Sibyl

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.