A comparative study of path performance metrics predictors

Using quality-of-service (QoS) metrics for Internet traf- fic is expected to improve greatly the performance of many network enabled applications, such as Voice-over-IP(VoIP) and video conferencing. However, it is not possible to con- stantly measure path performance metrics (PPMs) such as delay and throughput without interfering with the network. In this work, we focus on PPMs measurement scalabil- ity by considering machine learning techniques to estimate predictive models from past PPMs observations. Using real data collected from PlanetLab, we provide a comparison be- tween three different predictors: AR(MA) models, Kalman filters and support vector machines (SVMs). Some predic- tors use delay and throughput jointly to take advantage of the possible relationship between PPMs, while other pre- dictors consider PPMs individually. Our current results illustrate that the best performing model is an individual SVM specific to each time series. Overall, delay can be pre- dicted with very good accuracy while accurate forecasting of throughput remains an open problem.