Reference : A Hybrid Machine Learning and Schedulability Method for the Verification of TSN Networks
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/38990
A Hybrid Machine Learning and Schedulability Method for the Verification of TSN Networks
English
Mai, Tieu Long mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Navet, Nicolas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Migge, Jörn [RealTime-at-Work (RTaW)]
Mar-2019
15th IEEE International Workshop on Factory Communication Systems (WFCS2019)
IEEE
Yes
International
15th IEEE International Workshop on Factory Communication Systems (WFCS2019)
from 27-05-2019 to 29-05-2019
Sundsvall
Sweden
[en] timing verification ; machine learning ; Time-Sensitive Networking (TSN) ; schedulability analysis ; real-time systems ; design-space exploration ; in-vehicle networks ; industrial communication systems
[en] Machine learning (ML), and supervised learning in particular, can be used to learn what makes it hard for a network to be feasible and try to predict whether a network configuration will be feasible without executing a conventional schedulability analysis. A disadvantage of ML-based timing verification with respect to schedulability analysis is the possibility of "false positives": configurations deemed feasible while they are not. In this work, in order to minimize the rate of false positives, we propose the use of a measure of the uncertainty of the prediction to drop it when the uncertainty is too high, and rely instead on schedulability analysis. In this hybrid verification strategy, the clear-cut decisions are taken by ML, while the more difficult ones are taken by a conventional schedulability analysis. Importantly, the trade-off achieved between prediction accuracy and computing time can be controlled. We apply this hybrid verification method to Ethernet TSN networks and obtain, for instance in the case of priority scheduling with 8 traffic classes, a 99% prediction accuracy with a speedup factor of 5.7 with respect to conventional schedulability analysis and a reduction of 46% of the false positives compared to ML alone.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/38990
10.1109/WFCS.2019.8757948
https://ieeexplore.ieee.org/document/8757948

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