Ensemble-based modeling abstractions for modern self-optimizing systems

• URL: http://arxiv.org/abs/2309.05823v1
• Date: Mon, 11 Sep 2023 21:01:11 GMT
• Title: Ensemble-based modeling abstractions for modern self-optimizing systems
• Authors: Michal T\"opfer, Milad Abdullah, Tom\'a\v{s} Bure\v{s}, Petr Hn\v{e}tynka, Martin Kruli\v{s}
• Abstract summary: We extend our ensemble-based component model DEECo with the capability to use machine-learning and Industry optimizations in establishing and reconfiguration of component ensembles. We argue that machine-learning and optimizations 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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