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| | Network Digital Twin (NDT): Concepts and Reference Architecture |
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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 concept of Network Digital Twin (NDT), provides the basic definitions and a reference architecture, lists a set of application scenarios, and discusses such technology's benefits and key challenges. This document is a product of the Network Management Research Group (NMRG) of the Internet Research Task Force (IRTF). This document reflects the consensus of the research group. It is not a candidate for any level of Internet Standard and is published for informational purposes. |
| | Research Challenges in Coupling Artificial Intelligence and Network Management |
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| | draft-irtf-nmrg-ai-challenges-06.txt |
| | Date: |
06/07/2026 |
| | Authors: |
Jerome Francois, Alexander Clemm, Dimitri Papadimitriou, Stenio Fernandes, Stefan Schneider |
| | Working Group: |
Network Management (nmrg) |
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This document is intended to introduce the challenges to overcome when Network Management (NM) problems may require coupling with Artificial Intelligence (AI) solutions. On the one hand, many difficult NM problems still lack good solutions, or existing approaches come with significant limitations. Artificial Intelligence may help produce novel solutions to those problems. On the other hand, due to the high computational costs of AI solutions and stringent data privacy constraints, the distributed execution of AI workloads has become paramount. Consequently, networks must be operated efficiently to sustain these distributed processing requirements. To identify the right set of challenges, the document defines a method based on the evolution and nature of NM problems. This will be done in parallel with advances and the nature of existing solutions in AI in order to highlight where AI and NM have already been coupled together or could benefit from a closer integration. So, the method aims at evaluating the gap between NM problems and AI solutions. Challenges are derived accordingly, assuming that solving these challenges will help to reduce the gap between NM and AI. This document is a product of the Network Management Research Group (NMRG) of the Internet Research Task Force (IRTF). This document reflects the consensus of the research group. It is not a candidate for any level of Internet Standard and is published for informational purposes. |
| | Considerations of network/system for AI services |
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| | draft-irtf-nmrg-ai-deploy-03.txt |
| | Date: |
06/07/2026 |
| | 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, the execution environment has evolved from dedicated high-performance servers to commodity servers and 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 request an AI service. It describes some use cases for deploying network-based AI services, such as self-driving vehicles and network digital twins. |