = 0.06, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.00, Expected Age = 0.03\n", - "Object 1: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.00, Expected Age = 0.02\n", - "Object 2: Hit Rate = 0.40, Expected Hit Rate = 0.40, Average Time spend in Cache: 0.35, Average Age = 0.03, Expected Age = 0.09\n", - "Object 3: Hit Rate = 0.24, Expected Hit Rate = 0.23, Average Time spend in Cache: 0.19, Average Age = 0.01, Expected Age = 0.08\n", - "Object 4: 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 5: 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 6: Hit Rate = 0.79, Expected Hit Rate = 0.78, Average Time spend in Cache: 0.75, Average Age = 0.05, Expected Age = 0.05\n", - "Object 7: 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 8: Hit Rate = 0.22, Expected Hit Rate = 0.23, Average Time spend in Cache: 0.19, Average Age = 0.01, Expected Age = 0.08\n", - "Object 9: 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 10: 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 11: 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 12: Hit Rate = 0.23, Expected Hit Rate = 0.23, Average Time spend in Cache: 0.19, Average Age = 0.01, Expected Age = 0.08\n", - "Object 13: 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 14: 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 15: 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 16: 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 17: 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 18: Hit Rate = 0.65, Expected Hit Rate = 0.65, Average Time spend in Cache: 0.60, Average Age = 0.06, Expected Age = 0.08\n", - "Object 19: 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 20: 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 21: 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 22: Hit Rate = 0.68, Expected Hit Rate = 0.67, Average Time spend in Cache: 0.63, Average Age = 0.06, Expected Age = 0.08\n", - "Object 23: 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 24: 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 25: 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 26: 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 27: Hit Rate = 0.33, Expected Hit Rate = 0.33, Average Time spend in Cache: 0.28, Average Age = 0.02, Expected Age = 0.09\n", - "Object 28: Hit Rate = 0.05, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.00, Expected Age = 0.02\n", - "Object 29: 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 30: 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 31: 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 32: 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 33: 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 34: Hit Rate = 0.39, Expected Hit Rate = 0.40, Average Time spend in Cache: 0.35, Average Age = 0.03, Expected Age = 0.09\n", - "Object 35: 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 36: Hit Rate = 0.05, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.00, Expected Age = 0.03\n", - "Object 37: 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 38: 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 39: 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 40: Hit Rate = 0.33, Expected Hit Rate = 0.33, Average Time spend in Cache: 0.28, Average Age = 0.02, Expected Age = 0.09\n", - "Object 41: Hit Rate = 0.45, Expected Hit Rate = 0.45, Average Time spend in Cache: 0.39, Average Age = 0.04, Expected Age = 0.09\n", - "Object 42: Hit Rate = 0.40, Expected Hit Rate = 0.40, Average Time spend in Cache: 0.34, Average Age = 0.03, Expected Age = 0.09\n", - "Object 43: 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 44: 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 45: Hit Rate = 0.69, Expected Hit Rate = 0.69, Average Time spend in Cache: 0.65, Average Age = 0.06, Expected Age = 0.07\n", - "Object 46: 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 47: 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 48: 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 49: 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 50: Hit Rate = 0.05, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.00, Expected Age = 0.03\n", - "Object 51: 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 52: Hit Rate = 0.22, Expected Hit Rate = 0.23, Average Time spend in Cache: 0.18, Average Age = 0.01, Expected Age = 0.08\n", - "Object 53: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.00, Expected Age = 0.02\n", - "Object 54: 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 55: 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 56: Hit Rate = 0.57, Expected Hit Rate = 0.58, Average Time spend in Cache: 0.52, Average Age = 0.05, Expected Age = 0.08\n", - "Object 57: 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 58: Hit Rate = 0.05, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.00, Expected Age = 0.03\n", - "Object 59: 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 60: 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 61: 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 62: 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 63: 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 64: 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 65: 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 66: 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 67: Hit Rate = 0.22, Expected Hit Rate = 0.23, Average Time spend in Cache: 0.19, Average Age = 0.01, Expected Age = 0.08\n", - "Object 68: 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 69: 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 70: 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 71: 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 72: 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 73: 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 74: 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 75: 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 76: 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 77: 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 78: 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 79: 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 80: 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 81: 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 82: 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 83: 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 84: 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 85: Hit Rate = 0.55, Expected Hit Rate = 0.55, Average Time spend in Cache: 0.50, Average Age = 0.05, Expected Age = 0.09\n", - "Object 86: 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 87: Hit Rate = 0.22, Expected Hit Rate = 0.23, Average Time spend in Cache: 0.18, Average Age = 0.01, Expected Age = 0.08\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 = 0.40, Expected Hit Rate = 0.40, Average Time spend in Cache: 0.34, Average Age = 0.03, Expected Age = 0.10\n", - "Object 90: 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 91: 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 92: Hit Rate = 0.40, Expected Hit Rate = 0.40, Average Time spend in Cache: 0.35, Average Age = 0.03, Expected Age = 0.10\n", - "Object 93: 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 94: 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 95: 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 96: 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 97: 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 98: Hit Rate = 0.55, Expected Hit Rate = 0.55, Average Time spend in Cache: 0.50, Average Age = 0.05, Expected Age = 0.09\n", - "Object 99: 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 0: Hit Rate = 0.05, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 1.02\n", + "Object 1: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.80\n", + "Object 2: Hit Rate = 0.03, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.01, Expected Age = 0.64\n", + "Object 3: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.83\n", + "Object 4: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.01, Expected Age = 0.68\n", + "Object 5: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.80\n", + "Object 6: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.76\n", + "Object 7: Hit Rate = 0.03, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.01, Expected Age = 0.56\n", + "Object 8: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.73\n", + "Object 9: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.80\n", + "Object 10: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.76\n", + "Object 11: Hit Rate = 0.05, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.88\n", + "Object 12: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.73\n", + "Object 13: Hit Rate = 0.04, Expected Hit Rate = 0.05, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.77\n", + "Object 14: Hit Rate = 0.03, Expected Hit Rate = 0.06, Average Time spend in Cache: 0.04, Average Age = 0.01, Expected Age = 0.60\n", + "Object 15: Hit Rate = 0.05, Expected Hit Rate = 0.06, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.81\n", + "Object 16: Hit Rate = 0.05, Expected Hit Rate = 0.06, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.84\n", + "Object 17: Hit Rate = 0.04, Expected Hit Rate = 0.06, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.73\n", + "Object 18: Hit Rate = 0.03, Expected Hit Rate = 0.06, Average Time spend in Cache: 0.04, Average Age = 0.02, Expected Age = 0.55\n", + "Object 19: Hit Rate = 0.04, Expected Hit Rate = 0.06, Average Time spend in Cache: 0.04, Average Age = 0.01, Expected Age = 0.63\n", + "Object 20: Hit Rate = 0.04, Expected 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+ " -0.432202\n", " \n", " \n", "\n", @@ -910,44 +956,44 @@ "" ], "text/plain": [ - " access_count hits misses mu lambda hit_rate optimal_hitrates \\\n", - "0 2194 122 2072 None 2.0 0.055606 0.0513 \n", - "1 2237 98 2139 None 2.0 0.043809 0.0513 \n", - "2 6160 2458 3702 None 5.0 0.399026 0.4000 \n", - "3 3576 842 2734 None 3.0 0.235459 0.2254 \n", - "4 1106 0 1106 None 1.0 0.000000 0.0000 \n", - ".. ... ... ... ... ... ... ... \n", - "95 1000 0 1000 None 1.0 0.000000 0.0000 \n", - "96 1055 0 1055 None 1.0 0.000000 0.0000 \n", - "97 1063 0 1063 None 1.0 0.000000 0.0000 \n", - "98 11421 6278 5143 None 9.0 0.549689 0.5528 \n", - "99 1091 0 1091 None 1.0 0.000000 0.0000 \n", + " access_count hits misses mu lambda hit_rate optimal_hitrates \\\n", + "0 1022 52 970 None 0.0502 0.050881 0.0513 \n", + "1 1045 42 1003 None 0.0506 0.040191 0.0513 \n", + "2 1009 33 976 None 0.0511 0.032706 0.4000 \n", + "3 1059 45 1014 None 0.0515 0.042493 0.2254 \n", + "4 1045 37 1008 None 0.0519 0.035407 0.0000 \n", + ".. ... ... ... ... ... ... ... \n", + "95 10811 3365 7446 None 0.5519 0.311257 0.0000 \n", + "96 13157 4684 8473 None 0.6598 0.356008 0.0000 \n", + "97 16227 6542 9685 None 0.8305 0.403155 0.0000 \n", + "98 22767 11201 11566 None 1.1487 0.491984 0.5528 \n", + "99 39079 24517 14562 None 2.0000 0.627370 0.0000 \n", "\n", " expected_hit_rate expected_hit_rate_delta avg_cache_time \\\n", - "0 0.051241 0.004366 0.039414 \n", - "1 0.051241 -0.007432 0.040331 \n", - "2 0.400105 -0.001079 0.346128 \n", - "3 0.225316 0.010143 0.187388 \n", - "4 0.000000 0.000000 0.000000 \n", + "0 0.048961 0.001920 0.038411 \n", + "1 0.049341 -0.009150 0.040332 \n", + "2 0.049816 -0.017111 0.039100 \n", + "3 0.050196 -0.007703 0.041095 \n", + "4 0.050576 -0.015169 0.041100 \n", ".. ... ... ... \n", - "95 0.000000 0.000000 0.000000 \n", - "96 0.000000 0.000000 0.000000 \n", - "97 0.000000 0.000000 0.000000 \n", - "98 0.552733 -0.003044 0.495182 \n", - "99 0.000000 0.000000 0.000000 \n", + "95 0.424145 -0.112888 0.308356 \n", + "96 0.483045 -0.127037 0.353400 \n", + "97 0.564169 -0.161013 0.407435 \n", + "98 0.682951 -0.190967 0.489995 \n", + "99 0.864665 -0.237295 0.627601 \n", "\n", " cache_time_delta avg_age expected_age age_delta age_delta in % \n", - "0 0.016193 0.000781 0.027889 -0.027108 -0.971993 \n", - "1 0.003478 0.000547 0.021946 -0.021399 -0.975066 \n", - "2 0.052898 0.030540 0.094918 -0.064378 -0.678247 \n", - "3 0.048071 0.012580 0.083093 -0.070513 -0.848607 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "0 0.012470 0.023130 1.016189 -0.993059 -0.977238 \n", + "1 -0.000141 0.016009 0.795581 -0.779572 -0.979877 \n", + "2 -0.006394 0.013227 0.640718 -0.627490 -0.979355 \n", + "3 0.001398 0.015914 0.826598 -0.810683 -0.980747 \n", + "4 -0.005693 0.011848 0.683066 -0.671218 -0.982655 \n", ".. ... ... ... ... ... \n", - "95 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "96 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "97 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "98 0.054507 0.048121 0.087522 -0.039401 -0.450188 \n", - "99 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "95 0.002901 0.137645 0.624473 -0.486829 -0.779583 \n", + "96 0.002608 0.160020 0.617881 -0.457862 -0.741019 \n", + "97 -0.004280 0.185865 0.579650 -0.393785 -0.679350 \n", + "98 0.001989 0.224034 0.565071 -0.341037 -0.603530 \n", + "99 -0.000231 0.293713 0.517285 -0.223572 -0.432202 \n", "\n", "[100 rows x 15 columns]" ] @@ -1003,7 +1049,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -1037,7 +1083,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -1177,6 +1223,64 @@ { "cell_type": "code", "execution_count": 25, + "id": "5b6dac2e-8596-4e7c-97d8-aaf9632e4154", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 0.0\n", + "mean NaN\n", + "std NaN\n", + "min NaN\n", + "25% NaN\n", + "50% NaN\n", + "75% NaN\n", + "max NaN\n", + "Name: lambda, dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged[merged['hits']==0]['lambda'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "29393374-e379-42c8-8333-abfecc18e828", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 100.000000\n", + "mean 0.162692\n", + "std 0.247757\n", + "min 0.050200\n", + "25% 0.063050\n", + "50% 0.086800\n", + "75% 0.148775\n", + "max 2.000000\n", + "Name: lambda, dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged[merged['hits']>0]['lambda'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "id": "b47990b1-0231-43ac-8bc5-8340abe4a8b3", "metadata": {}, "outputs": [], @@ -1189,7 +1293,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "id": "db83cad4-7cc6-4702-ae3a-d1af30a561d2", "metadata": {}, "outputs": [], diff --git a/00_aoi_caching_simulation/file_experiment/details.csv b/00_aoi_caching_simulation/file_experiment/details.csv deleted file mode 100644 index 0ef2266..0000000 --- a/00_aoi_caching_simulation/file_experiment/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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