Adaptive Intelligent Tiering for modern storage systems

Abstract

Enterprise systems routinely use tiered storage consisting of a hierarchy of storage devices that vary in speed and size. One key to obtaining good performance in such a hierarchy is to migrate data elements intelligently to the appropriate tier. For example, moving the most used data towards the fastest tier and the least used data towards the slowest tier. Tiering is typically done based on usage statistics over relatively long time periods. In this paper, we consider a much more agile tiering mechanism called Adaptive Intelligent Tiering (AIT). It can dynamically adapt to the changing behavior of storage accesses by the running applications. The AIT mechanism uses a deep learning model to generate a set of candidate movements and employs a reinforcement learning mechanism to further refine the candidates. Based on extensive simulations in a 3-tier system, we show that the proposed scheme, compared with several other methods, enhances workload performance up to 85% on storage traces with a wide range of characteristics.