Autonomic Closed-Loop Service Management based on Interworking Multiple AI Models

Constant monitoring and agile management of cloud-native network functions (CNFs) are the essential to prevent serious degradation of quality of services (QoS) in network service infrastructure. To monitor and agilely manage services against dynamically changing situations in complicated cloud-native (CN) network infrastructure, automation of closed-loop service management should be discussed. In this paper, we discuss a unified interface of interworking multiple functions including different AI models to establish autonomic closed-loop service management that adapts not only to the past failure events, but also to the future ones according to the failure prediction. In addition, we developed the prototype of automatic closed-loop service management for CN infrastructure that automatically constructs network services and maintains QoS of the existing services based on the interworking three AI models dedicated for self-monitoring, self-configuration, and self-optimization.