aiqbench analysis
This research proposes a composable network digital twin framework that decomposes complex topologies into reusable subgraph units to improve latency prediction under dynamic conditions. By leveraging machine learning on modular components rather than monolithic representations, the approach maintains high accuracy while offering superior reusability for evolving network configurations. The study validates this method across synthetic environments and real-world datasets, demonstrating stability even when facing out-of-distribution scenarios.
Toward Composable Network Digital Twins: A Subgraph-Based Latency Prediction Study
arXiv:2609.18704v1 Announce Type: cross 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.
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.
Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
arXiv:2609.18704 [cs.NI]
(or arXiv:2609.18704v1 [cs.NI] for this version)
https://doi.org/10.48550/arXiv.2609.18704 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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