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

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Computer science Encryption Series (stratigraphy) Computer network Network monitoring Time series Web traffic World Wide Web The Internet Machine learning Paleontology Biology

Nikolas Wehner , Michael Seufert , Joshua Schüler , Sarah Wassermann , Pedro Casas , Tobias Hoßfeld ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1145/3466826.3466840 · OA: W3094429220

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 machinelearning 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.