[nmrg] Agenda - NMRG @ IETF 118 - Prague & Online

["Laurent Ciavaglia (Nokia)" <[e-mail]>]

Dear all,



The agenda for the NMRG session at IETF 118 is available at https://datatracker.ietf.org/doc/agenda-118-nmrg/, https://notes.ietf.org/notes-ietf-118-nmrg and copied below for your convenience.



We have received a high number of requests for this NMRG meeting and could not accommodate all of them in the allocated time.

We have decided i) to prioritize topics in scope of the current research agenda, ii) to give each presentation sufficient time to go deeper and iii) to allow for discussion.

Requests that could not be included for this meeting will be invited to present in one of the next NMRG meetings (interim or IETF 119).

Thank you for your understanding .



Looking forward to meet you all next week in Prague and online.



Best regards, Jérôme & Laurent

***

NMRG 72nd meeting
IETF 118, Prague + Online

  *   Friday 2023-11-10 13:00-15:00 CET
  *   https://datatracker.ietf.org/meeting/118/session/31741.ics

________________________________

Contacts:
- RG Chairs
- Laurent Ciavaglia <[e-mail]<mailto:[e-mail]>>
- Jérôme François <[e-mail]<mailto:[e-mail]>>
- RG Secretaries
- Jéferson Campos Nobre <[e-mail]<mailto:[e-mail]>>
- Pedro Martinez-Julia <[e-mail]<mailto:[e-mail]>>

________________________________

Useful links:
- Materials: https://datatracker.ietf.org/meeting/118/session/nmrg
- Meetecho: https://meetings.conf.meetecho.com/ietf118/?group=nmrg
- Notes: https://notes.ietf.org/notes-ietf-118-nmrg
- Video recording: https://www.youtube.com/user/ietf/playlists (available post-meeting)

________________________________

Agenda:

  *   13:00 Introduction, RG Chairs, 05 min

NMRG Research Agenda topics

  *   13:05 Knowledge Graphs for Network Management, Ignacio Dominguez-Martinez, 15 min
Abstract: The advent of AI opens the door for new opportunities that represent a step closer to the promised land of autonomous networks. However, AI applications are expected to consume combinations of data from multiple heterogenous sources, scattered throughout the network. In this sense, Knowledge graphs have appeared as promising technology due to their data integration and semantic interoperability capabilities. This presentation highlights some of the key aspects and challenges when building knowledge graphs within the scope of network management.
  *   13:20 Semantic Metadata Annotation for Network Anomaly Detection, Thomas Graf, 15 min
https://datatracker.ietf.org/doc/html/draft-netana-opsawg-nmrg-network-anomaly-semantics
https://wiki.ietf.org/en/meeting/118/hackathon, example implementation (IETF Hackathon)
  *   13:35 Artificial Intelligence Framework for Network Management, Pedro Martinez-Julia, 15 min
Abstract: The adoption of artificial intelligence (AI) in network management (NM) solutions is the way to resolve many of the complex management problems arising from the adoption of NFV and SDN technologies. The AINEMA framework, as discussed in this document, includes the functions, capabilities, and components that MUST be provided by AI modules and models to be successfully applied to NM. This is enhanced by the consideration of seamless integration of different services, including the ability of having multiple AI models working in parallel, as well as the ability of complex reasoning and event processing. In addition, disparate sources of information are put together without increasing complexity, through the definition of a control and management service bus. It allows, for instance, to involve external events in NM operations. Using all available sources of information --- namely, intelligence sources --- allows NM solutions to apply proper intelligence processes that provide explainable results instead of simple AI-based guesses. Such processes are highly based in reasoning and formal and target-based intelligence analysis and decision --- providing evidence-based conclusions and proofs for the decisions --- in the form of AI method outputs with explanations. This will allow computer and network system infrastructures --- and user demands --- to grow in complexity while keeping dependability.
https://datatracker.ietf.org/doc/html/draft-pedro-nmrg-ai-framework
  *   13:50 AI-Based Distributed Processing Automation in Digital Twin Network ;
Considerations of deploying AI services in a distributed method, Yong-Geun Hong, 15min
https://datatracker.ietf.org/doc/draft-oh-nmrg-ai-adp/
https://datatracker.ietf.org/doc/html/draft-hong-nmrg-ai-deploy
  *   14:05 Performance-Oriented Digital Twins for Packet and Optical Networks, Albert Cabellos, 15 min
https://datatracker.ietf.org/doc/draft-paillisse-nmrg-performance-digital-twin/
  *   14:20 Intent-Based Network Management Automation in 5G Networks, Jaehoon Paul Jeong, 15 min
Abstract: This document describes Network Management Automation (NMA) of cellular network services in 5G networks. For NMA, it proposes a framework empowered with Intent-Based Networking (IBN). The NMA in this document deals with a closed-loop network control, network policy translation, and network management audit. To support these three features in NMA, it specifies an architectural framework with system components and interfaces. Also, this framework can support the use cases of NMA in 5G networks such as the data aggregation of Internet of Things (IoT) devices, network slicing, and the Quality of Service (QoS) in Vehicle-to-Everything (V2X).
https://datatracker.ietf.org/doc/html/draft-jeong-nmrg-ibn-network-management-automation-02
https://wiki.ietf.org/en/meeting/118/hackathon, IETF-118 Hackathon Project related to this draft
  *   14:35 Challenges and Opportunities in Green Networking, Alex Clemm, 10 min

Open forum

  *   14:50 KIRA: Scalable Zero-Touch Routing for Autonomous Control Planes, Roland Bless, 15 min
Abstract: KIRA is a scalable zero-touch distributed routing solution that is tailored to control planes, i.e., in contrast to commonly used routing protocols like OSPF, ISIS, BGP etc., it prioritizes resilient connectivity over route efficiency. It scales to 100,000s of nodes in a single network, it uses ID-based addresses, is zero-touch (i.e., it requires no configuration for and after deployment) and is able to work well in various network topologies. Moreover, it offers a flexible memory/stretch trade-off per node, shows fast recovery from link or node failures, and is loop-free, even during convergence. Additionally, it includes a built-in Distributed Hash Table (DHT) that can be used for simple name service registration and resolution, thereby helping to realize autonomic network management and control and zero-touch deployments.
https://datatracker.ietf.org/doc/draft-bless-rtgwg-kira/
https://s.kit.edu/KIRA, more background information