CFP last date
22 April 2024
Reseach Article

Smart Workflow Scheduling using the Hybridization of Random Weight Model with Ant Colony Optimization (RWM-ACO)

by Deepak Sharma, Talwinder Kaur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 164 - Number 4
Year of Publication: 2017
Authors: Deepak Sharma, Talwinder Kaur
10.5120/ijca2017913618

Deepak Sharma, Talwinder Kaur . Smart Workflow Scheduling using the Hybridization of Random Weight Model with Ant Colony Optimization (RWM-ACO). International Journal of Computer Applications. 164, 4 ( Apr 2017), 33-36. DOI=10.5120/ijca2017913618

@article{ 10.5120/ijca2017913618,
author = { Deepak Sharma, Talwinder Kaur },
title = { Smart Workflow Scheduling using the Hybridization of Random Weight Model with Ant Colony Optimization (RWM-ACO) },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 164 },
number = { 4 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 33-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume164/number4/27473-2017913618/ },
doi = { 10.5120/ijca2017913618 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:11:08.037923+05:30
%A Deepak Sharma
%A Talwinder Kaur
%T Smart Workflow Scheduling using the Hybridization of Random Weight Model with Ant Colony Optimization (RWM-ACO)
%J International Journal of Computer Applications
%@ 0975-8887
%V 164
%N 4
%P 33-36
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The cloud based platforms are designed specifically for the provision of the high performance clusters (HPC), which is realized by using the multiple techniques all together for the realization of the distributed computing environment. The cloud platforms are designed to handle the independent queries either in the groups or individually for the minimization or optimization of the response time for the rich user experience. For this, the cloud models utilize the versatile task scheduling models, which are based upon the various types of parameter either in individuality or aggregate. In this paper, the random weight based calculation for the scheduling of the tasks over the target cloud systems, which is further channelized using the ant colony optimization (ACO) based swarm intelligence. The performance of the ACO with random weights based algorithm based upon the response time and energy consumption on a primary note. The proposed model has been found efficient in the terms of the obtained performance parameters.

References
  1. “Gartner Highlights Five Attributes of Cloud Computing” [on-line] available from: http://www.gartner.com/newsroom/id/1035013 [0n13 Dec 2013].
  2. Z. Tari, "Security and Privacy in Cloud Computing", IEEE Cloud Computing, vol. 1, no. 1, pp. 54-57, 2014.
  3. Y. Xianfeng and L. Tao, "Load Balancing of Virtual Machines in Cloud Computing Environment Using Improved Ant Colony Algorithm", International Journal of Grid and Distributed Computing, vol. 8, no. 6, pp. 19-30, 2012.
  4. L.M Vaquero., L. R Merino., J. Caceres and M. Lindner “A break in the clouds: towards a cloud definition,” ACM SIGCOMM Computer Communication Review, vol.39, issue 1, pp. 50-55, 2008.
  5. Basic concept and terminology of cloud computing-[on-line] available from: http://whatiscloud.com [on 9 Jan 2015].
  6. Q. Zhao, C. Xiong, C. Yu, C. Zhang and X. Zhao, "A new energy-aware task scheduling method for data-intensive applications in the cloud", Journal of Network and Computer Applications, vol. 59, pp. 14-27, 2016.
  7. Anthony T.Velte, Toby J.Velte, Robert Elsenpeter, “Cloud Computing - A Practical Approach,” Tata McGraw-Hill Edition (ISBN-13:978-0-07-068351-8).
  8. S. Bharti and K. Pattanaik, "Dynamic Distributed Flow Scheduling with Load Balancing for Data Center Networks", Procedia Computer Science, vol. 19, pp. 124-130, 2013.
  9. Y. M., "A Survey of Cloud Computing Fault Tolerance: Techniques and Implementation", International Journal of Computer Applications, vol. 138, no. 13, pp. 34-38, 2016.
  10. W. Zhao, P. M. Melliar Smith, and L.E. Moser, “Fault Tolerance Middleware for Cloud Computing,” Proceedings of IEEE 3rd International Conference on Cloud Computing, USA pp 67-74, 2010.
  11. Dorigo M, Optimization, learning and natural algorithms. PhD thesis, Dipartimento di `Elettronica, Politecnico di Milano, Italy, 1992 [in Italian].
  12. M Rahman, S. Iqbal, and J.Gao “Load Balancer as a service in Cloud Computing,” IEEE 8th International Symposium on Service Oriented System Engineering, pp.204-211, April 2014.
  13. “Cloud Computing: A delicate balance of risk and benefit” [0n-line] available from: http://www.zdnet.com/blog/hinchcliffe/eight-ways-that-cloud-computing-will-change- business/488 [10 Feb 2015].
  14. S. Choi, "Fault-tolerance Scheduling for Service Sharing in Mobile Social Cloud Computing", Korea Institute of Information Technology Review, vol. 11, no. 1, 2013.
  15. Gaochao Xu, Junjie Pang, and Xiaodong Fu “A Load Balancing Model Based on Cloud Partitioning for the Public Cloud,” IEEE Transactions in Tsinghua Science and Technology,  vol -18, issue 1, pp. 34-39, 2013.
  16. Liu, Zhanghui, and X.Wang. "A PSO-based algorithm for load balancing in virtual machines of cloud computing environment," Advances in Swarm Intelligence, Springer Berlin, pp.142-147, 2012.
  17. K.Li, G.Xu, G. Zhao, Y. Dong, and D. Wang “ Cloud Task scheduling based on Load Balancing Ant Colony Optimization,” 6th IEEE Annual China Grid Conference , pp 3-9, 2011.
  18. Chang, Haihua, and X.Tang. "A load-balance based resource-scheduling algorithm under cloud computing environment," 9th International Conference on Web-based Learning, Springer, Berlin, pp. 85–90, 2011.
  19. S.Cavić, Vesna, and E. Kühn, “Self-Organized Load Balancing through Swarm Intelligence,” In Next Generation Data Technologies for Collective Computational Intelligence, Springer, Berlin pp. 195-224, 2011.
  20. A. Jain and  R. Singh, “An Innovative Approach of Ant Colony Optimization for Load Balancing Peer to Peer Grid Environment,” IEEE International Conference of Issues and Challenges in Intelligent Computing Techniques , pp. 1-5, 2014.
  21. R. Chaukwale and S.S. Kamath, “A Modified Ant Colony Optimization Algorithm with Load balancing for Job Shop Scheduling,” 15th IEEE International Conference onAdvanced Computing Technologies , pp. 1–5, 2013.
  22. S. Dam, G. Mandal, K. Dasgupta, and P. Dutta "An Ant Colony Based Load Balancing Strategy in Cloud Computing," In Advanced Computing, Networking and Informatics Springer, vol 2, pp. 403-413, 2014.
  23. S. K Goyal and M. Singh “Adaptive and Dynamic Load Balancing in Grid Using Ant Colony Optimization,” International Journal of Engineering and Technology, vol. 4, issue 4, pp.167-174, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Swarm intelligence random weight computation cloud task scheduling workflow management.