工业互联网中设备任务的处理需要大量计算资源, 有低时延需求的任务显著增多. 边缘计算将算力等资源放置到靠近需求一侧, 为任务处理提供有效支撑. 但由于边缘计算资源有限, 无法同时满足设备任务的低时延和高完成率需求. 如何确定合理的卸载决策与任务调度, 仍然存在巨大挑战. 针对以上问题, 本文提出了一种基于深度学习的动态优先级任务调度算法DPTSA, 首先根据动态优先级选择待处理任务, 通过神经网络产生任务调度决策, 然后通过交叉变异等操作产生一组可行解, 再筛选最优解存储到经验缓冲区, 最后通过经验缓冲区样本优化神经网络参数. 基于Google的Brog任务调度数据集的实验结果表明, 相比于4种基准算法, DPTSA在任务等待时间和任务完成率方面都有出色表现.
The processing of device tasks in the industrial Internet requires a large amount of computing resources, and the tasks with low latency requirements have increased significantly. Edge computing places computing power and other resources on the side close to the demand to provide effective support for task processing. However, due to the limited edge computing resources, the requirements of low latency and high completion rate of the device tasks cannot be satisfied at the same time. It is still a great challenge to determine a reasonable offloading decision and task scheduling. Given the above problem, a deep learning-based dynamic priority task scheduling algorithm DPTSA is proposed in this study. Firstly, the tasks to be processed are selected according to dynamic priority and task scheduling decisions are generated through neural networks. Then, a set of feasible solutions are generated through cross-variance and other operations, and the optimal solutions are screened out and stored in the empirical buffer area. Finally, the neural network parameters are optimized through the empirical buffer samples. The experimental results based on Google’s Brog task scheduling dataset show that DPTSA is superior to the four benchmark algorithms in terms of task waiting time and task completion rate.