Advertisement

Allocating QoS-Constrained Workflow-Based Jobs in a Multi-cluster Grid Through Queueing Theory Approach

  • Yash Patel
  • John Darlington
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4330)

Abstract

Clusters are increasingly interconnected to form multi-cluster systems, which are becoming popular for scientific computation. End-users often submit their applications in the form of workflows with certain Quality of Service (QoS) requirements imposed on the workflows. These workflows describe the execution of a complex application built from individual application components, which form the workflow tasks. This paper addresses workload allocation techniques for Grid workflows. We model individual clusters as M/M/k queues and obtain a numerical solution for missed deadlines (failures) of tasks of Grid workflows. The approach is evaluated through an experimental simulation and the results confirm that the proposed workload allocation strategy combined with traditional scheduling algorithms performs considerably better in terms of satisfying QoS requirements of Grid workflows than scheduling algorithms that don’t employ such workload allocation techniques.

Keywords

Execution Time Arrival Rate Schedule Algorithm Task Failure General Algebraic Modeling System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    General Algebraic Modeling System (GAMS), http://www.gams.com/
  2. 2.
    Enabling Grids for E-sciencE (EGEE) (2004), http://www.eu-egee.org/
  3. 3.
    Mayer, A., et al.: Workflow Expression: Comparison of Spatial and Temporal Approaches. In: Workflow in Grid Systems Workshop (2004)Google Scholar
  4. 4.
    Kao, B., Garcia-Molina, H.: Scheduling Soft Real-Time Jobs over Dual Non-Real-Time Servers. IEEE Trans. Parallel and Distributed Systems 7(1), 56–68 (1996)CrossRefGoogle Scholar
  5. 5.
    Howell, F., et al.: SimJava, http://www.dcs.ed.ac.uk/home/hase/simjava
  6. 6.
    Nudd, G., Jarvis, S.: Performance-based middleware for Grid computing. Concurrency and Computation: Practice and Experience (2004)Google Scholar
  7. 7.
    Chen, K., Decreusefond, L.: Just How Bad is the FIFO Discipline for Handling Randomly Arriving Time-Critical Messages. In: Proc. 1995 IEEE International Workshop Factory Communication Systems (1995)Google Scholar
  8. 8.
    He, L., Jarvis, S.A., Spooner, D.P., Nudd, G.R.: Optimising Static Workload Allocation in Multiclusters. In: Proc. 18th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2004) (2004)Google Scholar
  9. 9.
    He, L., Han, Z., Jin, H., Pang, L.: DAG-Based Parallel Real Time Task Scheduling Algorithm on a Cluster. In: Proc. Seventh International Conference Parallel and Distributed Processing Techniques and Applications (PDPTA 2000) (2000)Google Scholar
  10. 10.
    Kleinrock, L.: Queueing Systems. John Wiley and Sons, Chichester (1975)MATHGoogle Scholar
  11. 11.
    Duran, M.A., Grossmann, I.E.: An Outer-Approximation Algorithm for a Class of Mixed-Integer Nonlinear Programs. Mathematical Programming 36, 307–339 (1986)MATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Barreto, M., Avila, R., Navaux, P.: The MultiCluster Model to the Integrated Use of Multiple Workstation Clusters. In: Proc. Third Workshop Personal Computer-Based Networks of Workstations, pp. 71–80 (2000)Google Scholar
  13. 13.
    Abramowitz, M., Stegun, I.A.: Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables (1972)Google Scholar
  14. 14.
    Metropolis, N., Ulam, S.: The Monte Carlo Method. Journal of the American Statistical Association (1949)Google Scholar
  15. 15.
    Shivaratri, N.G., Krueger, P., Singhal, M.: Load Distribution for Locally Distributed Systems. Computer 8(12), 33–44 (1992)CrossRefGoogle Scholar
  16. 16.
    Aumage, O.: Heterogeneous Multi-Cluster Networking with the Madeleine III Communication Library. In: Proc. 16th International Parallel and Distributed Processing Symposium (IPDPS 2002) (2002)Google Scholar
  17. 17.
    Leslie, R., McKenzie, S.: Evaluation of Load Sharing Algorithms for Heterogeneous Distributed Systems. In: Computer Communications (1999)Google Scholar
  18. 18.
    Banawan, S.A., Zeidat, N.M.: A Comparative Study of Load Sharing in Heterogeneous Multicomputer Systems. In: Proc. 25th Annual Simulation Symposium (1992)Google Scholar
  19. 19.
    Zhu, W., Fleisch, B.: Performance Evaluation of Soft Real-Time Scheduling on a Multicomputer Cluster. In: Proc. 20th International Conference Distributed Computing Systems (ICDCS 2000), pp. 610–617 (2000)Google Scholar
  20. 20.
    Zhang, X., Schopf, J.M.: Performance Analysis of the Globus Toolkit Monitoring and Discovery Service, MDS2. In: Proceedings of the International Workshop on Middleware Performance (MP 2004) (April 2004)Google Scholar
  21. 21.
    Tang, X.Y., Chanson, S.T.: Optimizing Static Job Scheduling in a Network of Heterogeneous Computers. In: Proc. 29th International Conference on Parallel Processing, pp. 373–382 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yash Patel
    • 1
  • John Darlington
    • 1
  1. 1.London e-Science Centre, Imperial CollegeLondon

Personalised recommendations