抽水蓄能电站中基于边缘计算的任务卸载算法

为降低抽水蓄能电站中终端设备密集计算型任务的处理时延,针对抽水蓄能电站的物联网体系,提出了一种基于边缘计算的任务卸载算法.在该文方案中,首先基于层次分析法对计算任务进行优先级划分,并以终端能耗为约束、以终端计算任务处理时延为优化目标建立卸载模型,其次基于Q学习算法(Q-Learning,QL)探索系统的状态转移信息,以...

Task Offloading Algorithm Based on Edge Computing in Pumped Storage Power Station

To reduce the delay of processing intensive computing tasks by terminal equipment in pumped storage power stations , this paper proposes a task offloading algorithm based on edge computing for the Internet of Things (IoT) system of pumped storage power stations. Firstly, the computing tasks are prioritized based on the analytic hierarchy process, and an offloading model is built with the terminal energy consumption as the constraint and the processing delay of terminal computing tasks as the optimization objective. Secondly, the Q-Learning (QL) algorithm is adopted to collect the state transition information, in the hope to obtain the best offloading strategy between the terminal device and the edge node. Finally, Deep Learning (DL) is used to map the relationship between states and actions to avoid a dimensional explosion in the iterative solution of the algorithm. The simulation results show that the proposed method greatly reduces the average delay of computing tasks and can greatly improve the execution efficiency of production operations and safety monitoring associated with pumped storage power stations.