age_cache_simulation/details.csv at b540aafc61de728573c7dad56a22d69271a7bec8

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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	328	336	1	22.17
3	2	212	47	165	3	50.07
4	3	673	337	336	1	50.63
5	4	711	360	351	1	32.75
6	5	345	113	232	2	49.46
7	6	649	321	328	1	18.98
8	7	137	26	111	5	49.63
9	8	681	338	343	1	50.22
10	9	689	346	343	1	49.33
11	10	667	329	338	1	37.8
12	11	328	124	204	2	52.31
13	12	692	362	330	1	48.46
14	13	683	331	352	1	49.86
15	14	696	347	349	1	31.98
16	15	344	110	234	2	29.14
17	16	326	95	231	2	48.48
18	17	689	334	355	1	49.93
19	18	691	345	346	1	22.83
20	19	219	50	169	3	51.09
21	20	732	374	358	1	52.77
22	21	739	390	349	1	51.1
23	22	683	349	334	1	49.55
24	23	670	332	338	1	32.02
25	24	331	106	225	2	50.97
26	25	724	369	355	1	50.58
27	26	692	350	342	1	51.49
28	27	705	363	342	1	15.33
29	28	137	21	116	5	49.55
30	29	672	333	339	1	48.56
31	30	694	337	357	1	50.3
32	31	674	339	335	1	17.36
33	32	144	25	119	4	51.19
34	33	670	343	327	1	12.77
35	34	141	18	123	4	50.99
36	35	659	336	323	1	50.36
37	36	691	348	343	1	53.05
38	37	737	391	346	1	24.66
39	38	219	54	165	3	9.0
40	39	100	9	91	8	49.72
41	40	714	355	359	1	22.28
42	41	184	41	143	4	18.63
43	42	161	30	131	4	37.57
44	43	362	136	226	2	49.57
45	44	696	345	351	1	47.05
46	45	627	295	332	1	48.77
47	46	648	316	332	1	5.66
48	47	53	3	50	10	48.22
49	48	676	326	350	1	48.12
50	49	638	307	331	1	49.93
51	50	673	336	337	1	18.75
52	51	128	24	104	5	9.41
53	52	85	8	77	9	51.54
54	53	747	385	362	1	49.86
55	54	700	349	351	1	51.24
56	55	687	352	335	1	53.75
57	56	720	387	333	1	50.43
58	57	690	348	342	1	2.5
59	58	40	1	39	17	32.08
60	59	318	102	216	2	47.92
61	60	674	323	351	1	17.39
62	61	23	4	19	26	50.97
63	62	724	369	355	1	49.2
64	63	628	309	319	1	36.52
65	64	345	126	219	2	50.66
66	65	681	345	336	1	6.85
67	66	73	5	68	10	50.23
68	67	651	327	324	1	0.0
69	68	10	0	10	69	49.06
70	69	636	312	324	1	48.17
71	70	656	316	340	1	34.95
72	71	372	130	242	2	51.33
73	72	678	348	330	1	49.69
74	73	638	317	321	1	50.95
75	74	685	349	336	1	20.9
76	75	201	42	159	3	35.76
77	76	344	123	221	2	36.23
78	77	345	125	220	2	27.2
79	78	261	71	190	3	8.51
80	79	47	4	43	15	50.3
81	80	660	332	328	1	49.53
82	81	634	314	320	1	13.87
83	82	137	19	118	5	30.74
84	83	309	95	214	2	50.44
85	84	686	346	340	1	49.41
86	85	682	337	345	1	32.61
87	86	322	105	217	2	50.0
88	87	676	338	338	1	31.69
89	88	325	103	222	2	49.93
90	89	689	344	345	1	49.06
91	90	695	341	354	1	36.73
92	91	324	119	205	2	33.53
93	92	337	113	224	2	26.13
94	93	222	58	164	3	49.85
95	94	686	342	344	1	35.61
96	95	351	125	226	2	52.44
97	96	717	376	341	1	49.25
98	97	664	327	337	1	0.0
99	98	23	0	23	37	21.55
100	99	181	39	142	4	35.31

2.4 KiB Raw Blame History

2.4 KiB

Raw Blame History