The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2017.080104
PDF

Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Author 1: Frederic Nzanywayingoma
Author 2: Yang Yang

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 1, 2017.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Since the beginning of cloud computing technology, task scheduling problem has never been an easy work. Because of its NP-complete problem nature, a large number of task scheduling techniques have been suggested by different researchers to solve this complicated optimization problem. It is found worth to employ heuristics methods to get optimal or to arrive at near-optimal solutions. In this work, a combination of two heuristics algorithms was proposed: particle swarm optimization (PSO) and genetic algorithm (GA). Firstly, we list pros and cons of each algorithm and express its best interest to maximize the resource utilization. Secondly, we conduct a performance comparison approach based on two most critical objective functions of task scheduling problems which are execution time and computation cost of tasks in cloud computing. Thirdly, we compare our results with other existing heuristics algorithms from the literatures. The experimental results was examined with benchmark functions and results showed that the particle swarm optimization (PSO) performs better than genetic algorithm (GA) but they both present a similarity because of their population based search methods. The results also showed that the proposed hybrid models outperform the standard PSO and reduces dramatically the execution time and lower the processing cost on the computing resources.

Keywords: Execution Time; Task Scheduling Algorithms; Particle Swarms (PSO); Genetic Algorithm (GA); Virtual Machines (VMs)

Frederic Nzanywayingoma and Yang Yang, “Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment” International Journal of Advanced Computer Science and Applications(IJACSA), 8(1), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080104

@article{Nzanywayingoma2017,
title = {Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080104},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080104},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {1},
author = {Frederic Nzanywayingoma and Yang Yang}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org