A Novel Task-Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization

A Novel Task-Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization

Zhou Wu (Guangdong Songshan Polytechnic College, China) and Jun Xiong (Guangdong Songshan Polytechnic College, China)
Copyright: © 2021 |Pages: 15
DOI: 10.4018/IJGCMS.2021040101
OnDemand PDF Download:
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

With the characteristics of low cost, high availability, and scalability, cloud computing has become a high demand platform in the field of information technology. Due to the dynamic and diversity of cloud computing system, the task and resource scheduling has become a challenging issue. This paper proposes a novel task scheduling algorithm of cloud computing based on particle swarm optimization. Firstly, the resource scheduling problem in cloud computing system is modeled, and the objective function of the task execution time is formulated. Then, the modified particle swarm optimization algorithm is introduced to schedule applications' tasks and enhance load balancing. It uses Copula function to explore the relation of the random parameters random numbers and defines the local attractor to avoid the fitness function to be trapped into local optimum. The simulation results show that the proposed resource scheduling and allocation model can effectively improve the resource utilization of cloud computing and greatly reduce the completion time of tasks.
Article Preview
Top

In recent years, many scholars have carried out in-depth research on cloud computing resource scheduling problems, and proposed a variety of resource scheduling algorithms. Static scheduling algorithm focuses on global planning according to all task requirements and resource pool size to maximize resource utilization.

Complete Article List

Search this Journal:
Reset
Open Access Articles
Volume 14: 4 Issues (2022): Forthcoming, Available for Pre-Order
Volume 13: 4 Issues (2021): 3 Released, 1 Forthcoming
Volume 12: 4 Issues (2020)
Volume 11: 4 Issues (2019)
Volume 10: 4 Issues (2018)
Volume 9: 4 Issues (2017)
Volume 8: 4 Issues (2016)
Volume 7: 4 Issues (2015)
Volume 6: 4 Issues (2014)
Volume 5: 4 Issues (2013)
Volume 4: 4 Issues (2012)
Volume 3: 4 Issues (2011)
Volume 2: 4 Issues (2010)
Volume 1: 4 Issues (2009)
View Complete Journal Contents Listing