ML-DEECo: a Machine-Learning-Enabled Framework for Self-organizing Components

Töpfer, Michal; Abdullah, Milad; Kruliš, Martin; Bureš, Tomáš; Hnětynka, Petr

doi:10.1109/acsosc56246.2022.00033

Cited by 2 publications

(2 citation statements)

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“…To experiment with online supervised learning in selfadaptive systems, we developed a machine-learning-enabled component model called ML-DEECo [6] that allows modeling systems with ML-based predictions of the future. Alongside the component model, we provide its implementation as a Python framework [7] to facilitate prototyping and experimenting with the systems.…”

Section: Introductionmentioning

confidence: 99%

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Online ML Self-adaptation in Face of Traps

Töpfer,

Plášil,

Bureš

et al. 2023

2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

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Online machine learning (ML) is often used in selfadaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report its limitations. Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties -traps -that, to our knowledge, are not discussed enough in the community. In this paper, we report our experience with these traps. Specifically, we discuss several traps that relate to the specification and online training of the ML-based estimators, their impact on selfadaptation, and the approach used to evaluate the estimators. Our overview of these traps provides a list of lessons learned, which can serve as guidance for other researchers and practitioners when applying online ML for self-adaptation.

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Section: Introductionmentioning

confidence: 99%

“…Technically, for the experiments presented in this paper, we used our Python implementation of ML-DEECo[7].…”

mentioning

confidence: 99%