mlsanityData to 14 Oct 2026

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

arXiv 2507.10903 ↗First posted 15 Jul 2025cs.NI cs.CL cs.LGcited by 2

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Abstract (arXiv)

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).

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 22 references that are in the map. First posted on arXiv 15 Jul 2025.

Builds on

7 of 22 references.

References · 7

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  4. 2024The Larger the Better? Improved LLM Code-Generation via Budget Reallocationcited by 43
  5. 2024SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Modelscited by 32
  6. 2024A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directionscited by 25
  7. 2025Integrating Language Models for Enhanced Network State Monitoring in DRL-Based SFC Provisioningcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Structure-Aware NL-to-SQL for SFC Provisioning via AST-Masking Empowered Language Modelscited by 0
  2. 2026Proactive SFC Provisioning with Forecast-Driven DRL in Data Centerscited by 0

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  3. 2024Safe Load Balancing in Software-Defined-Networking
  4. 2024Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges
  5. 2024Statistical QoS Provision in Business-Centric Networks
  6. 2026DRLM: Deep Reinforcement Learning-Based LLM Query Orchestration in Edge Environments
  7. 2024A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks
  8. 2025Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review