迈向可组合的网络数字孪生：基于子图的延迟预测研究
Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study
- University of Glasgow（格拉斯哥大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于子图的可组合网络数字孪生方法，通过可重用单元孪生分解网络并轻量组合，实现跨拓扑与流量变化的端到端延迟预测，兼具高精度与可重用性。
AI中文摘要：
现代网络必须支持不断变化的拓扑、配置和性能目标，这促使了对快速且可靠的性能估计的需求。网络数字孪生（NDT）能够在这些网络场景中实现用于性能估计的假设分析，然而，现有的基于机器学习的NDT方法通常依赖于整个拓扑的表示，这些表示本质上是整体式的，并且在网络拓扑或流量变化时缺乏可重用性。本文提出了一种可组合的NDT方法，将网络分解为子图，这些子图由可重用的单元孪生表示，捕获子图结构、配置和流量行为。一个轻量级的组合器聚合单元孪生的组合，以创建NDT，通过整体拓扑预测每条路由的端到端延迟。在受控的合成拓扑和多种流量场景、真实世界的Topology Zoo拓扑以及一个公开的NDT挑战数据集上的评估表明，可组合的NDT在分布内场景中实现了高精度，同时在分布外场景下保持稳定。与整体式全拓扑NDT的比较表明，我们的可组合方法实现了可重用性，同时达到了相当或更优的精度。
英文摘要：
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.
