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

Md. Kamrul Hossain, Walid Aljoby

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.

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

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.
Paper Structure (9 sections, 1 figure)

This paper contains 9 sections, 1 figure.

Table of Contents

  1. Introduction and Motivation
  2. Related Work
  3. Proposed Approach
  4. LLM-Assisted Intent Realization with Verifiability
  5. Conflict-Aware Activation and Enforcement Checks
  6. Proactive Multi-Intent Assurance and Remediation
  7. Telemetry Signals for Multi-Intent Assurance
  8. Challenges and Open Questions
  9. Conclusion

Figures (1)

  • Figure 1: Closed-loop thesis framework: LLM-assisted realization (translate+activate) coupled with proactive multi-intent assurance and ranked remediation.