Ensemble-based modeling abstractions for modern self-optimizing systems
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.
Cite
@article{arxiv.2309.05823,
title = {Ensemble-based modeling abstractions for modern self-optimizing systems},
author = {Michal Töpfer and Milad Abdullah and Tomáš Bureš and Petr Hnětynka and Martin Kruliš},
journal= {arXiv preprint arXiv:2309.05823},
year = {2023}
}
Comments
This is the authors' version of the paper - M. T\"opfer, M. Abdullah, T. Bure\v{s}, P. Hn\v{e}tynka, M. Kruli\v{s}: 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