Ensemble-based modeling abstractions for modern self-optimizing systems

TL;DR

This paper enhances the DEECo ensemble-based model with machine learning and optimization heuristics, enabling self-optimizing, adaptive systems for Industry 4.0 applications that learn and reconfigure at runtime.

Contribution

It introduces a model-level extension to DEECo for integrating learning and optimization, facilitating the development of adaptive, self-optimizing systems.

Findings

Modeling access-control in Industry 4.0 using the extended DEECo.

Demonstrates benefits of incorporating ML and heuristics for system adaptation.

Supports runtime learning and reconfiguration for uncertainty management.

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.