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	224	440	1	10.38
3	2	212	22	190	3	32.39
4	3	673	218	455	1	34.6
5	4	711	246	465	1	19.13
6	5	345	66	279	2	31.74
7	6	649	206	443	1	10.22
8	7	137	14	123	5	32.45
9	8	681	221	460	1	34.4
10	9	689	237	452	1	34.18
11	10	667	228	439	1	21.95
12	11	328	72	256	2	34.97
13	12	692	242	450	1	32.21
14	13	683	220	463	1	33.62
15	14	696	234	462	1	20.35
16	15	344	70	274	2	18.4
17	16	326	60	266	2	29.9
18	17	689	206	483	1	32.85
19	18	691	227	464	1	11.87
20	19	219	26	193	3	34.84
21	20	732	255	477	1	35.05
22	21	739	259	480	1	34.55
23	22	683	236	447	1	35.22
24	23	670	236	434	1	19.94
25	24	331	66	265	2	34.67
26	25	724	251	473	1	32.08
27	26	692	222	470	1	34.18
28	27	705	241	464	1	8.76
29	28	137	12	125	5	34.23
30	29	672	230	442	1	31.7
31	30	694	220	474	1	33.09
32	31	674	223	451	1	9.03
33	32	144	13	131	4	34.63
34	33	670	232	438	1	7.09
35	34	141	10	131	4	32.78
36	35	659	216	443	1	32.27
37	36	691	223	468	1	36.91
38	37	737	272	465	1	14.16
39	38	219	31	188	3	5.0
40	39	100	5	95	8	32.77
41	40	714	234	480	1	12.5
42	41	184	23	161	4	7.45
43	42	161	12	149	4	21.55
44	43	362	78	284	2	32.61
45	44	696	227	469	1	29.98
46	45	627	188	439	1	32.56
47	46	648	211	437	1	1.89
48	47	53	1	52	10	31.51
49	48	676	213	463	1	31.35
50	49	638	200	438	1	32.69
51	50	673	220	453	1	12.5
52	51	128	16	112	5	3.53
53	52	85	3	82	9	33.33
54	53	747	249	498	1	33.0
55	54	700	231	469	1	35.08
56	55	687	241	446	1	37.5
57	56	720	270	450	1	31.3
58	57	690	216	474	1	0.0
59	58	40	0	40	17	19.18
60	59	318	61	257	2	31.31
61	60	674	211	463	1	13.04
62	61	23	3	20	26	35.22
63	62	724	255	469	1	32.32
64	63	628	203	425	1	24.93
65	64	345	86	259	2	35.1
66	65	681	239	442	1	4.11
67	66	73	3	70	10	33.79
68	67	651	220	431	1	0.0
69	68	10	0	10	69	33.02
70	69	636	210	426	1	32.77
71	70	656	215	441	1	22.58
72	71	372	84	288	2	33.48
73	72	678	227	451	1	33.7
74	73	638	215	423	1	33.28
75	74	685	228	457	1	10.45
76	75	201	21	180	3	20.35
77	76	344	70	274	2	21.16
78	77	345	73	272	2	17.24
79	78	261	45	216	3	4.26
80	79	47	2	45	15	33.79
81	80	660	223	437	1	30.28
82	81	634	192	442	1	5.84
83	82	137	8	129	5	18.45
84	83	309	57	252	2	34.11
85	84	686	234	452	1	32.99
86	85	682	225	457	1	19.25
87	86	322	62	260	2	32.99
88	87	676	223	453	1	20.92
89	88	325	68	257	2	35.27
90	89	689	243	446	1	32.95
91	90	695	229	466	1	21.91
92	91	324	71	253	2	20.47
93	92	337	69	268	2	13.96
94	93	222	31	191	3	31.78
95	94	686	218	468	1	19.94
96	95	351	70	281	2	33.89
97	96	717	243	474	1	32.53
98	97	664	216	448	1	0.0
99	98	23	0	23	37	11.6
100	99	181	21	160	4	22.85

2.4 KiB Raw Blame History

2.4 KiB

Raw Blame History