A support vector machine based approach for forecasting of network weather services

We present forecasting related results using a recently introduced technique called Support Vector Machines (SVM) for measurements of processing, memory, disk space, communication latency and bandwidth derived from Network Weather Services (NWS). We then compare the performance of support vector machines with the forecasting techniques existing in network weather services using a set of metrics like mean absolute error, mean square error among others. The models are used to make predictions for several future time steps as against the present network weather services method of just the immediate future time step. The number of future time steps for which the prediction is done is referred to as the depth of prediction set. The support vector machines forecasts are found to be more accurate and outperform the existing methods. The performance improvement using support vector machines becomes more pronounced as the depth of the prediction set increases. The data gathered is from a production environment (i.e., non-experimental). © Springer Science+Business Media, Inc. 2006.