Abstract
One of the most challenging problems when facing the implementation of computational grids is the system resources effective management commonly referred as to grid scheduling. A rule-based scheduling system is presented here to schedule computationally intensive Bag-of-Tasks applications on grids for virtual organizations. There exist diverse techniques to develop rule-base scheduling systems. In this work, we suggest the joining of a gathering and sorting criteria for tasks and a fuzzy scheduling strategy. Moreover, in order to allow the system to learn and thus to improve its performance, two different off-line optimization procedures based on Michigan and Pittsburgh approaches are incorporated to apply Genetic Algorithms to the fuzzy scheduler rules. A complex objective function considering users differentiation is followed as a performance metric. It not only provides the conducted system evaluation process a comparison with other classical approaches in terms of accuracy and convergence behaviour characterization, but it also analyzes the variation of a wide set of evolution parameters in the learning process to achieve the best performance.
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This work has been financially supported by the Andalusian Government (Research Project P06-SEJ-01694).
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Prado, R.P., García-Galán, S., Yuste, A.J. et al. Genetic fuzzy rule-based scheduling system for grid computing in virtual organizations. Soft Comput 15, 1255–1271 (2011). https://doi.org/10.1007/s00500-010-0660-5
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DOI: https://doi.org/10.1007/s00500-010-0660-5