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.

翻译：本文提出了一种聊天驱动的网络管理框架，该框架将自然语言处理（NLP）与基于优化的虚拟网络资源分配相结合，实现了对虚拟网络服务直观且可靠的重新配置。传统的基于意图的网络（IBN）方法依赖统计语言模型来解析用户意图，但无法保证生成配置的可行性。为克服此问题，我们开发了一个两阶段框架，包含一个使用NLP从自然语言提示中提取意图的解析器，以及一个通过整数线性规划计算可行虚拟机（VM）放置与路由的优化器。具体而言，解析器将用户聊天内容转化为更新方向，即对CPU需求、延迟边界等参数是增加、减少还是维持，从而实现网络配置的迭代优化。本文介绍了两种意图提取器：一种是结合支持向量机（SVM）分类器的Sentence-BERT模型，另一种是大型语言模型（LLM）。在单用户与多用户场景下的实验表明，该框架能在保持配置可行性的同时，动态更新VM放置与路由。基于LLM的提取器能以更少的标注样本实现更高的准确率，而结合SVM分类器的Sentence-BERT则能提供显著更低的延迟，适用于实时操作。这些结果凸显了将NLP驱动的意图提取与基于优化的资源分配相结合，对于实现安全、可解释且用户友好的虚拟网络管理的有效性。