智能网联汽车计算卸载与边缘缓存联合优化策略

Joint Optimization Strategy of Computing Offloading and Edge Caching for Intelligent Connected Vehicles

为了保障智能网联汽车的低时延通信,利用蜂窝车联网中V2X(vehicle to everything)信道模型、边缘计算技术,研究计算卸载与边缘缓存联合优化策略。设计了一种智能网联汽车计算卸载与边缘缓存协同模型L-DDPG(least-deep deterministic policy gradient),通过对车载本地与边缘计算资源的整合,支持V2X场景下对不同计算任务的分类处理。由边缘平台对车载计算请求进行预判决,确保对连续的计算任务快速响应;结合基于最近最少使用(least recently used,LRU)的边缘缓存策略,实现对新计算任务的高效管理;基于DDPG算法对计算卸载与边缘缓存进行联合卸载决策。仿真结果表明:L-DDPG模型性能优于传统模型,能够有效提升系统的工作效能,在保障业务服务质量的同时降低时延和系统资源消耗。

To guarantee the low-delay communication of intelligent connected vehicles,the V2X channel model and the multi-access edge computing(MEC) technology,are used to carry out the research of the joint optimization strategy of computing offloading and edge caching.An intelligent connected vehicle with task offloading and edge caching model least-deep deterministic policy gradient(L-DDPG) is developed.By integrating the vehicular local and edge computing resources,the classification processing of different computing tasks in V2X scenarios is supported.The vehicular computing request is prejudged by edge platform to ensure the rapid response of continuous homogeneous computing tasks.Combining with the least recently used strategy,the new computing tasks are efficiently managed.A joint offloading decision for computing offloading and edge caching is carried out based on deep deterministic policy gradient(DDPG) algorithm.Simulation results show that the performance of L-DDPG model is better than that of traditional models,which can effectively improve the system performance,ensure the service quality,and reduce the time delay and resource consumption.