TSN Scheduler Benchmarking: Scenarios v2.0.0

TSN Scheduler Benchmarking Scenarios This dataset provides scenarios for testing Time-Sensitive Networking (TSN) schedulers, originally developed as part of the following PhD thesis (to be published): E. Schweissguth, "Routing and Scheduling of Time-Triggered Ethernet Networks: ILP Models and S...

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Bibliographische Detailangaben
1. Verfasser: Schweissguth, Eike
Format: Dataset
Sprache:eng
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Zusammenfassung:TSN Scheduler Benchmarking Scenarios This dataset provides scenarios for testing Time-Sensitive Networking (TSN) schedulers, originally developed as part of the following PhD thesis (to be published): E. Schweissguth, "Routing and Scheduling of Time-Triggered Ethernet Networks: ILP Models and Scheduler Benchmarking.". University of Rostock. An earlier version (v1.0.0) of benchmarking scenarios was published alongside the following publication: E. Schweissguth, S. Mehner, D. Hellmanns, J. Falk, H. Parzyjegla, P. Danielis, O. Hohlfeld, G. Muehl, and D. Timmermann, "TSN Scheduler Benchmarking." in WFCS 2023. IEEE. Scenarios of this earlier version are considered deprecated, but may still be useful for testing schedulers which do not support heterogenous cycle times. They are still available in the history (see releases). Compared to v1.0.0, this version contains scenarios with heterogeneous cycle times and frame sizes, reworked scenario parameters, and additional multicast scenarios.  Please refer to the thesis for detailed background information. Refer to "TSN Scheduler Benchmarking: Results" (https://github.com/EikeSG/TSNBenchResults or linked projects on Zenodo) for a comprehensive comparison of ILP-based scheduling models based on this scenario dataset. When accessing this dataset through Zenodo, also check the corresponding GitHub repository (https://github.com/EikeSG/TSNBenchScenarios) for updates and for a good in-browser view (especially for markdown files). The scenarios are input files for scheduler benchmarking, intended for re-use by other researchers. For further details about the dataset as well as licensing and citation please see README.md.
DOI:10.5281/zenodo.7738089