Parallelism and Garbage Collection aware I/O Scheduler with Improved SSD Performance

被引：32

作者：

Guo, Jiayang ^{[1
,2
]}

Hu, Yiming ^{[1
]}

Mao, Bo ^{[2
]}

Wu, Suzhen ^{[3
]}

机构：

[1] Univ Cincinnati, Sch Elect & Comp Engn, Cincinnati, OH 45220 USA

[2] Xiamen Univ, Software Sch, Xiamen 361005, Peoples R China

[3] Xiamen Univ, Dept Comp Sci, Xiamen 361005, Peoples R China

来源：

2017 31ST IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS) | 2017年

基金：

中国国家自然科学基金;

关键词：

Solid State Disk; Internal Parallelism; Workload Characteristics; Garbage Collection; I/O Scheduler;

D O I：

10.1109/IPDPS.2017.55

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose PGIS, a parallelism and garbage collection aware I/O Scheduler, which identifies the hot data based on trace characteristics to exploit the channel level internal parallelism of flash-based storage systems. PGIS not only fully exploits abundant channel resource in the SSD, but also it introduces a hot data identification mechanism to reduce the garbage collection overhead. By dispatching hot read data to different channel, the channel level internal parallelism is fully exploited. By dispatching hot write data to the same physical block, the garbage collection overhead has been alleviated. The experiment results show that compared with existing I/O schedulers, PGIS improves the response time and garbage collection performance significantly. Consequently, PGIS reduces the garbage collection overhead up to 30.9%, while exploiting channel level internal parallelism.

引用

页码：1184 / 1193

页数：10

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