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| | Network Digital Twin: Concepts and Reference Architecture |
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Digital Twin technology has been seen as a rapid adoption technology in Industry 4.0. The application of Digital Twin technology in the networking field is meant to develop various rich network applications, realize efficient and cost-effective data-driven network management, and accelerate network innovation. This document presents an overview of the concepts of Network Digital Twin, provides the basic definitions and a reference architecture, lists a set of application scenarios, and discusses such technology's benefits and key challenges. |
| | Considerations of network/system for AI services |
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| | draft-irtf-nmrg-ai-deploy-02.txt |
| | Date: |
20/10/2025 |
| | Authors: |
Yong-Geun Hong, Joo-Sang Youn, Seung-Woo Hong, Pedro Martinez-Julia, Qin WU |
| | Working Group: |
Network Management (nmrg) |
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As the development of AI technology has matured and AI technology has begun to be applied in various fields, it has changed from running only on very high-performance servers to running on commodity servers, with affordable, small-scale hardware, including microcontrollers, low-performance CPUs, and AI chipsets. This document outlines how to configure the network and system for an AI inference service, providing AI services in a distributed manner. It also outlines the factors to consider when a client connects to a cloud server and an edge device to requests an AI service. It describes some use cases for deploying network-based AI services, such as self-driving vehicles and network digital twins. |
| | Use Cases and Practices for Intent-Based Networking |
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| | draft-irtf-nmrg-ibn-usecases-01.txt |
| | Date: |
19/10/2025 |
| | Authors: |
Kehan Yao, Danyang Chen, Jaehoon Jeong, Qin WU, Chungang Yang, Luis Contreras, Giuseppe Fioccola |
| | Working Group: |
Network Management (nmrg) |
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This document proposes several use cases of Intent-Based Networking (IBN) and the methodologies to differ each use case by following the lifecycle of a real IBN system. It includes the initial system awareness and data collection for the IBN system and the construction of the IBN system, which consists of intent translation, deployment, verification, evaluation, and optimization. Practice learning and general learning are also summarized to instruct the construction of next generation network management systems with the integration of IBN techniques. |