Work-in-Progress: Maximizing I/O throughput and Minimizing Performance Variation via Reinforcement Learning based I/O Merging for SSDs

被引：0

作者：

Wu, Chao ^{[1
]}

Ji, Cheng ^{[1
]}

Li, Qiao ^{[1
]}

Fu, Chenchen ^{[1
]}

Xue, Chun Jason ^{[1
]}

机构：

[1] City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China

来源：

2018 INTERNATIONAL CONFERENCE ON COMPILERS, ARCHITECTURES AND SYNTHESIS FOR EMBEDDED SYSTEMS (CASES) | 2018年

关键词：

Merge technique; I/O scheduler; Reinforcement Learning; throughput; performance variation; worst-case latency;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Merge technique is widely adopted by I/O schedulers to maximize system I/O throughput. However merging operation could degrade the latency of individual I/O, thus incurring prolonged I/O latencies and enlarged I/O variations of I/O requests. In this case, the requirement of QoS (Quality of Service) performance will be violated. In order to improve QoS performance meanwhile providing high I/O throughput, this paper proposed a Reinforcement Learning based I/O merge approach. Through learning the characteristic of various I/O patterns, the proposed approach make merge decisions adaptive to different I/O workloads.

引用

页数：2

共 9 条

[1] Maximizing I/O Throughput and Minimizing Performance Variation via Reinforcement Learning Based I/O Merging for SSDs
Wu, Chao
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Li, Qiao
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