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Title	Novel Workload-Aware Approach To Mobile User Reallocation In Crowded Mobile Edge Computing Environment
ID_Doc	39507
Authors	Xiao X.; Ma Y.; Xia Y.; Zhou M.; Luo X.; Wang X.; Fu X.; Wei W.; Jiang N.
Year	2022
Published	IEEE Transactions on Intelligent Transportation Systems, 23, 7
DOI	http://dx.doi.org/10.1109/TITS.2021.3086827
Abstract	A mobile edge computing (MEC) paradgim is evolving as an increasingly popular means for developing and deploying smart-city-oriented applications. MEC servers can receive a great deal of requests from devices of mobile users, especially in crowded scenes, e.g., a city's central business district and school areas. It thus remains a great challenge for appropriate scheduling and managing strategies to avoid hotspots, guarantee load-fairness among MEC servers, and maintain high resource utilization at the same time. To address this challenge, we propose a coalitional-game-based and location-aware approach to MEC service migration for mobile user reallocation in crowded scenes. Our proposed method includes: 1) dividing MEC servers into multiple coalitions according to their inter-Euclidean distance by using a modified k-means clustering method; 2) discovering hotspots in every coalition area and scheduling services based on their corresponding cooperations; and 3) migrating services to appropriate edge servers to achieve high utilization and load-fairness among coalition members. Experimental results based on a real-world mobile trajectory dataset for crowded scenes, and an urban-edge-server-position dataset demonstrate that our method outperforms existing ones in terms of load fairness, number of migrations, and utilization rate of edge servers. © 2000-2011 IEEE.
Author Keywords	coalitional game; crowded scenes; Edge computing; hotspot discovery; load fairness; service migration; workload

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