Intelligent Resource Allocation Algorithm for Multi-platform Offloading in Vehicular Networks

Abstract

In order to reduce the delay of computing tasks and the total cost of the system, Mobile Eedge Computing (MEC) technology is applied to vehicular networks to improve further the service quality. The delay problem of vehicular networks is studied with the consideration of computing resources. In order to improve the performance of the next generation vehicular networks, a multi-platform offloading intelligent resource allocation algorithm is proposed to allocate the computing resources. In the proposed algorithm, the K-Nearest Neighbor (KNN) algorithm is used to select the offloading platform (i.e., cloud computing, mobile edge computing, local computing) for computing tasks. For the computing resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation in vehicular networks using the mobile edge computing technology. Simulation results demonstrate that compared with the baseline algorithms (i.e., all tasks offload to the local or MEC server), the proposed multi-platform offloading intelligent resource allocation algorithm achieves a significant reduction in latency cost, and the average system cost can be saved by 80%.