Vehicular Network Edge Intelligent Management : A Deep Deterministic Policy Gradient Approach for Service Offloading Decision
Yinlin Ren, Xiuming Yu, Xingyu Chen, Shaoyong Guo, Qiu Xue-Song
Abstract
The development of edge computing has alleviated the problem of limited vehicular computing capabilities in VANET. The vehicular edge computing (VEC) provide resources for the implementation of multiple intelligent services. However, the mobility of vehicles and the diversity of edge computing nodes pose huge challenges for service offloading. Deep reinforcement learning (DRL) in artificial intelligence (AI) is an effective technology to solve such challenges. Based on this scenario, we first introduce a software-defined vehicular networks (SDV) architecture that takes full advantage of the characteristics of SDN technology and can effectively and dynamically obtain a global view in VANET to facilitate the management of resources in the network. Then, we propose a new intelligent service offloading decision model, which introduces the Deep Deterministic Policy Gradient (DDPG) algorithm in DRL to solve the joint optimization of service offloading with multiple constraints. Simulation results show that the DDPG-based service offloading model has better performance and better stability than similar algorithms.