Two-stage Scheduling of Stream Computing for Industrial Cloud-edge Collaboration

Abstract

As the Industrial Internet of Things (IIoT) develops, intelligent services applying stream computing, such as industrial robot health management, are requiring higher timeliness of data processing, which may involve scheduling of stream tasks. However, traditional scheduling methods are no longer suitable for the currently widely used cloud-edge collaboration mode, not considering the cloud-edge heterogeneity, and focusing on the scheduling of single tasks instead of the optimization of the total tasks. To improve the performance of the cloud-edge collaboration, this paper establishes a practical model for task scheduling considering respectively cloud-edge environment collaboration models. We propose a novel two-stage scheduling method for IIoT. The algorithm utilizes the idea of maximum flow to divide the task into cloud-edge deployment schemes and find the best partitioning scheme, and then deploy the operator for the edge domain based on the network topology by using dynamic programming. Experimental results show that the proposed method could reduce 7.27% the cloud-edge bandwidth usage compared with the highest greedy algorithm for traffic difference, 24.33% end-to-end latency and 11.18% back-pressure rate compared with SBON.