Paper Info
Title
Sibyl: adaptive and extensible data placement in hybrid storage systems using online reinforcement learning
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 benefits of such a hybrid system. Recent research proposes various techniques that aim to accurately identify performance-critical data to place it in a "best-fit" 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 configurations, and (2) makes it difficult for designers to extend these techniques to different storage system configurations (e.g., with a different number or different types of storage devices) than the configuration 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 configurations. We introduce Sibyl , the first 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 configurations, 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 performance-oriented/cost-oriented HSS configuration compared to the best previous data placement technique. Our evaluation using an HSS configuration with three different storage devices shows that Sibyl outperforms the state-of-the-art data placement policy by 23.9%-48.2%, while significantly 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 offuture access patterns while incurring a very modest storage overhead of only 124.4 KiB.
Year
DOI
Venue
2022
10.1145/3470496.3527442
ISCA: International Symposium on Computer Architecture
Keywords
DocType
ISSN
solid-state drives (SSDs), reinforcement learning, hybrid storage systems, data placement, hybrid systems, machine learning
Conference
1063-6897
Citations 
PageRank 
References 
0
0.34
16
Authors
10
Name
Order
Citations
PageRank
Gagandeep Singh141.03
Rakesh Nadig200.68
Jisung Park362.08
Rahul Bera441.04
Nastaran Hajinazar500.34
David Novo692.04
Juan Gómez-Luna7223.88
Sander Stuijk8107870.45
Henk Corporaal91787166.20
Onur Mutlu109446357.40