Adaptive intent realisation (AIR) - inductive intent realisation through NLOP enabled intent matching

Abstract

There is a strong interest in designing systems that can simplify the interactions between humans and complex digital systems. Network Operators want more straightforward mechanisms to engage with their networks and inform their actions and goals. Intent was proposed to meet this challenge, but comes at a cost. Intent introduces large modelling efforts, requiring Network Operators to gain expertise in formal model notation and the integration of these models with their network. The cost is compounded by the speed which modern networks evolve, requiring constant adaption to maintain intent-driven features. This work aims to leverage the concepts of intent-based management for private net works, without component and formal model expertise. This will be achieved through the coordination of three enablers, Adaptive Policy, Machine Learning and Intent. Adap tive Policy provides a flexible framework for context-aware decision making, utilising a state-based approach to policy execution. Machine Learning informs the decision mak ing process to produce impact-aware responses based on closed-loop reporting. Intent structures the realisation process, how abstraction is handled through inductive pro cesses to generate actionable output. This work is highly experimental, developed on site at the Network Management Lab in an Ericsson Product Development Unit based in Ireland. This work concludes with the Adaptive Intent Realisation (AIR) reference ar chitecture successfully demonstrated in three use cases hosted in industrial grade private 5G networks.