Time effective workflow scheduling using genetic algorithm in cloud computing
Автор: Rohit Nagar, Deepak K. Gupta, Raj M. Singh
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 1 Vol. 10, 2018 года.
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Cloud computing is service based technology on internet which facilitates users to access plenty of resources on demand from anywhere and anytime in a metered manner i.e. pay per usage without paying much heed to the maintenance and implementation details of application. As cloud technology is evolving day by day it is being confronted by numerous challenges, such as time and cost under deadline constraints. Research work done so far mainly focused on reducing cost as well as execution time. In order to minimize cost and execution time previously existing workflow scheduling model known as predict earliest finish time is used. In this research work we have proposed a new PEFT genetic algorithm approach to further reduce the execution time on this model. A strategy is developed to let GA focus on to optimize chromosomes objective to get best suitable mutated children. After obtaining a feasible solution, the genetic algorithm focuses on optimizing the execution time. Experimental results show that our algorithm can find better solution within lesser time.
Cloud computing, Task Scheduling, Earliest finish time, Genetic Algorithm, Makespan
Короткий адрес: https://sciup.org/15016229
IDR: 15016229 | DOI: 10.5815/ijitcs.2018.01.08
Список литературы Time effective workflow scheduling using genetic algorithm in cloud computing
- Mell, Peter, and Tim Grance, “The NIST definition of cloud computing”, Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology Gaithersburg, pp. 20-23, Year 2011.
- Kaur, P.D., I. “Unfolding the distributed computing paradigm”. International Conference on Advances in Computer Engineering, pp. 339-342 (2010).
- Gibson, Joel, Robin Rondeau, Darren Eveleigh, and Qing Tan.“Benefits and challenges of three cloud computing service models”, Fourth International Conference on Computational Aspects of Social Networks, IEEE, pp. 198-205, Year 2012.
- Silva, J.N., Veiga, L., Ferreira, P.: “Heuristics for Resource Allocation on Utility Computating Infrastructures,” 6th International Workshop on Middleware for Grid Computing, New York (2008).
- Bridi,T., Bartolini,A., Lombardi, M., Milano, M., and Benini, L., “A Constraint Programming Scheduler for Heterogeneous High-Performance Computing Machines,” pp. 1–14, (2016).
- Meena,J., Kumar, M., M., “Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint,” vol. 4, pp. 5065–5082, (2016).
- Verma, A., “Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing,” I.J. Information Technology and Computer Science, 08, pp. 37–43, (2015).
- Arabnejad., H., and Barbosa G.J., “List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table,” IEEE Transactions on Parallel and Distributed Systems, Vol: 25(3) March (2014).
- Topcuoglu,H., Hariri, S., and Wu, M., “Performance-effective and low-complexity task scheduling for heterogeneous computing,” IEEE transaction on Parallel and Distributed System, vol. 13, no. 3, pp. 260–274, (2002).
- Daoud, I.M., and Kharma, N., “A Hybrid Heuristics- Genetic Algorithm for Task Scheduling in Heterogeneous Processor Networks” Journal of Parallel and Distributed Computing, Vol. 71(11), pp. 1518-1531, (2011).
- Kaur,S., and Verma, “An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment,” I.J. of Information Technology and Computer Science, pp. 74–79, (2012).
- Ahmad,G.S., Munir,U.E., Nisar, W., Avenue, Q., and Cantt, W., “PEGA: A Performance Effective Genetic Algorithm for Task Scheduling in Heterogeneous Systems,” IEEE 14th International Conference on High Performance Computing and Communications, pp. 1082–1087, (2012).
- Shekhar Singh, and Mala kalra, “Scheduling of Independent tasks in cloud computing using modified genetic algorithm”, IEEE, Pages: 565 - 569, DOI: 10.1109/CICN.2014.128, Year 2014.
- A Saima Gulzar Ahmad, Ehsan Ullah Munir, and Wasif Nisar, “A Performance Effective Genetic Algorithm for Task Scheduling in Heterogeneous Systems (PEGA)”, IEEE, Year: 2012, Pages: 1082 - 1087, DOI: 10.1109/HPCC.2012.158, Year 2012.
- Chuan Wang, Jianhua Gu,Yunlan Wang, and Tianhai Zhao, “Hybrid Heuristic-Genetic Algorithm for Task Scheduling in Heterogeneous Multi-Core System (HSCGS)” springer, DOI: 10.1007/978-3-642-33078-0_12, Year 2012.
- Saeid Abrishami, Mahmoud Naghibzadeh, “Deadline constrained Workflow Scheduling Algorithms for Infrastructure as a Service Clouds” IEEE, Volume 29, Issue 1, Pages 158-169, Year 2013.
- Amandeep Verma,and Sakshi Kaushal, “Deadline constraint heuristic-based Genetic Algorithm for Workflow Scheduling in Cloud” IEEE, Volume 5, Issue 2, Pages 96-106, Year 2014.
- Beibei Zhu, Hongze, “Modified genetic algorithm for DAG scheduling in grid systems” IEEE, Pages: 465 - 468, DOI: 10.1109/ICSESS.2012.6269505, Year 2012.
- S. Selvarani, and G. Sudha Sadhasivam, “Improved cost-based algorithm for task scheduling in cloud computing” IEEE, Pages: 1 - 5, DOI: 10.1109/ICCIC.2010.5705847, Year 2010.
