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Solving Scheduling Problems in Grid Resource Management Using an Evolutionary Algorithm

Stucky, Karl-Uwe; Jakob, Wilfried; Quinte, Alexander; Süß, Wolfgang

Evolutionary Algorithms (EA) are well suited for solving optimisation problems, especially NPcomplete 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 coscheduling and a 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.

DOI: 10.1007/11914952_14
Zitationen: 9
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik (IAI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2006
Sprache Englisch
Identifikator ISBN: 3-540-48274-1
ISSN: 0302-9743
KITopen-ID: 170066296
HGF-Programm 46.03.02 (POF I, LK 01) Simul.u.Optim.kompl.Berechnungssysteme
Erschienen in On the Move to Meaningful Internet Systems 2006 : CoopIS, DOA, GADA, and ODBASE : Proceedings of the OTM Confederated International Conferences, CoopIS, DOA, GADA, and ODBASE 2006, Part II, Montpellier, France, 29th October - 3rd November 2006. Ed.: R. Meersman
Verlag Springer Verlag
Seiten 1252-62
Serie Lecture Notes in Computer Science ; 4276
Nachgewiesen in Dimensions
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