Task Scheduling Strategy for Industrial Control Network based on Cloud-Edge-End Fusion Framework

Abstract

Industrial tasks with traditional cloud architecture have many disadvantages, which are mainly longer delay of data transmission and difficulty in software deployment. In view of the above disadvantages, this paper used containers to solve the problems for industrial software deployment and upgrade. This paper uses a cloud-edge-end framework to deploy tasks with high delay on the edge of the network or even on the device side, so as to reduce the delay of data transmission and computing pressure. At the same time, the task scheduling strategy based on the particle swarm optimization algorithm is used to search for the node with the minimum task delay among multiple nodes to realize the task allocation optimization. Finally, through the comparison experiment, it is concluded that the task scheduling strategy for industrial control networks based on a cloud-edge-end fusion framework can better reduce the task execution delay and reduce the difficulty of software deployment.