LAUSR

Internet of Vehicles (IoV) has become a significant research area due to its specific features and applications such as efficient traffic management, road safety, and entertainment. Vehicles are expected to carry relatively more communication systems, onboard computing facilities, storage, and increased sensing power. Vehicular Cloud Network (VCN) is a hybrid technology that has a remarkable impact on IoV by instantly using vehicular resources. Merging software-defined networking with VCN is valuable to vehicular networks. Network function virtualization can be deployed across the servers and vehicles within the VCN to provide customized agile services. Its deployment requires an appropriate management strategy that often manifests as the difficult online decision making tasks. This paper proposes an intelligent Virtualized Network Functions (VNFs) selection strategy. For satisfying the quality requirements of different vehicular services, this strategy is based on the results of traffic identification that utilizes deep neural network and Multi-Grained Cascade Forest to distinguish service behaviors. According to the order of VNFs, the vehicular network is divided into several layers where each arrived packet needs to be queued. The scheduler of each layer selects a layer to host the next VNF for the packets in the queue. Our experiments demonstrate that the proposed traffic identification method increases the precision by 7.7% and improves the real-time performance. Furthermore, the proposed model of VNFs selection reduces network congestion compared to traditional scheduling models.