Optimization of Computation Offloading Strategy for Multi-access Edge Computing in 5G Internet of Vehicles

• 1、School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876

Abstract：With the development of 5G technologies and Internet of Vehicles (IoV), a large number of IoV applications have appeared. The lack of computing capacity of vehicle equipment has become a major limitation for reducing the application experience of users. The emergence of Multi-access Edge Computing (MEC) can effectively solve this problem. By offloading tasks to the MEC server located at the edge of the network, the data computing can be accelerated, allowing users to enjoy uninterrupted high-quality network experience. In order to effectively reduce the latency and energy consumption of tasks, we design a vehicle-aware Multi-access Edge Computing network (VAMECN), and propose a deep reinforcement learning-based joint computation offloading and resourceallocation optimization algorithm (CORA), taking into account the offloading requirements of users, vehicle mobility, computing resource distribution, and communication bandwidth, etc. Experiments have proved that compared with other computation offloading schemes, the CORA algorithm has the lowest offloading cost under different number of vehicles, data size, number of MEC servers, and computing capacity. It indicates that the CORA algorithm can effectively improve system resource utilization and optimize the offloading and resource allocation strategy.

Keywords： multi-access edge computing internet of vehicles 5G computation offloading deep reinforcement learning