Selecting Mobile Services in Cloud and Edge Environment by Moth-Flame Optimization Algorithm

Ming Zhu, Xiukun Yan, Jing Li, Cong Liu and Yawen Cao
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Ming Zhu: Shandong University of Technology, China
Xiukun Yan: Shandong University of Technology, China
Jing Li: Shandong University of Technology, China
Cong Liu: Shandong University of Technology, China
Yawen Cao: Jinan Foreign Language School, China

International Journal of Web Services Research (IJWSR), 2022, vol. 19, issue 1, 1-23

Abstract: Mobile edge computing is playing an increasingly important role in the rise of mobile internet technology. Services deployed on edge servers nearby mobile users would provide computing capabilities with low latency and high scalability. Usually, a single service is challenging to meet a complex user request, which asks for composing services. With the increasing number of services in the cloud and edge computing environment and the user mobility, selecting appropriate services to meet the complex mobile user's requests becomes a crucial problem. This paper proposes a modified moth-flame optimization algorithm using overall QoS for service selection. Specifically, the overall QoS of services is calculated by combining the subjective and objective QoS with the ordinal relationship and coefficient of variation, and the moth-flame optimization algorithm is improved by adding the differential evolution algorithm. The experimental results show that the proposed approach outperforms some other services selection approaches.

Date: 2022
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