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Machine learning has been recently applied in real-time systems to predict whether Ethernet network configurations are feasible in terms of meeting deadline constraints without executing conventional schedulability analysis. However, the existing prediction techniques require domain expertise to choose the relevant input features and do not perform consistently when topologies or traffic patterns differ significantly from the ones in the training data. To overcome these problems, we propose a Graph Neural Network (GNN) prediction model that synthesizes relevant features directly from the raw data. This deep learning model possesses the ability to exploit relations among flows, links, and queues in switched Ethernet networks and generalizes to unseen topologies and traffic patterns. We also explore the use of ensembles of GNNs and show that it enhances the robustness of the predictions. An evaluation on heterogeneous testing sets comprising realistic automotive networks shows that ensembles of 32 GNN models feature a prediction accuracy ranging from 79.3% to 90% for Ethernet networks using priorities as the Quality-of-Service mechanism. The use of ensemble models provides a speedup factor ranging from 77 to 1,715 compared to schedulability analysis, which allows a far more extensive design space exploration.

Graph neural network (GNN) is an advanced machine learning model, which has been recently applied to encode Ethernet configurations as graphs and predict their feasibility in terms of meeting deadlines constraints. Ensembles of GNN models have proven to be robust to changes in the topology and traffic patterns with respect to the training set. However, the moderate prediction accuracy of the model, 79.3% at the lowest, hinders the application of GNN to real-world problems.This study proposes improvements to the base GNN model in the construction of the training set and the structure of the model itself. We first introduce new training sets that are more diverse in terms of topologies and traffic patterns and focus on configurations that are difficult to predict. We then enhance the GNN model with more powerful activation functions, multiple channels and implement a technique called global pooling. The prediction accuracy of ensemble of GNNs with a combination of the suggested improvements increases significantly, up to 11.9% on the same 13 testing sets. Importantly, these improvements increase only marginally the time it takes to predict unseen configurations, i.e., the speedup factor is still from 50 to 1125 compared to schedulability analysis, which allows a far more extensive exploration of the design space.