SERVICE ORIENTED GRID RESOURCE MODELING AND MANAGEMENT

Youcef Derbal

Abstract

Computational grids (CGs) are large scale networks of geographically distributed aggregates of resource clusters that may be contributed by distinct providers. The exploitation of these resources is enabled by a collection of decision-making processes; including resource management and discovery, resource state dissemination, and job scheduling. Traditionally, these mechanisms rely on a physical view of the grid resource model. This entails the need for complex multi-dimensional search strategies and a considerable level of resource state information exchange between the grid management domains. Consequently, it has been difficult to achieve the desirable performance properties of speed, robustness and scalability required for the management of CGs. In this paper we argue that with the adoption of the Service Oriented Architecture (SOA), a logical service-oriented view of the resource model provides the necessary level of abstraction to express the grid capacity to handle the load of hosted services. In this respect, we propose a Service Oriented Model (SOM) that relies on the quantification of the aggregated resource behaviour using a defined service capacity unit that we call servslot. The paper details the development of SOM and highlights the pertinent issues that arise from this new approach. A preliminary exploration of SOM integration as part of a nominal grid architectural framework is provided along with directions for future works.

References

  1. Al-Ali, R., Hafid, A., Rana, O., and Walker, D., 2004. An approach for quality of service adaptation in service oriented Grids. Concurrency Computation Practice and Experience. Vol. 16, no. 5, pp. 401-412.
  2. Barabási, A., and Albert, R., 1999. Emergence of Scaling in Random Networks. Science. Vol. 286, no. 5489, pp. 509-512.
  3. Bukhari, U., and Abbas, F., 2004. A comparative study of naming, resolution and discovery schemes for networked environments. In Proceedings - Second Annual Conference on Communication Networks and Services Research, pp. 265 - 272.
  4. Cangussu, J. W., Cooper, K., and Li, C., 2004. A control theory based framework for dynamic adaptable systems. In Proceedings of the ACM Symposium on Applied Computing, pp. 1546-1553.
  5. Casavant, T. L., and Kuhl, J. G., 1988. A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems. IEEE Transactions on Software Engineering. Vol. 14, no. 2, pp. 141-155.
  6. Desktop Management Task Force, 1999., Common Information Model (CIM), http://www.dmtf.org/spec/cims.html.
  7. Dimakopoulos, V. V., and Pitoura, E., 2003. A peer-topeer approach to resource discovery in multi-agent systems. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), pp. 272.
  8. Faloutsos, M., Faloutsos, P., and Faloutsos, C., 1999. On power-law relationships of the Internet topology. Computer Communication Review. Vol. 29, no. 4, pp. 251-262.
  9. Foster, I., and Kesselman, C., 2004, The grid: blueprint for a new computing infrastructure, Morgan Kaufmann;Elsevier Science, San Francisco, Calif.Oxford, pp. 748.
  10. Graupner, S., Kotov, V., Andrzejak, A., and Trinks, H., 2003. Service-centric globally distributed computing. IEEE Internet Computing. Vol. 7, no. 4, pp. 36 - 43.
  11. He, X., Sun, X., and Von Laszewski, G., 2003. QoS guided Min-Min heuristic for grid task scheduling. Journal of Computer Science and Technology. Vol. 18, no. 4, pp. 442-451.
  12. Huang, Z., Gu, L., Du, B., and He, C., 2004. Grid resource specification language based on XML and its usage in resource registry meta-service. In Proceedings - 2004 IEEE International Conference on Services Computing, SCC 2004, pp. 467 - 470.
  13. Iyengar, V., Tilak, S., Lewis, M. J., and Abu-Ghazaleh, N. B., 2004. Non-Uniform Information Dissemination for Dynamic Grid Resource Discovery. In, pp.
  14. Krauter, K., Buyya, R., and Maheswaran, M., 2002. A taxonomy and survey of grid resource management systems for distributed computing. Software - Practice and Experience. Vol. 32, no. 2, pp. 135-164.
  15. Lanfranchi, G., Peruta, P. D., and Perrone, A., 2003. Toward a new landscape of systems management in an autonomic computing environment. IBM Systems Journal [H.W. Wilson - AST]. Vol. 42, no. 1, pp. 119.
  16. Ludwig, S. A., 2003. Comparison of centralized and decentralized service discovery in a grid environment. In Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Systems, pp. 12.
  17. Maheswaran, M., 2001. Data dissemination approaches for performance discovery in grid computing systems. In Proceedings of the 15th International Parallel and Distributed Processing Symposium (IPDPS 7801), pp. 910 - 923.
  18. Reed, D. A., and Mendes, C. L., 2005. Intelligent Monitoring for Adaptation in Grid Applications. Proceedings of the IEEE. Vol. 93, no. 2, pp. 426 - 435.
  19. Spooner, D. P., Jarvis, S. A., Cao, J., Saini, S., and Nudd, G. R., 2003. Local grid scheduling techniques using performance prediction. IEE Proceedings: Computers and Digital Techniques. Vol. 150, no. 2, pp. 87-96.
  20. Sun, X.-H., and Ming, W., 2003. Grid Harvest Service: a system for long-term, application-level task scheduling. In Parallel and Distributed Processing Symposium, pp.
  21. Tuecke, S., Czajkowski, K., Foster, I., Frey, J., Graham, S., Kesselman, C., Maquire, T., Sandholm, T., Snelling, D., and Vanderbilt, P., 2003, Open Grid Services Infrastructure (OGSI), Global Grid Forum.
  22. Wu, X.-C., Li, H., and Ju, J.-B., 2004. A prototype of dynamically disseminating and discovering resource information for resource managements in computational grid. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics, pp. 2893-2898.
  23. Yang, L., Schopf, J. M., and Foster, I., 2003. Conservative Scheduling: Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments. In Proceedings of Supercomputing 2003, pp.
  24. Zhu, Y., and Pagilla, P. R., 2003. Adaptive Estimation of Time-Varying Parameters in Linear Systems. In Proceedings of the American Control Conference, pp. 4167-4172.
  25. Zhu, Y., and Zhang, J.-L., 2004. Distributed storage based on intelligent agent. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics, pp. 297-301.
Download


Paper Citation


in Harvard Style

Derbal Y. (2005). SERVICE ORIENTED GRID RESOURCE MODELING AND MANAGEMENT . In Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 972-8865-20-1, pages 146-153. DOI: 10.5220/0001235001460153


in Bibtex Style

@conference{webist05,
author={Youcef Derbal},
title={SERVICE ORIENTED GRID RESOURCE MODELING AND MANAGEMENT},
booktitle={Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2005},
pages={146-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001235001460153},
isbn={972-8865-20-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - SERVICE ORIENTED GRID RESOURCE MODELING AND MANAGEMENT
SN - 972-8865-20-1
AU - Derbal Y.
PY - 2005
SP - 146
EP - 153
DO - 10.5220/0001235001460153