Integrating Language Models for Enhanced Network State Monitoring in DRL-Based SFC Provisioning

• URL: http://arxiv.org/abs/2502.11298v1
• Date: Sun, 16 Feb 2025 22:52:14 GMT
• Title: Integrating Language Models for Enhanced Network State Monitoring in DRL-Based SFC Provisioning
• Authors: Parisa Fard Moshiri, Murat Arda Onsu, Poonam Lohan, Burak Kantarci, Emil Janulewicz,
• Abstract summary: This paper integrates Deep Reinforcement Learning (DRL) with Language Models (LMs) to enhance network management. By feeding final VNF allocations from DRL into the LM, the system can process and respond to queries related to SFCs, DCs, and VNFs, enabling real-time insights into resource utilization, bottleneck detection, and future demand planning.
• Score: 5.37102888813454
• License:
• Abstract: Efficient Service Function Chain (SFC) provisioning and Virtual Network Function (VNF) placement are critical for enhancing network performance in modern architectures such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV). While Deep Reinforcement Learning (DRL) aids decision-making in dynamic network environments, its reliance on structured inputs and predefined rules limits adaptability in unforeseen scenarios. Additionally, incorrect actions by a DRL agent may require numerous training iterations to correct, potentially reinforcing suboptimal policies and degrading performance. This paper integrates DRL with Language Models (LMs), specifically Bidirectional Encoder Representations from Transformers (BERT) and DistilBERT, to enhance network management. By feeding final VNF allocations from DRL into the LM, the system can process and respond to queries related to SFCs, DCs, and VNFs, enabling real-time insights into resource utilization, bottleneck detection, and future demand planning. The LMs are fine-tuned to our domain-specific dataset using Low-Rank Adaptation (LoRA). Results show that BERT outperforms DistilBERT with a lower test loss (0.28 compared to 0.36) and higher confidence (0.83 compared to 0.74), though BERT requires approximately 46% more processing time.

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