Metadata-intensive I/O Optimizations in Parallel File Systems

Xie Ke; Li Xiuqiao; Wu Qimeng; Xiao Limin; Ruan Li

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Metadata-intensive I/O Optimizations in Parallel File Systems

Xie Ke 1,Li Xiuqiao 2,Wu Qimeng 2,Xiao Limin 2 *,Ruan Li 2

1.School of Computer Science and Engineering, Beihang University, Beijing 100191

2.School of Computer Science and Engineering, Beihang University, Beijing 100191, China

*Correspondence author

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Funding: the Beijing Natural Science Foundation（No.No. 4122042）, the National Natural Science Foundation of China（No.No.61003015）, the State Key Laboratory of Software Development Environment （No.SKLSDE-2012ZX-23）, the Doctoral Fund of Ministry of Education of China （No.No. 20101102110018）, the High-tech Research and Development Program of China （No.No. 2011AA01A205）

Opened online: 3 May 2013

Accepted by: none

Citation: Xie Ke,Li Xiuqiao,Wu Qimeng.Metadata-intensive I/O Optimizations in Parallel File Systems[OL]. [ 3 May 2013] http://en.paper.edu.cn/en_releasepaper/content/4539533

With parallel file systems increasingly growing in size, the performance of metadata I/O becomes critical for overall performance. Metadata-intensive applications create a lot of metadata I/O requests with small amount of data, making metadata access become the bottleneck of system. We propose an optimization method based on aggregating and merging requests for metadata-intensive I/O to deal with this problem. Extensive simulations show that the aggregate throughput of intensive file creating can be increased by up to 15.28 times and average response time can be decreased by factors of up to 99.27 percent when the aggregation period and request interval is configured as 0.8ms and 0.025ms respectively. Simulations also show that the aggregate throughput of intensive metadata access can be increased by up to 8.45 times and average response time can be decreased by factors of up to 99.02 percent when the merging period and request interval is configured as 0.4ms and 0.025ms respectively. Meanwhile, experiments show that our method can scale well with the number of metadata servers and clients.

Keywords:parallel file system; metadata-intensive I/O; aggregating and merging requests

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