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

TL;DR

This paper explores integrating MAPE, control theory, and machine learning to enhance adaptive systems, analyzing their relationships and potential benefits through a cloud-based scenario.

Contribution

It investigates how combining MAPE, control theory, and ML can improve adaptive system design and discusses open research questions.

Findings

Combining approaches can lead to more robust adaptation.

Analysis shows potential benefits of integration.

Open questions for future research are identified.

Abstract

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.