Abstract:Telecom networks scale with growing users and data-intensive applications, generating heavy traffic that causes congestion, reducing throughput, increasing delay, and raising computational costs. Traditional routing protocols act only after performance degradation, making them unsuitable for dynamic traffic and topological changes. Addressing these challenges requires a routing approach that adapts in real time, scales with network growth, operates without disrupting active services, and provides continuous feedback for congestion-aware traffic optimisation. The Network Digital Twin (NDT) addresses these needs by mirroring global network behaviour using Message Passing Neural Networks (MPNNs) through bidirectional communication with the physical network. To align the NDT with physical network behaviour, synthetic traffic is generated with increasing load across topological structures that incrementally scale as routers are added. These topologies are created by graph-generating models such as Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz, customised with vertex degree limitations. The NDT collects performance metrics from routers and links, and MPNNs classify edges based on local vertex and global network behaviours. Based on these classifications, feedback is sent as Policy-Based Routing (PBR) protocol commands to each router, enabling optimal traffic distribution across links of the physical network.
| Subjects: | Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2605.24318 [cs.NI] |
| (or arXiv:2605.24318v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24318 arXiv-issued DOI via DataCite (pending registration) |
From: Umer Iqbal Mr [view email]
[v1]
Sat, 23 May 2026 00:53:07 UTC (13,183 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。