Research on task scheduling based on improved particle swarm optimization in cloud computing environment

Biying Zhang; Lei Zhang; Jianliang Sun

doi:10.1117/12.2641288

4 August 2022 Research on task scheduling based on improved particle swarm optimization in cloud computing environment

Biying Zhang, Lei Zhang, Jianliang Sun

Proceedings Volume 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022); 123061W (2022) https://doi.org/10.1117/12.2641288
Event: Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 2022, Changchun, China

Abstract

With the arrival of THE 5G era and the further growth of computing volume, how to rapidly process a large number of computing tasks is a problem that needs to be solved in the cloud computing field. In order to make the cloud computing task scheduling process with better performance, in this paper, the traditional particle swarm optimization (pso) algorithm was improved and applied to the task scheduling in cloud computing, in view of the task completion time as the optimization goal, main of population is initialized by the method of reverse learning, and the algorithm of the inertia weight and learning factor for dynamic adjustment. Through setting contrast experiments, it is confirmed that the improved algorithm has better performance and reduces the task completion time in cloud computing task scheduling.

Citation Download Citation

Biying Zhang, Lei Zhang, and Jianliang Sun "Research on task scheduling based on improved particle swarm optimization in cloud computing environment", Proc. SPIE 12306, Second International Conference on Digital Signal and Computer Communications (DSCC 2022), 123061W (4 August 2022); https://doi.org/10.1117/12.2641288

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