Augur: Predictive and Adaptive Data Management in SSD-SMR Hybrid Storage System Using Reinforcement Learning

In SSD-SMR hybrid storage systems, the Persistent Cache (PC) cleaning process in Shingled Magnetic Recording (SMR) disks involves frequent Read-Modify-Write (RMW) operations, significantly increasing system tail latency. Although traditional Q-learning methods alleviate this issue to some extent, the expansion of state space results in an increase in the dimensions of the Q-Table, leading to significant rises in computational and storage costs, thus limiting its ability to handle complex problems. In this paper, we propose an intelligent data prediction management strategy named Augur, based on Deep Reinforcement Learning (DRL), that predicts nodes where high latency is imminent and directs the agent named Pythia to timely redirect the flow of suspect data, effectively reducing RMW operations in SMR. We implement our technique on a real SSD-SMR hybrid storage system. Experimental results show that, compared to Q-learning and Skylight, our method reduces the system tail latency at the 99.9<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile by 23.61% and 58.35%, and reduces average request latency by 22.54% and 47.56%, respectively.