Nikolas Wehner - Lehrstuhl für Informatik III

Telefon	(0931) 31-88150
Telefax	(0931) 31-86632
E-Mail	nikolas.wehner@uni-wuerzburg.de
Raum	A213a
Anschrift	Lehrstuhl für Informatik III Am Hubland D-97074 Würzburg

Contents: Overview | Publications | Teaching | Working Group | Projects

Research Interests

My research interests include the following topics:

Quality of Experience (QoE) of Internet applications
Measurement and analytics of (encrypted) network traffic
Artificial intelligence and machine learning for networks
Performance analysis and modeling of communication systems

Recent Publications

1.
Seufert, M. et al. Hot Topic Abstract: Marina: Realizing ML-driven Real-time Network Traffic Monitoring at Terabit Scale. in International Conference on Networked Systems (NetSys) (2025).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{info3-inproceedings-2025-7, address = {Ilmenau, Germany}, author = {Seufert, Michael and Dietz, Katharina and Wehner, Nikolas and Geißler, Stefan and Schüler, Joshua and Wolz, Manuel and Hotho, Andreas and Casas, Pedro and Hoßfeld, Tobias and Feldmann, Anja}, booktitle = {International Conference on Networked Systems (NetSys)}, keywords = {sys:hidden:inproceedings}, month = {09}, title = {Hot Topic Abstract: Marina: Realizing ML-driven Real-time Network Traffic Monitoring at Terabit Scale}, year = 2025 }

%0 Conference Paper %1 info3-inproceedings-2025-7 %A Seufert, Michael %A Dietz, Katharina %A Wehner, Nikolas %A Geißler, Stefan %A Schüler, Joshua %A Wolz, Manuel %A Hotho, Andreas %A Casas, Pedro %A Hoßfeld, Tobias %A Feldmann, Anja %B International Conference on Networked Systems (NetSys) %C Ilmenau, Germany %D 2025 %T Hot Topic Abstract: Marina: Realizing ML-driven Real-time Network Traffic Monitoring at Terabit Scale
1.
Dietz, K. et al. I Choose You: Evaluating the Impact of Feature Selection on XAI Consensus for ML-NIDS. in 3rd Workshop on Machine Learning & Networking (MaLeNe) as part of NetSys’25 (2025).
- [ BibTeX ]
- [ EndNote ]
@inproceedings{info3-inproceedings-2025-5, address = {Ilmenau, Germany}, author = {Dietz, Katharina and Schleicher, Johannes and Wehner, Nikolas and Hajizadeh, Mehrdad and Casas, Pedro and Geißler, Stefan and Seufert, Michael and Hoßfeld, Tobias}, booktitle = {3rd Workshop on Machine Learning & Networking (MaLeNe) as part of NetSys'25}, keywords = {vipnano}, month = {09}, title = {I Choose You: Evaluating the Impact of Feature Selection on XAI Consensus for ML-NIDS}, year = 2025 }

%0 Conference Paper %1 info3-inproceedings-2025-5 %A Dietz, Katharina %A Schleicher, Johannes %A Wehner, Nikolas %A Hajizadeh, Mehrdad %A Casas, Pedro %A Geißler, Stefan %A Seufert, Michael %A Hoßfeld, Tobias %B 3rd Workshop on Machine Learning & Networking (MaLeNe) as part of NetSys'25 %C Ilmenau, Germany %D 2025 %T I Choose You: Evaluating the Impact of Feature Selection on XAI Consensus for ML-NIDS
1.
Wehner, N., Seufert, A., Hoßfeld, T. & Seufert, M. A Tutorial on Data-Driven Quality of Experience Modeling with Explainable Artificial Intelligence. IEEE Communications Surveys & Tutorials (2025).
- [ BibTeX ]
- [ EndNote ]
@article{info3-article-2025-2, author = {Wehner, Nikolas and Seufert, Anika and Hoßfeld, Tobias and Seufert, Michael}, journal = {IEEE Communications Surveys & Tutorials}, keywords = {sys:hidden:article}, month = {06}, title = {A Tutorial on Data-Driven Quality of Experience Modeling with Explainable Artificial Intelligence}, year = 2025 }

%0 Journal Article %1 info3-article-2025-2 %A Wehner, Nikolas %A Seufert, Anika %A Hoßfeld, Tobias %A Seufert, Michael %D 2025 %J IEEE Communications Surveys & Tutorials %T A Tutorial on Data-Driven Quality of Experience Modeling with Explainable Artificial Intelligence
1.
Wehner, N. et al. Exploring the Application of Time Series Foundation Models to Network Monitoring Tasks. Computer Networks, Special Issue on Generative and Explainable AI for Internet Traffic and Network Architectures (2025).
- [ BibTeX ]
- [ EndNote ]
@article{info3-article-2025-1, author = {Wehner, Nikolas and Casas, Pedro and Dietz, Katharina and Geißler, Stefan and Hoßfeld, Tobias and Seufert, Michael}, journal = {Computer Networks, Special Issue on Generative and Explainable AI for Internet Traffic and Network Architectures}, keywords = {sys:hidden:article}, month = {05}, title = {Exploring the Application of Time Series Foundation Models to Network Monitoring Tasks}, year = 2025 }

%0 Journal Article %1 info3-article-2025-1 %A Wehner, Nikolas %A Casas, Pedro %A Dietz, Katharina %A Geißler, Stefan %A Hoßfeld, Tobias %A Seufert, Michael %D 2025 %J Computer Networks, Special Issue on Generative and Explainable AI for Internet Traffic and Network Architectures %T Exploring the Application of Time Series Foundation Models to Network Monitoring Tasks
1.
Wehner, N. et al. To Cap or not to Cap: Bandwidth Capping Effects on Network Interactions and QoE of Competing Short Video Streams. in 16th ACM Multimedia Systems Conference (MMSys) (2025).
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Delivering popular short video streaming services like TikTok, Instagram Reels, or YouTube Shorts, poses substantial challenges for service providers and network operators. This is not only due to high download volumes but also due to high-volume pre-loading strategies that cause high bandwidth demand variations. These strategies, designed to reduce initial delays, can additionally lead to bandwidth excess when users swipe quickly through videos and consume only a fraction of downloaded content. This creates inefficiencies and unbalanced network resource utilization, particularly in competitive bandwidth environments. To address these challenges, we investigate the effectiveness of bandwidth capping in this paper, i.e., limiting the throughput of video flows in the network. We conduct measurement studies to analyze the impact of capping on network interactions and Quality of Experience (QoE) of three popular short video services in different scenarios. We find that capping substantially reduces download volume (15% - 45% median reduction) and bandwidth excess (18% - 52% mean reduction), while bandwidth utilization fairness improves. Meanwhile, QoE surprisingly remains nearly unaffected in most cases, with only minor statistical differences in a few scenarios.

@inproceedings{info3-inproceedings-2025-1, abstract = {Delivering popular short video streaming services like TikTok, Instagram Reels, or YouTube Shorts, poses substantial challenges for service providers and network operators. This is not only due to high download volumes but also due to high-volume pre-loading strategies that cause high bandwidth demand variations. These strategies, designed to reduce initial delays, can additionally lead to bandwidth excess when users swipe quickly through videos and consume only a fraction of downloaded content. This creates inefficiencies and unbalanced network resource utilization, particularly in competitive bandwidth environments. To address these challenges, we investigate the effectiveness of bandwidth capping in this paper, i.e., limiting the throughput of video flows in the network. We conduct measurement studies to analyze the impact of capping on network interactions and Quality of Experience (QoE) of three popular short video services in different scenarios. We find that capping substantially reduces download volume (15% - 45% median reduction) and bandwidth excess (18% - 52% mean reduction), while bandwidth utilization fairness improves. Meanwhile, QoE surprisingly remains nearly unaffected in most cases, with only minor statistical differences in a few scenarios.}, address = {Stellenbosch, South Africa}, author = {Wehner, Nikolas and Karagkioules, Theodoros and Halepovic, Emir and Simonovski, Filip and Hoßfeld, Tobias and Seufert, Michael}, booktitle = {16th ACM Multimedia Systems Conference (MMSys)}, keywords = {sys:hidden:inproceedings}, month = {04}, title = {To Cap or not to Cap: Bandwidth Capping Effects on Network Interactions and QoE of Competing Short Video Streams}, year = 2025 }

%0 Conference Paper %1 info3-inproceedings-2025-1 %A Wehner, Nikolas %A Karagkioules, Theodoros %A Halepovic, Emir %A Simonovski, Filip %A Hoßfeld, Tobias %A Seufert, Michael %B 16th ACM Multimedia Systems Conference (MMSys) %C Stellenbosch, South Africa %D 2025 %T To Cap or not to Cap: Bandwidth Capping Effects on Network Interactions and QoE of Competing Short Video Streams %X Delivering popular short video streaming services like TikTok, Instagram Reels, or YouTube Shorts, poses substantial challenges for service providers and network operators. This is not only due to high download volumes but also due to high-volume pre-loading strategies that cause high bandwidth demand variations. These strategies, designed to reduce initial delays, can additionally lead to bandwidth excess when users swipe quickly through videos and consume only a fraction of downloaded content. This creates inefficiencies and unbalanced network resource utilization, particularly in competitive bandwidth environments. To address these challenges, we investigate the effectiveness of bandwidth capping in this paper, i.e., limiting the throughput of video flows in the network. We conduct measurement studies to analyze the impact of capping on network interactions and Quality of Experience (QoE) of three popular short video services in different scenarios. We find that capping substantially reduces download volume (15% - 45% median reduction) and bandwidth excess (18% - 52% mean reduction), while bandwidth utilization fairness improves. Meanwhile, QoE surprisingly remains nearly unaffected in most cases, with only minor statistical differences in a few scenarios.

A comprehensive list of all publications can be found here: Publications

Nikolas Wehner M. Sc.

Research Interests

Recent Publications

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