Research Article
Location-Aware Edge Service Migration for Mobile User Reallocation in Crowded Scenes
@INPROCEEDINGS{10.1007/978-3-030-67537-0_27, author={Xuan Xiao and Yin Li and Yunni Xia and Yong Ma and Chunxu Jiang and Xingli Zhong}, title={Location-Aware Edge Service Migration for Mobile User Reallocation in Crowded Scenes}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Service migration Load fairness Coalitional game Crowded scenes Hotspot discovery Mobile trajectory User reallocation Backbone network}, doi={10.1007/978-3-030-67537-0_27} }- Xuan Xiao
Yin Li
Yunni Xia
Yong Ma
Chunxu Jiang
Xingli Zhong
Year: 2021
Location-Aware Edge Service Migration for Mobile User Reallocation in Crowded Scenes
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_27
Abstract
The 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 equipments of highly mobile users, especially in crowded scenes, e.g., city’s central business district (CBD) 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 multiple steps: 1) dividing MEC servers into multiple coalitions according to their inter-euclidean distance by using a modifiedk-means clustering method; 2) discovering hotspots in every coalition area and scheduling services based on their corresponding cooperations; 3) migrating services to appropriate edge servers to achieve load-fairness among coalition members by using a migration budget mechanism; 4) transferring workloads to nearby coalitions by backbone network in case of workloads beyond the limit. 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 approaches in terms of load-fairness, migration times, and energy consumption of migrations.
