Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing

In cloud computing, a shared, configurable pool of computing resources is made accessible online and allocated to users according to their needs. It is essential for a cloud provider to schedule jobs in the cloud to keep up service quality and boost system efficiency. In this paper, we present a sch...

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Published inMathematical problems in engineering Vol. 2023; no. 1
Main Authors Chaudhary, Shikha, Sharma, Vijay Kumar, Thakur, R. N., Rathi, Amit, Kumar, Pramendra, Sharma, Sachin
Format Journal Article
LanguageEnglish
Published New York Hindawi 2023
Hindawi Limited
Subjects
Aging
Algorithms
Cloud computing
Energy consumption
Genetic algorithms
Leadership
Mathematical problems
Optimization
Particle swarm optimization
Privacy
Schedules
Scheduling
Software services
Surveillance
Task scheduling
Utility functions
Virtual environments
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Summary:In cloud computing, a shared, configurable pool of computing resources is made accessible online and allocated to users according to their needs. It is essential for a cloud provider to schedule jobs in the cloud to keep up service quality and boost system efficiency. In this paper, we present a scheduling technique based on modified particle swarm optimization to combat the issues of excessively long scheduling time and high computation costs associated with scheduling jobs in a cloud environment. The modified PSO is used to allocate the jobs to virtual machines in order to minimize the objective function consisting of cost and makespan. The algorithm relies on biological changes that occur in organisms to regulate premature convergence and improve local search capability. The technique is analyzed and simulated using CloudSim, and the simulation results demonstrate that the proposed approach decreases makespan and cost effectively as compared to standard PSO.
ISSN:1024-123X
1563-5147
DOI:10.1155/2023/3916735