ST-NDT: A Topological Framework for Reducing Communication Overhead in Network Digital Twins
TL;DR
The proposed Sparse Topological Network Digital Twin (ST-NDT) framework consistently outperforms all four baseline frameworks across most of the edge monitoring budgets, and exploits the Hodge spectral structure of the graph to identify a minimal set of maximally informative sensor edges.
Abstract
Next-generation networks (NGN) are becoming increasingly complex, especially with the increasing size of topologies. To manage this complexity requires the adoption of frameworks that enable real-time monitoring, optimization, and ``what-if''case scenario analysis. Network Digital Twins (NDTs) have emerged as a key enabler technology for supporting these capabilities because of their ability to provide digital replicas of physical networks operations, enabling scenario testing without interfering with the live network. Although NDTs are considered a key enabler technology, maintaining continuous synchronization with the Physical Twin (PT) introduces considerable communication overhead and excessive bandwidth utilization, limiting sustainability in resource-constrained networks. This paper proposes a topological signal processing framework for sparse network monitoring in NDTs. We represent network edge flows as signals defined on a cell complex, and exploit the Hodge spectral structure of the graph to identify a minimal set of maximally informative sensor edges, thereby reducing the measurement overhead between the physical network and its digital twin replica. Results show that the proposed Sparse Topological Network Digital Twin (ST-NDT) framework consistently outperforms all four baseline frameworks across most of the edge monitoring budgets. For example, at a 20\% monitoring budget (48 of 243 edges monitored), ST-NDT achieves improvements of 10.85\%, 11.24\%, 7.36\%, and 6.15\% over degree-based, core-number, betweenness centrality, and random-selection frameworks, respectively.