← 研踪 PaperTrailarXiv:2606.24853 · v3
A Lightweight, Open-Source Network Digital Twin Blueprint for IoT-Edge Environments
首次提交 2026-06-24 01:32
版本更新 2026-11-11 01:22
雷达观测 2026-11-11 13:07
摘要
IoT-Edge-Cloud infrastructures are increasingly heterogeneous, and experimenting across the continuum is difficult and risks disrupting live systems. Network Digital Twins (NDT) are a pragmatic answer, yet practitioners lack a structured account of three things: how to build an NDT from open tools, how to model wireless impairments, and how to understand whether the twin is faithful to the physical network, or not. This tutorial introduces NDT through the IRTF reference model and walks through a lightweight open-source blueprint built on Containerlab, Open vSwitch, \textit{Open Network Operating System (ONOS)}, and Prometheus/Grafana. It then explains how to define wireless impairment profiles (static, model-based, trace-driven, AI-generated, and live), and what each approach can and cannot reproduce. Finally, it presents a workflow to validate the twin against the physical network. A proof-of-concept Edge case study illustrates the procedure, including the pitfalls encountered and the lessons learned. Directions for future research are outlined.
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v3 · 2026-11-11 01:22 · 最新观测版本
A Lightweight, Open-Source Network Digital Twin Blueprint for IoT-Edge Environments
IoT-Edge-Cloud infrastructures are increasingly heterogeneous, and experimenting across the continuum is difficult and risks disrupting live systems. Network Digital Twins (NDT) are a pragmatic answer, yet practitioners lack a structured account of three things: how to build an NDT from open tools, how to model wireless impairments, and how to understand whether the twin is faithful to the physical network, or not. This tutorial introduces NDT through the IRTF reference model and walks through a lightweight open-source blueprint built on Containerlab, Open vSwitch, \textit{Open Network Operating System (ONOS)}, and Prometheus/Grafana. It then explains how to define wireless impairment profiles (static, model-based, trace-driven, AI-generated, and live), and what each approach can and cannot reproduce. Finally, it presents a workflow to validate the twin against the physical network. A proof-of-concept Edge case study illustrates the procedure, including the pitfalls encountered and the lessons learned. Directions for future research are outlined.
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