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arXiv · 2309.05823

Ensemble-based modeling abstractions for modern self-optimizing systems

Michal Töpfer·Milad Abdullah·Tomáš Bureš·Petr Hnětynka·Martin Kruliš
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Abstract

In this paper, we extend our ensemble-based component model DEECo with the capability to use machine-learning and optimization heuristics in establishing and reconfiguration of autonomic component ensembles. We show how to capture these concepts on the model level and give an example of how such a model can be beneficially used for modeling access-control related problem in the Industry 4.0 settings. We argue that incorporating machine-learning and optimization heuristics is a key feature for modern smart systems which are to learn over the time and optimize their behavior at runtime to deal with uncertainty in their environment.

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Michal Töpfer, Milad Abdullah, Tomáš Bureš, Petr Hnětynka, Martin Kruliš. 2023-09-11. Ensemble-based modeling abstractions for modern self-optimizing systems. https://doi.org/10.1007/978-3-031-19759-8_20

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