In this paper we focus on the problem of predicting Quality of Service (QoS), and in particular end-to-end delay, by using traffic matrix samples. To this aim, we study different models based on machine learning as a promising tool to characterize performance in complex computer networks. More specifically, we first provide a simulation platform, based on NS 3 network simulator, in which each Origin-Destination (OD) flow is a mixture of UDP and TCP traffic and we generate useful data for our study. We present three datasets over which we gradually vary the network characteristics: incoming traffic intensity, link capacities, and propagation delays. The datasets are leveraged to train machine learning models, namely Neural Networks and Random Forests, to predict end-to-end delay starting from the knowledge of OD traffic matrix samples. The robustness of these models is evaluated in different test scenarios. Numerical results show that both models are able to accurately forecast the end-to-end delay over all tested datasets, with Random Forests outperforming Neural Networks with gaps as high as 40%.

End-to-end delay prediction based on traffic matrix sampling

Krasniqi F.;Elias J.;Redondi A. E. C.
2020

Abstract

In this paper we focus on the problem of predicting Quality of Service (QoS), and in particular end-to-end delay, by using traffic matrix samples. To this aim, we study different models based on machine learning as a promising tool to characterize performance in complex computer networks. More specifically, we first provide a simulation platform, based on NS 3 network simulator, in which each Origin-Destination (OD) flow is a mixture of UDP and TCP traffic and we generate useful data for our study. We present three datasets over which we gradually vary the network characteristics: incoming traffic intensity, link capacities, and propagation delays. The datasets are leveraged to train machine learning models, namely Neural Networks and Random Forests, to predict end-to-end delay starting from the knowledge of OD traffic matrix samples. The robustness of these models is evaluated in different test scenarios. Numerical results show that both models are able to accurately forecast the end-to-end delay over all tested datasets, with Random Forests outperforming Neural Networks with gaps as high as 40%.
IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020
978-1-7281-8695-5
End-to-end delay
Machine Learning
QoS prediction
Traffic Measurement
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1153629
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