Refining Network Intents for Self-Driving Networks

TL;DR

This paper presents a novel intent-refinement process for intent-based networking that uses machine learning and operator feedback to improve translation of high-level policies into network configurations, enhancing accuracy and usability.

Contribution

It introduces a sequence-to-sequence learning model with an intermediate natural language-like representation for translating operator intents into network configurations, incorporating feedback for continuous improvement.

Findings

Achieves an R-squared of 0.99 on a 5000-entry dataset.

Operator feedback significantly improves translation accuracy.

Uses a natural language-like intermediate representation for better feedback collection.

Abstract

Recent advances in artificial intelligence (AI) offer an opportunity for the adoption of self-driving networks. However, network operators or home-network users still do not have the right tools to exploit these new advancements in AI, since they have to rely on low-level languages to specify network policies. Intent-based networking (IBN) allows operators to specify high-level policies that dictate how the network should behave without worrying how they are translated into configuration commands in the network devices. However, the existing research proposals for IBN fail to exploit the knowledge and feedback from the network operator to validate or improve the translation of intents. In this paper, we introduce a novel intent-refinement process that uses machine learning and feedback from the operator to translate the operator's utterances into network configurations. Our refinement process uses a sequence-to-sequence learning model to extract intents from natural language and the feedback from the operator to improve learning. The key insight of our process is an intermediate representation that resembles natural language that is suitable to collect feedback from the operator but is structured enough to facilitate precise translations. Our prototype interacts with a network operator using natural language and translates the operator input to the intermediate representation before translating to SDN rules. Our experimental results show that our process achieves a correlation coefficient squared (i.e., R-squared) of 0.99 for a dataset with 5000 entries and the operator feedback significantly improves the accuracy of our model.