Hierarchical and balanced scheduling method of data-intensive workflow in industrial internet of things

To improve the throughput of the data-intensive workflow scheduling process and shorten the task completion time, a hierarchical and balanced scheduling method for data-intensive workflow in the industrial internet of things (IIoT) was proposed. Firstly, according to the structure of the workflow system, workflow tasks are classified and processed in a top-down manner. Secondly, calculate the completion time and load balancing degree of the workflow, and construct a workflow analysis balanced scheduling objective function under the constraints of time and load balancing degree. Finally, the frog position is updated, and the frog jumping algorithm is used to solve the objective function to obtain the optimal solution, thereby generating the optimal scheduling plan. The experimental results show that the task completion time of the proposed method does not exceed 30 s, and the maximum load balancing rate reaches 37%. Copyright © 2024 Inderscience Enterprises Ltd.