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SA-LSM: Optimize Data Layout for LSM-tree Based Storage using Survival Analysis

Summary: SA-LSM applies survival analysis to LSM access traces and semantic history, predicting cold records to drive data layout beyond write/compaction policies. An externalized training/inference architecture integrates with X-Engine, cutting tail latency by up to 78.9%. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12900
Venue
VLDB
Year
2022
Pagerank
5.4444442e-05
Overall Rank
8,360 | 42.65%
DOI
10.14778/3547305.3547320

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhang_vldb22,
        title = {{SA-LSM: Optimize Data Layout for LSM-tree Based Storage using Survival Analysis}},
        author = {Zhang, Teng and Tan, Jian and Cai, Xin and Wang, Jianying and Li, Feifei and Sun, Jianling},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {10},
        pages = {2161--2174},
        doi = {10.14778/3547305.3547320},
        url = {https://doi.org/10.14778/3547305.3547320},
        year = {2022}
}

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