Predicting the execution time of grid workflow applications through local learning

Workflow execution time prediction is widely seen as a key service to understand the performance behavior and support the optimization of Grid workflow applications. In this paper, we present a novel approach for estimating the execution time of workflows based on Local Learning. The workflows are characterized in terms of different attributes describing structural and runtime information about workflow activities, control and data flow dependencies, number of Grid sites, problem size, etc. Our local learning framework is complemented by a dynamic weighing scheme that assigns weights to workflow attributes reflecting their impact on the workflow execution time. Predictions are given through intervals bounded by the minimum and maximum predicted values, which are associated with a confidence value indicating the degree of confidence about the prediction accuracy. Evaluation results for three real world workflows on a real Grid are presented to demonstrate the prediction accuracy and overheads of the proposed method.