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	Novel Workload-Aware Approach to Mobile User Reallocation in Crowded Mobile Edge Computing Environment
	Xiao, Xuan1; Ma, Yong2; Xia, Yunni1; Zhou, Mengchu3,4; Luo, Xin5; Wang, Xu6; Fu, Xiaodong7; Wei, Wei8; Jiang, Ning9
	2022-07-01
摘要	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.
关键词	Edge computing workload service migration load fairness coalitional game crowded scenes hotspot discovery
DOI	10.1109/TITS.2021.3086827
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
卷号	23期号:7页码:8846-8856
通讯作者	Ma, Yong(may@jxnu.edu.cn) ; Xia, Yunni(xiayunni@hotmail.com)
收录类别	SCI
WOS记录号	WOS:000838694400304
语种	英语