Intent-based Industrial Network Management Using Natural Language Instructions

Intent-based Networks (IBNs) allow specifying net-work operations using natural languages, which can significantly reduce network management cost and effort, as well as improve efficiency. In this work, we discuss the design of a typical IBN. In particular, we define several intent classes corresponding to different network operations. Subsequently, we use machine learning classification to map a natural language intent into one of those classes. Relevant network entities, if any, are also identified. After an intent has been resolved, the corresponding networking task is triggered. We design an IBN prototype based on the proposed architecture by integrating natural language intent processing with a network and a network controller. Subsequently, we evaluate the IBN prototype with sample intents as inputs. The IBN prototype was found to successfully execute the networking tasks, based on input intents, and installed the desired effects, which were verified with network tests.