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An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms

by Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 2
Year of Publication: 2014
Authors: Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das
10.5120/15473-4158

Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das . An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms. International Journal of Computer Applications. 89, 2 ( March 2014), 11-18. DOI=10.5120/15473-4158

@article{ 10.5120/15473-4158,
author = { Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das },
title = { An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 2 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 11-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number2/15473-4158/ },
doi = { 10.5120/15473-4158 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:11.974286+05:30
%A Saurabh Bilgaiyan
%A Santwana Sagnika
%A Madhabananda Das
%T An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 2
%P 11-18
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing is a popular computing paradigm that performs processing of huge volumes of data using highly available geographically distributed resources that can be accessed by users on the basis of Pay As per Use policy. In the modern computing environment where the amount of data to be processed is increasing day by day, the costs involved in the transmission and execution of such amount of data is mounting significantly. So there is a requirement of appropriate scheduling of tasks which will help to manage the escalating costs of data intensive applications. This paper analyzes various evolutionary and swarm based task scheduling algorithms that address the above mentioned problem.

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Index Terms

Computer Science
Information Sciences

Keywords

Ant Colony Optimization (ACO) Bee Colony Optimization (BCO) cloud computing Genetic Algorithm (GA) Particle Swarm Optimization (PSO) Quality of Services (QoS) task scheduling.

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