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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 Digital Twin Network, provides the basic definitions and a reference architecture, lists a set of application scenarios, and discusses such technology's benefits and key challenges. |
| | Research Challenges in Coupling Artificial Intelligence and Network Management |
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| | draft-irtf-nmrg-ai-challenges-04.txt |
| | Date: |
28/11/2024 |
| | 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, there are many difficult problems in NM that to this date have no good solutions, or where any solutions come with significant limitations and constraints. Artificial Intelligence may help produce novel solutions to those problems. On the other hand, for several reasons (computational costs of AI solutions, privacy of data), distribution of AI tasks became primordial. It is thus also expected that network are operated efficiently to support those tasks. 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 been already coupled together or could benefit from a higher integration. So, the method aims at evaluating the gap between NM problems and AI solutions. Challenges are derived accordingly, assuming 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. |
| | Challenges and Opportunities in Management for Green Networking |
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| | draft-irtf-nmrg-green-ps-03.txt |
| | Date: |
28/06/2024 |
| | Authors: |
Alexander Clemm, Cedric Westphal, Jeff Tantsura, Laurent Ciavaglia, Carlos Pignataro, Marie-Paule Odini |
| | Working Group: |
Network Management (nmrg) |
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Reducing humankind's environmental footprint and making technology more environmentally sustainable are among the biggest challenges of our age. Networks play an important part in this challenge. On one hand, they enable applications that help to reduce this footprint. On the other hand, they contribute to this footprint themselves in no insignificant way. Therefore, methods to make networking technology itself "greener" and to manage and operate networks in ways that reduce their environmental footprint without impacting their utility need to be explored. This document outlines a corresponding set of opportunities, along with associated research challenges, for networking technology in general and management technology in particular to become "greener", i.e., more sustainable, with reduced greenhouse gas emissions and less negative impact on the environment. 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. |
| | Use Cases and Practices for Intent-Based Networking |
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| | draft-kdj-nmrg-ibn-usecases-02.txt |
| | Date: |
21/10/2024 |
| | 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. |
| | Considerations of network/system for AI services |
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| | draft-irtf-nmrg-ai-deploy-00.txt |
| | Date: |
15/11/2024 |
| | Authors: |
Yong-Geun Hong, Joo-Sang Youn, Seung-Woo Hong, Ho-Sun Yoon, Pedro Martinez-Julia |
| | Working Group: |
Network Management (nmrg) |
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As the development of AI technology matured and AI technology began to be applied in various fields, AI technology is changed from running only on very high-performance servers with small hardware, including microcontrollers, low-performance CPUs and AI chipsets. In this document, we consider how to configure the network and the system in terms of AI inference service to provide AI service in a distributed method. Also, we describe the points to be considered in the environment where a client connects to a cloud server and an edge device and requests an AI service. Some use cases of deploying network-based AI services, such as self-driving vehicles and network digital twins, are described. |