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

TL;DR

This paper proposes integrating language models with deep reinforcement learning to improve network state monitoring and decision-making in service function chain provisioning, enabling real-time insights and better adaptability.

Contribution

It introduces a novel framework combining DRL with fine-tuned BERT models for enhanced network management and resource analysis in SDN and NFV environments.

Findings

BERT outperforms DistilBERT in test loss and confidence.

The integrated system provides real-time resource insights.

BERT requires 46% more processing time.

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.