Datasets
(Last update: May 29, 2026)
- Efficient Gradient-based Network Calculus for Scalable Synthesis of Network Configurations, Proceedings of the 31st IEEE International Conference on Embedded and Real-Time Computing Systems and Application (RTCSA). Fabien Geyer, Steffen Bondorf [dataset] [doi:10.1109/RTCSA66114.2025.00029]
- Evaluation of Graph Neural Networks in Airborne Networks, Proceedings of the 31st IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN). Ali Yilmaz Yildirim, Fabien Geyer, Georg Carle [dataset] [doi:10.1109/LANMAN66415.2025.11154539]
- Differentiable Programming & Network Calculus: Configuration Synthesis under Delay Constraints, . Fabien Geyer, Steffen Bondorf [link] [dataset]
- Network calculus with flow prolongation - a feedforward FIFO analysis enabled by ML, IEEE Transactions on Computers. Fabien Geyer, Alexander Scheffler, Steffen Bondorf [arxiv] [dataset] [doi:10.1109/TC.2022.3204225]
- Analyzing real-time video delivery over cellular networks for remote piloting aerial vehicles, Proceedings of the ACM Internet Measurement Conference (IMC 2022). Aygün Baltaci, Hendrik Cech, Nitinder Mohan, Fabien Geyer, Vaibhav Bajpai, Jörg Ott, Dominic Schupke [code] [dataset] [doi:10.1145/3517745.3561465]
- Network Synthesis under Delay Constraints: The Power of Network Calculus Differentiability, Proceedings of the 41st IEEE International Conference on Computer Communications (INFOCOM 2022). Fabien Geyer, Steffen Bondorf [link] [dataset] [doi:10.1109/INFOCOM48880.2022.9796777]
- Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the FIFO Analysis, Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2021). Fabien Geyer, Alexander Scheffler, Steffen Bondorf [link] [dataset] [doi:10.1109/RTAS52030.2021.00021]
- Graph-based Deep Learning for Fast and Tight Network Calculus Analyses, IEEE Transactions on Network Science and Engineering. Fabien Geyer, Steffen Bondorf [link] [dataset] [doi:10.1109/TNSE.2020.3025806]
- On the Robustness of Deep Learning-predicted Contention Models for Network Calculus, Proceedings of the 25th IEEE Symposium on Computers and Communications (ISCC 2020). Fabien Geyer, Steffen Bondorf [link] [arxiv] [dataset] [doi:10.1109/ISCC50000.2020.9219693]
- On the Learnability of Software Router Performance via CPU Measurements, Proceedings of the 2019 CoNEXT Student Workshop. Charles Shelbourne, Leonardo Linguaglossa, Aldo Lipani, Tianzhu Zhang, Fabien Geyer [link] [dataset] [doi:10.1145/3360468.3366776]
- DeepMPLS: Fast Analysis of MPLS Configurations Using Deep Learning, IFIP Networking 2019. Fabien Geyer, Stefan Schmid [link] [code] [dataset] [doi:10.23919/IFIPNetworking.2019.8816842]
- DTLS Performance - How Expensive is Security?, . Sebastian Gallenmüller, Dominik Schöffmann, Dominik Scholz, Fabien Geyer, Georg Carle [link] [code] [dataset]
- DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks, Proceedings of the 38th IEEE International Conference on Computer Communications (INFOCOM 2019). Fabien Geyer, Steffen Bondorf [link] [dataset] [doi:10.1109/INFOCOM.2019.8737496]
- The Case for a Network Calculus Heuristic: Using Insights from Data for Tighter Bounds, Proceedings of the 2018 International Workshop on Network Calculus and Applications (NetCal 2018). Fabien Geyer, Georg Carle [link] [dataset] [doi:10.1109/ITC30.2018.10060]