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

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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.

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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).

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Correspondence to Lu Xu.

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Jian Liu received his MS in computer application form Yanshan University, China in 2008. He is currently a PhD candidate at the University of Chinese Academy of Science, China. He works in the Data Storage and Management Technology Research Center of Institute of Computing Technology, Chinese Academy of Sciences. His reresearch interests include load balance and high availability of cluster file system.

Huanqing Dong received his PhD in computer science and technology from Northwestern Polytechnical University, China in 2005. He is currently a senior engineer at the Data Storage and Management Technology Research Center of Institute of Computing Technology, Chinese Academy of Sciences, China. His research interests include cluster file systems and network storage.

Junwei Zhang received his PhD in computer architecture from Graduate University of Chinese Academy of Science, China in 2010. He is currently an associate professor at the Data Storage and Management Technology Research Center of Institute of Computing Technology, Chinese Academy of Sciences. His research interests include cluster file systems and network storage.

Zhenjun Liu received his PhD in computer architecture from Graduate University of Chinese Academy of Science, China in 2006. Now, he is currently an associate professor at the Data Storage and Management Technology Research Center of Institute of Computing Technology, Chinese Academy of Sciences. His research interests include WAN storage, distributed file system and raid management.

Lu Xu received his PhD in computer systems software from Purdue University, USA in 1995. Now, He is currently a professor and the director at the Data Storage and Management Technology Research Center of Institute of Computing Technology, Chinese Academy of Sciences, China. He is an associate director of the Division of Information Storage Technology, China Computer Federation, China. His research interests include computer architecture, high performance network storage, and computer system software.

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Liu, J., Dong, H., Zhang, J. et al. HWM: a hybrid workload migration mechanism of metadata server cluster in data center. Front. Comput. Sci. 11, 75–87 (2017). https://doi.org/10.1007/s11704-016-6036-y

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