Ensemble-based modeling abstractions for modern self-optimizing systems
arXiv:2309.05823 · doi:10.1007/978-3-031-19759-8_20
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.
This is the authors' version of the paper - M. Töpfer, M. Abdullah, T. Bureš, P. Hnětynka, M. Kruliš: Ensemble-Based Modeling Abstractions for Modern Self-optimizing Systems, in Proceedings of ISOLA 2022, Rhodes, Greece, pp. 318-334, 2022. The final authenticated publication is available online at https://doi.org/10.1007/978-3-031-19759-8_20