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Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16–18, 2020, Proceedings, Part I

Research Article

Location-Aware Edge Service Migration for Mobile User Reallocation in Crowded Scenes

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  • @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
Xuan Xiao1, Yin Li2, Yunni Xia1,*, Yong Ma3, Chunxu Jiang4, Xingli Zhong5
  • 1: College of Computer Science, Chongqing University
  • 2: Institute of Software Application Technology, Guangzhou and Chinese Academy of Sciences
  • 3: School of Computer and Information Engineering, Jiangxi Normal University
  • 4: Chongqing Key Laboratory of Smart Electronics Reliability Technology
  • 5: CISDI R&D Co. Ltd.
*Contact email: xiayunni@hotmail.com

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.

Keywords
Service migration Load fairness Coalitional game Crowded scenes Hotspot discovery Mobile trajectory User reallocation Backbone network
Published
2021-01-22
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-67537-0_27
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