An accurate performance prediction service can be very useful for resource management and the scheduler service and help them make better resource utilization decisions by providing better execution time estimates. In this paper we present a novel approach of predicting the execution time of computational tasks for Grid infrastructures using machine learning models based on multilayer perceptron combined with a principal feature selection algorithm for selecting the most important runtime features.Our technique uses runtime provenance information as input to multilayer perceptron (MLP) based neural network which trains a model that can predict the execution time of programs with reasonably good accuracy. For the development and training of our machine learning models, we used provenance data collected from three different computational Grids by executing real-world applications. By using our MLP based method we were able to minimise the prediction error to as low as 22% for real-world applications on various Grid infrastructures.
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