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Reseach Article

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

by Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das
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.

References
  1. Rimal, B. P. , Choi, E. , and Lumb, Ian. 2009. A taxonomy and survey of cloud computing systems. In Proceedings of the 5th IEEE International joint conference of INC, IMS and IDC, 44-51.
  2. Patel, R. , and Patel, S. , "Survey on Resource Allocation Strategies in Cloud Computing", International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 2, February- 2013, 1-5.
  3. Guo, L. , Zhao, S. , Shen, S. , and Jiang, C. , "Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm", Journal Of Networks, Vol. 7, No. 3, March 2012, 547-553.
  4. Lakhani, J. , and Bheda, H. 2012. Scheduling Technique of Data Intensive Application Workflows in Cloud Computing. In Proceedings of the Nirma University International Conference On Engineering, 1-5.
  5. Lin, C. T. , "Comparative Based Analysis of Scheduling Algorithms for Resource Management in Cloud Computing Environment", JCSE International Journal of Computer Science and Engineering, Vol. -1, Issue-1, July 2013, 17-23.
  6. Ge, Y. , and Wei, G. 2010. GA-Based Task Scheduler for the Cloud Computing Systems. In Proceedings of theIEEE International Conference on Web Information Systems and Mining, 181-186.
  7. Zhao, C. , Zhang, S. , Liu, Q. , Xie, J. , and Hu, J. 2009. Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing. In Proceedings of 5th IEEE International Conference on Wireless Communications, Networking and Mobile Computing, 1-4.
  8. Guo-ning, G. , Ting-Iei, H. , and Shuai, G. 2010. Genetic Simulated Annealing Algorithm for Task Scheduling based on Cloud Computing Environment. In Proceedings of IEEE International Conference on Intelligent Computing and Integrated Systems, 60-63.
  9. Zhu, K. , Song, H. , Liu, L. , Gao, J. , and Cheng, G. 2011. Hybrid Genetic Algorithm for Cloud Computing Applications. In Proceedings of IEEE Asia-Pacific Services Computing Conference, 182-187.
  10. Wang, X. , and Wang, Y. 2012. An Energy and Data Locality Aware Bi-level Multiobjective Task Scheduling Model Based on MapReduce for Cloud Computing. In Proceedings of IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 648-655.
  11. Chang-Tian, Y. , and Jiong, Y. 2012. Energy-aware Genetic Algorithms for Task Scheduling in Cloud Computing. In Proceedings of Seventh IEEE ChinaGrid Annual Conference, 43-48.
  12. Jang, S. H. , Kim, T. Y. , Kim, J. K. , and Lee, J. S. , "The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing", International Journal of Control and Automation Vol. 5, No. 4, December, 2012, 157-162.
  13. Junwei, G. , and Yongsheng, Y. 2013. Research of cloud computing task scheduling algorithm based on improved genetic algorithm. In Proceedings of 2nd International Conference on Computer Science and Electronics Engineering, 2134-2137.
  14. Liu, J. , Luo, X. G. , Zhang, X. M. , Zhang, F. , and Li, B. N. , "Job Scheduling Model for Cloud Computing Based on Multi-Objective Genetic Algorithm", IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 3, January 2013, 134-139.
  15. Pop, F. , Cristea, V. , Bessis, N. , and Sotiriadis, S. 2013. Reputation guided Genetic Scheduling Algorithm for Independent Tasks in Inter-Clouds Environments. In Proceedings of 27th IEEE International Conference on Advanced Information Networking and Applications Workshops, 772-776.
  16. Qing, W. , and Han-Chao, Z. 2011. Optimization of Task Allocation And Knowledge Workers Scheduling Based-on Particle Swarm Optimization. In Proceedings of IEEE International Conference on Electric Information and Control Engineering, 574-578.
  17. Pandey, S. , Wu, L. , Guru, S. M. , and Buyya, R. 2010. A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. In Proceedings of 24th IEEE International Conference on Advanced Information Networking and Applications, 400-407.
  18. Netjinda, N. , Sirinaovakul, B. , and Achalakul, T. 2012. Cost Optimization in Cloud Provisioning using Particle Swarm Optimization. In Proceedings of 9th IEEE International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 1-4.
  19. Guo, L. , Shao, G. , and Zhao, S. 2012. Multi-objective Task Assignment in Cloud Computing by Particle Swarm Optimization. In Proceedings of 8th International Conference on Wireless Communications, Networking and Mobile Computing, 1-4.
  20. Guo, L. , Zhao, S. , Shen, S. , and Jiang, C. , "Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm", Journal Of Networks, Vol. 7, No. 3, March 2012, 547-553.
  21. Chiang, C. W. , Lee, Y. C. , Lee, C. N. , and Chou, T. Y. , "Ant colony optimization for task matching and scheduling", IEE Proceedings - Computers and Digital Techniques, Vol. 153, No. 6, November 2006, 373-380.
  22. Li, K. , Xu, G. , Zhao, G. , Dong, Y. , and Wang, D. 2011. Cloud Task scheduling based on Load Balancing Ant Colony Optimization. In Proceedings of Sixth IEEE Annual ChinaGrid Conference, 3-9.
  23. TSai, P. W. , Pan, J. S. , Liao, B. Y. , and Chu, S. C. , "Enhanced Artificial Bee Colony Optimization", International Journal of Innovative Computing, Information and Control, Volume 5, Number 12, December 2009, 1-12.
  24. Bitam, S. 2012. Bees Life Algorithm for Job Scheduling in Cloud Computing. In Proceedings of The Third International Conference on Communications and Information Technology, 186-191.
  25. Mizan, T. , Masud, S. M. R. A. , Latip, R. , "Modified Bees Life Algorithm for Job Scheduling in Hybrid Cloud", International Journal of Engineering and Technology Volume 2 No. 6, June, 2012, 974-979.
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.