Leveraging Large Language Models for VNF Resource Forecasting

The evolution of the Network Function Virtualization (NFV) paradigm has revolutionized the way network services are deployed, managed, and scaled. Within this transformative landscape, Virtual Network Function (VNF) resource prediction emerges as a cornerstone for optimizing network resource allocation and ensuring service reliability and efficiency. Traditional resource forecasting methods often struggle to adapt to the dynamic and non-linear nature of changes in resource consumption patterns in modern telecommunication networks. We address this challenge by leveraging the inherent pattern recognition and next-token prediction capabilities of Large Language Model (LLM) without requiring any domain-specific fine-tuning. Our study utilizes Llama2 as the foundation model to evaluate the performance against widely used probability-based models on a public VNF dataset that encompasses real-world resource consumption data of various VNFs for comparative analysis. Our findings suggest that LLM offers a highly effective alternative for VNF resource forecasting, demonstrating significant potential in enhancing network resource management.