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A Goal-oriented Workflow Scheduling in Heterogeneous Distributed Systems

by Arash Ghorbannia Delavar, Yalda Aryan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 8
Year of Publication: 2012
Authors: Arash Ghorbannia Delavar, Yalda Aryan
10.5120/8223-1656

Arash Ghorbannia Delavar, Yalda Aryan . A Goal-oriented Workflow Scheduling in Heterogeneous Distributed Systems. International Journal of Computer Applications. 52, 8 ( August 2012), 27-33. DOI=10.5120/8223-1656

@article{ 10.5120/8223-1656,
author = { Arash Ghorbannia Delavar, Yalda Aryan },
title = { A Goal-oriented Workflow Scheduling in Heterogeneous Distributed Systems },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 8 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number8/8223-1656/ },
doi = { 10.5120/8223-1656 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:51:45.510327+05:30
%A Arash Ghorbannia Delavar
%A Yalda Aryan
%T A Goal-oriented Workflow Scheduling in Heterogeneous Distributed Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 8
%P 27-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In heterogeneous distributed systems like grid and cloud computing infrastructures, the major problem is the task scheduling which can have much impact on system performance. For some reasons, such as heterogeneous and dynamic features and the dependencies among the requests, this issue is known as a NP-hard problem. In this article a hybrid meta-heuristic method based on Genetic Algorithm (GMSW) is being proposed in order to find a suitable solution for mapping the requests on resources. The proposed method tries to obtain the response quickly, with some goal-oriented operations. It begins, through making a good initial population by merging some features of the Best-Fit and Round Robin methods and a bi-directional tasks prioritization in unbalanced-structured workflow, considering their impact on each other, based on graph topology. Some other operations control and lead the algorithm steps in order to obtain the solution by using efficient parameters in the mentioned systems. Here the focus is on optimizing the makespan and reliability, by considering a good distribution of workload on resources. The experiments here indicate that the GMSW improves the results, with the increasing number of tasks in application graph, for the mentioned objectives. The results are compared with other studied algorithms.

References
  1. Sahoo, B. Avinash Ekka, A. 2006. Performance Analysis Of Concurrent Tasks Scheduling Schemes In a Heterogeneous Distributed Computing System. National Conference on Computer Science & Technology.
  2. BRENT, R. P. 1989. Efficient Implementation of the First-Fit Strategy for Dynamic Storage Allocation. Australian National University, ACM Transactions on Programming Languages and Systems, Vol. 11, No. 3, (July 1989).
  3. Nurmi, D. Wolski, R. Grzegorczyk, C. Obertelli, G. So-man, S. Youseff, L. and Zagorodnov, D. 2009. The Eucalyptus open-source cloud-computing system. IEEE International Symposium on Cluster Computing and the Grid (CCGrid).
  4. Izakian, H. Abraham, A. Member, S. Comparison of Heuristics for Scheduling Independent Tasks on Heterogeneous Distributed Environments.
  5. Casanova, H. Desprez, F. Suter, F. 2010. On cluster resource allocation for multiple parallel task graphs. ELSEVIER, J. Parallel and Distributed Computing, 70 (2010) 1193–1203.
  6. Pandey, S. 2010. Scheduling and Management of Data Intensive Application Workflows in Grid and Cloud computing Environments. Doctoral Thesis. Department of Computer Science and Software Engineering, the University of Melbourne, Australia (December 2010).
  7. Porto, S. Ribeiro, C. 1995. A tabu search approach to task scheduling on heterogeneous processors under precedence constraints. International Journal of High Speed Computing, 7 (1995) 45–72.
  8. Kalashnikov, A. Kostenko, V. 2008. A parallel algorithm of simulated annealing for multiprocessor scheduling, International Journal of Computer and Systems Sciences 47 (2008) 455–463.
  9. Yu, J. Buyya, R. Ramamohanarao, K. 2009. Workflow Scheduling Algorithms for Grid computing. Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3010, Australia. http://www. cloudbus. org/reports.
  10. Yoo, M. 2009. Real-time task scheduling by multiobjective genetic algorithm. ELSEVIER, The Journal of Systems and Software, 82 (2009) 619–628.
  11. Omara, F. A. Arafa, M. M. 2010. Genetic algorithms for task scheduling problem. ELSEVIER, J. Parallel and Distributed Computing, 70 (2010) 13_22.
  12. Fida, A. 2008. Workflow Scheduling for Service Oriented Cloud Computing. MSc Thesis, College of Graduate Studies and Research In Partial Fulfillment, Department of Computer Science University of Saskatchewan Saskatoon.
  13. Ghorbannia Delavar, A. Aryan, Y. 2011. A Synthetic Heuristic Algorithm for Independent Task Scheduling in Cloud Systems. IJCSI International Journal of Computer Science Issues, 1694-0814.
  14. Ilavarasan E. and Thambidurai, P. 2007. Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments. Journal of Computer Sciences 3 (2): 94-103, 2007.
  15. Padmavathi, S. Mercy Shalinie, S. 2010. Scable Low Complexity Task Scheduling Algorithm for Cluster of Workstations. Journal of Engineering Science and Technology Vol. 5, No. 3 (2010) 332 – 341.
  16. Shi, Z. Dongarra, J. J. 2006. Scheduling workflow applications on processors with different capabilities. Future Generation Computer Systems 22 (2006) 665–675.
  17. Tang, X. Li, K. Li, R. Veeravalli, B. 2010. Reliability-aware scheduling strategy for heterogeneous distributed computing systems, J. Parallel and Distributed Computing, 70 (2010) 941_952.
  18. Tang, X. Li, K. Liao, G. Li, R. 2010. List scheduling with duplication for heterogeneous computing systems. J. Parallel and Distributed Computing, 70 (2010) 323_329.
  19. Wen, Y. Xu, H. Yang, J. 2011. A heuristic-based hybrid genetic-variable neighbourhood search algorithm for task scheduling in heterogeneous multiprocessor system. ELSEVIER, Information Sciences, 181 (2011) 567–581.
  20. Li, J. Qiu, M. Ming, Z. Quan, G. Qin, X. Gue, Z. 2012. Online optimization for scheduling preemptable tasks on IaaS cloud systems. ELSEVIER, Journal of Parallel and Distributed Computing, 72 (2012) 666–677.
  21. Wang, X. Shin Yeo, Ch. Buyya, R. Su, J. 2011. Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm, ELSEVIER, Future Generation Computer Systems 27 (2011) 1124–1134.
  22. Ge, J. Zhang, B. and Fang, Y. 2010. Research on the Resource Monitoring Model Under Cloud Computing Environment. WISM 2010, LNCS 6318, pp. 111–118, Springer, Verlag Berlin Heidelberg.
  23. Ghorbannia Delavar, A. Aghazarian, V. Litkouhi S. and Khajeh naeini, M. 2011. A Scheduling Algorithm for Increasing the Quality of the Distributed Systems by using Genetic Algorithm. International Journal of Information and Education Technology, Vol. 1, No. 1, ISSN: 2010-3689.
  24. Mezmaz, M. Melab, N. Kessaci, Y. Lee c, Y. C. Talbi, E. -G. Zomaya, A. Y. Tuyttens, D. 2011. A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. ELSEVIER, J. Parallel and Distributed Computing (2011).
  25. Chitra, P. Rajaram, R. Venkatesh, P. 2011. Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems, Applied Soft Computing 11 (2011) 2725–2734.
  26. Lee, Y. C. Zomaya, A. Y. 2010. Rescheduling for reliable job completion with the support of clouds. Future Generation Computer Systems 26 (2010) 1192_1199.
Index Terms

Computer Science
Information Sciences

Keywords

Heterogeneous distributed systems Grid computing Cloud computing Workflow scheduling Reliability Genetic Algorithm

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