Ontology-Based Intelligent Agent for Grid Resource Management

  • Kyu Cheol Cho
  • Chang Hyeon Noh
  • Jong Sik Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5796)

Abstract

The intelligent agent works powerful jobs for handling system complexity and making systems more modular. Especially a reasoning agent is effective on organizing for decision-making process of systems. This paper introduces an Ontology-based Intelligent Agent for a Grid Resource Management System (OIAGRMS), which uses ontology reasoning to select a suitable resource supplier, is proposed. This paper focuses on effective grid resource management and the improvement of resource utilization through transaction management for the OIAGRMS. For performance evaluation with accuracy and reliability, the OIAGRMS is compared with the Prediction-based Agent for Grid Resource Management System(PAGRMS) and the Random-based Agent for Grid Resource Management System(RAGRMS). The OIAGRMS recorded over 90 percents trade success, but the PAGRMS and RAGRMS recorded less than a 90 percents trade success. In comparing of resource utilization rate, maximum deviation, standard deviation, the OIAGRMS were about 9.4 and 9.8 percents but the PAGRMS are about 22.9 and 16.3 percents, the RAGRMS were about 61.6 and 21.7 percents. The empirical results demonstrate the usefulness and improvement utilization with stable performances of the intelligent agent base on ontology reasoning in grid environment.

Keywords

Intelligent Agent Grid Resource Grid Environment User Demand Grid User 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Wooldridge, M., Jennings, N.R.: Intelligent Agents: Theory and Practice. The Knowledge Engineering Review 10(2), 115–152 (1995)CrossRefGoogle Scholar
  2. 2.
    Foster, I., Kesselman, C. (eds.): The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann Publishers, USA (1999)Google Scholar
  3. 3.
    Xing, W., Dikaiakos, M.D., Sakellariou, R., Orlando, S., Laforenza, D.: Design and development of a core grid ontology. In: Gorlatch, Danelutto, M. (eds.) CoreGRID Integration Workshop, Italy, pp. 21–31 (2005)Google Scholar
  4. 4.
    Buyya, R., Chapin, S., DiNucci, D.: Architectural Models for Resource Management in the Grid. Grid Computing GIRD 2000. In: First IEEE/ACM International Workshop, pp. 20–33 (2000) Google Scholar
  5. 5.
    Chapin, S., Clement, M., Snell, Q.: A Grid Resource Management Architecture, Grid Forum Scheduling Working Group (1999)Google Scholar
  6. 6.
    Buyya, R., Abramson, D., Giddy, J.: A Case for Economy Grid Architecture for Service Oriented Grid Computing. In: Proceedings of International Parallel and Distributed Processing Symposium: Heterogeneous Computing Workshop (2001)Google Scholar
  7. 7.
    Cho, K.C., Kim, T.Y., Lee, J.S.: User Demand Prediction-based Resource Management Model in Grid Computing Environment. In: ICHIT 2008, Korea, pp. 627–632 (2008)Google Scholar
  8. 8.
    Thompson, C.: Characterizing the Agent Grid, Technical Report, Object Services and Consulting, Inc., http://www.objs.com/agility/tech-reports/9812-grid.html
  9. 9.
    Tierney, B., Johnston, W., Lee, J., Thompson, M.: A data intensive distributed computing architecture for grid applications. Future Generation Computer Systems 16(5), 473–481 (2000)CrossRefGoogle Scholar
  10. 10.
    Rana, O.F., Walker, D.W.: The agent grid: agent-based resource integration in PSEs. In: Proceedings of 16th IMACS World Congress on Scientific Computation, Applied Mathematics and Simulation (2000)Google Scholar
  11. 11.
    Shen, W., Li, Y., Ghenniwa, H., Wang, C.: Adaptive negotiation for agent-based grid computing. In: Proceedings of AAMAS Workshop on Agentcities Challenges in Open Agent Environments, pp. 32–36 (2002)Google Scholar
  12. 12.
    Horridge, M., Knublach, H., Rector, A., Stevens, R., Wroe, C.: A practical guide to building OWL ontologies using the Protégé-OWL plugin and CO-ODE tools. University of Manchester (2004)Google Scholar
  13. 13.
    Fikes, R., Hayes, P., Horrocks, I.: Owl-ql: A language for deductive query answering on the semantic web, Technical Report KSL 03-14, Stanford University (2003)Google Scholar
  14. 14.
    Roure, D.D.: A brief history of the semantic grid. In: Semantic Grid: The Convergence of Technologies, number 05271, Dagstuhl Seminar Proceedings. IBFI, Dagstuhl, Germany (2005)Google Scholar
  15. 15.
    McGuinness, D.L., Harmelen, F.V.: OWL Web Ontology Language Overview, W3C Proposed Recommendation (2003), http://www.w3.org/TR/2003/PR-owl-features-20031215/
  16. 16.
    Brickley, D., Guha, R.V.: FDF Vocabulary Description Language 1.0:RDF Scheman, W2C Working Draft (2003)Google Scholar
  17. 17.
  18. 18.
    Zeigler, B.P., et al.: The DEVS Environment for High-Performance Modeling and Simulation. IEEE C S & E 4(3), 61–71 (1997)CrossRefGoogle Scholar
  19. 19.
    Kalpakam, S., Arivarignan, G.: Inventory System with Random Supply Quantity, OR Spektrum, pp. 139–145 (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kyu Cheol Cho
    • 1
  • Chang Hyeon Noh
    • 1
  • Jong Sik Lee
    • 1
  1. 1.School of Computer Science and EngineeringInha UniversityIncheonSouth Korea

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