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

Abstract

The mobile edge computing (MEC) paradgim is evolving as an increasingly popular means for developing and deploying smart-cityoriented 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 modified k-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.