Adaptive Orchestration of Service Function Chains in SAGIN-MEC via Graph Reinforcement Learning

Space-air-ground integrated networks (SAGINs) augmented with mobile edge computing (MEC) provide a unified yet heterogeneous substrate for latency-sensitive services. Deploying service function chains (SFCs) over satellites, aerial platforms, and ground nodes, however, is difficult due to hierarchical resource heterogeneity, time-varying network states, and stringent end-to-end (E2E) delay requirements. In this paper, we study online SFC embedding in a three-layer SAGIN-MEC architecture under coupled computing and networking constraints. We model the deployment as a two-stage process: (i) placing each virtual network function (VNF) onto feasible nodes subject to computing-capacity constraints, and (ii) mapping inter-VNF traffic onto feasible paths subject to bandwidth and delay constraints. To achieve adaptive decisions under dynamic states, we cast the problem as graph reinforcement learning by jointly encoding the substrate topology and each SFC into a unified graph state, and propose a structure-aware PPO agent that combines graph convolution with domain features and an action-masking mechanism to eliminate infeasible placement/routing actions. Extensive experiments in dynamic large-scale scenarios show that the proposed method consistently outperforms competitive baselines, improving the acceptance rate by 6.991% under high load, reducing the average E2E delay by 13.556%, and increasing the long-term revenue-to-cost ratio by 27.763% on average.