Tackling the Grid Job Planning and Resource Allocation Problem Using a Hybrid Evolutionary Algorithm

  • Karl-Uwe Stucky
  • Wilfried Jakob
  • Alexander Quinte
  • Wolfgang Süß
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4967)

Abstract

This paper presents results of new experiments with the Global Optimising Resource Broker and Allocator GORBA for grid systems. The scheduling algorithm is based on the Evolutionary Algorithm GLEAM (General Learning Evolutionary Algorithm and Method) and several heuristics. The task of planning grid resource allocation is compared to pure NP-complete job shop scheduling and it is shown in which way it is of greater complexity. Two different gene models and two repair methods are described in detail and assessed by the experimental results. Based on the analysis of the experimental results, directions of further work and improvements will be outlined.

Keywords

Schedule Problem Service Level Agreement Grid Resource Grid Environment Resource Allocation Problem 
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.
    Jakob, W., Quinte, A., Süß, W., Stucky, K.-U.: Optimised Scheduling of Grid Resources Using Hybrid Evolutionary Algorithms. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds.) PPAM 2005. LNCS, vol. 3911, pp. 406–413. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Blume, C., Jakob, W.: GLEAM – An Evolutionary Algorithm for Planning and Control Based on Evolution Strategy. In: Cantú-Paz, E. (ed.) GECCO 2002, vol. LBP, pp. 31–38 (2002)Google Scholar
  3. 3.
    Süß, W., Jakob, W., Quinte, A., Stucky, K.-U.: GORBA: Resource Brokering in Grid Environments using Evolutionary Algorithms. In: 17th IASTED Int. Conf. on Parallel and Distributed Computing Systems (PDCS), Phoenix, AZ, pp. 19–24 (2005)Google Scholar
  4. 4.
    Schmeck, H., Merkle, D., Middendorf, M.: Ant Colony Optimization for Resource-Constrained Project Scheduling. In: Whitley, D., et al. (eds.) Conf. Proc GECCO 2000, pp. 893–900. Morgan Kaufmann, San Francisco (2000)Google Scholar
  5. 5.
    Schmitz, F., Schneider, O.: The CampusGrid test bed at Forschungszentrum Karlsruhe. In: Sloot, P.M.A., Hoekstra, A.G., Priol, T., Reinefeld, A., Bubak, M. (eds.) EGC 2005. LNCS, vol. 3470, pp. 1139–1142. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Hovestadt, M., Kao, O., Keller, A., Streit, A.: Scheduling in HPC Resource Management Systems: Queuing vs. Planning. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2003. LNCS, vol. 2862, pp. 1–20. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    Prodan, R., Fahringer, T.: Dynamic Scheduling of Scientific Workflow Applications on the Grid Using a Modular Optimisation Tool: A Case Study. In: 20th Symposium of Applied Computing, SAC 2005, pp. 687–694. ACM Press, New York (2005)CrossRefGoogle Scholar
  8. 8.
    Wieczorek, M., Prodan, R., Fahringer, T.: Comparison of Workflow Scheduling Strategies on the Grid. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds.) PPAM 2005. LNCS, vol. 3911, pp. 792–800. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Padgett, J., Djemame, K., Dew, P.: Grid Service Level Agreements Combining Resource Reservation and Predictive Run-time Adaptation. In: Proc. of the UK e-Science All Hands Meeting, Nottingham, UK (September 2005)Google Scholar
  10. 10.
    Brucker, P.: Scheduling Algorithms. Springer, Heidelberg (2004)MATHGoogle Scholar
  11. 11.
    Brucker, P.: Complex Scheduling. Springer, Heidelberg (2006)Google Scholar
  12. 12.
    Di Martino, V., Mililotti, M.: Sub optimal scheduling in a grid using genetic algorithms. Parallel Computing 30, 553–565 (2004)CrossRefGoogle Scholar
  13. 13.
    Gao, Y., Rong, H.Q., Huang, J.Z.: Adaptive grid job scheduling with genetic algorithms. Future Generation Computer Systems 21, 151–161 (2005)CrossRefGoogle Scholar
  14. 14.
    Stucky, K.-U., Jakob, W., Quinte, A., Süß, W.: Solving Scheduling Problems in Grid Resource Management Using an Evolutionary Algorithm. In: Meersman, R., Tari, Z. (eds.) OTM 2006. LNCS, vol. 4276, pp. 1252–1262. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Jakob, W., Gorges-Schleuter, M., Blume, C.: Application of Genetic Algorithms to Task Planning and Learning. In: Männer, R., Manderick, B. (eds.) Conf. Proc. PPSN II, pp. 291–300. North-Holland, Amsterdam (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Karl-Uwe Stucky
    • 1
  • Wilfried Jakob
    • 1
  • Alexander Quinte
    • 1
  • Wolfgang Süß
    • 1
  1. 1.Forschungszentrum Karlsruhe GmbHInstitute for Applied Computer ScienceKarlsruheGermany

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