age_cache_simulation/details.csv at 673302a6b2b10e075bcc5af94156a6cfb56a1aaf

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	494	170	1	45.28
3	2	212	96	116	3	74.15
4	3	673	499	174	1	76.65
5	4	711	545	166	1	60.0
6	5	345	207	138	2	73.96
7	6	649	480	169	1	38.69
8	7	137	53	84	5	74.3
9	8	681	506	175	1	74.6
10	9	689	514	175	1	74.66
11	10	667	498	169	1	60.37
12	11	328	198	130	2	75.29
13	12	692	521	171	1	74.96
14	13	683	512	171	1	74.71
15	14	696	520	176	1	60.76
16	15	344	209	135	2	59.2
17	16	326	193	133	2	75.47
18	17	689	520	169	1	75.11
19	18	691	519	172	1	51.6
20	19	219	113	106	3	75.96
21	20	732	556	176	1	76.32
22	21	739	564	175	1	75.26
23	22	683	514	169	1	74.78
24	23	670	501	169	1	57.4
25	24	331	190	141	2	75.83
26	25	724	549	175	1	74.86
27	26	692	518	174	1	75.74
28	27	705	534	171	1	37.23
29	28	137	51	86	5	75.15
30	29	672	505	167	1	74.64
31	30	694	518	176	1	74.78
32	31	674	504	170	1	38.89
33	32	144	56	88	4	74.93
34	33	670	502	168	1	31.91
35	34	141	45	96	4	74.66
36	35	659	492	167	1	75.4
37	36	691	521	170	1	76.12
38	37	737	561	176	1	48.86
39	38	219	107	112	3	28.0
40	39	100	28	72	8	75.91
41	40	714	542	172	1	42.39
42	41	184	78	106	4	43.48
43	42	161	70	91	4	61.88
44	43	362	224	138	2	75.43
45	44	696	525	171	1	73.21
46	45	627	459	168	1	75.15
47	46	648	487	161	1	22.64
48	47	53	12	41	10	74.85
49	48	676	506	170	1	73.98
50	49	638	472	166	1	74.59
51	50	673	502	171	1	39.06
52	51	128	50	78	5	21.18
53	52	85	18	67	9	76.57
54	53	747	572	175	1	76.0
55	54	700	532	168	1	75.25
56	55	687	517	170	1	75.42
57	56	720	543	177	1	75.36
58	57	690	520	170	1	10.0
59	58	40	4	36	17	60.69
60	59	318	193	125	2	74.93
61	60	674	505	169	1	21.74
62	61	23	5	18	26	75.55
63	62	724	547	177	1	74.2
64	63	628	466	162	1	60.29
65	64	345	208	137	2	74.89
66	65	681	510	171	1	23.29
67	66	73	17	56	10	74.35
68	67	651	484	167	1	0.0
69	68	10	0	10	69	73.9
70	69	636	470	166	1	74.7
71	70	656	490	166	1	61.56
72	71	372	229	143	2	75.52
73	72	678	512	166	1	73.51
74	73	638	469	169	1	74.89
75	74	685	513	172	1	44.28
76	75	201	89	112	3	59.88
77	76	344	206	138	2	61.45
78	77	345	212	133	2	50.19
79	78	261	131	130	3	10.64
80	79	47	5	42	15	75.3
81	80	660	497	163	1	74.29
82	81	634	471	163	1	37.96
83	82	137	52	85	5	57.28
84	83	309	177	132	2	75.51
85	84	686	518	168	1	74.63
86	85	682	509	173	1	58.39
87	86	322	188	134	2	74.7
88	87	676	505	171	1	58.15
89	88	325	189	136	2	75.62
90	89	689	521	168	1	75.11
91	90	695	522	173	1	62.35
92	91	324	202	122	2	60.53
93	92	337	204	133	2	47.75
94	93	222	106	116	3	75.07
95	94	686	515	171	1	62.39
96	95	351	219	132	2	76.29
97	96	717	547	170	1	74.4
98	97	664	494	170	1	8.7
99	98	23	2	21	37	46.96
100	99	181	85	96	4	59.64

2.4 KiB Raw Blame History

2.4 KiB

Raw Blame History