Abstract
This paper presents results of new experiments with the Global Optimising Resource Broker and Allocator GORBA for grid systems. The scheduling algorithm is based on the Evolutionary Algorithm GLEAM (General Learning Evolutionary Algorithm and Method) and several heuristics. The task of planning grid resource allocation is compared to pure NP-complete job shop scheduling and it is shown in which way it is of greater complexity. Two different gene models and two repair methods are described in detail and assessed by the experimental results. Based on the analysis of the experimental results, directions of further work and improvements will be outlined.
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Stucky, KU., Jakob, W., Quinte, A., Süß, W. (2008). Tackling the Grid Job Planning and Resource Allocation Problem Using a Hybrid Evolutionary Algorithm. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2007. Lecture Notes in Computer Science, vol 4967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68111-3_61
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DOI: https://doi.org/10.1007/978-3-540-68111-3_61
Publisher Name: Springer, Berlin, Heidelberg
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