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Wehner, N., Seufert, M., Schüler, J., Wassermann, S., Casas, P., Hoßfeld, T.: Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling. ACM SIGMETRICS Performance Evaluation Review. (2020).
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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Wehner, N., Seufert, M., Schüler, J., Wassermann, S., Casas, P., Hoßfeld, T.: Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling. 2nd Workshop on AI in Networks and Distributed Systems (WAIN). , Milan, Italy (2020).
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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Seufert, M., Wehner, N., Wieser, V., Casas, P., Capdehourat, G.: Mind the (QoE) Gap: On the Incompatibility of Web and Video QoE Models in the Wild. 16th International Conference on Network and Service Management (CNSM). , Izmir, Turkey (2020).
Education Service Providers (ESPs) have a paramount role in the digitization of education, providing reliable devices for students and teachers and high quality Internet access at schools. In this paper, a large-scale, passive, in-device Quality of Experience (QoE) monitoring system is presented, which was deployed into a nationwide network of education-purpose devices. Four months' worth of continuous measurements were conducted by an ESP, covering more than 800 education centers and about 4000 devices, used both in schools and at home. When analyzing the QoE of web sessions in school networks, we identify a fundamental issue with the compatibility of web browsing and video QoE models, which inhibits the successful application of QoE-aware network management for multiple services.
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Wassermann, S., Casas, P., Ben Houidi, Z., Huet, A., Seufert, M., Wehner, N., Schüler, J., Cai, S.-M., Shi, H., Xu, J., Hoßfeld, T., Rossi, D.: Are you on Mobile or Desktop? On the Impact of End-User Device on Web QoE Inference from Encrypted Traffic. 16th International Conference on Network and Service Management (CNSM). , Izmir, Turkey (2020).
Web browsing is one of the key applications of the Internet, if not the most important one. We address the problem of Web Quality of Experience (QoE) monitoring from the ISP perspective, relying on in-network, passive measurements. As a proxy to Web QoE, we focus on the analysis of the well-known SpeedIndex (SI) metric. Given the lack of app-level data visibility introduced by the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI and the QoE level of individual web page loading sessions, using as input only packet- and flow-level data. Our study targets the impact of different end-user device types (e.g., smartphone, desktop, tablet) on the performance of such models. Empirical evaluations on a large, multi-device, heterogeneous corpus of Web QoE measurements for top popular websites demonstrate that the proposed solution can infer the SI as well as estimate QoE ranges with high accuracy, using either packet-level or flow-level measurements. In addition, we show that the device type adds a strong bias in the feasibility of these Web QoE models, putting into question the applicability of previously conceived approaches on single-device measurements. To improve the state of affairs, we conceive cross-device generalizable models operating at both packet and flow levels, offering a feasible solution for Web QoE monitoring in operational, multi-device networks. To the best of our knowledge, this is the first study tackling the analysis of Web QoE from encrypted network traffic in multi-device scenarios.
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Wassermann, S., Wehner, N., Casas, P.: Machine Learning Models for YouTube QoE and User Engagement Prediction in Smartphones. WAIN - Workshop on AI in Networks and Distributed Systems. , Toulouse, France (2018).
Measuring and monitoring YouTube Quality of Experience is a challenging task, especially when dealing with cellular networks and smartphone users. Using a large-scale database of crowdsourced YouTube-QoE measurements in smartphones, we conceive multiple machine-learning models to infer different YouTube-QoE-relevant metrics and userbehavior- related metrics from network-level measurements, without requiring root access to the smartphone, video-player embedding, or any other reverse-engineering-like approaches. The dataset includes measurements from more than 360 users worldwide, spanning over the last five years. Our preliminary results suggest that QoE-based monitoring of YouTube mobile can be realized through machine learning models with high accuracy, relying only on network-related features and without accessing any higher-layer metric to perform the estimations.
