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A QPSO Algorithm Based on Hierarchical Weight and Its Application in Cloud Computing Task Scheduling

Guolong Yu¹, Yong Zhao², Zhongwei Cui¹ and Yu Zuo¹

1. School of Mathematics and Big Data, Guizhou Education University
   Guiyang, 550000, China
   heihuzhiguang@163.com
2. School of Information Engineering, Shenzhen Graduate School of Peking
   University, Shenzhen, 518000, China
   516636425@qq.com

Abstract

The computing method of the average optimal position is one of the most important factors that affect the optimization performance of the QPSO algorithm. Therefore, a particle position weight computing method based on particle fitness value grading is proposed, which is called HWQPSO (hierarchical weight QPSO). In this method, the higher the fitness value of a particle, the higher the level of the particle, and the greater the weight. Particles at different levels have different weights, while particles at the same level have the same weight. Through this method, the excellent particles have higher average optimal position weight, and at the same time, the absolute weight of a few particles is avoided, so that the algorithm can quickly and stably converge to the optimal solution, and improve the optimization ability and efficiency of the algorithm. In order to verify the effectiveness of the method, five standard test functions are selected to test the performance of HWQPSO, QPSO, DWC-QPSO and LTQPSO algorithm, and the algorithms are applied to the task scheduling of the cloud computing platform. Through the test experiment and application comparison, the results show that the HWQPSO algorithm can converge to the optimal solution of the test function faster than the other three algorithms. It can also find the task scheduling scheme with the shortest time consumption and the most balanced computing resource load in the cloud platform. In the experiment, compared with QPSO, DWC-QPSO and LTQPSO algorithm, HWQPSO execution time of the maximum task scheduling was reduced by 35%, 23% and 21% respectively.

Key words

QPSO algorithm, hierarchical weight, cloud computing, task scheduling, average optimal location

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200223033Y

Publication information

Volume 18, Issue 1 (January 2021)
Year of Publication: 2021
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Available in PDF
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How to cite

Yu, G., Zhao, Y., Cui, Z., Zuo, Y.: A QPSO Algorithm Based on Hierarchical Weight and Its Application in Cloud Computing Task Scheduling. Computer Science and Information Systems, Vol. 18, No. 1, 189–212. (2021), https://doi.org/10.2298/CSIS200223033Y