Reinforcement Learning-Assisted Cache Cleaning to Mitigate Long-Tail Latency in DM-SMR - Pan, Yungang; Jia, Zhiping; Shen, Zhaoyan; Li, Bingzhe; Chang, Wanli; Shao, Zili (2021 58th ACM/IEEE Design Automation Conference (DAC), 2021)
DM-SMR adopts Persistent Cache (PC) to accommodate non-sequential write operations. However, the PC cleaning process induces severe long-tail latency. In this paper, we propose to mitigate the tail latency of PC cleaning by using Reinforcement Learning (RL). Specifically, a real-time lightweight Q-learning model is built to analyze the idle window of I/O workloads, based on which PC cleaning is judiciously scheduled, thereby maximally utilizing the I/O idle window and effectively hiding the tail latency from regular requests. We implement our technique inside a Linux device driver with an emulated SMR drive. Experimental results show that our technique can reduce the tail latency by 57.65% at 99.9th percentile and the average response time by 46.11% compared to a typical SMR design.