Reference : Using Machine Learning to Speed Up the Design Space Exploration of Ethernet TSN networks
Reports : External report
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/38604
Using Machine Learning to Speed Up the Design Space Exploration of Ethernet TSN networks
English
Navet, Nicolas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Mai, Tieu Long mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Migge, Jörn [RealTime-at-Work (RTaW)]
29-Jan-2019
University of Luxembourg
[en] timing verification ; machine learning ; Time-Sensitive Networking (TSN) ; supervised learning ; unsupervised learning ; schedulability analysis ; real-time systems ; design-space exploration
[en] In this work, we ask if Machine Learning (ML) can provide a viable alternative to conventional schedulability analysis to determine whether a real-time Ethernet network meets a set of timing constraints. Otherwise said, can an algorithm learn what makes it difficult for a system to be feasible and predict whether a configuration will be feasible without executing a schedulability analysis? In this study, we apply standard supervised and unsupervised ML techniques and compare them, in terms of their accuracy and running times, with precise and approximate schedulability analyses in Network-Calculus. We show that ML techniques are efficient at predicting the feasibility of realistic TSN networks and offer new trade-offs between accuracy and computation time especially interesting for design-space exploration algorithms.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/38604

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