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)


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.


Schedule Algorithm Pareto Front User Group Rule Base Schedule Strategy 
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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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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