Research PaperOpen Access

Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Sangita M. Jaybhaye

Research Scholar, Department of Computer and IT, College of Engineering Pune, Pune, Maharashtra, India

E-mail Address: smjaybhaye@gmail.com

Corresponding author.

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and

Vahida Z. Attar

Research Scholar, Department of Computer and IT, College of Engineering Pune, Pune, Maharashtra, India

E-mail Address: vahida.comp@coep.ac.in

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https://doi.org/10.1142/S2196888820500104Cited by:1 (Source: Crossref)

Abstract

Cloud services are used to achieve diverse computing needs such as cost, security, scalability, and availability. Acceleration evolution in the distributed and cloud domains is common for large and dynamic workflows deployment. Resources and task mapping depend on the user’s objectives such as reduction in cost or execution completion within the stipulated time in consideration with certain quality of services. Multiple virtual machine instances can be launched by defining different configurations such as operating system, server types, and applications. Though workflow scheduling is an NP-Hard problem, variety of decision-making techniques are available for optimum resource allocation. In this research paper, different algorithms are studied and compared with evolutionary approaches. Workflow scheduling using genetic algorithm is implemented and discussed. This paper aims to design a decision-making technique to optimize resources of cloud. It is an adaptive scheduling to maximize profit by reducing execution time. The approach implemented is useful to cloud service providers to maximize profit and resource efficiency in their services.

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References

1. J. Sahni and D. Prakash Vidyarthi, A cost-effective deadline- constrained dynamic scheduling algorithm for scientific workflows in a cloud environment, IEEE Trans. Cloud Comput. 6(1) (2018) 1–18. Crossref, Google Scholar
2. V. Singh, I. Gupta and P. K. Jana, A novel cost effective approach for deadline-constrained workflow scheduling by dynamic provisioning of resources, Future Gener. Comput. Syst. 79 (2018) 95–110. Crossref, Google Scholar
3. A. M. Chirkin, M. A. Melnik, A. S. Z. Belloum and S. V. Kovalchuk, Execution time estimation for workflow scheduling, Future Gener. Comput. Syst. 75 (2017) 376–387. Crossref, Google Scholar
4. L. Wei, C. H. Foh, B. He and J. Cai, Towards efficient resource allocation for heterogeneous workloads in IaaS clouds, IEEE Trans. Cloud Comput. 6(1) (2015) 264–276. Crossref, Google Scholar
5. F. Nzanywayingoma and Y. Yang, Efficient resource management techniques in cloud computing environment: A review an discussion, J. Comput. Appl. 15(4) (2017) 1917–1933. Google Scholar
6. J. Meena, M. Kumar and M. Vardhan, Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint, IEEE Access 4 (2016) 5065–5082. Crossref, Google Scholar
7. T. Phan Thanh, L. Nguyen and C. N. Doan, A robust and effective MODE algorithm for workflow scheduling in cloud, in Seventh Symp. Inf. Commun. Technol. (ACM, 2016), pp. 259–264. Google Scholar
8. D. Yin and T. Kosar, Data-aware approximate workflow scheduling, arXiv:1805.10499. Google Scholar
9. B. L. Muhammad-Bello and M. Aritsugi, Robust deadline constrained resource provisioning and workflow scheduling algorithm for handling performance uncertainty in IaaS clouds, in Proc. 10th Int. Conf. Utility and Cloud Computing (ACM, 2017), pp. 29–34. Crossref, Google Scholar
10. V. Arabnejad, K. Bubendorfer and B. Ng, Deadline distribution strategies for scientific workflow scheduling in commercial clouds, in 9th Int. Conf. Utility and Cloud Comput. (IEEE/ACM, 2016), pp. 70–78. Crossref, Google Scholar
11. V. Arabnejad, K. Bubendorfer and B. Ng, Scheduling deadline constrained scientific workflows on dynamically provisioned cloud resources, Future Gener. Comput. Syst. 75 (2017) 348–364. Crossref, Google Scholar
12. Z. Li, B. Luo, J. Ge, H. Yang, L. Huang, H. Hu, H. Hu and B. Luo, A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds, Future Gener. Comput. Syst. 65 (2016) 140–152. Crossref, Google Scholar
13. J. Liu, E. Pacitti, P. Valduriez, D. de Oliveira and M. Mattoso, Multi-objective scheduling of scientific workflows in multisite clouds, Future Gener. Comput. Syst. 63 (2016) 76–95. Crossref, Google Scholar
14. J. R. Almeida and J. L. Oliveira, A modular workflow management framework, in Proc. 11th Int. Conf. Health Informatics, SCITEPRESS — Science and Technology Publications (2018) pp. 414–421. Crossref, Google Scholar
15. J. Liu, E. Pacitti and M. Mattoso, A survey of data-intensive scientific workflow management, J. Grid Comput. 13 (2015) 1–44. https://doi.org/10.1007/s10723-015-9329-8 Crossref, Google Scholar
16. S. Bharathi, A. Chervenak, E. Deelman, G. Mehta and M.-H. Su, Characterization of scientific workflows, in 2008 Third Workshop on Workflows in Support of Large-scale Science (IEEE, 2008), pp. 1–10. Crossref, Google Scholar
17. G. L. Stavrinides, F. R. Duro, H. D. Karatza, J. G. Blas and J. Carretero, Different aspects of workflow scheduling in large-scale distributed systems, Simul. Modelling Pract. Theory 70 (2017) 120–134. Crossref, Google Scholar
18. K. Kanagaraj and S. Swamynathan, Structure aware resource estimation for effective scheduling and execution of data intensive workflow in cloud, Future Gener. Comput. Syst. 79 (2018) 878–891. Crossref, Google Scholar
19. L. Teylo, U. de Paula, Y. Frota, D. de Oliveira and L. M. A. Drummond, A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds, Future Gener. Comput. Syst. 76 (2017) 1–17. Crossref, Google Scholar
20. M. Zhang, R. Buyya, H. Li and L. Liu, An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in clouds, Distrib. Parallel Databases 36 (2018) 339–368. Crossref, Google Scholar
21. M. Zhang, H. Li, L. Liu and R. Buyya, An adaptive multi-objective evolutionary algorithm for constrained workflow scheduling in clouds, Distrib. Parallel Databases 36(2) (2017) 339–368. Crossref, Google Scholar
22. Z. Zhu, G. Zhang, M. Li and X. Liu, Evolutionary multi-objective workflow scheduling in cloud, IEEE Trans. Parallel Distrib. Syst. 27(5) (2016) 1344–1357. Crossref, Google Scholar
23. S. Yassa, J. Sublime, R. Chelouah, H. Kadima, G.-S. Jo and B. Granado, A genetic algorithm for multi-objective optimization in workflow scheduling with hard constraints, Int. J. Metaheuristics 2(4) (2013) 415–433. Crossref, Google Scholar
24. A. Choudhary, I. Gupta, V. Singh and P. K. Jana, A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing, Future Gener. Comput. Syst. 83 (2018) 14–26. Crossref, Google Scholar
25. L. Wang and E. Gelenbe, Adaptive dispatching of tasks in the cloud, IEEE Trans. Cloud Comput. 6(1) (2015) 33–45. Crossref, Google Scholar
26. M. Kalra and S. Singh, A review of meta heuristic scheduling techniques in cloud computing, Egypt. Inform. J. 16(3) (2015) 275–295. Crossref, Google Scholar
27. A. A. Visheratin, M. Melnik and D. Nasonov, Workflow scheduling algorithms for hard-deadline constrained cloud environments, Procedia Comput. Sci. 80 (2016) 2098–2106. Crossref, Google Scholar
28. A. A. Visheratin, M. Melnik and D. Nasonov, Workflow scheduling algorithms for hard-deadline constrained cloud environments, Procedia Comput. Sci. 80 (2016) 2098–2106. Crossref, Google Scholar
29. J.-T. Tsai, J.-C. Fang and J.-H. Chou, Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm, Comput. Oper. Res. 40(12) (2013) 3045–3055. Crossref, Google Scholar
30. N. Anwar and H. Deng, Elastic scheduling of scientific workflows under deadline constraints in cloud computing environments, Future Internet 10(1) (2018) 1–23. https://doi.org/10.3390/10010005 Crossref, Google Scholar

Vol. 07, No. 02

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History

Received 21 June 2019

Accepted 20 January 2020

Published: 26 February 2020

Information

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Adaptive Workflow Scheduling Using Evolutionary Approach in Cloud Computing

Abstract

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