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	550	114	1	58.49
3	2	212	124	88	3	83.21
4	3	673	560	113	1	84.11
5	4	711	598	113	1	72.17
6	5	345	249	96	2	82.43
7	6	649	535	114	1	50.36
8	7	137	69	68	5	83.41
9	8	681	568	113	1	83.74
10	9	689	577	112	1	82.91
11	10	667	553	114	1	71.34
12	11	328	234	94	2	83.67
13	12	692	579	113	1	83.46
14	13	683	570	113	1	83.48
15	14	696	581	115	1	70.64
16	15	344	243	101	2	71.78
17	16	326	234	92	2	83.45
18	17	689	575	114	1	83.65
19	18	691	578	113	1	62.56
20	19	219	137	82	3	84.29
21	20	732	617	115	1	84.44
22	21	739	624	115	1	83.31
23	22	683	569	114	1	83.13
24	23	670	557	113	1	71.3
25	24	331	236	95	2	83.84
26	25	724	607	117	1	83.38
27	26	692	577	115	1	83.69
28	27	705	590	115	1	48.18
29	28	137	66	71	5	83.04
30	29	672	558	114	1	83.43
31	30	694	579	115	1	83.23
32	31	674	561	113	1	51.39
33	32	144	74	70	4	83.13
34	33	670	557	113	1	48.94
35	34	141	69	72	4	83.0
36	35	659	547	112	1	83.36
37	36	691	576	115	1	84.67
38	37	737	624	113	1	62.56
39	38	219	137	82	3	41.0
40	39	100	41	59	8	83.61
41	40	714	597	117	1	55.98
42	41	184	103	81	4	57.14
43	42	161	92	69	4	73.76
44	43	362	267	95	2	83.91
45	44	696	584	112	1	82.46
46	45	627	517	110	1	82.72
47	46	648	536	112	1	30.19
48	47	53	16	37	10	83.14
49	48	676	562	114	1	82.29
50	49	638	525	113	1	83.06
51	50	673	559	114	1	49.22
52	51	128	63	65	5	34.12
53	52	85	29	56	9	84.34
54	53	747	630	117	1	83.86
55	54	700	587	113	1	83.41
56	55	687	573	114	1	83.75
57	56	720	603	117	1	83.33
58	57	690	575	115	1	17.5
59	58	40	7	33	17	70.13
60	59	318	223	95	2	82.94
61	60	674	559	115	1	21.74
62	61	23	5	18	26	83.84
63	62	724	607	117	1	82.17
64	63	628	516	112	1	71.59
65	64	345	247	98	2	83.11
66	65	681	566	115	1	31.51
67	66	73	23	50	10	83.1
68	67	651	541	110	1	0.0
69	68	10	0	10	69	82.55
70	69	636	525	111	1	82.62
71	70	656	542	114	1	73.12
72	71	372	272	100	2	83.48
73	72	678	566	112	1	82.13
74	73	638	524	114	1	82.92
75	74	685	568	117	1	57.71
76	75	201	116	85	3	72.67
77	76	344	250	94	2	72.17
78	77	345	249	96	2	64.75
79	78	261	169	92	3	19.15
80	79	47	9	38	15	83.48
81	80	660	551	109	1	82.18
82	81	634	521	113	1	51.09
83	82	137	70	67	5	69.58
84	83	309	215	94	2	83.53
85	84	686	573	113	1	82.84
86	85	682	565	117	1	71.12
87	86	322	229	93	2	83.28
88	87	676	563	113	1	68.62
89	88	325	223	102	2	83.16
90	89	689	573	116	1	83.31
91	90	695	579	116	1	73.15
92	91	324	237	87	2	72.11
93	92	337	243	94	2	63.51
94	93	222	141	81	3	83.09
95	94	686	570	116	1	71.79
96	95	351	252	99	2	84.66
97	96	717	607	110	1	83.28
98	97	664	553	111	1	21.74
99	98	23	5	18	37	56.91
100	99	181	103	78	4	70.62

2.4 KiB Raw Blame History

2.4 KiB

Raw Blame History