AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks
This work addresses automation challenges for network operators in next-generation networks, representing an incremental improvement by integrating large language models into existing IBN frameworks.
The paper tackles the problem of dependable automation in Intent-Based Networking by addressing brittle natural language to policy translation and reactive assurance, proposing an end-to-end closed-loop pipeline using large language models with structured validation and proactive multi-intent failure prediction to achieve operator-trustworthy automation with actionable early warnings and interpretable explanations.
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.