A2ProSFC: Agentic AI-Enabled Proactive SFC Orchestration in Embodied Edge Intelligence Networks

Abstract

Embodied Artificial Intelligence (AI) integrates multimodal large models into Embodied Agents (EAs), driving the evolution of Embodied Edge Intelligence Networks (EEINs) to handle the heterogeneous requests generated by EAs. To guarantee service performance for heterogeneous requests, Service Function Chain (SFC) orchestration has emerged as a critical solution, involving the sequential deployment of Network Functions (NFs) to satisfy customized service requirements. However, realizing SFC orchestration in EEINs presents several challenges, including limited forwarding performance, dynamic environment evolution, and high-dimensional decision spaces. To tackle these issues, we present A2ProSFC, an agentic AI-enabled SFC orchestration system that facilitates perception–reasoning–action loops by leveraging programmable switches. Specifically, we formulate a long-term SFC orchestration problem aimed at maximizing served SFC throughput while ensuring system load balancing. Subsequently, we employ Lyapunov optimization to decouple the long-term orchestration into a sequence of online optimization subproblems and design DiffOrch, a diffusion-based SFC orchestration algorithm. By leveraging In-band Network Telemetry (INT), DiffOrch perceives network state information and adaptively generates orchestration decisions. Furthermore, we design a pipeline integrating INT perception and SFC orchestration to validate the system’s effectiveness. Experimental results demonstrate that A2ProSFC improves throughput by 40.53% and enhances load balancing efficiency by 36.97% compared to existing baselines.