Chat-Driven Network Management with NLP and Optimization
Analysis
This paper addresses the limitations of intent-based networking by combining NLP for user intent extraction with optimization techniques for feasible network configuration. The two-stage framework, comprising an Interpreter and an Optimizer, offers a practical approach to managing virtual network services through natural language interaction. The comparison of Sentence-BERT with SVM and LLM-based extractors highlights the trade-off between accuracy, latency, and data requirements, providing valuable insights for real-world deployment.
Key Takeaways
- •Combines NLP for intent extraction with optimization for feasible network configuration.
- •Offers a two-stage framework (Interpreter and Optimizer) for chat-driven network management.
- •Compares Sentence-BERT with SVM and LLM-based intent extractors, highlighting trade-offs.
- •Provides a user-friendly and interpretable approach to virtual network management.
Reference
“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.”