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Frontiers of Computer Science

, Volume 11, Issue 1, pp 75–87 | Cite as

HWM: a hybrid workload migration mechanism of metadata server cluster in data center

  • Jian Liu
  • Huanqing Dong
  • Junwei Zhang
  • Zhenjun Liu
  • Lu XuEmail author
Research Article

Abstract

In data center, applications of big data analytics pose a big challenge to massive storage systems. It is significant to achieve high availability, high performance and high scalability for PB-scale or EB-scale storage systems. Metadata server (MDS) cluster architecture is one of the most effective solutions to meet the requirements of applications in data center. Workload migration can achieve load balance and energy saving of cluster systems. In this paper, a hybrid workload migration mechanism of MDS cluster is proposed and named as HWM. In HWM, workload of MDS is classified into two categories: metadata service and state service, and they can be migrated rapidly from a source MDS to a target MDS in different ways. Firstly, in metadata service migration, all the dirty metadata of one sub file system is flushed to a shared storage pool by the source MDS, and then is loaded by the target MDS. Secondly, in state service migration, all the states of that sub file system are migrated from source MDS to target MDS through network at file granularity, and then all of the related structures of these states are reconstructed in targetMDS. Thirdly, in the process of workload migration, instead of blocking client requests, the source MDS can decide which MDS will respond to each request according to the operation type and the migration stage. The proposed mechanismis implemented in the BlueWhaleMDS cluster. The performance measurements show that the HWM mechanism is efficient to migrate the workload of a MDS cluster system and provides low-latency access to metadata and states.

Keywords

data center metadata server cluster hybrid workload migration metadata service state service lowlatency access 

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Notes

Acknowledgements

This work was partially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA06010401), and the Tianjin Science and Technology Program (15ZXDSGX00020).

Supplementary material

11704_2016_6036_MOESM1_ESM.ppt (133 kb)
Supplementary material, approximately 133 KB.

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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Jian Liu
    • 1
    • 2
  • Huanqing Dong
    • 1
  • Junwei Zhang
    • 1
  • Zhenjun Liu
    • 1
  • Lu Xu
    • 1
    Email author
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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