Establishing Trust for Using Natural Language for Intent-Based Networking

Abstract

Today’s enterprise networks wrestle with accommodating an ever-growing number of devices of different types, supporting increasingly demanding applications and ever more complex services, and protecting their users from sophisticated and disrupting cyber threats. In response, a proposed architectural approach for improving network management, referred to as Intent-Based Networking (IBN), has attracted significant attention. It is built on the premise that network operators specify network policies in natural language and the network correctly translates these spoken intents (e.g., policies) into proper device-specific configurations that are then deployed across the network to reliably act on the operators’ expressed intents. Unfortunately, IBN has not yet fully delivered on its promise of automated, fast, and reliable policy deployment, mainly due to the significant challenges that the reliance on methods from Natural Language Processing (NLP) or more recent techniques from Machine Learning (ML) and Artificial Intelligence (AI) poses for unambiguously and accurately translating the myriad of intents that operators can express in natural language into “trustworthy” device configurations. This paper usesLumi, a recently designed end-to-end prototype of a system that allows operators “to manage their network by talking to the network,” as an illustrative case study. In particular, we use it to elaborate on the different functionalities such systems should have to realize IBN’s vision of automating the fast deployment of policies. At the same time, we leverageLumi to highlight the extra efforts that are required to ensure that the deployed policies can be entrusted to accurately express and execute the operators’ original intents.