Decentralized Grid Scheduling with Evolutionary Fuzzy Systems

  • Alexander Fölling
  • Christian Grimme
  • Joachim Lepping
  • Alexander Papaspyrou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5798)

Abstract

In this paper, we address the problem of finding workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a non-invasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC) centers and which leaves the local resource managers completely untouched. In this environment of fully autonomous sites, independent users are assumed to submit their jobs to the Grid middleware layer of their local site, which in turn decides on the delegation and execution either on the local system or on remote sites in a situation-dependent, adaptive way. We find for different scenarios that the exchange policies show good performance characteristics not only with respect to traditional metrics such as average weighted response time and utilization, but also in terms of robustness and stability in changing environments.

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References

  1. 1.
    Cordón, O., Herrera, F., Hoffmann, F., Magdalena, L.: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases. In: Genetic Fuzzy Systems. Advances in Fuzzy Systems - Applications and Theory, vol. 19. World Scientific, Singapore (2001)Google Scholar
  2. 2.
    Ernemann, C., Hamscher, V., Yahyapour, R.: Benefits of global grid computing for job scheduling. In: Proceedings of the Fifth IEEE/ACM International Workshop on Grid Computing (GRID 2004), pp. 374–379. IEEE Computer Society, Los Alamitos (2004)CrossRefGoogle Scholar
  3. 3.
    Feitelson, D.G., Nitzberg, B.: Job characteristics of a production parallel scientific workload on the NASA ames iPSC/860. In: Feitelson, D.G., Rudolph, L. (eds.) IPPS-WS 1995 and JSSPP 1995. LNCS, vol. 949, pp. 337–360. Springer, Heidelberg (1995)Google Scholar
  4. 4.
    Franke, C., Hoffmann, F., Lepping, J., Schwiegelshohn, U.: Development of Scheduling Strategies with Genetic Fuzzy Systems. Applied Soft Computing 8(1), 706–721 (2008)CrossRefGoogle Scholar
  5. 5.
    Franke, C., Lepping, J., Schwiegelshohn, U.: Genetic Fuzzy Systems applied to Online Job Scheduling. In: Proceedings of the 2007 IEEE International Conference on Fuzzy Systems, London, June 2007, pp. 1573–1578. IEEE Press, Los Alamitos (2007)Google Scholar
  6. 6.
    Gagliardi, F., Jones, B., Grey, F., Begin, M.-E., Heikkurinen, M.: Building an infrastructure for scientific grid computing: status and goals of the egee project. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 363(1833), 1729–1742 (2005)CrossRefGoogle Scholar
  7. 7.
    Grimme, C., Lepping, J., Papaspyrou, A.: Prospects of Collaboration between Compute Providers by means of Job Interchange. In: Frachtenberg, E., Schwiegelshohn, U. (eds.) JSSPP 2007. LNCS, vol. 4942, pp. 132–151. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Grimme, C., Lepping, J., Papaspyrou, A.: Discovering Performance Bounds for Grid Scheduling by using Evolutionary Multiobjective Optimization. In: Keijzer, M., et al. (eds.) Prococeedings of the Genetic and Evolutionary Computation Conference (GECCO 2008), Atlanta, Georgia, USA, July 2008, pp. 1491–1498. ACM Press, New York (2008)CrossRefGoogle Scholar
  9. 9.
    Juang, C.-F., Lin, J.-Y., Lin, C.-T.: Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design. IEEE Transactions on System, Man and Cybernetics 30(2), 290–302 (2000)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Marinescu, D.C., Boloni, L., Hao, R., Jun, K.K.: An alternative model for scheduling on a computational grid. In: Proceedings of ISCIS 1998, the Thirteenth International Symposium on Computer and Information Sciences, Antalya, pp. 473–480. IOP Press, Amsterdam (1998)Google Scholar
  11. 11.
    Schwefel, H.-P.: Evolution and Optimum Seeking. John Wiley & Sons, New York (1995)Google Scholar
  12. 12.
    Schwiegelshohn, U., Tchernykh, A., Yahyapour, R.: Online scheduling in grids. In: 22nd IEEE International Parallel and Distributed Processing Symposium (IPDPS 2008). IEEE Press, Los Alamitos (2008)Google Scholar
  13. 13.
    Schwiegelshohn, U., Yahyapour, R.: Fairness in parallel job scheduling. Journal of Scheduling 3(5), 297–320 (2000)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems, Man, and Cybernetics, SMC 15(1), 116–132 (1985)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexander Fölling
    • 1
  • Christian Grimme
    • 1
  • Joachim Lepping
    • 1
  • Alexander Papaspyrou
    • 1
  1. 1.Robotics Research InstituteTU Dortmund UniversityDortmundGermany

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