Scheduling Multiple Workflow Using De-De Dodging Algorithm and PBD Algorithm in Cloud: Detailed Study
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Scheduling Multiple Workflow Using De-De Dodging Algorithm and PBD Algorithm in Cloud: Detailed Study

Authors: B. Arun Kumar, T. Ravichandran

Abstract:

Workflow scheduling is an important part of cloud computing and based on different criteria it decides cost, execution time, and performances. A cloud workflow system is a platform service facilitating automation of distributed applications based on new cloud infrastructure. An aspect which differentiates cloud workflow system from others is market-oriented business model, an innovation which challenges conventional workflow scheduling strategies. Time and Cost optimization algorithm for scheduling Hybrid Clouds (TCHC) algorithm decides which resource should be chartered from public providers is combined with a new De-De algorithm considering that every instance of single and multiple workflows work without deadlocks. To offset this, two new concepts - De-De Dodging Algorithm and Priority Based Decisive Algorithm - combine with conventional deadlock avoidance issues by proposing one algorithm that maximizes active (not just allocated) resource use and reduces Makespan.

Keywords: Workflow Scheduling, cloud workflow, TCHC algorithm, De-De Dodging Algorithm, Priority Based Decisive Algorithm (PBD), Makespan.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1108190

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[1] Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Services and Applications 1(1), 7–18 (2010).
[2] Smith, J.E., Nair, R.: The architecture of virtual machines. Computer 38(5), 32–38 (2005).
[3] E. Deelman, D. Gannon, M. Shields, and I. Taylor, “Workflows and escience: An overview of workflow system features and capabilities,” Future Gener. Comput. Syst., vol. 25, no. 5, pp. 528– 540, May 2009.
[4] A. Ramakrishnan, G. Singh, H. Zhao, E. Deelman, R. Sakellariou, K. Vahi, K. Blackburn, D. Mayers, and M. Samidi, “Scheduling dataintensive workflows onto storage-constrained distributed resources,” in Proceedings of the 7th IEEE International Symposium on Cluster Computing and the Grid, 2007, pp. 401–409.
[5] J. Bent, D. Thain, A. Arpaci-Dusseau, R. H. Arpaci-Dusseau, and M. Livny, “Explicit control in a batch-aware distributed file system,” in Proceedings of Networked Systems Design and Implementation (NSDI), San Francisco, California, USA, 2004, pp. 365–378.
[6] Fakhfakh, F., Kacem, H. H., & Kacem, A. H. (2014, September). Workflow Scheduling in Cloud Computing: A Survey. In Enterprise Distributed Object Computing Conference Workshops and Demonstrations (EDOCW), 2014 IEEE 18th International (pp. 372-378). IEEE.
[7] Verma, A., & Kaushal, S. (2013, September). Budget constrained priority based genetic algorithm for workflow scheduling in cloud. In Communication and Computing (ARTCom 2013), Fifth International Conference on Advances in Recent Technologies in (pp. 216-222). IET.
[8] Watanabe, E. N., Campos, P. P., Braghetto, K. R., & Batista, D. M. (2014, May). Energy Saving Algorithms for Workflow Scheduling in Cloud Computing. In Computer Networks and Distributed Systems (SBRC), 2014 Brazilian Symposium on (pp. 9-16). IEEE.
[9] Udomkasemsub, O., Xiaorong, L., & Achalakul, T. (2012, May). A multiple-objective workflow scheduling framework for cloud data analytics. In Computer Science and Software Engineering (JCSSE), 2012 International Joint Conference on (pp. 391-398). IEEE.
[10] Arya, L. K., & Verma, A. (2014, March). Workflow scheduling algorithms in cloud environment-A survey. In Engineering and Computational Sciences (RAECS), 2014 Recent Advances in (pp. 1-4). IEEE.
[11] Rahman, M., Li, X., & Palit, H. (2011, May). Hybrid heuristic for scheduling data analytics workflow applications in hybrid cloud environment. In Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on (pp. 966- 974). IEEE.
[12] Bittencourt, L. F., Senna, C. R., & Madeira, E. R. (2010, October). Scheduling service workflows for cost optimization in hybrid clouds. In Network and Service Management (CNSM), 2010 International Conference on (pp. 394-397). IEEE.
[13] Prakash, V., & Bala, A. (2014, July). A novel scheduling approach for workflow management in cloud computing. In Signal Propagation and Computer Technology (ICSPCT), 2014 International Conference on (pp. 610-615). IEEE.
[14] Zhu, M., Wu, Q., & Zhao, Y. (2012, December). A cost-effective scheduling algorithm for scientific workflows in clouds. In Performance Computing and Communications Conference (IPCCC), 2012 IEEE 31st International (pp. 256-265). IEEE.
[15] Xu, M., Cui, L., Wang, H., & Bi, Y. (2009, August). A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing. In Parallel and Distributed Processing with Applications, 2009 IEEE International Symposium on (pp. 629-634). IEEE.