Hierarchical and Dynamic Information Management Framework on Grid Computing

  • Eun-Ha Song
  • Yang-Seung Jeon
  • Sung-Kook Han
  • Young-Sik Jeong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4096)


This paper presents a GridIMF framework that provides support for adaptive grid services in response to the change of resource supplies and demands in a dynamic computing environment. The framework features a 3-tier hierarchical resource management structure. At the top is a global information manager that serves as a service broker between resource requesters and providers. Resource providers form virtual organizations, each of which is controlled by a local resource manager. The resource managers schedule the execution of tasks adaptively for efficiency and fault tolerance according to the dynamic resource availability information. The framework provides a common API through an application proxy. The proxy decouples grid applications from the implementation details of the framework.


Task Allocation Grid Resource Virtual Organization Grid Application Grid User 
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  1. 1.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  2. 2.
    Foster, I., Kesselman, C., Tueche, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International Journal Supercomputer Applications 15(3) (2001)Google Scholar
  3. 3.
    Foster, I.: The Grid: A New Infrastructure for 21st Century Science. Physics (2002)Google Scholar
  4. 4.
    Katzy, B.R.: Design and Implementation of Virtual Organizations, University St. Gallen and Erasmus University RotterdamGoogle Scholar
  5. 5.
    Bajaj, R., Agrawal, D.P.: Fellow, Improving Scheduling of Tasks in a Heterogeneous Environment. IEEE Trans. Parallel and Distributed Systems 15(2), 107–118 (2004)CrossRefGoogle Scholar
  6. 6.
    Takefusa, A., Casanova, H., Matsuoka, S., Berman, F.: A Study of Deadline Scheduling for Client-Server Systems on the Computational Grid. In: High Performance Distributed Computing, Proceedings 10th IEEE International Symposium (August 2001)Google Scholar
  7. 7.
    Buyya, R., Chapin, S., DiNucci, D.: Architectural Models for Resource Management in the Grid. In: Grid Computing GIRD 2000. First IEEE/ACM International Workshop, Bangalore, India, pp. 20–33 (2000)Google Scholar
  8. 8.
    Haban, D., Shin, K.G.: Application of RealTime Monitoring to Scheduling Tasks with Random Execution Times. IEEE Transactions on Software Engineering 16, 1374–1389 (1990)CrossRefGoogle Scholar
  9. 9.
    Angulo, D., Foster, I., Liu, C., Yang, L.: Design and Evaluation of a Resource Selection Framework for Grid Applications. In: Proceedings of IEEE International Symposium on High Performance Distributed Computing (HPDC-11), Edinburgh, Scotland (July 2002)Google Scholar
  10. 10.
    Foster, I., Kesselman, C.: Globus: A Metacomputing Infrastructure Toolkit. International Journal supercomputer Applications 11(2), 115–128 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eun-Ha Song
    • 1
  • Yang-Seung Jeon
    • 1
  • Sung-Kook Han
    • 1
  • Young-Sik Jeong
    • 1
  1. 1.Dept. of Computer Eng.Wonkwang Univ.IksanKorea

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