Ensemble-Based Modeling Abstractions for Modern Self-optimizing Systems

Title:

Ensemble-Based Modeling Abstractions for Modern Self-optimizing Systems

Authors:

M. Töpfer, M. Abdullah, T. Bureš, P. Hnětynka, M. Kruliš

Publication:

Proceedings of ISOLA 2022, Rhodes, Greece

DOI:

Year:

2022

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.

BibTeX:

@inproceedings{topfer_ensemblebased_2022,
    title = {{Ensemble-Based Modeling Abstractions for Modern Self-optimizing Systems}},
    author = {Töpfer, Michal and Abdullah, Milad and Bureš, Tomas and Hnětynka, Petr and Kruliš , Martin},
    year = {2022},
    booktitle = {{Proceedings of ISOLA 2022, Rhodes, Greece}},
    publisher = {Springer},
    series = {{LNCS}},
    doi = {10.1007/978-3-031-19759-8_20},
    pages = {318--334},
    volume = {13703},
}