Spectral graph theory-based virtual network embedding for vehicular fog computing: A deep reinforcement learning architecture

Abstract

In the intelligent transportation system (ITS), the vehicular fog computing network (VFCN) can effectively alleviate the bottleneck existing in the cloud computing framework, such as high latency-sensitive applications, through edge computing offloading. It uses vehicles as the infrastructure, and fog nodes can communicate, perceive and share resources, so resource orchestration has become an essential issue of VFCN. To reduce the communication transportation cost and improve the resource utilization of VFCN, we propose a spectral graph theory-based resource orchestration algorithm by combining Virtual Network Embedding (VNE) and Deep Reinforcement Learning (DRL). Specifically, we propose a four-layer strategy network based on Graph Convolutional Networks (GCNs) for computing node embedding probability, where fog nodes fully mine spatial structure information by fusing themselves with neighborhood information to compensate for the lack of traditional heuristic VNE. Moreover, fog link embedding is performed by breadth-first search (BFS). Finally, the effectiveness of the proposed strategy is scientifically and rigorously proved in terms of long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate through simulation cases, which can reasonably arrange the resources of VFCN.