Effective Decision Making in Self-adaptive Systems Using Cost-Benefit Analysis at Runtime and Online Learning of Adaptation Spaces

Donckt, Jeroen Van Der; Weyns, Danny; Iftikhar, M. Usman; Buttar, Sarpreet Singh

doi:10.1007/978-3-030-22559-9_17

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…E.g., in [28] neural networks are employed for such a reduction. A similar approach [27] (by a similar group of authors) combines machine-learning techniques with a cost-based analysis to reduce the space and choose an adaptation with the best cost. In [17], a theorem defining a theoretical bound on the impact of applying a machinelearning method during adaptation was defined, and an approach for reducing an adaptation space was proposed.…”

Section: Related Workmentioning

confidence: 99%

Ensemble-Based Modeling Abstractions for Modern Self-optimizing Systems

Töpfer¹,

Abdullah²,

Bureš³

et al. 2022

Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning

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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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Section: Related Workmentioning

confidence: 99%