Dr. Fabien Geyer

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My current personal homepage with latest updates is here: fabgeyer.github.io.


Selected publications

Last update: Mai 2022

  • Network Synthesis under Delay Constraints: The Power of Network Calculus Differentiability
    Fabien Geyer, Steffen Bondorf
    In Proceedings of the 41th IEEE International Conference on Computer Communications (INFOCOM 2022), May 2022
    [pdf] [dataset].
  • Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the FIFO Analysis
    Fabien Geyer, Alexander Scheffler, Steffen Bondorf
    In Proceedings of the 27th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2021), May 2021
    doi:10.1109/RTAS52030.2021.00021
    [pdf] [dataset].
  • Experimental UAV Data Traffic Modeling and Network Performance Analysis
    Aygün Baltaci, Markus Klügel, Fabien Geyer, Svetoslav Duhovnikov, Vaibhav Bajpai, Jörg Ott, Dominic Schupke
    In Proceedings of the 40th IEEE International Conference on Computer Communications (INFOCOM 2021), May 2021
    [pdf] [code].
  • Graph-based Deep Learning for Fast and Tight Network Calculus Analyses
    Fabien Geyer, Steffen Bondorf
    In IEEE Transactions on Network Science and Engineering, January 2021
    doi:10.1109/TNSE.2020.3025806
    [pdf] [dataset].
  • Virtual Cross-Flow Detouring in the Deterministic Network Calculus Analysis
    Steffen Bondorf, Fabien Geyer
    In IFIP Networking 2020, June 2020
    ISBN: 978-3-903176-28-7
    [pdf].
  • Cryptographic Hashing in P4 Data Planes
    Dominik Scholz, Andreas Oeldemann, Fabien Geyer, Sebastian Gallenmüller, Henning Stubbe, Thomas Wild, Andreas Herkersdorf, Georg Carle
    In Proceedings of the 2nd P4 Workshop In Europe (EuroP4 2019), September 2019
    doi:10.1109/ANCS.2019.8901886
    [pdf] [slides].
  • Reproducible Measurements of TCP BBR Congestion Control
    Benedikt Jaeger, Dominik Scholz, Daniel Raumer, Fabien Geyer, Georg Carle
    In Computer Communications, Elsevier BV, May 2019
    doi:10.1016/j.comcom.2019.05.011
    [pdf] [code].
  • DeepTMA: Predicting Effective Contention Models for Network Calculus using Graph Neural Networks
    Fabien Geyer, Steffen Bondorf
    In Proceedings of the 38th IEEE International Conference on Computer Communications (INFOCOM 2019), April 2019
    doi:10.1109/INFOCOM.2019.8737496
    [pdf] [dataset].
  • DeepComNet: Performance Evaluation of Network Topologies using Graph-Based Deep Learning
    Fabien Geyer
    In Performance Evaluation, April 2019
    doi:10.1016/j.peva.2018.12.003
    [pdf].
  • Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning
    Fabien Geyer, Georg Carle
    In Proceedings of the 2018 SIGCOMM Workshop on Big Data Analytics and Machine Learning For Data Communication Networks (Big-dama 2018), August 2018
    doi:10.1145/3229607.3229610
    [pdf].
  • Towards a Deeper Understanding of TCP BBR Congestion Control
    Dominik Scholz, Benedikt Jaeger, Lukas Schwaighofer, Daniel Raumer, Fabien Geyer, Georg Carle
    In IFIP Networking 2018, May 2018
    doi:10.23919/IFIPNetworking.2018.8696830
    [pdf] [code].
  • Performance Evaluation of Network Topologies using Graph-Based Deep Learning
    Fabien Geyer
    In Proceedings of the 11th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS 2017), December 2017
    doi:10.1145/3150928.3150941
    [pdf].
  • End-to-End Flow-Level Quality-of-Service Guarantees for Switched Networks
    Fabien Geyer
    PhD thesis, Technische Universität München, July 2015
    ISBN: 978-3-937201-49-8
    [pdf].