ST-NDT：一种降低网络数字孪生通信开销的拓扑框架
ST-NDT: A Topological Framework for Reducing Communication Overhead in Network Digital Twins
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中文总结 AI 辅助
针对网络数字孪生中物理孪生同步带来的高通信开销问题，提出基于拓扑信号处理和Hodge谱结构的ST-NDT框架，以最小化传感器边选择，在20%监控预算下较四种基线提升6.15%至11.24%。
中文摘要 AI 辅助
下一代网络（NGN）正变得越来越复杂，尤其是随着拓扑规模的不断增大。为了管理这种复杂性，需要采用能够实现实时监控、优化和“假设”场景分析的框架。网络数字孪生（NDTs）已成为支持这些能力的关键使能技术，因为它们能够提供物理网络运行的数字副本，从而在不干扰实时网络的情况下进行场景测试。尽管NDTs被认为是关键使能技术，但保持与物理孪生（PT）的持续同步会引入相当大的通信开销和过度的带宽利用，限制了在资源受限网络中的可持续性。本文提出了一种用于NDTs中稀疏网络监控的拓扑信号处理框架。我们将网络边流表示为定义在胞腔复形上的信号，并利用图的Hodge谱结构来识别一组最小的、信息量最大的传感器边，从而减少物理网络与其数字孪生副本之间的测量开销。结果表明，所提出的稀疏拓扑网络数字孪生（ST-NDT）框架在大多数边监控预算下始终优于所有四个基线框架。例如，在20%的监控预算（监控243条边中的48条）下，ST-NDT相对于基于度、核心数、介数中心性和随机选择的框架分别实现了10.85%、11.24%、7.36%和6.15%的提升。
英文摘要
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.
发表机构
- University of Trento（特伦托大学）
- C.N.I.T（信息与通信技术研究中心）
机构由 AI 辅助整理，请以论文原文为准。
