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Selecting the appropriate scheduling algorithm in distributed heterogeneous systems is a difficult problem. In order to avoid an exhaustive search it is possible to design an automatic selection procedure based on a classification model trained using various characteristics of the tasks to be scheduled. This paper presents a comparative study on the effectiveness of several classification models used to select an effective algorithm for a given scheduling problem. The main contribution of the paper is the hybrid classifier based on non-nested generalized exemplars and an evolutionary selection of attributes and exemplars. The experiments show the ability of the proposed hybrid classifier to identify the appropriate scheduling algorithm when new configurations arrive to the grid scheduler.

Key words and phrases. grid scheduler, evolutionary pruning, non-nested generalized exemplars, classification.