1.
Supervisor: Wehner, N.: QoE Monitoring using State-of-the-Art NLP Techniques, (2021).
Contact: nikolas.wehner@uni-wuerzburg.de In recent years, the Natural Language Processing (NLP) domain has been transformed severely by novel learning mechanisms like attention and novel Deep Learning models like the general Transformer, BERT, and GPT-3. The attention mechanism aligns input sequences with output sequences in a way that the importance of each input element can be estimated for an output element. This limits its applicability not only to NLP, but it could also be a highly interesting concept for the networking domain in general. The novel models like the Transformer make use of attention mechanisms in order to be able to perform for example neural machine translation with high accuracy. In this work, the applicability of the attention mechanism and for example the Transformer model shall be evaluated for the use case of (Web/Video) QoE monitoring. For this work, the data will be provided. The main task is the modelling of the network traces and the optimization of the learning processes. Therefore, various approaches for the modelling and for the feature extraction shall be tested. (Also available as Bachelor's thesis or Praktikum)
2.
Supervisor: Wehner, N.: Flow-based Web QoE Monitoring for Encrypted Network Traffic, (2021).
Contact: nikolas.wehner@uni-wuerzburg.de The goal of this work is to estimate Web QoE KPIs like the Real-User-Monitoring Speed Index (RUMSI) or Page Load Time (PLT) directly from encrypted network traces using flow-based information only. This requires a reasonable feature extraction and engineering process as well as the use of machine learning techniques. Therefore, different feature sets and models shall be tested, e.g., using the response times of flows and modelling the data as time series. These approaches are however subject to change. The data for this work will be provided.
3.
Supervisor: Wehner, N.: Simulation Framework for Web QoE Monitoring, (2021).
Contact: nikolas.wehner@uni-wuerzburg.de Abstract: Web browsing is a key application in the Internet today. To describe the delight or frustration with web browsing the Quality of Experience (QoE) concept is used. Web browsing QoE mainly depends on the loading process of a web page and can be approximated with different models like the Page Load Time (PLT). To guarantee high quality services, service providers, e.g., the Internet Service Provider, should implement a QoE monitoring. However, obtaining relevant data is often difficult, cumbersome, and does not scale very well. Thus, a simulative approach could be helpful for future evaluations. The goal of this work is the creation and evaluation of a simulation which is able to provide realistic web browsing in a shared access network. Therefore, an existing Java simulation, already capable of web browsing, has to be extended for more realistic web browsing characteristics by analysing an existing data set and integrating the characteristics of this data set. The final performance evaluation is then twofold: First, it shall be validated that the simulated web browsing requests are close to reality. Further, a simulation study with a varying number of clients and network capacities shall be conducted and the Web QoE for the clients shall be evaluated afterwards.
4.
Supervisor: Wehner, N.: Implementation of a Low Latency QoE Fair HAS Logic, (2021).
Contact: nikolas.wehner@uni-wuerzburg.de Abstract: Nowadays, basically all videos are streamed via HTTP Adaptive Streaming (HAS). An important concept in terms of HAS is the Quality of Experience (QoE), which describes how users perceive video streaming. QoE fairness extends the concept of QoE by comparing the QoE of mutliple clients in a shared network. An important component influencing the QoE and consequently the QoE fairness is the HAS adaptation logic, which is responsible for downloading the segments with the goal to obtain the best possible video quality without causing stalling, i.e., playback interruption due to buffer underrun. The goal of this work is to implement an adaptation logic, currently implemented in a simulation, into a real video player and evaluating the QoE and QoE fairness of the adaptation logic when confronted with low-latency situations in real scenarios. (Also available as Bachelor's thesis)
