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ReStore: A Reinforcement Learning Approach for Data Migration in Multi-Tiered Storage

Summary: ReStore uses per-tier reinforcement-learning agents for adaptive, page-level migration, jointly modeling access patterns and SSD read/write asymmetry and parallelism. It delivers up to 6× lower runtime and 48× fewer migrations across benchmarks and traces. (summarized by gpt-5.6-luna on Jul 26 2026)

Paper ID
h6570eb261637f162
Venue
SIGMOD
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,496 | 29.44%
DOI
10.1145/3802104

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Authors

BibTeX Citation

@inproceedings{zhang_sigmod26,
        title = {{ReStore: A Reinforcement Learning Approach for Data Migration in Multi-Tiered Storage}},
        author = {Zhang, Tianru and Papon, Tarikul Islam and Bagashvili, Teona and Toor, Salman and Athanassoulis, Manos},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3802104},
        url = {https://dl.acm.org/doi/10.1145/3802104},
        year = {2026}
}

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Rank Citing Paper Year Venue Pagerank
10,994 ReStore-in-Action: Adaptive Multi-Tier Storage Management via Intelligent Data Migration Policy 2026 VLDB 4.9793485e-05
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