Chat-Driven Optimal Management for Virtual Network Services

TL;DR

This paper introduces a chat-driven network management framework that combines natural language processing with optimization techniques to enable intuitive, reliable, and feasible virtual network reconfiguration based on user prompts.

Contribution

It develops a two-stage framework with novel intent extractors, integrating NLP and optimization for dynamic, safe virtual network management.

Findings

LLM-based intent extractor achieves higher accuracy with fewer samples.

Sentence-BERT with SVM offers lower latency for real-time use.

Framework successfully updates VM placement and routing while maintaining feasibility.

Abstract

This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend on statistical language models to interpret user intent but cannot guarantee the feasibility of generated configurations. To overcome this, we develop a two-stage framework consisting of an Interpreter, which extracts intent from natural language prompts using NLP, and an Optimizer, which computes feasible virtual machine (VM) placement and routing via an integer linear programming. In particular, the Interpreter translates user chats into update directions, i.e., whether to increase, decrease, or maintain parameters such as CPU demand and latency bounds, thereby enabling iterative refinement of the network configuration. In this paper, two intent extractors, which are a Sentence-BERT model with support vector machine (SVM) classifiers and a large language model (LLM), are introduced. Experiments in single-user and multi-user settings show that the framework dynamically updates VM placement and routing while preserving feasibility. The LLM-based extractor achieves higher accuracy with fewer labeled samples, whereas the Sentence-BERT with SVM classifiers provides significantly lower latency suitable for real-time operation. These results underscore the effectiveness of combining NLP-driven intent extraction with optimization-based allocation for safe, interpretable, and user-friendly virtual network management.