Intent‐Based Automated NFV Management Methods Using Large Language Models

The growing demands for network capacity and the increasing complexities of modern network environments pose significant challenges for efficient network management and orchestration. To solve these problems, artificial intelligence (AI) techniques have attracted attention to enable automated network management, to enhance the quality of service (QoS), and to ensure the service level objectives (SLOs). However, there still remain significant limitations in automated network management within the paradigm of intent‐based networking (IBN). In this paper, we propose intent‐based network management methods using large language models (LLMs) for NFV environment. The proposed methods translate user's high‐level intents expressed in natural language and generate the executable network and service management policies such as service function chaining (SFC), autoscaling, and network security. To improve domain understanding and generation accuracy, we apply domain adaptation techniques for LLMs including prompt engineering, retrieval‐augmented generation (RAG), and iterative feedback mechanisms. Evaluations conducted on a real Kubernetes cluster showed that the proposed methods significantly improve intent translation and policy generation performance and effectively fulfill user intents, compared with baseline approaches. These results indicate the feasibility of leveraging LLMs to enable practical end‐to‐end intent‐based automation for cloud‐native NFV management.