ReStore-in-Action: Adaptive Multi-Tier Storage Management via Intelligent Data Migration Policy
Summary: ReStore uses lightweight page-level reinforcement learning to adapt multi-tier placement to workload shifts and device-specific asymmetries, reducing runtime and migrations. A web demo interactively compares it with five policies under configurable tiers and workloads. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Shadman Saqib Eusuf (University of Massachusetts Boston)
- 2. Tianru Zhang (Uppsala University)
- 3. Teona Bagashvili (Boston University)
- 4. Manos Athanassoulis (Boston University)
- 5. Tarikul Islam Papon (University of Massachusetts Boston)
BibTeX Citation
@article{eusuf_vldb26,
title = {{ReStore-in-Action: Adaptive Multi-Tier Storage Management via Intelligent Data Migration Policy}},
author = {Eusuf, Shadman Saqib and Zhang, Tianru and Bagashvili, Teona and Athanassoulis, Manos and Papon, Tarikul Islam},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4702--4705},
doi = {10.14778/3827998.3828101},
url = {https://doi.org/10.14778/3827998.3828101},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 212 | The LRU-K Page Replacement Algorithm For Database Disk Buffering | 1993 | SIGMOD | 0.00024767296 |
| 4,008 | Automating Distributed Tiered Storage Management in Cluster Computing | 2020 | VLDB | 6.8591707e-05 |
| 10,496 | ReStore: A Reinforcement Learning Approach for Data Migration in Multi-Tiered Storage | 2026 | SIGMOD | 4.9793485e-05 |
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