Large Language Models for Zero Touch Network Configuration Management

TL;DR

This paper presents LLM-NetCFG, a novel approach using large language models to automate, verify, and implement network configurations from natural language, advancing zero-touch network management.

Contribution

Introduction of LLM-NetCFG, a large language model-based system for automating and verifying network configurations with minimal human intervention.

Findings

Successfully generated network configurations from natural language.

Automated verification of network configurations.

Reduced human intervention in network management processes.

Abstract

The Zero-touch Network & Service Management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this paper, taking advantage of recent advances in generative Artificial Intelligence, we introduce the Network ConFiguration Generator (LLM-NetCFG) that employs Large Language Model and architects ZSM configuration agents by Large Language Models. LLM-NetCFG can automatically generate configurations, verify them, and configure network devices based on intents expressed in natural language. We also show the automation and verification of network configurations with minimum human intervention. Moreover, we explore the opportunities and challenges of integrating LLM in functional areas of network management to fully achieve ZSM.