1.
Casas, P., Wassermann, S., Wehner, N., Seufert, M., Schüler, J., Hoßfeld, T.: Mobile Web and App QoE Monitoring for ISPs - from Encrypted Traffic to Speed Index through Machine Learning. 13th Wireless and Mobile Networking Conference (WMNC), Best Paper Award. , Montreal, Canada (Virtual Conference) (2021).
2.
Wehner, N., Seufert, M., Schüler, J., Casas, P., Hoßfeld, T.: How are your Apps Doing? QoE Inference and Analysis in Mobile Devices. 17th International Conference on Network and Service Management (CNSM). , Izmir, Turkey (Virtual Conference) (2021).
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.
3.
Wehner, N., Seufert, M., Wieser, V., Casas, P., Capdehourat, G.: Quality that Matters: QoE Monitoring in Education Service Provider (ESP) Networks. IFIP/IEEE International Symposium on Integrated Network Management (IM 2021). , Bordeaux, France (Virtual Conference) (2021).
Education Service Providers (ESPs) play a crucial role in the digitization of education as they equip students and teachers with reliable devices and provide high quality Internet access at schools. This paper investigates four months worth of continuous measurements conducted by an ESP using a largescale, passive, in-device Quality of Experience (QoE) monitoring system deployed into a nationwide network of education-purpose devices. These measurements cover more than 800 education centers and about 4000 devices, used both in schools and at home. Using this rich dataset, we present an exhaustive characterization of the browsing behavior, and a quantification of the web and video QoE in this educational context. Web QoE results showed a better performance for school Wi-Fi networks compared to home connections, suggesting that several issues may arise for ESPs due to the increasing relevance of home-schooling caused by the COVID-19 pandemic.
4.
Grigorjew, A., Seufert, M., Wehner, N., Hofmann, J., Hoßfeld, T.: ML-Assisted Latency Assignments in Time-Sensitive Networking. IFIP/IEEE International Symposium on Integrated Network Management (IM 2021). , Bordeaux, France (Virtual Conference) (2021).
Recent developments in industrial automation and invehicle communication have raised the requirements of real-time networking. Bus systems that were traditionally deployed in these fields cannot provide sufficient bandwidth and are now shifting towards Ethernet for their real-time communication needs. In this field, standardization efforts from the IEEE and the IETF have developed new data plane mechanisms such as shapers and schedulers, as well as control plane mechanisms such as reservation protocols to support their new requirements. However, their implementation and their optimal configuration remain an important factor for their efficiency. This work presents a machine learning framework that takes on the configuration task. Four different models are trained for the configuration of per-hop latency guarantees in a distributed resource reservation process and compared with respect to their real-time traffic capacity. The evaluation shows that all models provide good configurations for the provided scenarios, but more importantly, they represent a first step for a semi-automated configuration of parameters in Time-Sensitive Networking.
5.
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.
