We conduct research in relation to Internet applications: we evaluate applications, we model Internet applications. A frequently-performed investigation is the study of user satisfaction (Quality of Experience) with a particular Internet application.
Mobi-QoE (Monitoring and Analysis of Quality of Experience in Mobile Broadband Networks, EU H2020)
The objective of Mobi-QoE is to extend MONROE’s testbed to the QoE domain by integrating novel software-based QoE-capable measurement tools and QoE models for popular end-user services (e.g., YouTube, Facebook, Spotify).
SmartQoE(Measurement Concept and Trendscouting for QoE in Mobile Context)
The focus of the project "SmartQoE" is on three major goals: 1. Development of a measurement concept to measure QoE-relevant application data with a smartphone app, 2. Analysis of collected data with respect to QoE criteria, 3. Trendscouting for QoE in mobile networks.
DFG QoE-DZ(Analysis and Optimization of the Trade-off between QoE and Energy-Efficiency in Data Centers)
This project focuses on quantifying and adjusting the trade-off between QoE and energy-efficiency in data centers for highly relevant use cases. An interesting use case for the interconnection between data centers, QoE, and energy-efficiency that is considered to have an increasing impact in the following years, are Virtual Desktop Infrastructures (VDIs). A VDI enables users to use very lightweight systems, e.g., so-called thin clients, whereby the actual operating system including all computations and software runs in a data center.
Related Research Areas
YouTube is one of the most popular services in today’s Internet. It has more than 1 billion users and every day people watch hundreds of millions of hours of YouTube videos. Half of those YouTube views are on mobile devices. On the one hand, (mobile) operators want to handle the huge amount of video traffic as efficiently as possible (high revenue per bit), on the other hand, they want to deliver a high Quality of Experience (QoE) to satisfy their customers. Therefore, it is very important for operators to understand the performance of their networks with respect to YouTube traffic.
In online social networks (OSNs) users voluntarily provide information about themselves, their interests, their friends and their activities, especially about their current situation or exceptional events. Nowadays these so called social signals are ubiquitous and can not only be collected from OSNs (e.g., friendships, interests, trust-relevant metadata), but also from applications (e.g., messaging or call patterns) and sensors (e.g., location). Social awareness harvests these signals, extracts useful and re-usable information (e.g., users’ social relationships, activity patterns, and interests), and exploits them in order to improve a service.