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

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), and Yawen Cao (Jinan Foreign Language School, China)

Source Title: International Journal of Web Services Research (IJWSR)19(1)

ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781799893462|DOI: 10.4018/ijwsr.302888

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MLA

Zhu, Ming, et al. "Selecting Mobile Services in Cloud and Edge Environment by Moth-Flame Optimization Algorithm." IJWSR vol.19, no.1 2022: pp.1-23. https://doi.org/10.4018/ijwsr.302888

APA

Zhu, M., Yan, X., Li, J., Liu, C., & Cao, Y. (2022). Selecting Mobile Services in Cloud and Edge Environment by Moth-Flame Optimization Algorithm. International Journal of Web Services Research (IJWSR), 19(1), 1-23. https://doi.org/10.4018/ijwsr.302888

Chicago

Zhu, Ming, et al. "Selecting Mobile Services in Cloud and Edge Environment by Moth-Flame Optimization Algorithm," International Journal of Web Services Research (IJWSR) 19, no.1: 1-23. https://doi.org/10.4018/ijwsr.302888

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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.