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| 한글제목(Korean Title) |
심층강화학습을 활용한 멀티 액세스 컴퓨팅 환경에서 효율적인 서비스 모빌리티 |
| 영문제목(English Title) |
Efficient Service Mobility Management in Multi-Access Edge Computing: A Deep Reinforcement Learning Approach |
| 저자(Author) |
Lusungu J. Mwasinga
Huigyu Yang
Moonseong Kim
Hyunseung Choo
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| 원문수록처(Citation) |
VOL 23 NO. 02 PP. 0071 ~ 0072 (2022. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The Multi-access Edge Computing(MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. The paradigm 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 causes quality of experience deterioration. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. Among others, the proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users. Efficacy of the proposed approach is validated through extensive simulation experiments, whose results are benchmarked against another scheme and reveal outstanding performance of 54.6% in terms of service migration rejection rate reduction. |
| 키워드(Keyword) |
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| 파일첨부 |
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