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.
Original language | English (US) |
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Pages (from-to) | 8846-8856 |
Number of pages | 11 |
Journal | IEEE Transactions on Intelligent Transportation Systems |
Volume | 23 |
Issue number | 7 |
DOIs | |
State | Published - Jul 1 2022 |
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications
Keywords
- Edge computing
- coalitional game
- crowded scenes
- hotspot discovery
- load fairness
- service migration
- workload