%0 Journal Article %1 info3-article-2020-4 %A Wehner, Nikolas %A Seufert, Michael %A Schüler, Joshua %A Wassermann, Sarah %A Casas, Pedro %A Hoßfeld, Tobias %D 2021 %J ACM SIGMETRICS Performance Evaluation Review %K myown ucn copy %N 4 %P 37-40 %T Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling %V 48 %X 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.

@article{info3-article-2020-4, 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. }, added-at = {2020-10-20T11:50:35.000+0200}, author = {Wehner, Nikolas and Seufert, Michael and Schüler, Joshua and Wassermann, Sarah and Casas, Pedro and Hoßfeld, Tobias}, biburl = {https://www.bibsonomy.org/bibtex/23452b71cac893e9cd83f21da195c437a/uniwue_info3}, interhash = {ed5d096ad892d54e4fb1e7c3a59b4ca5}, intrahash = {3452b71cac893e9cd83f21da195c437a}, journal = {ACM SIGMETRICS Performance Evaluation Review}, keywords = {myown ucn copy}, month = {3}, number = 4, pages = {37-40}, timestamp = {2022-03-14T00:10:59.000+0100}, title = {Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling}, volume = 48, year = 2021 }