LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning

2025-07-14

AI Summary

Optimizing Service Function Chain (SFC) deployment in Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) environments remains challenging due to dynamic network states, heterogeneous resource constraints, and complex SFC requirements. Method: This paper proposes a deep reinforcement learning (DRL) framework that integrates lightweight language models (FLAN-T5/BART) with relational databases. Instead of relying on rigid, rule-based interfaces, the framework employs semantic parsing to interpret natural-language queries regarding real-time network conditions—including SFC specifications, infrastructure resource availability, and VNF status—and directly informs VNF placement decisions. Contribution/Results: Experimental evaluation shows FLAN-T5 achieves a test loss of 0.00161 and 94.79% accuracy, with inference latency of only 2 hours and 2 minutes—96% faster than SQLCoder. The approach significantly enhances DRL’s adaptability, responsiveness, and decision interpretability under unpredictable network dynamics.

Technology Category

Planning, Routing, and Scheduling: Planning with Language ModelsNatural Language Processing: Learning & Optimization for NLPSearch and Optimization: Learning to Search

Application Category

Search and Retrieval-Augmented AI: Large language models for searchSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Effective management of Service Function Chains (SFCs) and optimal Virtual Network Function (VNF) placement are critical challenges in modern Software-Defined Networking (SDN) and Network Function Virtualization (NFV) environments. Although Deep Reinforcement Learning (DRL) is widely adopted for dynamic network decision-making, its inherent dependency on structured data and fixed action rules often limits adaptability and responsiveness, particularly under unpredictable network conditions. This paper introduces LiLM-RDB-SFC, a novel approach combining Lightweight Language Model (LiLM) with Relational Database (RDB) to answer network state queries to guide DRL model for efficient SFC provisioning. Our proposed approach leverages two LiLMs, Bidirectional and Auto-Regressive Transformers (BART) and the Fine-tuned Language Net T5 (FLAN-T5), to interpret network data and support diverse query types related to SFC demands, data center resources, and VNF availability. Results demonstrate that FLAN-T5 outperforms BART with a lower test loss (0.00161 compared to 0.00734), higher accuracy (94.79% compared to 80.2%), and less processing time (2h 2min compared to 2h 38min). Moreover, when compared to the large language model SQLCoder, FLAN-T5 matches the accuracy of SQLCoder while cutting processing time by 96% (SQLCoder: 54 h 43 min; FLAN-T5: 2 h 2 min).

Problem

• Optimizing Service Function Chain provisioning in SDN/NFV
• Overcoming DRL limitations in dynamic network decision-making
• Enhancing VNF placement with lightweight language models

Innovation

• Combines Lightweight Language Model with Relational Database
• Uses BART and FLAN-T5 for network data interpretation
• FLAN-T5 reduces processing time by 96%