End-to-End Quality of Service Seen by Applications: A Statistical Learning Approach

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The focus of this paper is on the estimation of Quality of Service (QoS) parameters seen by an application. The authors' proposal is based on end-to-end active measurements and statistical learning tools. They propose a methodology where the system is trained during short periods with application flows and probe packets bursts. They learn the relation between QoS parameters seen by the application and the state of the network path, which is inferred from the inter-arrival times of the probe packets bursts. They obtain a continuous non intrusive QoS monitoring methodology. They propose two different estimators of the network state and analyze them using Nadaraya - Watson estimator and Support Vector Machines (SVM) for regression.