Large Language Models for Zero Touch Network Configuration Management

doi:10.1109/MCOM.001.2400368

Large Language Models for Zero Touch Network Configuration Management

2024-01-01

0

OA

AI

O

Oscar Maurício Caicedo Rendón

N

Nelson L. S. da Fonseca

DOI：10.1109/MCOM.001.2400368

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Abstract

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The zero-touch network and service management (ZSM) paradigm, a direct response to the increasing complexity of communication networks, is a problem-solving approach. In this article, taking advantage of recent advances in generative artificial intelligence, we introduce the network configuration generator (LLM-NetCFG) that 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.

Keywords:

Autonomous networks

Transformers

Training

Data models

Large language models

Analytical models

Adaptation models

Prompt engineering

Knowledge engineering

Generators

Large Language Models for Zero Touch Network Configuration Management

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