Ensemble-based modeling abstractions for modern self-optimizing systems
This work addresses modeling challenges for smart systems in Industry 4.0, but it appears incremental as it builds on an existing component model.
The paper tackles the problem of modeling self-optimizing systems by extending the DEECo ensemble-based component model to incorporate machine-learning and optimization heuristics for autonomic ensemble reconfiguration, demonstrating its application in access-control for Industry 4.0 settings.
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.