Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing

Full Text (PDF, 865KB), PP.37-43

Views: 0 Downloads: 0

Author(s)

Amandeep Verma 1,* Sakshi Kaushal 1

1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2015.08.06

Received: 7 Oct. 2014 / Revised: 11 Feb. 2015 / Accepted: 20 Apr. 2015 / Published: 8 Jul. 2015

Index Terms

Workflow, Bi-Criteria Scheduling, Resource Reservation, HEFT, PSO, Priority

Abstract

Cloud computing is a collection of heterogeneous virtualized resources that can be accessed on-demand to service applications. Scheduling large and complex workflows becomes a challenging issue in cloud computing with a requirement that the execution time as well as cost incurred by using a set of heterogeneous cloud resources should be minimizes simultaneously. In this paper, we have extended our previously proposed Bi-Criteria Priority based Particle Swarm Optimization (BPSO) algorithm to schedule workflow tasks over the available cloud resources under given the deadline and budget constraints while considering the confirmed reservation of the resources. The extended heuristic is simulated and comparison is done with state-of-art algorithms. The simulation results show that extended BPSO algorithm also decreases the execution cost of schedule as compared to state-of-art algorithms under the same deadline and budget constraint while considering the exiting load of the resources too.

Cite This Paper

Amandeep Verma, Sakshi Kaushal, "Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing", International Journal of Information Technology and Computer Science(IJITCS), vol.7, no.8, pp.37-43, 2015. DOI:10.5815/ijitcs.2015.08.06

Reference

[1]I. Taylor, E. Deelman, D. Gannon, and M Shields., “Workflows for e-science: scientific workflows for grid”, 1st Edition, Springer, 2007.

[2]S. Pandey, “Scheduling and management of data intensive application workflows in grid and cloud computing environment”, PhD Thesis, University of Melbourne, Australia, 2010.

[3]L. Ke, J. Hai, C. X. L. Jinjun, Y. Dong, “A compromised time-cost scheduling algorithm in SwinDeW-C for instance-intensive cost-constrained workflows on cloud computing platform”, Journal of High Performance Computing Applications , vol.24, no.4, pp: 445–456, 2010.

[4]A. Verma, and S. Kaushal, “Bi-criteria priority based particle swram optimization workflow scheduling algorithm for cloud”, Proceeding of International Conference on Recent Advances in Engineering and Computational Sciences (RAECS),pp:1-6, March 2014 .

[5]J. Yu, and R. Buyya, “Workflow scheduling algorithms for grid computing”, In: Xhafa F, Abraham A (eds) Metaheuristics for scheduling in distributed computing environment, Springer, Berlin, 2008, ISBN: 978-3-540-69260-7.

[6]J. Yu, and R. Buyya, “Taxonomy of workflow management systems for grid computing”, Journal of Grid Computing, vol.3, no.1-2, pp: 171–200, 2005.

[7]T. Haluk, H. Salim, and M.Y. Wu, “Performance-effective and low-complexity task scheduling for heterogenous computing”, IEEE Transaction on Parallel and Distributed Systems, vol. 13, no. 3, pp: 260-274, 2002.

[8]Z. Aimin, Q. Bo-Yang, L.C. Hui, Z. Shi Zheng, N.S. Ponnuthurai, Qingfu Z., “Multiobjective evolutionary algorithms: A survey of the state of the art”, Journal of Swarm and Evolutionary Computation, vol. 1, no.1, pp:32–49, 2011.

[9]M. Malawki, G. Juve, E. Deelman, J. Nabrzyski, “ Cost and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds”, in proceeding of IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, UT, pp:1-11, 2012.

[10]R. Sakellariou, H. Zhao, E. Tsiakkouri, M.D. Dikaiakos, “Scheduling workfloes with budget constraint”, In: Gorlatch S, Danelutto M.(eds.) Integrated Research in GRID Computing,pp:189-202, Springer, 2007.

[11]S. Pandey, W. Linlin, M.G. Siddeswara, and R. Buyya, “A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments”, In. Proceeding International Conference on Advanced Information Networking and Applications Perth, WA, pp: 400-407, April 20-23, 2010

[12]W. Zhangjun, L. Xiao, N. Zhiwei, Y. Dong, and Y. Yun, “A market-oriented hierarchical scheduling strategy in cloud workflow systems”, Journal of Supercomputing, vol.63, no.1, pp: 256-293, Jan 2013.

[13]A. Verma, and S. Kaushal, “Deadline and budget distribution based cost-time optimization workflow scheduling algorithm for cloud”, In. IJCA Proceeding of International Conference on Recent Advances and Future Trends in IT, Patiala, India, pp: 1-4, 2012.

[14]A. Verma, and |S. Verma, “Deadline constraint heuristic based genetic algorithm for workflow scheduling in cloud”, Journal of Grid and Utility Computing, vol. 5, no. 2, pp:96-106, 2014.

[15]A. Verma, and S. Kaushal, “Budget constraint priority based genetic algorithm for workflow scheduling in cloud”, In. Proceeding of IET International Conference on Recent Trends in Information, Telecommunication and Computing, India pp: 8-14, 2013.

[16]S. Kaur and A. Verma, “An efficient approach to genetic algorithm for task scheduling in cloud computing environment”,IJITCS,vol.4, no. 10, Sept. 2012.

[17]W. Zheng, and R. Sakellariou, “Budget-deadline constrained workflow planning for admission control”, Journal of Grid Computing, vol. 11, no. 4,pp: 633-651, December 2013.

[18]S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M.H. Su, K. Vahi, “Characterization of scientific workflows”, In. Workshop on Workflows in Support of Large Scale Science, CA, USA, pp:1-10, 2008.

[19]Amazon Elastic Compute Cloud (Amazon EC2) Online Available: http://aws.amzon.com/ec2/.

[20]N. C. Rodrigo, R. Ranjan, B. Anton, A.F.D.R. Cesar, R. Buyya, “Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, Journal of Software: Practice and Experience (SPE), vol. 41, no. 1, pp: 23-50, 2011.