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Dynamic Job Scheduling on the Grid Environment Using the Great Deluge Algorithm

  • Paul McMullan
  • Barry McCollum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4671)

Abstract

The utilization of the computational Grid processor network has become a common method for researchers and scientists without access to local processor clusters to avail of the benefits of parallel processing for compute-intensive applications. As a result, this demand requires effective and efficient dynamic allocation of available resources. Although static scheduling and allocation techniques have proved effective, the dynamic nature of the Grid requires innovative techniques for reacting to change and maintaining stability for users. The dynamic scheduling process requires quite powerful optimization techniques, which can themselves lack the performance required in reaction time for achieving an effective schedule solution. Often there is a trade-off between solution quality and speed in achieving a solution. This paper presents an extension of a technique used in optimization and scheduling which can provide the means of achieving this balance and improves on similar approaches currently published.

Keywords

Grid Job Scheduling Great Deluge Simulated Annealing Network Parallel Processing 

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References

  1. 1.
    Fernandez-Baca, D.: Allocating Modules to Processors in a Distributed System. IEEE Transactions on Software Engineering 15(11), 1427–1436 (1989)CrossRefGoogle Scholar
  2. 2.
    Tracy, D., et al.: A Comparison of eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing 61, 810–837 (2001)CrossRefGoogle Scholar
  3. 3.
    Liu, L., Zhan, J., Lian, L.: A Runtime Scheduling Approach with Respect to Job Parallelism for Computational Grid. In: Proc. Of 3rd International Conference of Grid and Cooperative Computing (2004)Google Scholar
  4. 4.
    Mika, M., et al.: A Metaheuristic Approach to Scheduling Workflow Jobs on a Grid. In: Grid Resource Management: State of the Art and Future Trends, Kluwer Academic Publishers, Boston (2003)Google Scholar
  5. 5.
    Dueck, G.: Threshold Accepting: A General Purpose Optimization Algorithm Appearing Superior to Simulated Annealing. J. Computational Physics 90, 161–175 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Kirkpatrick, S., Gellat, J.C.D., Vecci, M.P.: Optimization by Simulated Annealing. Science 220, 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Yarkhan, A., Dongarra, J.: Experiments with Scheduling Using Simulated Annealing in a Grid Environment. In: Parashar, M. (ed.) GRID 2002. LNCS, vol. 2536, pp. 232–242. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Fidanova, S.: Simulated Annealing for Grid Scheduling Problem. In: IEEE John Vincent Atanasoft International Symposium on Modern Computing (JVA 2006), pp. 41–45 (2006)Google Scholar
  9. 9.
    McMullan, P.: An Extended Implementation of the Great Deluge Algorithm for Course Timetabling. In: ICCS 2007. International Conference on Computational Science. LNCS, Springer, Heidelberg (2007)Google Scholar
  10. 10.
    Kendall, G., Mohamad, M.: Channel Assignment in Cellular Communication Using a Great Deluge Hyper-Heuristic. In: Proc. of IEEE International Conference on Network (ICON 2004), pp. 769–773 (2004)Google Scholar
  11. 11.
    Petrovic, S., Burke, E.K.: University Timetabling, Handbook of Scheduling: Algorithms, Models and Performance Analysis, ch. 45. CRC Press, Boca Raton (2004)Google Scholar
  12. 12.
    McMullan, P., Roche, T.: An Intelligent Space Allocation and Planning Tool for Educational Requirements. Technical Report, RTS-TR-2005-2 (2005)Google Scholar
  13. 13.
    Berman, F., et al.: The GrADS Project: Software Support for high-level Grid application development. Int. Journal of High Performance Computing Applications 15(4), 327–344 (2001)CrossRefGoogle Scholar
  14. 14.
    Foster, I., Kesselman, C.: The Globus Toolkit. In: Foster, I., Kesselmanm, C. (eds.) The Grid: Blueprint for a New Computing Infrastructure, ch. 11, Morgan Kaufmann, San Francisco (1999)Google Scholar
  15. 15.
    Wolski, R., Spring, N., Hayes, J.: The Network Weather Service: a Distributed Resource Performance Forecasting System for Metacomputing. Future Generation Computing Systems 15(5-6), 757–768 (1999)CrossRefGoogle Scholar
  16. 16.
    Burke, E.K., Newall, J.P.: Solving Examination Timetabling Problems through Adaptation of Heuristic Orderings, Technical Report, Nottingham (2002)Google Scholar
  17. 17.
    Abramson, D., Krishnamoorthy, M., Dang, H.: Simulated Annealing Cooling Schedules for the School Timetabling Problem. Asia-Pacific Journal of Operation Research 16, 1–22 (1999)zbMATHMathSciNetGoogle Scholar
  18. 18.
    Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Journal Structural and Multidisciplinary Optimization 26(6), 369–395 (2004)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Paul McMullan
    • 1
  • Barry McCollum
    • 1
  1. 1.School of Electronics, Electrical Engineering and Computer Science, Queen’s University of BelfastNorthern Ireland

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