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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: 5725
Citations: Bibtex RefMan EndNote GB/T7714
MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning
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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.
@article{title="MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning",
author="Zhao-qi Wu, Jin Wei, Fan Zhang, Wei Guo, Guang-wei Xie",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="7",
pages="1034-1046",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900121"
}
%0 Journal Article
%T MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning
%A Zhao-qi Wu
%A Jin Wei
%A Fan Zhang
%A Wei Guo
%A Guang-wei Xie
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 7
%P 1034-1046
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900121
TY - JOUR
T1 - MDLB: a metadata dynamic load balancing mechanism based on reinforcement learning
A1 - Zhao-qi Wu
A1 - Jin Wei
A1 - Fan Zhang
A1 - Wei Guo
A1 - Guang-wei Xie
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 7
SP - 1034
EP - 1046
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900121
Abstract: 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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