AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks

TL;DR

This paper presents an AI-driven intent-based networking system that automates network configuration and assurance using large language models, aiming for reliable, proactive, and interpretable network management.

Contribution

It introduces an end-to-end closed-loop IBN pipeline utilizing large language models for natural language to policy translation and proactive failure prediction.

Findings

Enhanced automation reliability with structured validation.

Proactive multi-intent failure prediction with root-cause analysis.

Improved operator trust through early warnings and explanations.

Abstract

Intent-Based Networking (IBN) aims to simplify operating heterogeneous infrastructures by translating high-level intents into enforceable policies and assuring compliance. However, dependable automation remains difficult because (i) realizing intents from ambiguous natural language into controller-ready policies is brittle and prone to conflicts and unintended side effects, and (ii) assurance is often reactive and struggles in multi-intent settings where faults create cascading symptoms and ambiguous telemetry. This paper proposes an end-to-end closed-loop IBN pipeline that uses large language models with structured validation for natural language to policy realization and conflict-aware activation, and reformulates assurance as proactive multi-intent failure prediction with root-cause disambiguation. The expected outcome is operator-trustworthy automation that provides actionable early warnings, interpretable explanations, and measurable lead time for remediation.