基于深度强化学习的边云协同串行任务卸载算法

Edge Cloud Collaboration Serial Task Offloading Algorithm Based on Deep Reinforcement Learning

在移动边缘计算任务卸载问题中,传统卸载算法仅考虑移动设备和边缘服务器计算资源,在资源利用、系统效能上存在一定的局限性。该文基于RainbowDQN算法,考虑了延迟、能耗成本和服务质量保证等因素,提出了一种边缘云协同串行任务卸载算法(ECWS-RDQN)。该算法通过对串行任务的权重分配,实现了网络边缘和云端协同的串行任务动态分配处理,为不同的用户设备应用提供近似最优的任务分配卸载策略。实验表明,ECWS-RDQN算法比传统方案有更好的系统效能,提升了应用的服务质量。

In the offloading problem of mobile edge computing task,the traditional offloading algorithm only considers the computing resources of mobile devices and edge servers,and has some limitations in resource utilization and system efficiency.this paper proposes an edge-cloud weighted serial task offloading algorithm based on rainbowDQN(ECWS-RDQN)based on the RainbowDQN algorithm,considering the factors of delay,energy consumption cost and service quality assurance.This algorithm realizes the serial task dynamic assignment processing of network edge and cloud collaboration through the weight to provide approximately optimal task assignment offloading strategies for different user device applications.Experiments show that the ECWS-RDQN algorithm has better system efficiency than the traditional schemes and improves the service quality of the applications.