Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
This work addresses the integration of multiple adaptation approaches for engineers and researchers in adaptive systems, but it is incremental as it focuses on analysis and questions rather than new solutions.
The paper investigates the relationship and potential synergy between MAPE loops, control theory, and machine learning for building adaptive systems, using a cloud-based enterprise scenario to illustrate the analysis and proposing open research questions.
Two established approaches 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 rapidly 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 approaches 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 approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.