Full Text:   <7957>

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CLC number: TP338.8

On-line Access: 2020-07-10

Received: 2019-03-01

Revision Accepted: 2019-11-14

Crosschecked: 2020-06-02

Cited: 0

Clicked: 5743

Citations:  Bibtex RefMan EndNote GB/T7714


Zhao-qi Wu


Fan Zhang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.7 P.1034-1046


MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning

Author(s):  Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie

Affiliation(s):  National Digital Switching System Engineering & Technological R&D Center, Zhengzhou 450002, China; more

Corresponding email(s):   17034203@qq.com

Key Words:  Object-oriented storage system, Metadata, Dynamic load balancing, Reinforcement learning, Q_learning

Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie. MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 1034-1046.

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A1 - Jin Wei
A1 - Fan Zhang
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900121

With the growing amount of information and data, object-oriented storage systems have been widely used in many applications, including the Google File System, Amazon S3, Hadoop Distributed File System, and Ceph, in which load balancing of metadata plays an important role in improving the input/output performance of the entire system. Unbalanced load on the metadata server leads to a serious bottleneck problem for system performance. However, most existing metadata load balancing strategies, which are based on subtree segmentation or hashing, lack good dynamics and adaptability. In this study, we propose a metadata dynamic load balancing (MDLB) mechanism based on reinforcement learning (RL). We learn that the q_learning algorithm and our RL-based strategy consist of three modules, i.e., the policy selection network, load balancing network, and parameter update network. Experimental results show that the proposed MDLB algorithm can adjust the load dynamically according to the performance of the metadata servers, and that it has good adaptability in the case of sudden change of data volume.

This article has been corrected, see doi:10.1631/FITEE.19e0121





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