Enhancement of Cloud-native applications with Autonomic Features

Abstract

The Autonomic Computing paradigm reduces complexity in installing, configuring, optimizing, and maintaining heterogeneous systems. Despite first discussing it a long ago, it is still a top research challenge, especially in the context of other technologies. It is necessary to provide autonomic features to the Cloud-native execution environment to meet the rapidly changing demands without human support and continuous improvement of their capabilities. The present work attempts to answer how to explore autonomic features in Cloud-native environments. As a solution, we propose using the AMoCNA framework. It is rooted in Autonomic Computing. The success factors for the AMoCNA implementation are its execution controllers. They drive the management actions proceeding in a Cloud-native execution environment. A similar concept already exists in Kubernetes, so we compare both execution mechanisms. This research presents guidelines for including autonomic features in Cloud-native environments. The integration of Cloud-native Applications with AMoCNA leads to facilitating autonomic management. To show the potential of our concept, we evaluated it. The developed executor performs cluster autoscaling and ensures autonomic management in the infrastructure layer. The experiment also proved the importance of observations. The knowledge gained in this process is a good authority of information about past and current state of Cloud-native Applications. Combining this knowledge with defined executors provides an effective means of achieving the autonomic nature of Cloud-native applications.