Title: The QoS evaluation model for cloud resource node

Authors: Dan Liu; Leilei Zhu; Zetian Zhang; Yan Zeng; Zhengqi Bai; Li Li

Addresses: College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, 130022, China ' College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, 130022, China ' College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, 130022, China ' College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, 130022, China ' College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, 130022, China ' College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, 130022, China

Abstract: The service quality of resource nodes in a cloud service system can reflect the capability of the system in providing users with services. In this paper, we focus on the evaluation of cloud service quality to build Cloud_MQOSF, a cloud service quality of service (QoS) evaluation framework with availability, reliability, service performance, and scalability as the evaluation indexes. This architecture can rapidly and stably evaluate quantitatively the service capabilities of resource nodes in a cloud service system. Several sets of experiments are carried out using the operational data of a well-known cloud service provider and the quality of web service dataset. The experimental results show that the model not only effectively evaluates the quality of service of resource nodes in a data center, but is also fast and stable. This provides a favourable basis for resource allocation in the cloud.

Keywords: QoS; quality of service; multi-quantitative; resource node; cloud service system; Cloud_MQOSF.

DOI: 10.1504/IJSNET.2021.117480

International Journal of Sensor Networks, 2021 Vol.36 No.4, pp.194 - 203

Received: 21 Sep 2020
Accepted: 28 Dec 2020

Published online: 21 Aug 2021 *

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