Toward Dynamic Enterprise Architecture Model using MAPE-K, Control theory and Machine Learning to achieve autonomic adaptiveness

Nowadays, it is no longer a secret that organizations must be able to accommodate changes to survive the turbulent and stormy changes we are going through. Enterprise architecture is a tool used to guarantee the alignment of strategy, business, and IT. In this study, we focus on giving the organization, through a dynamic model of Enterprise architecture, an adaptive tool to respond to external and internal constraints keeping the alignment and stability of the enterprise during the response to change. With the prospect of allowing autonomic adaptiveness of our EA model, we propose applying an integrated approach based on the well-known loop MAPE-K in the domain of autonomic computing combined with the Control Theory (CT). We use Machine Learning techniques to give the self-learning ability to our model. This defined pattern, inspired from Dynamico and enriched by ML techniques was applied to our theme of study, Enterprise architecture.