Name
Abstract | ||
|---|---|---|
Intelligent and connected vehicles rely on edge computing to offload their perception and planning tasks, so the scheduling of communication and computing resources is critical to the driving safety and efficiency. However, the imbalanced distribution of road traffic and offloading demands impedes the quality of vehicular edge computing. In this paper, we propose a multi-timescale load balancing approach to improve the service quality and resource utility of vehicular edge computing. Specifically, vehicle mobility optimization is leveraged to perform long-term load balancing, and resource allocation is used to achieve real-time load balancing. As the multi-timescale optimization is confronted with the curse of dimensionality, multi-agent deep reinforcement learning is utilized to optimized vehicle mobility and resource allocation in parallel. Experimental results show that the proposed method can significantly reduce the service delay of vehicular edge computing. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VTC2020-Fall49728.2020.9348791 | 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) |
Keywords | DocType | ISSN |
Edge computing,load balancing,route planning,resource allocation,multi-agent deep reinforcement learning | Conference | 1090-3038 |
ISBN | Citations | PageRank |
978-1-7281-9485-1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Lin | 1 | 0 | 0.34 |
| Quan Yuan | 2 | 55 | 11.07 |
| Jinglin Li | 3 | 150 | 30.39 |
| Shu Yang | 4 | 0 | 0.34 |