An Online and Predictive Method for Grid Scheduling Based on Data Mining and Rough Set

  • Asgarali Bouyer
  • Mohammadbagher Karimi
  • Mnsour Jalali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5592)

Abstract

Since Grid is a distributed and heterogeneous environment, scheduling and resource management are important in Grid. One of the fundamental problems in Grid is designing a suitable method for management of resources. Many approaches have been proposed to increase performance of scheduling process, but each method has special conditions and they act well only in some special conditions. Moreover for resources scheduling, most of them use GIS’s data that maybe is encountered with old data. In this paper, we use an online approach for finding resources with less time spending for resource discovery rather than other proposed methods; and then by using Rough Set and Decision Tree data mining technique, in order to classification of Grid nodes, scheduler will select proper nodes for desired job based on job’s condition. This approach also has a fair treat in dealing with The Least Deadline jobs. The obtained results show this approach is one of the promising methods for resource selecting in scheduling phase with high accuracy and performance.

Keywords

Grid Scheduling Rough Set Decision Tree Scheduler 

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References

  1. 1.
    Bouyer, A., Karimi, M., Jalali, M., Noor, M.: A new Approach for Selecting Best Resources Nodes by Using Fuzzy Decision Tree in Grid Resource Broker. International Journal of Grid and Distributed Computing (IJGDC), SERSC (2008), ISSN: 2005-4262Google Scholar
  2. 2.
    Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, USA (2003)Google Scholar
  3. 3.
    Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. International Journal of High Performance Computing Applications (3), 200–222 (2001)Google Scholar
  4. 4.
    Chen, Y., Li, Y., Gong, Z., Zhu, Q.: A framework of a tree-based grid information service. IEEE International Conference on Services Computing 2, 255–256 (2005)Google Scholar
  5. 5.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  6. 6.
    Gao, K., Chen, K., Liu, M., Chen, J.: Rough Set Based Data Mining Tasks Scheduling on Knowledge Grid. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS(LNAI), vol. 3528, pp. 150–155. Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Zhang, W., Fang, B., He, H., Zhang, H., Hu, M.: Multisite Resource Selection and Scheduling Algorithm on Computational Grid. In: Proc. 18th Parallel and Distributed Processing Symp., pp. 105–115 (2004)Google Scholar
  8. 8.
    Buyya, R., Abramson, D., Giddy, J.: Nimrod/G: an architecture for a resource management and scheduling system in a global computational grid. High Performance Computing in the Asia-Pacific Region. In: Proceedings The Fourth International Conference/ Exhibition. 14-17, May 2000, vol. 1, pp. 14–17, 283–289 (2000)Google Scholar
  9. 9.
    Tang, B., Yin, Y., Liu, Q., Zhou, Z.: Research on the Application of Ant Colony Algorithm in Grid Resource Scheduling. In: 4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, October 12-14, 2008, pp. 1–4 (2008)Google Scholar
  10. 10.
    yue, W., Tao, H.: Application in TSP Based on Ant Colony Optimization. Journal of Wuhan University of Technology (Information and Managing Engineering Edition) 28(11), 24–26 (2006)Google Scholar
  11. 11.
    Huang, J., Jin, H., Xie, X., Zhang, Q.: An Approach to Grid Optimization Based on Fuzzy Association Rule Mining. In: Proceedings of the First International Conference on e-Science and Grid Computing (2005)Google Scholar
  12. 12.
    Yu, K.-M., Chen, C.-K.: An Adaptive Scheduling Algorithm for Scheduling Tasks in Computational Grid. In: Proc. in Seventh International Conference on Grid and Cooperative Computing, IEEE, Los Alamitos (2008)Google Scholar
  13. 13.
    Huang, P.-J., Peng, H., Zheng, Q.-L.: Mining optimal resource combination in computational grid. In: Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, August 13-16, 2006, IEEE, Los Alamitos (2006)Google Scholar
  14. 14.
    Chunlin, L., Layuan, L.: Pricing and Resource Allocation in Computational Grid with Utility Functions. In: International conference on Information Technology: Coding and Computing 2005, ITCC 2005, April 4-6, 2005, vol. 2, pp. 175–180 (2005)Google Scholar
  15. 15.
    Zhihong, X., Jizhou, S.: An Ant Algorithm Based Grid Computing and Task Scheduling. Journal of Tianjin University 37(5), 414–418 (2004)Google Scholar
  16. 16.
    Yu, K.-M., Luo, Z.-J., Chou, C.-H., Chen, C.-K., Zhou, J.: A Fuzzy Neural Network Based Scheduling Algorithm for Job Assignment on Computational Grids. In: Enokido, T., Barolli, L., Takizawa, M. (eds.) NBiS 2007. LNCS, vol. 4658, pp. 533–542. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Zhihong, X., Junhua, G.: Research on Ant Algorithm Based Classified Task Scheduling in Grid Computing. Journal of Hebei University of Technology 35(3), 68–71 (2006)MATHGoogle Scholar
  18. 18.
    Subrata, R., Zomaya, A.Y., Landfeldt, B.: Artificial life techniques for load balancing in computational grids. Journal of Computer and System Sciences 73(8), 1176–1190 (2007)CrossRefMATHGoogle Scholar
  19. 19.
    Xu, Z.H., Hou, X.D., Sun, J.Z.: Ant algorithm-based task scheduling in grid computing. In: Proceedings of 2003 IEEE Canadian Conference on Electrical and Computer Engineering, pp. 1107–1110 (2003)Google Scholar
  20. 20.
    Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications on the ACM 38(11), 89–95 (1995)CrossRefGoogle Scholar
  21. 21.
    Wei, J., Huang, D., Wang, S., Ma, Z.: Rough set based decision tree. In: Proceedings of the 4th World Congress on Intelligent Control and Automation, vol. 1, pp. 426–431 (2002)Google Scholar
  22. 22.
    Moura-Pires, F., Steiger-Garcao, A.: A decision tree algorithm with segmentation. In: Proceedings, International Conference on Industrial Electronics, Control and Instrumentation, IECON 1991, October 28-November 1, 1991, vol. 3, pp. 2077–2082 (1991)Google Scholar
  23. 23.
    Ke-wu, Y., Jin-fu, Z., Qiang, S.: The application of ID3 algorithm in aviation marketing. In: IEEE International Conference on Grey Systems and Intelligent Services, GSIS 2007, November 18-20, 2007, pp. 1284–1288 (2007)Google Scholar
  24. 24.
    Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data, p. 256. Kluwer, Dordrecht (1991)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Asgarali Bouyer
    • 1
  • Mohammadbagher Karimi
    • 2
  • Mnsour Jalali
    • 1
  1. 1.Islamic Azad Unversity-Miyandoab branchMiyandoab, West AzerbayjanIran
  2. 2.Islamic Azad Unversity-Tabriz branch, Tabriz, East AzerbayjanIran

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