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Issue title: Artificial Intelligence and Advanced Manufacturing (AIAM 2020)
Guest editors: Shengzong Zhou
Article type: Research Article
Authors: Jiang, Xiaominga; b | Yang, Huamina; b; * | Yang, Yaa; b | Chen, Zhanfanga; b
Affiliations: [a] Changchun University of Science and Technology, Changchun, China | [b] Chongqing Research Institute Changchun University Of Science and Technology, Changchun, China
Correspondence: [*] Corresponding author. Huamin Yang, Changchun University of Science and Technology, Changchun, China. E-mail: chenzhanfang@cust.edu.cn.
Abstract: In server clusters, the static scheduling algorithm has superior performance when the user visits are relatively stable. In the face of sudden increase in user traffic, the dynamic scheduling algorithm has a better load balancing effect than the static scheduling algorithm. However, in the face of complex network environments, the static scheduling algorithm cannot adjust the load according to the performance of the server in real time. The dynamic scheduling algorithm using a single weight to evaluate server performance is unreliable, and load scheduling with reference to the number of connections has uncertainty. In view of this problem, this paper proposes a cluster load balancing algorithm based on dynamic consistent hash based on the study of load balancing based on LVS clusters. By analyzing the performance and load parameters, we divide the request process into in-cycle and out-of-cycle. By setting up the LVS cluster system, the performance weights, load parameters, number of virtual nodes and cycles of the algorithm in this paper are determined experimentally. Finally, the response time and throughput of the algorithm in this paper are compared with the WRR algorithm and WLC algorithm. The results show that the time and throughput of this algorithm are better than WRR algorithm and WLC algorithm.
Keywords: Dynamic, consistent hash, load balancing, cluster
DOI: 10.3233/JIFS-189706
Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 3, pp. 4461-4468, 2021
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