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Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

Abstract : This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI-an efficient approximation to SI, with machine-learning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.
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https://hal.archives-ouvertes.fr/hal-02973134
Contributor : Sarah Wassermann <>
Submitted on : Tuesday, October 20, 2020 - 10:04:25 PM
Last modification on : Saturday, October 24, 2020 - 12:45:56 AM
Long-term archiving on: : Thursday, January 21, 2021 - 7:34:47 PM

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  • HAL Id : hal-02973134, version 1

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Nikolas Wehner, Michael Seufert, Joshua Schüler, Sarah Wassermann, Pedro Casas, et al.. Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling. 2nd Workshop on AI in Networks and Distributed Systems (WAIN), Nov 2020, Milano, Italy. ⟨hal-02973134⟩

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