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An Upgraded Algorithm of Resource Scheduling using PSO and SA in Cloud Computing

by Talwinder Kaur, Seema Pahwa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 8
Year of Publication: 2013
Authors: Talwinder Kaur, Seema Pahwa
10.5120/12906-9909

Talwinder Kaur, Seema Pahwa . An Upgraded Algorithm of Resource Scheduling using PSO and SA in Cloud Computing. International Journal of Computer Applications. 74, 8 ( July 2013), 28-32. DOI=10.5120/12906-9909

@article{ 10.5120/12906-9909,
author = { Talwinder Kaur, Seema Pahwa },
title = { An Upgraded Algorithm of Resource Scheduling using PSO and SA in Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 8 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number8/12906-9909/ },
doi = { 10.5120/12906-9909 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:43.950126+05:30
%A Talwinder Kaur
%A Seema Pahwa
%T An Upgraded Algorithm of Resource Scheduling using PSO and SA in Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 8
%P 28-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing is a subscription-based service whose primary benefit is application scalability which allows real-time provisioning of resources to meet application requirements. Scheduling is the most prominent issue in cloud computing. Generally the goal of scheduling is to properly dispatch parallel jobs to slave node machines according to different scheduling policies. In this paper previously existing algorithms i. e. Particle Swarm Optimization (PSO), Improved Particle Swarm Optimization (IPSO), Simulated Annealing (SA) Algorithm, and Hybrid Particle Swarm Optimization-Simulated Annealing based on utilization time are studied which were proposed to handle problems posed by network properties between user and resources. A new algorithm is designed using shortest path theory, Particle Swarm Optimization and Simulated Annealing technique which achieve the target consuming less average execution time to obtain more efficiency in resource utilization and minimize the cost of the processing.

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Index Terms

Computer Science
Information Sciences

Keywords

Scheduling IPSO-Improved Particle Swarm Optimization PSO-Particle Swarm Optimization SA-Simulated Annealing

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