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Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning

Danny Weyns, Bradley Schmerl, Masako Kishida, Alberto Leva, Marin Litoiu, Necmiye Ozay, Colin Paterson and Kenji Tei.


2020. Submitted for publication.

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Abstract
Two established techniques to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapid growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper we are concerned with the question of how these techniques are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation techniques using scenarios from two different domains and illustrate the analysis involved in combining different adaptation techniques. The paper concludes with suggestions for further research in this interesting area.

Keywords: Control Theory, Machine Learning, Self-adaptation.  
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