Journal of Engineering and Applied Sciences

Year: 2019
Volume: 14
Issue: 2
Page No. 562 - 566

Using Simulation to Improve Max-Min Algorithm for Minimizing the Workflow Execution Cost in the Cloud

Authors : Ali S.A. Al-Haboobi, Mohammed R.A.M. Hammoodi and Ahmed Raheem Kadhim

References

Bharathi, S., A. Chervenak, E. Deelman, G. Mehta and M.H. Su et al., 2008. Characterization of scientific workflows. Proceedings of the 3rd Workshop on Workflows in Support of Large-Scale Science, November 17, 2008, IEEE, Austin, Texas, USA., ISBN:978-1-4244-2827-4, pp: 1-10.

Buyya, R., C.S. Yeo, S. Venugopal, J. Broberg and I. Brandic, 2009. Cloud computing and emerging IT platforms: Vision, hype and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst., 25: 599-616.
CrossRef  |  Direct Link  |  

Calheiros, R.N., R. Ranjan, A. Beloglazov, C.A.F. de Rose and R. Buyya, 2011. CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Pract. Experience, 41: 23-50.
CrossRef  |  Direct Link  |  

Chen, W. and E. Deelman, 2012. Workflowsim: A toolkit for simulating scientific workflows in distributed environments. Proceedings of the 2012 IEEE 8th International Conference on E-Science (E-Science’12), October 8-12, 2012, IEEE, Chicago, Illinois, ISBN:978-1-4673-4467-8, pp: 1-8.

Deelman, E., D. Gannon, M. Shields and I. Taylor, 2006. Workflows and e-Science: An overview of workflow system features and capabilities. Future Gener. Comput. Syst., 25: 528-540.
CrossRef  |  

Dong, F. and S.G. Akl, 2006. Scheduling algorithms for grid computing: state of the art and open problems. Technical Report No. 2006-504, School of Computing, Queen, s University, Kingston. http://www.dcs.warwick.ac.uk http: //cs. felk.cvut. cz /~xobitko /ga/ about.html.

Elzeki, O.M., M.Z. Reshad and M.A. Elsoud, 2012. Improved max-min algorithm in cloud computing. Int. J. Comput. Applic., 50: 22-27.
Direct Link  |  

Ming, G. and H. Li, 2012. An Improved Algorithm based on Max-Min for Cloud Task Scheduling. In: Recent Advances in Computer Science and Information Engineering, Qian, Z., L. Cao, W. Su, T. Wang, H. Yang (Eds.). Springer, Berlin, Heidelberg, ISBN:978-3-642-25788-9, pp: 217-223.

Parsa, S. and R.E. Maleki, 2009. RASA: A new task scheduling algorithm in grid environment. World Appl. Sci. J., 7: 152-160.

Sun, W., N. Zhang, H. Wang, W. Yin and T. Qiu, 2013. PACO: A period ACO based scheduling algorithm in cloud computing. Proceedings of the 2013 International Conference on Cloud Computing and Big Data, December 16-19, 2013, IEEE, Fuzhou, China, ISBN:978-1-4799-2829-3, pp: 482-486.

Topcuoglu, H., S. Hariri and M.Y. Wu, 2002. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst., 13: 260-274.
CrossRef  |  

Tsai, C.W., W.C. Huang, M.H. Chiang, M.C. Chiang and C.S. Yang, 2014. A hyper-heuristic scheduling algorithm for cloud. Cloud Comput. IEEE. Trans., 2: 236-250.
CrossRef  |  

Ullman, J.D., 1975. NP-complete scheduling problems. J. Comput. Syst. Sci., 10: 384-393.
CrossRef  |  Direct Link  |  

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