Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study

2026-09-16

AI Summary

本文提出了一种基于子图的可组合网络数字孪生方法，通过重用单元孪生来预测端到端延迟，解决了现有方法在拓扑或流量变化时缺乏复用性的问题。

Abstract

Modern networks must support changing topologies, configurations, and performance objectives, motivating fast and reliable performance estimation. Network digital twins (NDTs) enable what-if analysis for performance estimation in such network scenarios, however, existing machine learning-based NDT approaches often rely on entire topology representations, which are inherently monolithic and lack reusability under topological or traffic changes in the network. This paper introduces a composable NDT approach that decomposes networks into subgraphs represented by reusable unit twins that capture subgraph structure, configuration and traffic behaviours. A lightweight composer aggregates unit twin combinations to create NDTs that predict per-route end-to-end latency through an overall topology. Evaluation across controlled synthetic topologies and diverse traffic scenarios, real-world Topology Zoo topologies, and a public NDT challenge dataset demonstrates that the composable NDTs achieve high in-distribution accuracy while remaining stable under out-of-distribution scenarios. Comparison with monolithic full topology NDTs demonstrates that our composable approach achieves reusability, while achieving comparable or superior accuracy.

Problem

• Network Digital Twins
• Subgraph-Based
• Latency Prediction
• Topology Changes
• Traffic Changes

Innovation

• composable NDTs
• subgraph-based
• unit twins
• latency prediction
• reusability