Intelligent Task Offloading Algorithm for Mobile Edge Computing in Vehicular Networks

Abstract

Based on the research of network and computing, while considering the delay of vehicular networks, this paper proposes an intelligent task offloading framework that can dynamically schedule network and computing resources to improve the performance of next-generation vehicular networks. Considering the task computing problem of the vehicles and the applications of the mobile edge computing (MEC), an intelligent task offloading joint optimization algorithm is proposed in this paper. The algorithm firstly uses the K-Nearest Neighbors (KNN) method to select the offloading platform (i.e., cloud computing, mobile edge computing, local computing) of the computing task. Considering the computing resource allocation problem and the complexity of the system, the algorithm secondly uses the reinforcement learning method to solve the resource allocation problem effectively. Simulation results show that, comparing to the baseline algorithm that all tasks are offloaded to the local or MEC server, the proposed algorithm achieves a significant reduction in latency cost. Compared with the Ful1 MEC" algorithm, the proposed algorithm can save 80% of the average system cost.