1. A Multiagent Reinforcement Learning-Assisted Cache Cleaning Scheme for DM-SMR
- Author
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Shen, Zhaoyan, Yang, Yuhan, Pan, Yungang, Zhang, Yuhao, Jia, Zhiping, Cai, Xiaojun, Li, Bingzhe, Shao, Zili, Shen, Zhaoyan, Yang, Yuhan, Pan, Yungang, Zhang, Yuhao, Jia, Zhiping, Cai, Xiaojun, Li, Bingzhe, and Shao, Zili
- Abstract
To support nonsequential writes, persistent cache (PC) is constructed in drive managed SMR (DM-SMR) drive. However, PC cleaning introduces drastic performance degradation and enlarges tail latencies. In this article, we propose to utilize reinforcement learning (RL) to mitigate the longtail latency of PC cleaning. Our scheme uses the lightweight Q-learning method to monitor and learn the idle time of I/O workloads, based on which PC cleaning is intelligently guided, thus maximally exploit idle time between requests and hiding tail latency from normal requests. In addition, a multiagent RL scheme with clustering algorithm is adopted to further mitigate the tail latencies and adapt to variable workloads. We emulate a DM-SMR drive inside a Linux device driver to implement our proposed scheme. According to the experimental results, our scheme can effectively reduce the tail latency by 59.45% at the 99.9th percentile and the average latency by 48.75% compared with a typical shingled magnetic recording (SMR) design., Funding Agencies|National Science Foundation for Young Scientists of China [61902218]; National Natural Science Foundation of China [62272271]; National Natural Science Foundation of Shandong Joint Fund [U1806203]; Research Grants Council of the Hong Kong Special Administrative Region, China [GRF 15224918]; Chinese University of Hong Kong [4055151]; U.S. National Science Foundation [2208317, 2204657]
- Published
- 2023
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