age_cache_simulation/details.csv at 62fc02b20528ebffb18f131b05d7597674d1170a

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Tuan-Dat Tran 6e8a742705 feat: add cache simulation with experiments for TTL and eviction strategies

Introduce a new simulation for Age of Information (AoI) cache management, focusing on varying TTL values and eviction strategies (LRU and Random Eviction). This includes:
- New Python script  for event-driven cache simulations using .
- Experiments for "No Refresh" across multiple TTL configurations (, , ..., ) with:
  - Hit rate and object age tracking (, , etc.).
  - Visualizations (e.g., , ).
- Updated  to describe experimental setup and configurations.
- Log export file () for simulation results.
- Refactor of  with detailed strategy configurations and runtime notes.

### Reason
The commit enhances the project by enabling detailed experiments with configurable cache parameters, supporting analysis of cache efficiency and AoI under varying conditions. This provides a foundation for more sophisticated simulations and insights.

### Performance
- Runtime: ~4m 29s for .

Co-authored experiments introduce structured data files and visualizations, improving clarity for future iterations.

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

2024-11-27 16:31:46 +01:00

2.4 KiB

Raw Blame History

1		access_count	hits	misses	lambda	hit_rate
2	1	664	527	137	1	52.83
3	2	212	112	100	3	79.79
4	3	673	537	136	1	80.87
5	4	711	575	136	1	68.99
6	5	345	238	107	2	79.35
7	6	649	515	134	1	43.8
8	7	137	60	77	5	80.32
9	8	681	547	134	1	79.97
10	9	689	551	138	1	79.31
11	10	667	529	138	1	68.29
12	11	328	224	104	2	80.2
13	12	692	555	137	1	80.09
14	13	683	547	136	1	80.6
15	14	696	561	135	1	67.15
16	15	344	231	113	2	65.64
17	16	326	214	112	2	80.12
18	17	689	552	137	1	80.32
19	18	691	555	136	1	55.71
20	19	219	122	97	3	80.87
21	20	732	592	140	1	81.33
22	21	739	601	138	1	79.8
23	22	683	545	138	1	79.85
24	23	670	535	135	1	67.07
25	24	331	222	109	2	80.66
26	25	724	584	140	1	80.35
27	26	692	556	136	1	80.85
28	27	705	570	135	1	43.8
29	28	137	60	77	5	79.76
30	29	672	536	136	1	79.83
31	30	694	554	140	1	79.82
32	31	674	538	136	1	47.92
33	32	144	69	75	4	79.55
34	33	670	533	137	1	43.26
35	34	141	61	80	4	79.51
36	35	659	524	135	1	80.03
37	36	691	553	138	1	80.73
38	37	737	595	142	1	57.08
39	38	219	125	94	3	37.0
40	39	100	37	63	8	80.39
41	40	714	574	140	1	50.54
42	41	184	93	91	4	47.83
43	42	161	77	84	4	68.51
44	43	362	248	114	2	80.6
45	44	696	561	135	1	78.63
46	45	627	493	134	1	79.63
47	46	648	516	132	1	24.53
48	47	53	13	40	10	79.73
49	48	676	539	137	1	78.68
50	49	638	502	136	1	79.35
51	50	673	534	139	1	42.97
52	51	128	55	73	5	30.59
53	52	85	26	59	9	81.12
54	53	747	606	141	1	81.0
55	54	700	567	133	1	80.2
56	55	687	551	136	1	81.25
57	56	720	585	135	1	79.57
58	57	690	549	141	1	12.5
59	58	40	5	35	17	65.72
60	59	318	209	109	2	79.82
61	60	674	538	136	1	21.74
62	61	23	5	18	26	80.94
63	62	724	586	138	1	78.34
64	63	628	492	136	1	66.96
65	64	345	231	114	2	80.62
66	65	681	549	132	1	28.77
67	66	73	21	52	10	79.57
68	67	651	518	133	1	0.0
69	68	10	0	10	69	78.46
70	69	636	499	137	1	79.73
71	70	656	523	133	1	67.74
72	71	372	252	120	2	80.38
73	72	678	545	133	1	79.31
74	73	638	506	132	1	79.85
75	74	685	547	138	1	52.24
76	75	201	105	96	3	65.99
77	76	344	227	117	2	68.41
78	77	345	236	109	2	59.0
79	78	261	154	107	3	14.89
80	79	47	7	40	15	79.7
81	80	660	526	134	1	79.02
82	81	634	501	133	1	43.07
83	82	137	59	78	5	65.05
84	83	309	201	108	2	80.47
85	84	686	552	134	1	79.47
86	85	682	542	140	1	64.91
87	86	322	209	113	2	79.14
88	87	676	535	141	1	65.23
89	88	325	212	113	2	80.41
90	89	689	554	135	1	80.43
91	90	695	559	136	1	67.59
92	91	324	219	105	2	67.36
93	92	337	227	110	2	56.76
94	93	222	126	96	3	79.74
95	94	686	547	139	1	68.38
96	95	351	240	111	2	80.61
97	96	717	578	139	1	79.52
98	97	664	528	136	1	17.39
99	98	23	4	19	37	53.04
100	99	181	96	85	4	68.25

2.4 KiB Raw Blame History

2.4 KiB

Raw Blame History