تقرير
Ensemble-based modeling abstractions for modern self-optimizing systems
| العنوان: | Ensemble-based modeling abstractions for modern self-optimizing systems |
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| المؤلفون: | Töpfer, Michal, Abdullah, Milad, Bureš, Tomáš, Hnětynka, Petr, Kruliš, Martin |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control |
| الوصف: | 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. Comment: 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 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-031-19759-8_20 |
| URL الوصول: | http://arxiv.org/abs/2309.05823 |
| رقم الأكسشن: | edsarx.2309.05823 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-031-19759-8_20 |
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