Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning
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2022-06
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Conference Paper
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Abstract
Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Data placement across different devices is critical to maximize the benefts of such a hybrid system. Recent research proposes various techniques that aim to accurately identify performance-critical data to place it in a "best-ft"storage device. Unfortunately, most of these techniques are rigid, which (1) limits their adaptivity to perform well for a wide range of workloads and storage device confgurations, and (2) makes it difcult for designers to extend these techniques to different storage system confgurations (e.g., with a different number or different types of storage devices) than the confguration they are designed for. Our goal is to design a new data placement technique for hybrid storage systems that overcomes these issues and provides: (1) adaptivity, by continuously learning from and adapting to the workload and the storage device characteristics, and (2) easy extensibility to a wide range of workloads and HSS confgurations. We introduce Sibyl, the frst technique that uses reinforcement learning for data placement in hybrid storage systems. Sibyl observes different features of the running workload as well as the storage devices to make system-aware data placement decisions. For every decision it makes, Sibyl receives a reward from the system that it uses to evaluate the long-term performance impact of its decision and continuously optimizes its data placement policy online. We implement Sibyl on real systems with various HSS confgurations, including dual-and tri-hybrid storage systems, and extensively compare it against four previously proposed data placement techniques (both heuristic-and machine learning-based) over a wide range of workloads. Our results show that Sibyl provides 21.6%/19.9% performance improvement in a performanceoriented/cost-oriented HSS confguration compared to the best previous data placement technique. Our evaluation using an HSS confguration with three different storage devices shows that Sibyl outperforms the state-of-the-art data placement policy by 23.9%-48.2%, while signifcantly reducing the system architect's burden in designing a data placement mechanism that can simultaneously incorporate three storage devices. We show that Sibyl achieves 80% of the performance of an oracle policy that has complete knowledge of future access patterns while incurring a very modest storage overhead of only 124.4 KiB.
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ISCA '22: Proceedings of the 49th Annual International Symposium on Computer Architecture
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320 - 336
Publisher
Association for Computing Machinery
Event
49th Annual International Symposium on Computer Architecture (ISCA 2022)
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Software
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Subject
Solid-state drives (SSDs); Reinforcement learning; Hybrid storage systems; Data placement; Hybrid systems; Machine learning
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09483 - Mutlu, Onur / Mutlu, Onur