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

Wehner, Nikolas; Seufert, Michael; Schuler, Joshua; Wassermann, Sarah; Casas, Pedro; Hoßfeld, Tobias

doi:10.1145/3466826.3466840

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…However, metrics like SI require access to the application layer, which is totally hidden from ISPs due to the wide deployment of end-to-end network traffic encryption. Different from our previous work [3], [4] -which focuses exclusively on Web QoE for web browsing, this paper extends previous work by specifically considering smartphone apps for Web QoE monitoring and analysis, which account for a huge share of the traffic in mobile networks.…”

Section: Introductionmentioning

confidence: 93%

How are your Apps Doing? QoE Inference and Analysis in Mobile Devices

Wehner

Seufert

Schuler

et al. 2021

2021 17th International Conference on Network and Service Management (CNSM)

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Web browsing has become the most important application of the Internet for the end user. When it comes to mobile devices, web services are mainly accessed through apps. This paper tackles the problem of Web Quality of Experience (QoE) in mobile devices, with a specific focus on apps QoE monitoring and analysis, using in-network (encrypted) traffic measurements. Measuring apps QoE is complex, not only from an instrumentation point of view, but also from the heterogeneity of user interactions which might realize substantially different user experience. To this end, we conduct a feasibility study on four specific and popular Android apps and their corresponding web services. Our test automation framework emulates and measures different user interactions commonly executed during an app session, including the app startup, clicking, scrolling, and searching. The resulting traffic is characterized on different dimensions, and machine learning models are trained to identify web services, apps, and user interactions, and to infer their QoE. The proposed models can correctly identify the specific web service and app in 86% of the cases and accurately estimate the associated QoE with small errors. Our preliminary study represents a first step towards an in-network, web QoE monitoring solution for mobile-device apps.

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Section: Introductionmentioning

confidence: 93%

How are your Apps Doing? QoE Inference and Analysis in Mobile Devices

Wehner

Seufert

Schuler

et al. 2021

2021 17th International Conference on Network and Service Management (CNSM)

Self Cite

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show abstract

“…In order to avoid customer churn, network service providers and ISPs are thus interested in estimating QoE or, as a proxy, QoE influence factors from encrypted network traffic. For this purpose, most approaches rely on ML as can be seen for video streaming and web browsing in [157], [256]. For data-driven QoE modelling, the goal is to predict the Mean Opinion Score (MOS) of a service on the range from 1 (bad) to 5 (excellent) in a regression task given some QoE influence factors.…”

Section: ) Interpretable Modelsmentioning

confidence: 99%

Machine Learning With Computer Networks: Techniques, Datasets, and Models

Afifi,

Pochaba,

Boltres

et al. 2024

IEEE Access

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“…In [47], Wehner et al proposed an alternative to avoid the instrumentation of interactive web applications and obtain the SI without incurring high computational costs. Although the SI traditionally requires the instrumentation of the CIA, the authors suggested obtaining the metric through indirect measurements.…”

Section: Strategies Based On Indirect Measuresmentioning

confidence: 99%

Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications

Arellano-Uson,

Magaña,

Morato

et al. 2024

Applied Sciences

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A cloud-based interactive application (CIA) is an application running in the cloud with stringent interactivity requirements, such as remote desktop and cloud gaming. These services have experienced a surge in usage, primarily due to the adoption of new remote work practices during the pandemic and the emergence of entertainment schemes similar to cloud gaming platforms. Evaluating the quality of experience (QoE) in these applications requires specific metrics, including interactivity time, responsiveness, and the assessment of video- and audio-quality degradation. Despite existing studies that evaluate QoE and compare features of general cloud applications, systematic research into QoE for CIAs is lacking. Previous surveys often narrow their focus, overlooking a comprehensive assessment. They touch on QoE in broader contexts but fall short in detailed metric analysis. Some emphasise areas like mobile cloud computing, omitting CIA-specific nuances. This paper offers a comprehensive survey of QoE measurement techniques in CIAs, providing a taxonomy of input metrics, strategies, and evaluation architectures. State-of-the-art proposals are assessed, enabling a comparative analysis of their strengths and weaknesses and identifying future research directions.

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

Cited by 8 publications

References 9 publications

How are your Apps Doing? QoE Inference and Analysis in Mobile Devices

How are your Apps Doing? QoE Inference and Analysis in Mobile Devices

Machine Learning With Computer Networks: Techniques, Datasets, and Models

Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications

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