Max-Average: An Extended Max-Min Scheduling Algorithm for Grid Computing Environtment

Authors

  • J.Y. Maipan-uku Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E., Malaysia
  • A. Muhammed Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E., Malaysia
  • A. Abdullah Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E., Malaysia
  • M. Hussin Department of Communication Technology and Networks, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor D.E., Malaysia

Keywords:

Scheduling Algorithm, Grid Computing, Minimum Execution Time (MET), Minimum Completion Time (MCT),

Abstract

Sharing numerous computational and communication power from connected heterogeneous systems over the world are the two key points of Grid computing. Grid computing can also be referred as a computing platform for users to utilise the remote heterogeneous resources for solving their large scale jobs that require a huge amount of processing power or a huge data storage. Sharing these resources that way effectively requires a very good scheduling strategy, which is the focus of this research. This paper presents a new proposed grid based scheduling algorithm called Max-Average, inspired from Max-Min algorithm. In order to produce good quality solutions, the proposed algorithm is designed in two phases; firstly it uses an initial task queue like the traditional Max -Min for estimating task completion time for each of resources, and in the second phase choose the fitting resource for scheduling according to requirements. The results from our simulation showed that our proposed algorithm is performing better in producing good quality solutions, particularly in executing tasks fast and in balancing the load (resource utilisation) among the resources more effectively when compared to standard Minimum Execution Time (MET), Minimum Completion Time (MCT), Min-Min, and Max-Min heuristic approaches

References

Xhafa, F. (2008). Metaheuristics for Scheduling in Distributed Computing Environments. Springer-Verlag, pp. 2-9.

Jacob, B. (2005). Introduction to grid computing. United States: IBM, International Technical Support Organization. Vol. 1(1), pp. 100.

Kokilavani, T. and Amalarethinam, D.I.G., (2011). Load Balanced MinMin Algorithm for Static Meta-Task Scheduling in Grid Computing. International Journal of Computer Applications. Vol. 20(2), pp. 43-47.

Kokilavani, T., and Amalarethinam, D. I. (2010). Applying

Nontraditional Optimization Techniques to Task Scheduling In Grid Computing-An Overview. International Journal of Research & Reviews in Computer Science. Vol. 1(4), pp. 34-38.

Hemamalini, M., (2012). Review of Grid Task Scheduling in Distributed Heterogeneous Environment. International Journal of Computer Applications. Vol. 40 (2), pp. 24 – 26.

Maheswaran, M., Ali, Siegel, H. J., Hensgen, D. and Freund, F. R. (1999). Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems1. Journal of Parallel and Distributed Computing. Vol. 59(2), pp.107 – 131.

Braun, T. D., Siegel, H. J. and Beck, N., (2001). A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing. Vol. 61, pp. 823 – 831.

Fujimoto, N., and Hagihara, K. (2004). A Comparison among Grid Scheduling Algorithms for Independent Coarse-Grained Tasks. Vol. 2(4), pp. 7-7.

Xhafa, F., Barolli, L., and Durresi, A. (2007). Batch mode scheduling in grid systems. International Journal of Web and Grid Services, Vol. 3(1), pp. 19-19.

Luo, P., and Shi, Z. (2007). A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems. Journal of Parallel and Distributed Computing. Vol. 67(6), pp. 695-714.

Ming, G., and Li, H. (2011). An Improved Algorithm Based on MaxMin for Cloud Task Scheduling. Recent Advances in Computer Science and Information Engineering Lecture Notes in Electrical Engineering.Vol. 125, pp. 217-223.

Amalarethinam, G.D.I. and Kfatheen V.S., (2014). Max-min Average Algorithm for Scheduling Tasks in Grid Computing Systems. International Journal of Computer Science and Information Technologies. Vol. 3, pp. 3659-62.

Devipriya, S., and Ramesh, C. (2013). Improved Max-Min Heuristic Model for Task Scheduling in Cloud. IEEE, pp. 883-888.

Mao, Y., Chen, X., and Li, X. (2014). Max–Min Task Scheduling Algorithm for Load Balance in Cloud Computing. Proceedings of International Conference on Computer Science and Information Technology, Advances in Intelligent Systems and Computing. Vol. 255, pp. 457-465.

Li, X., Mao, Y., Xiao, X., and Zhuang, Y. (2014). An Improved MaxMin Task-Scheduling Algorithm for Elastic Cloud. International Symposium on Computer, Consumer and Control, pp. 340-343.

Etminani, K., Naghibzadeh, M., and Yanehsari, N.R., (2007). A Hybrid Min-Min Max-Min Algorithm with Improved Performance. Department of Computer Engineering, Ferdowsi University of Mashad, Iran. Vol.32, pp. 1 – 3.

Li, W., and Zhang, W. (2009). An improved Scheduling Algorithm for Grid Tasks. International Symposium on Intelligent Ubiquitous Computing and Education. Vol. 35, pp. 9-12.

Parsa, S and Reza, E. M., (2009). RASA: A New Task Scheduling Algorithm in Grid Environment. World Applied Sciences Journal. Vol. 7, pp. 152-155.

Gupta, K., and Singh, M., (2012). Heuristic Based Task Scheduling In Grid. International Journal of Engineering and Technology (IJET). Vol. 4, pp. 254 – 258.

Anousha, S., Shoeib, A., and Ahmadi, M. (2014). A New Heuristic Algorithm for Improving Total Completion Time in Grid Computing. Springer-Verlag Berlin Heidelberg, pp. 17-26.

Panda, S., Agrawal, P., Khilar, P., & Mohapatra, D. (2014). SkewnessBased Min-Min Max-Min Heuristic for Grid Task Scheduling. In 4thIEEE International Conference on Advanced Computing and Communication Technologies. pp. 282-289.

Vijayalakshmi, R., and Vasudevan, V. (2015). Static Batch Mode Heuristic Algorithm for Mapping Independent Tasks in

Computational Grid. Journal of Computer Science. Vol. 11(1), pp.

Downloads

Published

2016-09-01

How to Cite

Maipan-uku, J., Muhammed, A., Abdullah, A., & Hussin, M. (2016). Max-Average: An Extended Max-Min Scheduling Algorithm for Grid Computing Environtment. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(6), 43–47. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1243

Similar Articles

You may also start an advanced similarity search for this article.