age_cache_simulation/details.csv at 2040ea639f60a6ef788ddeeb7da46b26f236beeb

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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	445	219	1	37.74
3	2	212	80	132	3	66.42
4	3	673	447	226	1	67.37
5	4	711	479	232	1	51.01
6	5	345	176	169	2	64.56
7	6	649	419	230	1	29.2
8	7	137	40	97	5	66.37
9	8	681	452	229	1	66.76
10	9	689	460	229	1	66.57
11	10	667	444	223	1	52.13
12	11	328	171	157	2	67.92
13	12	692	470	222	1	66.03
14	13	683	451	232	1	66.67
15	14	696	464	232	1	48.55
16	15	344	167	177	2	48.47
17	16	326	158	168	2	66.33
18	17	689	457	232	1	65.99
19	18	691	456	235	1	41.1
20	19	219	90	129	3	68.58
21	20	732	502	230	1	68.88
22	21	739	509	230	1	66.76
23	22	683	456	227	1	66.57
24	23	670	446	224	1	49.85
25	24	331	165	166	2	68.09
26	25	724	493	231	1	66.91
27	26	692	463	229	1	68.09
28	27	705	480	225	1	28.47
29	28	137	39	98	5	65.92
30	29	672	443	229	1	66.28
31	30	694	460	234	1	66.62
32	31	674	449	225	1	29.17
33	32	144	42	102	4	66.72
34	33	670	447	223	1	26.95
35	34	141	38	103	4	66.77
36	35	659	440	219	1	67.15
37	36	691	464	227	1	69.06
38	37	737	509	228	1	39.27
39	38	219	86	133	3	21.0
40	39	100	21	79	8	66.81
41	40	714	477	237	1	32.07
42	41	184	59	125	4	34.78
43	42	161	56	105	4	53.87
44	43	362	195	167	2	66.52
45	44	696	463	233	1	64.59
46	45	627	405	222	1	64.97
47	46	648	421	227	1	9.43
48	47	53	5	48	10	64.79
49	48	676	438	238	1	65.52
50	49	638	418	220	1	66.12
51	50	673	445	228	1	29.69
52	51	128	38	90	5	16.47
53	52	85	14	71	9	68.14
54	53	747	509	238	1	67.71
55	54	700	474	226	1	66.67
56	55	687	458	229	1	68.89
57	56	720	496	224	1	67.25
58	57	690	464	226	1	5.0
59	58	40	2	38	17	48.11
60	59	318	153	165	2	66.32
61	60	674	447	227	1	17.39
62	61	23	4	19	26	68.09
63	62	724	493	231	1	64.49
64	63	628	405	223	1	52.46
65	64	345	181	164	2	67.11
66	65	681	457	224	1	16.44
67	66	73	12	61	10	66.36
68	67	651	432	219	1	0.0
69	68	10	0	10	69	65.41
70	69	636	416	220	1	65.7
71	70	656	431	225	1	50.81
72	71	372	189	183	2	67.7
73	72	678	459	219	1	66.14
74	73	638	422	216	1	65.69
75	74	685	450	235	1	32.84
76	75	201	66	135	3	52.33
77	76	344	180	164	2	51.88
78	77	345	179	166	2	41.76
79	78	261	109	152	3	10.64
80	79	47	5	42	15	66.52
81	80	660	439	221	1	65.3
82	81	634	414	220	1	29.2
83	82	137	40	97	5	46.93
84	83	309	145	164	2	67.49
85	84	686	463	223	1	66.72
86	85	682	455	227	1	49.07
87	86	322	158	164	2	64.94
88	87	676	439	237	1	48.92
89	88	325	159	166	2	66.62
90	89	689	459	230	1	67.05
91	90	695	466	229	1	51.23
92	91	324	166	158	2	51.63
93	92	337	174	163	2	39.64
94	93	222	88	134	3	66.18
95	94	686	454	232	1	50.71
96	95	351	178	173	2	67.78
97	96	717	486	231	1	66.72
98	97	664	443	221	1	0.0
99	98	23	0	23	37	38.12
100	99	181	69	112	4	50.15

2.4 KiB Raw Blame History

2.4 KiB

Raw Blame History