A Quality-driven Machine Learning Governance Architecture for Self-adaptive Edge Clouds

Abstract

Self-adaptive systems such as clouds and edge clouds are more and more using Machine Learning (ML) techniques if sufficient data is available to create respective ML models. Self-adaptive systems are built around a controller that, based on monitored system data as input, generate actions to maintain the system in question within expected quality ranges. Machine learning (ML) can help to create controllers for self-adaptive systems such as edge clouds. However, because ML-created controllers are created without a direct full control by expert software developers, quality needs to be specifically looked at, requiring a better understanding of the ML models. Here, we explore a quality-oriented management and governance architecture for self-adaptive edge controllers. The concrete objective here is the validation of a reference governance architecture for edge cloud systems that facilitates ML controller quality management in a feedback loop.