Ensemble-based modeling abstractions for modern self-optimizing systems
Michal Töpfer Milad Abdullah Tomáš Bureš Petr Hnětynka Martin Kruliš
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.