| Assessing effect sizes of influence factors towards a QoE model for HTTP adaptive streaming T Hoßfeld, M Seufert, C Sieber, T Zinner Quality of Multimedia Experience (QoMEX), 2014 Sixth International Workshop …, 2014 | 173 | 2014 |
| Implementation and User-centric Comparison of a Novel Adaptation Logic for DASH with SVC C Sieber, T Hoßfeld, T Zinner, P Tran-Gia, C Timmerer 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM …, 2013 | 119 | 2013 |
| Identifying QoE optimal adaptation of HTTP adaptive streaming based on subjective studies T Hoßfeld, M Seufert, C Sieber, T Zinner, P Tran-Gia Computer Networks 81, 320-332, 2015 | 92 | 2015 |
| Impact of intermediate layer on quality of experience of HTTP adaptive streaming M Seufert, T Hoßfeld, C Sieber 2015 11th International Conference on Network and Service Management (CNSM …, 2015 | 38 | 2015 |
| Sacrificing efficiency for quality of experience: YouTube's redundant traffic behavior C Sieber, P Heegaard, T Hoßfeld, W Kellerer IFIP Networking Conference (IFIP Networking) and Workshops, 2016, 503-511, 2016 | 28 | 2016 |
| The cost of aggressive HTTP adaptive streaming: Quantifying YouTube's redundant traffic C Sieber, A Blenk, M Hinteregger, W Kellerer 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM …, 2015 | 28 | 2015 |
| Network configuration with quality of service abstractions for SDN and legacy networks C Sieber, A Blenk, D Hock, M Scheib, T Höhn, S Köhler, W Kellerer 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM …, 2015 | 24 | 2015 |
| Towards Optimal Adaptation of NFV Packet Processing to Modern CPU Memory Architectures C Sieber, R Durner, M Ehm, W Kellerer, P Sharma Proceedings of the 2nd Workshop on Cloud-Assisted Networking, 7-12, 2017 | 23 | 2017 |
| Towards a programmable management plane for SDN and legacy networks C Sieber, A Blenk, A Basta, D Hock, W Kellerer 2016 IEEE NetSoft Conference and Workshops (NetSoft), 319-327, 2016 | 18 | 2016 |
| Online resource mapping for SDN network hypervisors using machine learning C Sieber, A Basta, A Blenk, W Kellerer NetSoft Conference and Workshops (NetSoft), 2016 IEEE, 78-82, 2016 | 17 | 2016 |
| Evaluation of the Benefits of Variable Segment Durations for Adaptive Streaming S Schwarzmann, T Zinner, S Geissler, C Sieber 2018 Tenth International Conference on Quality of Multimedia Experience …, 2018 | 16 | 2018 |
| Scalable Application-and User-aware Resource Allocation in Enterprise Networks Using End-host Pacing C Sieber, S Schwarzmann, A Blenk, T Zinner, W Kellerer ACM Transactions on Modeling and Performance Evaluation of Computing Systems …, 2020 | 14 | 2020 |
| Online learning and adaptation of network hypervisor performance models C Sieber, A Obermair, W Kellerer Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium on …, 2017 | 13 | 2017 |
| Keep Calm and Don’t Switch: About the Relationship Between Switches and Quality in HAS C Moldovan, K Hagn, C Sieber, W Kellerer, T Hoßfeld Teletraffic Congress (ITC 29), 2017 29th International 3, 1-6, 2017 | 11 | 2017 |
| How fast can you reconfigure your partially deployed SDN network? C Sieber, R Durner, W Kellerer IFIP Networking Conference (IFIP Networking) and Workshops, 2017, 1-9, 2017 | 10 | 2017 |
| Youtube can do better: Getting the most out of video adaptation C Moldovan, C Sieber, P Heegaard, W Kellerer, T Hoßfeld 2016 28th International Teletraffic Congress (ITC 28) 3, 7-12, 2016 | 10 | 2016 |
| hvbench: An open and scalable SDN network hypervisor benchmark C Sieber, A Blenk, A Basta, W Kellerer 2016 IEEE NetSoft Conference and Workshops (NetSoft), 403-406, 2016 | 10 | 2016 |
| Towards Reducing Last-Level-Cache Interference of Co-located Virtual Network Functions R Durner, C Sieber, W Kellerer 2019 28th International Conference on Computer Communication and Networks …, 2019 | 9 | 2019 |
| Close to Optimum? T Hoßfeld, M Seufert, C Sieber, T Zinner, P Tran-Gia PIK-Praxis der Informationsverarbeitung und Kommunikation 37 (4), 275-285, 2014 | 7 | 2014 |
| Towards Machine Learning-Based Optimal HAS C Sieber, K Hagn, C Moldovan, T Hoßfeld, W Kellerer arXiv preprint arXiv:1808.08065, 2018 | 6 | 2018 |
Tobias HossfeldUniversity of WürzburgVerified email at informatik.uni-wuerzburg.de
