Maximizing I/O Throughput and Minimizing Performance Variation via Reinforcement Learning Based I/O Merging for SSDs

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

1 Scopus Citations
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Author(s)

  • Congming Gao
  • Riwei Pan
  • Chenchen Fu
  • Liang Shi

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8823009
Pages (from-to)72-86
Journal / PublicationIEEE Transactions on Computers
Volume69
Issue number1
Online published3 Sep 2019
Publication statusPublished - Jan 2020

Abstract

Merging technique is widely adopted by I/O schedulers to maximize system I/O throughput. However, I/O merging could increase the latency of individual I/O, thus incurring prolonged I/O latencies and enlarged performance variations. Even with better system throughput, higher worst-case latency experienced by some requests could block the SSD storage system, which violates the QoS (Quality of Service) requirement. In order to improve QoS performance while providing higher I/O throughput, this paper proposes a reinforcement learning based I/O merging approach. Through learning the characteristic of various I/O patterns, the proposed approach makes merging decisions adaptively based on different I/O workloads. Evaluation results show that the proposed scheme is capable of reducing the standard deviation of I/O latency by 19.1 percent on average, worst-case latency by 7.3-60.9 percent at the 99.9th percentile compared with the latest I/O merging scheme, while maximizing system throughput.

Research Area(s)

  • I/O scheduler, Merging technique, performance variation, reinforcement learning, throughput, worst-case latency

Citation Format(s)

Maximizing I/O Throughput and Minimizing Performance Variation via Reinforcement Learning Based I/O Merging for SSDs. / Wu, Chao; Ji, Cheng; Li, Qiao; Gao, Congming; Pan, Riwei; Fu, Chenchen; Shi, Liang; Xue, Chun Jason.

In: IEEE Transactions on Computers, Vol. 69, No. 1, 8823009, 01.2020, p. 72-86.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal