Proactive and Intelligent Monitoring and Orchestration of Cloud-Native IP Multimedia Subsystem

As the cloud moves from monolithic infrastructure to a self-isolated cloud native microservice environment, automation is becoming an important aspect for the management of the application life cycle. In this context, there are many tools available that can monitor these applications and raise alarms. However, automated orchestration is still in its early stages, and the available solutions are not capable of monitoring the whole system from application to hardware levels and performing automated operations within the system. Moreover, IP Multimedia Subsystem (IMS), which is the core part of the Telecom industry, has switched to a microservice environment. These IMS services are critical and need to be proactively monitored to provide automated orchestration operations. In this paper, we address the aforementioned problem by proposing a new scheme for monitoring the metrics from different sources and proactively and automatically performing orchestration using machine learning while improving the scalability of the cloud native Virtual IMS. Experiments carried out with a real cloud-native IMS running in a kubernetes cluster explore the relevance, efficiency and scalability of the proposed scheme.