Leveraging Large Language Models for Multi-Objective and Adaptive SFC Deployment: Techniques, Case Study, and Promising Directions

Network function virtualization enables flexible network services through Service Function Chain (SFC) deployment. Existing SFC deployment solutions rely on mixed integer linear programming (MILP) solvers or heuristics. However, they fail to support multi-objective deployment and adaptive service operation. In this article, we propose a large language model (LLM) and mixture-of-experts (MoE)-enabled framework for multi-objective and adaptive SFC deployment (MoED). In detail, MoED considers a comprehensive set of fundamental optimization objectives and deploys specialized expert agents for them. Then, MoED leverages LLMs to analyze input user intents and formulates the optimization function. Next, it uses LLMs to dynamically activate expert agents and coordinate their output deployment strategies. Experimental results demonstrate that compared to existing solutions, MoED reduces the power consumption by up to 68.4% while achieving near-optimal performance in terms of throughput, latency, and resource consumption , and reduces deployment strategy solving time by several orders of magnitude.