Towards Intent-based Configuration for Network Function Virtualization using In-context Learning in Large Language Models

Network Function Virtualization (NFV) enables the execution of Virtual Network Functions (VNFs) on standard commodity servers. This brings flexibility, allowing for the rapid deployment of various network services while reducing costs. However, NFV configurations are becoming increasingly complex, necessitating experts for the setup. Intent-based network configuration has emerged as a solution to simplify NFV configuration and management. Nonetheless, it presents challenges, such as translating high-level natural language intents into low-level network configurations. In this work, we propose NFV-Intent - a system that leverages in-context learning in Large Language Models to perform the intent translation task. In-context learning enables NFV-Intent to work without retraining the Large Language Models, which is a difficult and expensive task. NFV-Intent uses a JSON template as the desired output, allowing Large Language Models to learn with a small number of examples and enabling easy verification of the configuration. Our evaluation showed that the intent can be translated into JSON configuration with high accuracy. To demonstrate the feasibility of NFV-Intent, we implemented and integrated it into the NI-testbed, our previously developed system for AI-based NFV life-cycle management.