End-to-end Delay Prediction Based on Traffic Matrix Sampling
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| Titel: | End-to-end Delay Prediction Based on Traffic Matrix Sampling |
|---|---|
| Autoren: | Krasniqi, Filip, Elias, Jocelyne, Leguay, Jeremie, Redondi, Alessandro E. C. |
| Quelle: | IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :774-779 |
| Verlagsinformationen: | IEEE, 2020. |
| Publikationsjahr: | 2020 |
| Schlagwörter: | Machine Learning, End-to-end delay, End-to-end delay, QoS prediction, Machine Learning, Traffic Measurement, QoS prediction, 0202 electrical engineering, electronic engineering, information engineering, Traffic Measurement, 02 engineering and technology |
| Beschreibung: | 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%. |
| Publikationsart: | Article Conference object |
| Dateibeschreibung: | application/pdf |
| DOI: | 10.1109/infocomwkshps50562.2020.9162765 |
| Zugangs-URL: | https://hdl.handle.net/11311/1153629 https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162765 https://hdl.handle.net/11585/795014 https://ieeexplore.ieee.org/document/9162765 https://doi.org/10.1109/INFOCOMWKSHPS50562.2020.9162765 |
| Rights: | IEEE Copyright |
| Dokumentencode: | edsair.doi.dedup.....20d8f1d10431063ff551f09e8f1c699a |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | 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%. |
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| DOI: | 10.1109/infocomwkshps50562.2020.9162765 |