Dynamic management of key states for reinforcement learning-assisted garbage collection to reduce long tail latency in SSD

Abstract

Garbage collection (GC) is one of main causes of the long-tail latency problem in storage systems. Long-tail latency due to GC is more than 100 times greater than the average latency at the 99th percentile. Therefore, due to such a long tail latency, real-time systems and quality-critical systems cannot meet the system requirements. In this study, we propose a novel key state management technique of reinforcement learning-assisted garbage collection. The purpose of this study is to dynamically manage key states from a significant number of state candidates. Dynamic management enables us to utilize suitable and frequently recurring key states at a small area cost since the full states do not have to be managed. The experimental results show that the proposed technique reduces by 22--25% the long-tail latency compared to a state-of-the-art scheme with real-world workloads.