On Advantages of Scheduling Using Genetic Fuzzy Systems

  • Carsten Franke
  • Joachim Lepping
  • Uwe Schwiegelshohn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4376)

Abstract

In this paper, we present a methodology for automatically generating online scheduling strategies for a complex scheduling objective with the help of real life workload data. The scheduling problem includes independent parallel jobs and multiple identical machines. The objective is defined by the machine provider and considers different priorities of user groups. In order to allow a wide range of objective functions, we use a rule based scheduling strategy. There, a rule system classifies all possible scheduling states and assigns an appropriate scheduling strategy based on the actual state. The rule bases are developed with the help of a Genetic Fuzzy System that uses workload data obtained from real system installations. We evaluate our new scheduling strategies again on real workload data in comparison to a probability based scheduling strategy and the EASY standard scheduling algorithm. To this end, we select an exemplary objective function that prioritizes some user groups over others.

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References

  1. 1.
    Bäck, T., Schwefel, H.–P.: An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation 1(1), 1–23 (1993)CrossRefGoogle Scholar
  2. 2.
    Beyer, H.–G., Schwefel, H.–P.: Evolution Strategies – A Comprehensive Introduction. Natural Computing 1(1), 3–52 (2002)CrossRefMathSciNetMATHGoogle Scholar
  3. 3.
    Bonarini, A.: Evolutionary Learning of Fuzzy rules: competition and cooperation. In: Pedrycz, W. (ed.) Fuzzy Modelling: Paradigms and Practice, pp. 265–284. Kluwer Academic Publishers, Dordrecht (1996)Google Scholar
  4. 4.
    Buyya, R., Abramson, D., Venugopal, S.: The Grid Economy. Proceedings of the IEEE, Special Issue on Grid Computing 93(3), 698–714 (2005)Google Scholar
  5. 5.
    Chapin, S.J., et al.: Benchmarks and standards for the evaluation of parallel job schedulers. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1999, IPPS-WS 1999, and SPDP-WS 1999. LNCS, vol. 1659, pp. 67–90. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Ernemann, C., et al.: On Advantages of Grid Computing for Parallel Job Scheduling. In: Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID2002), Berlin, pp. 39–46. IEEE Computer Society Press, Los Alamitos (2002)CrossRefGoogle Scholar
  7. 7.
    Ernemann, C., Hamscher, V., Yahyapour, R.: Economic Scheduling in Grid Computing. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 128–152. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Ernemann, C., et al.: Scheduling Algorithm Development based on Complex Owner Defined Objectives. Technical Report CI-190/05, Dortmund University, Germany (2005), http://sfbci.uni-dortmund.de/Publications/Reference/Downloads/19005.pdf
  9. 9.
    Ernemann, C., Song, B., Yahyapour, R.: Scaling of Workload Traces. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2003. LNCS, vol. 2862, pp. 166–183. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Ernemann, C., Yahyapour, R.: Applying Economic Scheduling Methods to Grid Environments. In: Grid Resource Management - State of the Art and Future Trends, pp. 491–506. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  11. 11.
    Feitelson, D.G.: Memory usage in the LANL CM-5 workload. In: Feitelson, D.G., Rudolph, L. (eds.) Job Scheduling Strategies for Parallel Processing. LNCS, vol. 1291, pp. 78–94. Springer, Heidelberg (1997)Google Scholar
  12. 12.
    Feitelson, D.G.: Metric and Workload Effects on Computer Systems Evaluation. Computer 36(9), 18–25 (2003)CrossRefGoogle Scholar
  13. 13.
    Feitelson, D.G.: Parallel Workload Archive (March 2006), http://www.cs.huji.ac.il/labs/parallel/workload/
  14. 14.
    Feitelson, D.G., Jette, M.A.: Improved Utilization and Responsiveness with Gang Scheduling. In: Feitelson, D.G., Rudolph, L. (eds.) Job Scheduling Strategies for Parallel Processing. LNCS, vol. 1291, pp. 238–261. Springer, Heidelberg (1997)Google Scholar
  15. 15.
    Graham, R.L.: Bounds for certain multiprocessor anomalies. Bell System Technical Journal 45, 1563–1581 (1966)Google Scholar
  16. 16.
    Heine, F., et al.: On the Impact of Reservations from the Grid on Planning-Based Resource Management Computational Science. In: Sunderam, V.S., et al. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 155–162. Springer, Heidelberg (2005)Google Scholar
  17. 17.
    Hoffmann, F.: Evolutionary Algorithms for Fuzzy Control System Design. Proceedings of the IEEE 89(9), 1318–1333 (2001)CrossRefGoogle Scholar
  18. 18.
    Hotovy, S.: Workload Evolution on the Cornell Theory Center IBM SP2. In: Feitelson, D.G., Rudolph, L. (eds.) Job Scheduling Strategies for Parallel Processing. LNCS, vol. 1162, pp. 27–40. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  19. 19.
    Jansen, T., Wiegand, R.P.: Exploring the Explorative Advantage of the Cooperative Coevolutionary (1+1) EA. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 310–321. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  20. 20.
    Jin, Y., von Seelen, W., Sendhoff, B.: On Generating FC 3 Fuzzy Rule Systems from Data Using Evolution Strategies. IEEE Transactions on System, Man and Cybernetics 29(6), 829–845 (1999)CrossRefGoogle Scholar
  21. 21.
    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
  22. 22.
    Panait, L., Wiegand, R.-P., Luke, S.: A Sensitivity Analysis of a Cooperative Coevolutionary Algorithm Biased for Optimization. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 573–584. Springer, Heidelberg (2004)Google Scholar
  23. 23.
    Mu’alem, A.W., Feitelson, D.G.: Utilization, Predictability, Workloads, and User Runtime Estimates in Scheduling the IBM SP2 with Backfilling. IEEE Transanctions on Parallel & Distributed Systems 12(6), 529–543 (2001)CrossRefGoogle Scholar
  24. 24.
    Paredis, J.: Coevolutionary Computation. Artificial Life 2(4), 355–375 (1995)CrossRefGoogle Scholar
  25. 25.
    Potter, M.A., De Jong, K.: A Cooperative Coevolutionary Approach to Function Optimization. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature - PPSN III. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994)Google Scholar
  26. 26.
    Schwefel, H.–P.: Numerical Optimization of Computer Models. John Wiley & Sons, Chichester (1981)MATHGoogle Scholar
  27. 27.
    Smith, S.F.: A Learning System Based on Genetic Adaptive Algorithms. PhD thesis, Department of Computer Science, University of Pittsburgh (1980)Google Scholar
  28. 28.
    Song, B., Ernemann, C., Yahyapour, R.: Parallel Computer Workload Modeling with Markov Chains. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2004. LNCS, vol. 3277, pp. 47–62. Springer, Heidelberg (2005)Google Scholar
  29. 29.
    Song, B., Ernemann, C., Yahyapour, R.: User Group-based Workload Analysis and Modeling (CD-ROM). In: Proceedings of the International Symposium on Cluster Computing and the Grid (CCGRID2005), IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  30. 30.
    Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems, Man, and Cybernetics 15(1), 116–132 (1985)MATHGoogle Scholar
  31. 31.
    Talby, D., Feitelson, D.G.: Supporting Priorities and Improving Utilization of the IBM SP Scheduler Using Slack-Based Backfilling. In: Proceedings of the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing, pp. 513–517. IEEE Computer Society Press, Los Alamitos (1999)CrossRefGoogle Scholar
  32. 32.
    Windisch, K., et al.: A comparison of workload traces from two production parallel machines. In: 6th Symp. Frontiers Massively Parallel Computing, pp. 319–326. IEEE Computer Society Press, Los Alamitos (1996)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Carsten Franke
    • 1
  • Joachim Lepping
    • 1
  • Uwe Schwiegelshohn
    • 1
  1. 1.Robotics Research Institute, Dortmund University, 44221 DortmundGermany

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