End-to-end quality of service seen by applications : a statistical learning approach

Abstract

The focus of this work is on the estimation of quality of servi ce (QoS) parameters seen by an application. Our proposal is based on end-to-end active measurements and sta tistical learning tools. We propose a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation be- tween QoS parameters seen by the application and the state of the network path, which is inferred from the interarrival times of the probe packets bursts. We obtain a continuous non intrusive QoS monitoring methodology. We propose two di ff erent estimators of the network state and analyze them using Nadaraya-Watson estimator and Support Vector Machines (SVM) for regression. We compare these approaches and we show results obtained by simulations and by measures in operational networks