Title: Resource monitoring method of the expandable cloud platform based on micro-service architecture

Authors: Dong He; Hongbing Huang; Yiyang Yao; Weiqiang Qi; Hong Li; Dong Mao

Addresses: State Grid ZheJiang Electric Power Corporation Information and Telecommunication Branch, Zhejiang, 310000, China ' State Grid ZheJiang Electric Power Corporation Information and Telecommunication Branch, Zhejiang, 310000, China ' State Grid ZheJiang Electric Power Corporation Information and Telecommunication Branch, Zhejiang, 310000, China ' State Grid ZheJiang Electric Power Corporation Information and Telecommunication Branch, Zhejiang, 310000, China ' State Grid ZheJiang Electric Power Corporation Information and Telecommunication Branch, Zhejiang, 310000, China ' State Grid ZheJiang Electric Power Corporation Information and Telecommunication Branch, Zhejiang, 310000, China

Abstract: In order to improve the ability of resource monitoring and adaptive forwarding control of the scalable cloud platform, this paper proposes an esource monitoring method based on the microservice architecture. Built the optimised microservice architecture of the scalable cloud platform, and used the distributed cloud composite storage method to design the modular storage structure. Fuzzy correlation detection method was used to detect the characteristics of scalable cloud platform resources and to process information fusion. The microservice platform system was built on the scalable cloud platform for adaptive clustering. Mining association rule set of scalable cloud platform resources in cluster center, based on microservice architecture. Simulation results show that when the SNR is - 2 dB, the output bit error rate of the proposed method is 0. The results show that this method has good information scheduling ability in scalable cloud platform resource monitoring, and can provide a stable resource output balance.

Keywords: micro-service architecture; expandable cloud platform; resource monitoring; scheduling; fuzzy correlation detection; adaptive clustering.

DOI: 10.1504/IJAACS.2022.122943

International Journal of Autonomous and Adaptive Communications Systems, 2022 Vol.15 No.1, pp.18 - 31

Received: 01 Jan 2020
Accepted: 06 May 2020
Published online: 18 May 2022 *

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