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Mobile Web and App QoE Monitoring for ISPs -from Encrypted Traffic to Speed Index through Machine Learning

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Abstract

Web browsing is one of the key applications of the Internet. In this paper, we address the problem of mobile Web and App QoE monitoring from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Our study targets the analysis of Web and App QoE in mobile devices, including mobile browsing in smartphones and tablets, as well as mobile apps. As a proxy to Web QoE, we focus on the analysis of the well-known Speed Index (SI) metric. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI of individual web page and app loading sessions, using as input only packet level data. Empirical evaluations on a large, multi mobile-device corpus of Web and App QoE measurements for top popular websites and selected apps demonstrate that the proposed solution can properly infer the SI from in-network, encrypted-traffic measurements, relying on learning-based models. Our study also reveals relevant network and web page content characteristics impacting Web QoE in mobile devices, providing a complete overview on the mobile Web and App QoE assessment problem.
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Dates and versions

hal-03365897 , version 1 (05-10-2021)
hal-03365897 , version 2 (30-10-2021)

Identifiers

  • HAL Id : hal-03365897 , version 2

Cite

Pedro Casas, Sarah Wassermann, Nikolas Wehner, Michael Seufert, Joshua Schüler, et al.. Mobile Web and App QoE Monitoring for ISPs -from Encrypted Traffic to Speed Index through Machine Learning. 13th IFIP Wireless and Mobile Networking Conference (WMNC), Oct 2021, Montréal, Canada. ⟨hal-03365897v2⟩
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