Identification, Modelling and Prediction of Non-periodic Bursts in Workloads

Abstract

Non-periodic bursts are prevalent in workloads of large scale applications. Existing workload models do not predict such non-periodic bursts very well because they mainly focus on repeatable base functions. We begin by showing the necessity to include bursts in workload models by investigating their detrimental effects in a petabyte-scale distributed data management system. This work then makes three contributions. First, we analyse the accuracy of five existing prediction models on workloads of data and computational grids, as well as derived synthetic workloads. Second, we introduce a novel averages-based model to predict bursts in arbitrary workloads. Third, we present a novel metric, mean absolute estimated distance, to assess the prediction accuracy of the model. Using our model and metric, we show that burst behaviour in workloads can be identified, quantified and predicted independently of the underlying base functions. Furthermore, our model and metric are applicable to arbitrary kinds of burst prediction for time series.