Solving Scheduling Problems in Grid Resource Management Using an Evolutionary Algorithm

  • Karl-Uwe Stucky
  • Wilfried Jakob
  • Alexander Quinte
  • Wolfgang Süß
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4276)


Evolutionary Algorithms (EA) are well suited for solving optimisation problems, especially NP-complete problems. This paper presents the application of the Evolutionary Algorithm GLEAM (General Learning and Evolutionary Algorithm and Method) in the field of grid computing. Here, grid resources like computing power, software, or storage have to be allocated to jobs that are running in heterogeneous computing environments. The problem is similar to industrial resource scheduling, but has additional characteristics like co-scheduling and high dynamics within the resource pool and the set of requesting jobs. The paper describes the deployment of GLEAM in the global optimising grid resource broker GORBA (Global Optimising Resource Broker and Allocator) and the first promising results in a grid simulation environment.


Grid Resource Grid Environment Resource Broker Minimum Fitness Conventional Planning 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

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