Lusungu Josh Mwasinga, Syed Muhammad Raza, Duc-Tai Le, Moonseong Kim, Hyunseung Choo, Journal of Internet Computing and Services, Vol. 24, No. 2, pp. 1-10, Apr. 2023
10.7472/jksii.2023.24.2.1, Full Text:  HTML
Keywords: Edge Computing, Service Mobility, Service Availability, Beyond 5G, Deep Reinforcement Learning

Abstract

The Multi-access Edge Computing (MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. It seeks to transform the edge into an IT services platform for hosting resource-intensive and delay-stringent services for mobile users, thereby significantly enhancing perceived service quality of experience. However, erratic user mobility impedes seamless service continuity as well as satisfying delay-stringent service requirements, especially as users roam farther away from the serving MEC resource, which deteriorates quality of experience. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. The proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users, thereby reducing service migration rejection rate and enhancing service availability. Efficacy of the proposed approach is confirmed through simulation experiments, where results show that on average, the proposed scheme reduces service delay by 8%, task computing time by 36%, and migration rejection rate by more than 90%, when comparing to a baseline scheme.

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