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QoS-Constrained Resource Allocation for a Grid-Based Multiple Source Electrocardiogram Application

  • Dong Su Nam
  • Chan-Hyun Youn
  • Bong Hwan Lee
  • Gari Clifford
  • Jennifer Healey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3043)

Abstract

QoS-constrained policy has an advantage to guarantee QoS requirements requested by users. Quorum systems can ensure the consistency and availability of replicated data despite the benign failure of data repositories. We propose a Quorum based resource management scheme, which resource Quorum includes middleware entity and network entity, both can satisfy requirements of application QoS. We also suggest a heuristic configuration algorithm in order to optimize performance and usage cost of Resource Quorum. We evaluate both simulations and experiments based on the electrocardiogram (ECG) application for health care, because this application requires transferring giga-bytes of data and analyzing complicated signal of ECG. Simulation results show that network capabilities are more important than computing capabilities, as both sizes of transferred data and computation task increases. Experimental results show that our scheme can reduce the total execution time of ECG application by using proposed heuristic algorithm compared to policy based management scheme.

Keywords

Usage Cost Total Execution Time Grid Application Network Capability Network Entity 
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.
    Douglis, F., Foster, I.: The Grid Grows Up. In: Internet Computing IEEE 2003, pp. 24–26 (2003)Google Scholar
  2. 2.
    Foster, I., Kesselman, C.: Globus: A Metacomputing Infrastructure Toolkit. International J. Supercomputing Applications 11(2), 115–128 (1997)CrossRefGoogle Scholar
  3. 3.
    Wolski, R.: Dynamically Forecasting Network Performance to Support Dynamic Scheduling Using the Network Weather Service. In: Proceedings of the 6th High-Performance Distributed Computing Conference (August 1997)Google Scholar
  4. 4.
    Yang, K., Galis, A., Todd, C.: A Policy-based Active Grid Management Architecture. In: Proceedings of 10th IEEE International Conference on Networks (ICOIN 2002), August 2002, pp. 243–248. IEEE Press, Los Alamitos (2002)Google Scholar
  5. 5.
    Liabotis, I., et al.: Self-organising management of Grid environments Google Scholar
  6. 6.
    Malkhi, D., Reiter, M.: Byzantine quorum systems. In: Proceedings of the 29th ACM Symposium on Theory of Computing (STOC) (May 1997)Google Scholar
  7. 7.
    Golberger, A., Amaral, L., Glass, L., Hausdorff, J.M., et al.: PhysioBank, PhysioToolkit, and PhysioNet:Component of a New Research Resource for Complex Physiologic Signals. Circulation 101(23) (June 2000)Google Scholar
  8. 8.
    Casanova, H.: Simgrid: A Toolkit for the Simulation of Application Scheduling. In: Proceedings of the First IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2001), Brisbane, Australia, May 15-18 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Dong Su Nam
    • 1
    • 5
  • Chan-Hyun Youn
    • 1
    • 3
  • Bong Hwan Lee
    • 2
  • Gari Clifford
    • 3
  • Jennifer Healey
    • 4
  1. 1.School of EngineeringInformation and Communications UniversityDaejeonKorea
  2. 2.Dept. of Information and Communications EngineeringDaejeon UniversityDaejeonKorea
  3. 3.Harvard-MIT Division of Health Science TechnologyMITCambridgeUSA
  4. 4.4 Dept. of Tranlational MedicineHarvard Medical School/BIDMCBostonUSA
  5. 5.Dept. of Information AssuranceNational Security Research InstituteDaejeonKorea

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