车载边缘计算中基于深度强化学习的协同计算卸载方案

车载边缘计算(Vehicular Edge Computing,VEC)是一种可实现车联网低时延和高可靠性的关键技术,用户将计算任务卸载到移动边缘计算(Mobile Edge Computing,MEC)服务器上,不仅可以解决车载终端计算能力不足的问题,而且可以减少能耗,降低车联网通信服务的时延。然而,高速公路场景下车辆移动性与边缘服务器静态部署的矛盾给计算卸载的可靠性带来了挑战。针对高速公路环境的特点,研究了临近车辆提供计算服务的可能性。通过联合MEC服务器和车辆的计算资源,设计并实现了一个基于深度强化学习的协同计算卸载方案,以实现在满足任务时延约束的前提下最小化所有任务时延的目标。仿真实验结果表明,相比于没有车辆协同的方案,所提方案可以有效降低时延和计算卸载失败率。

Deep Reinforcement Learning-based Collaborative Computation Offloading Scheme in Vehicular Edge Computing

Vehicular edge computing(VEC)is a key technology that can realize low latency and high reliability of internet of vehicles.Users offload computing tasks to mobile edge computing(MEC)servers,which can not only solve the problem of insufficient computing capability of vehicles,but also reduce the energy consumption and the latency of communication service.How-ever,the contradiction between the mobility of vehicles and the static deployment of edge servers in highway scenarios poses a challenge to the reliability of computing offloading.To solve this problem,this paper designs a collaborative deep reinforcement learning-based scheme for vehicles to adapt to the dynamic high-speed environment by combining the computing resources of MEC servers and neighboring vehicles.Simulation results show that compared with the scheme without vehicle collaboration,this scheme can reduce the delay and the failure rate of offloading.