A Comparative Study of Path Performance Metrics Predictors

Abstract

peer reviewedUsing quality-of-service (QoS) metrics for Internet traffic 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 constantly measure path performance metrics (PPMs) such as delay and throughput without interfering with the network. In this work, we focus on PPMs measurement scalability by considering machine learning techniques to estimate predictive models from past PPMs observations. Using real data collected from PlanetLab, we provide a comparison between 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 predictors 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 predicted with very good accuracy while accurate forecasting of throughput remains an open problem