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MBFS: a parallel metadata search method based on Bloomfilters using MapReduce for large-scale file systems

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Abstract

The metadata search is an important way to access and manage file systems. Many solutions have been proposed to tackle performance issue of metadata search. However, the existing solutions build a separate metadata index at the internal or external file system through the related data structure or database use semantics and event-notification method to construct the index structure, utilize the sampling-based method to conduct direct metadata search on the namespace, face problems of the high I/O overhead for maintaining consistency between metadata indexes and metadata, have enormous space overhead for metadata indexes storing and low accuracy of results and so on. To address these problems, this paper presents MBFS, a fast, accurate and lightweight metadata search method based on multi-dimensional Bloomfilters. We create a multi-dimensional Bloomfilter structure on the basis of the directory entry that can prune sub-trees to narrow the search scope of namespace. MBFS is capable of producing fast and accurate answers for a class of complex search over a file system after consuming a small number of disk accesses. MBFS residing in the file system does not need additional I/O overhead to maintain consistency. MBFS consists of Bloomfilters which are composed of bits, so it is a lightweight metadata search method that consumes marginal space overhead. Moreover, MBFS employs MapReduce for speeding up search under the environment of multiple metadata servers. Extensive experiments are conducted to prove the effectiveness of MBFS. The experimental results show that MBFS can achieve an excellent performance not only on the search latency, but also on the accuracy of results with low space and time overhead.

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Acknowledgments

This version has benefited greatly from the many detailed comments and suggestions from the anonymous reviewers. The authors gratefully acknowledge these comments and suggestions. The work described in this paper was supported by the National Natural Science Foundation of China under Grant No. 61370059 and 61232009, the Beijing Natural Science Foundation under Grant No. 4152030, the fund of the State Key Laboratory of Software Development Environment under Grant No. SKLSDE-2014ZX-05, the Open Research Fund of The Academy of Satellite Application under Grant NO. 2014_CXJJDSJ_04, the Fundamental Research Funds for the Central Universities under Grant NO. YWF-14-JSJXY-14 and YWF-15-GJSYS-085, the Open Project Program of National Engineering Research Center for Science & Technology Resources Sharing Service (Beihang University).

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Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Zhisheng Huo, Limin Xiao, Qiaoling Zhong, Shupan Li, Ang Li & Li Ruan

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

    Zhisheng Huo, Limin Xiao, Qiaoling Zhong, Shupan Li, Ang Li & Li Ruan

  3. Space Star Technology Co., Ltd, Beijing, 100086, China

    Shouxin Wang & Lihong Fu

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  1. Zhisheng Huo
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Correspondence to Zhisheng Huo.

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Huo, Z., Xiao, L., Zhong, Q. et al. MBFS: a parallel metadata search method based on Bloomfilters using MapReduce for large-scale file systems. J Supercomput 72, 3006–3032 (2016). https://doi.org/10.1007/s11227-015-1464-2

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