| Towards a deeper understanding of TCP BBR congestion control D Scholz, B Jaeger, L Schwaighofer, D Raumer, F Geyer, G Carle 2018 IFIP networking conference (IFIP networking) and workshops, 1-9, 2018 | 137 | 2018 |
| Learning and generating distributed routing protocols using graph-based deep learning F Geyer, G Carle Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning …, 2018 | 95 | 2018 |
| TSimNet: An industrial time sensitive networking simulation framework based on OMNeT++ P Heise, F Geyer, R Obermaisser 2016 8th IFIP International Conference on New Technologies, Mobility and …, 2016 | 59 | 2016 |
| Graph neural networks for communication networks: Context, use cases and opportunities J Suárez-Varela, P Almasan, M Ferriol-Galmés, K Rusek, F Geyer, ... IEEE network 37 (3), 146-153, 2022 | 51 | 2022 |
| Cryptographic hashing in p4 data planes D Scholz, A Oeldemann, F Geyer, S Gallenmüller, H Stubbe, T Wild, ... 2019 ACM/IEEE Symposium on Architectures for Networking and Communications …, 2019 | 46 | 2019 |
| Reproducible measurements of TCP BBR congestion control B Jaeger, D Scholz, D Raumer, F Geyer, G Carle Computer Communications 144, 31-43, 2019 | 45 | 2019 |
| DeepTMA: Predicting effective contention models for network calculus using graph neural networks F Geyer, S Bondorf IEEE INFOCOM 2019-IEEE Conference on Computer Communications, 1009-1017, 2019 | 44 | 2019 |
| A performance study of Audio Video Bridging in aeronautic Ethernet networks E Heidinger, F Geyer, S Schneele, M Paulitsch 7th IEEE International Symposium on Industrial Embedded Systems (SIES'12), 67-75, 2012 | 38 | 2012 |
| Deterministic OpenFlow: Performance evaluation of SDN hardware for avionic networks P Heise, F Geyer, R Obermaisser 2015 11th International Conference on Network and Service Management (CNSM …, 2015 | 37 | 2015 |
| Network engineering for real-time networks: comparison of automotive and aeronautic industries approaches F Geyer, G Carle IEEE Communications Magazine 54 (2), 106-112, 2016 | 32 | 2016 |
| Comparison of ieee avb and afdx S Schneele, F Geyer 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC), 7A1-1-7A1-9, 2012 | 30 | 2012 |
| DeepComNet: Performance evaluation of network topologies using graph-based deep learning F Geyer Performance Evaluation 130, 1-16, 2019 | 29 | 2019 |
| Performance perspective on private distributed ledger technologies for industrial networks F Geyer, H Kinkelin, H Leppelsack, S Liebald, D Scholz, G Carle, ... 2019 International Conference on Networked Systems (NetSys), 1-8, 2019 | 24 | 2019 |
| Performance evaluation of network topologies using graph-based deep learning F Geyer Proceedings of the 11th EAI International Conference on Performance …, 2017 | 24 | 2017 |
| Experimental UAV data traffic modeling and network performance analysis A Baltaci, M Klügel, F Geyer, S Duhovnikov, V Bajpai, J Ott, D Schupke IEEE INFOCOM 2021-IEEE Conference on Computer Communications, 1-10, 2021 | 22 | 2021 |
| Evaluation of audio/video bridging forwarding method in an avionics switched ethernet context F Geyer, E Heidinger, S Schneele, A von Bodisco 2013 IEEE Symposium on Computers and Communications (ISCC), 000711-000716, 2013 | 16 | 2013 |
| Generalizing Network Calculus Analysis to Derive Performance Guarantees for Multicast Flows. S Bondorf, F Geyer VALUETOOLS, 2016 | 15 | 2016 |
| Graph-based deep learning for fast and tight network calculus analyses F Geyer, S Bondorf IEEE Transactions on Network Science and Engineering 8 (1), 75-88, 2020 | 13 | 2020 |
| Tightening network calculus delay bounds by predicting flow prolongations in the FIFO analysis F Geyer, A Scheffler, S Bondorf 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium …, 2021 | 11 | 2021 |
| On the robustness of deep learning-predicted contention models for network calculus F Geyer, S Bondorf 2020 IEEE Symposium on Computers and Communications (ISCC), 1-7, 2020 | 11 | 2020 |
Georg CarleTechnische Universität München在 net.in.tum.de 的电子邮件经过验证
