Offline Metrics vs. Online Performance in SDN: A Performance Reversal Study of MLP and GraphSAGE
Mi Young Jo, Kee-Cheon Kim · Electronics · 2025
Software-Defined Networking (SDN) provides centralized control over routing paths through a logically centralized controller. Although Graph Neural Networks (GNNs) such as GraphSAGE have shown strong potential for network topology analysis, their superiority over simpler models like the Multi-Layer Perceptron (MLP) in dynamic SDN control remains unclear. In this study, we compare MLP and GraphSAGE using three training data volumes (70, 100, and 140) and spatio-temporal features that integrate spatial and temporal characteristics of each node. Experimental results reveal a distinct discrepancy between offline classification metrics and online SDN performance. Offline evaluation showed that MLP achieved a slightly higher F1-score (0.62) than GraphSAGE (0.59). However, when deployed in a SDN controller, GraphSAGE reduced latency by 17%, increased throughput by 8%, and improved jitter by 31%. These results demonstrate that higher offline accuracy does not necessarily translate into better real-time control performance, since offline metrics fail to capture topology-aware routing, congestion recovery, and dynamic adaptation effects. The findings provide a practical guideline for SDN-oriented AI model evaluation, emphasizing end-to-end system performance over isolated offline metrics.