Reinforcement Learning-Assisted Garbage Collection to Mitigate Long-Tail Latency in SSD

NAND flash memory is widely used in various systems, ranging from real-time embedded systems to enterprise server systems. Because the flash memory has erase-before-write characteristics, we need flash-memory management methods, i.e., address translation and garbage collection. In particular, garbage collection (GC) incurs long-tail latency, e.g., 100 times higher latency than the average latency at the 99th percentile. Thus, real-time and quality-critical systems fail to meet the given requirements such as deadline and QoS constraints. In this study, we propose a novel method of GC based on reinforcement learning. The objective is to reduce the long-tail latency by exploiting the idle time in the storage system. To improve the efficiency of the reinforcement learning-assisted GC scheme, we present new optimization methods that exploit fine-grained GC to further reduce the long-tail latency. The experimental results with real workloads show that our technique significantly reduces the long-tail latency by 29-36% at the 99.99th percentile compared to state-of-the-art schemes.