Reinforcement Learning-Assisted Cache Cleaning to Mitigate Long-Tail Latency in DM-SMR
Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Embedded Memory, Storage and Networking
Embedded Systems
DescriptionDM-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 effectively hiding the tail latency from regular requests. 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.