Ontology Driven Closed Control Loop Automation

Abstract

Autonomic network management approaches have not been widely adopted, mainly due to significant unsolved challenges. Challenges include technical complexity, lack of consistent models and knowledge bases describing the system, and the difficulty of evolving management methods and processes. Autonomic approaches often operate a closed control loop. Such loops enable dynamicity and are often intent driven, where system goals and requirements are declared, then automatically accomplished and maintained. These loops continuously monitor and analyze large amounts of information to infer knowledge about the system.Representing the knowledge as semantic graphs is well suited to automated inference, enabling hidden relationships, strategies and understanding to be identified. When applied in an autonomic network management system this automatic discovery of additional knowledge can be used in several ways to inform and improve intent driven closed control loops.This paper describes the design and evaluation of an ontology to represent and help interpret, validate and apply high level goals or ‘intents’ as part of a closed control loop. This approach enables these intents to be enforced, satisfied and maintained. The ontology forms part of a framework which generates graph-based data from network monitoring information collected in a commonly used network/cloud monitoring service (Prometheus). The ontology also models intents relative to the monitoring knowledge. Furthermore, the model has the capabilities to allow the monitored network to adapt, then helps plan how to continuously satisfy and maintain the intent. Finally, the ontology and framework are applied in a real-life use case, which relates to Quality of Service (QoS) assurance for a 5G Telecoms Network Slice. The use case is designed to motivate and demonstrate the usefulness of the approach.