Call for Paper - July 2019 Edition
IJCA solicits original research papers for the July 2019 Edition. Last date of manuscript submission is June 20, 2019. Read More

Scheduling Preemptive and Non-Preemptive Tasks of Scientific Workflows using Hybrid Instances in Cloud Environment

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Vinay K., S. M. Dilip Kumar, Venugopal K. R.

Vinay K., Dilip S M Kumar and Venugopal K R.. Scheduling Preemptive and Non-Preemptive Tasks of Scientific Workflows using Hybrid Instances in Cloud Environment. International Journal of Computer Applications 181(15):1-6, September 2018. BibTeX

	author = {Vinay K. and S. M. Dilip Kumar and Venugopal K. R.},
	title = {Scheduling Preemptive and Non-Preemptive Tasks of Scientific Workflows using Hybrid Instances in Cloud Environment},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2018},
	volume = {181},
	number = {15},
	month = {Sep},
	year = {2018},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2018917817},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


SWf (Scientific Workflows) are vastly used in scientific domains and typically include non-preemptive and preemptive tasks. Cloud computing facilitates an appropriate ways to access cloud resources as a “pay-as-you-go" model and several resources such as, reserved, on-demand and spot instances are offered by the cloud service providers. The spot instance renting price is less as compared to on-demand instances. But, failures happen due to difference in the instance bid price. Henceforth, it is a challenge to schedule the preemptive and non-preemptive tasks of SWf onto appropriate spot and on-demand spot instances. Therefore, in this paper a SWf scheduling problem using both spot and on-demand instances are considered and the main objective is to reduce the total execution cost under deadline constraints. An efficient rule-based scheduling algorithms are proposed to schedule non-preemptive and preemptive tasks of SWf. The algorithm considers three different rules such as, maximum number of successors, minimum processing time, and minimum slack time to schedule SWf efficiently. Experimental results demonstrate the effectiveness of the proposed rule-based task sequence initialization and virtual machine selection algorithms for different SWf sizes.


  1. Shishir Bharathi, Ann Chervenak, Ewa Deelman, Gaurang Mehta, Mei-Hui Su, and Karan Vahi. Characterization of scientific workflows. In Workflows in Support of Large-Scale Science, 2008. WORKS 2008. Third Workshop on, pages 1– 10. IEEE, 2008.
  2. AmazonEC2. Amazon elastic compute cloud (Amazon EC2)., 2014.
  3. Jia Yu and Rajkumar Buyya. Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Scientific Programming, 14(3):217–230, 2006.
  4. Apostolos Gerasoulis and Tao Yang. On the granularity and clustering of directed acyclic task graphs. IEEE Transactions on Parallel and Distributed Systems, 4(6):686–701, 1993.
  5. Haluk Topcuoglu, Salim Hariri, and Min-you Wu. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3):260–274, 2002.
  6. Michael A. Palis, Jing-Chiou Liou, and David S. L.Wei. Task clustering and scheduling for distributed memory parallel architectures. IEEE Transactions on Parallel and Distributed Systems, 7(1):46–55, 1996.
  7. Wei-Neng Chen and Jun Zhang. An ant colony optimization approach to a grid workflow scheduling problem with various qos requirements. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 39(1):29–43, 2009.
  8. James Blythe, Sonal Jain, Ewa Deelman, Yolanda Gil, Karan Vahi, Anirban Mandal, and Ken Kennedy. Task scheduling strategies for workflow-based applications in grids. In IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2005), volume 2, pages 759–767. IEEE, 2005.
  9. Jia Yu, Rajkumar Buyya, and Chen Khong Tham. Costbased scheduling of scientific workflow applications on utility grids. In First International Conference on e-Science and Grid Computing (e-Science 2005), page 8. IEEE, 2005.
  10. S. Abrishami, M. Naghibzadeh, and DHJ Epema. Costdriven scheduling of grid workflows using partial critical paths. IEEE Transactions on Parallel and Distributed Systems, 23(8):1400–1414, 2012.
  11. K Vinay, SM Dilip Kumar, S Raghavendra, and KR Venugopal. Cost and fault-tolerant aware resource management for scientific workflows using hybrid instances on clouds. Multimedia Tools and Applications, 77(8):10171–10193, 2018.
  12. Deepak Poola, Kotagiri Ramamohanarao, and Rajkumar Buyya. Fault-tolerant workflow scheduling using spot instances on clouds. Procedia Computer Science, 29:523–533, 2014.
  13. Deepak Poola, Kotagiri Ramamohanarao, and Rajkumar Buyya. Enhancing reliability of workflow execution using task replication and spot instances. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 10(4):30, 2016.
  14. Daeyong Jung, JongBeom Lim, JoonMin Gil, Eunyoung Lee, and Heonchang Yu. Task balanced workflow scheduling technique considering task processing rate in spot market. Journal of Applied Mathematics, 2014, 2014.
  15. Daeyong Jung, Taeweon Suh, Heon-Chang Yu, and Joon-Min Gil. A workflow scheduling technique using genetic algorithm in spot instance-based cloud. TIIS, 8(9):3126–3145, 2014.
  16. Rodrigo N Calheiros, Rajiv Ranjan, Anton Beloglazov, César AF De Rose, and Rajkumar Buyya. Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience, 41(1):23–50, 2011.


Cloud Computing, Scientific Workflows, Scheduling, Preemptive, Non-Preemptive, On-demand, Spot, Virtual Machine