DS-Route: GNN-based Flow-Level Latency Prediction in Software-Defined LEO Satellite Networks

Abstract

Low Earth Orbit (LEO) satellite networks are emerging as a promising solution for providing global Internet access. However, their expansive scale and dynamic topologies present significant challenges for network management and routing optimization. While software-defined satellite networks (SDSNs) provide centralized control and global visibility, their effective operation hinges on accurate modeling of network states under constrained control-plane resources. Existing learning-based methods typically rely on resource-intensive link-level measurements, which are impractical in LEO constellations due to limited telemetry and high synchronization overhead. In this paper, we propose DS-Route, a graph neural network-based framework for flow-level delay prediction that relies solely on flow attributes and routing information, eliminating the need for fine-grained link-level measurements. To address the scalability and topological complexity of large-scale LEO satellite networks, DS-Route incorporates a dual-scale modeling strategy that integrates global topological embeddings with local path-level dependencies. Comprehensive evaluations using Kuiper and Starlink topologies demonstrate that DS-Route reduces mean absolute relative error (MARE) by 15.1% and 22.6%, respectively, compared to best-performing baselines, while maintaining robust performance under realistic satellite conditions. Furthermore, DS-Route’s routing performance is validated through consistent improvements in key Quality of Service indicators compared to existing predictors, highlighting its potential for effective satellite traffic engineering.