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 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> 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.