边缘计算下基于深度强化学习的车联网任务调度

Task Scheduling for Internet of Vehicles Based on Deep Reinforcement Learning in Edge Computing

针对边缘计算下车联网中时延约束型计算任务的卸载执行问题,提出一种基于深度强化学习的任务调度方法。在多边缘服务器场景下,构建软件定义网络辅助的车联网任务卸载系统,给出车辆计算卸载的任务调度模型;根据任务调度的特点,设计一种基于改进指针网络的调度方法,综合考虑任务调度和计算资源分配的复杂性,采用深度强化学习算法对指针网络进行训练;运用训练好的指针网络对车辆卸载任务进行调度。仿真结果表明:在边缘服务器计算资源相同的情况下,该方法在处理时延约束型计算任务的数量方面优于其他方法,有效提高了车联网任务卸载系统的服务能力。

Aiming at the offloading and execution of delay-constrained computing tasks for internet of vehicles in edge computing,a task scheduling method based on deep reinforcement learning is proposed.In multi-edge server scenario,a software-defined network-aided internet of vehicles task offloading system is built.On this basis,the task scheduling model of vehicle computation offloading is given.According to the characteristics of task scheduling,a scheduling method based on an improved pointer network is designed.Considering the complexity of task scheduling and computing resource allocation,the deep reinforcement learning algorithm is used to train the pointer network.The vehicle offloading tasks is scheduled by the trained pointer network.The simulation results show that with the same computing resources of edge servers,the proposed method is better than other methods in processing the number of delay-constrained computing tasks,and effectively improves the service capability of the internet of vehicles task offloading system.