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Numrering	Referens	Omslagsbild	Hitta
1.	Topfer, Michal, et al. (författare) Online ML Self-adaptation in Face of Traps 2023 Ingår i: Proceedings - 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2023. - : IEEE. - 9798350337440 ; , s. 57-66 Konferensbidrag (refereegranskat)abstract 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.

Topfer, Michal, et al. (författare)
Online ML Self-adaptation in Face of Traps
2023
Ingår i: Proceedings - 2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2023. - : IEEE. - 9798350337440 ; , s. 57-66
Konferensbidrag (refereegranskat)abstract
- 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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