NMRG 72nd meeting
IETF 118, Prague + Online
Contacts:
RG Chairs
RG Secretaries
Useful links:
Agenda:
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)
Alfons: Is this architecture with all the level of controllers
suitable for ML applications?
Qin: Work related: ECA draft (how to respond to network changes)
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
Alfons: the control and management service bus looks very similar to
SMO architecture in ORAN.
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/
Roland: your definition is more generic
Roland: SIGCOMM papers on data-driven model of networks
Marco Liebsch
14:20
To Albert: Agree that NTD should answer the question ‘how changes
the behaviour of my network if THAT happens?” One point about THAT:
Do you think the enforced change or event (THAT) is more on the load
on a path, or more in the configuration of a node in a NDT, e.g. its
queue size, scheduler, etc. The latter may require creating a new
model, while the impact of adding load to one interface/path is what
an existing well trained model can help to answer. Do you agree?
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
Laurent: we have sevral IBN use cases. Discuss with other authors of
other IBN use cases, commonolaties, connections (for example
measurment intent)
Alex: interesting to have this as a use case and also an experience
report.
14:35 Challenges and Opportunities in Green Networking, Alex
Clemm, 15 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
Laurent: different domains: have you investigated the cases where
different areas are disconnected?
NMRG Research Agenda topics
13:05 Knowledge Graphs for Network Management, Ignacio
Dominguez-Martinez, 15 min
13:20 Semantic Metadata Annotation for Network Anomaly Detection,
Thomas Graf, 15 min
13:35 Artificial Intelligence Framework for Network Management,
Pedro Martinez-Julia, 15 min
13:50 AI-Based Distributed Processing Automation in Digital Twin
Network ;
Considerations of deploying AI services in a distributed method,
Yong-Geun Hong, 15min
14:05 Performance-Oriented Digital Twins for Packet and Optical
Networks, Albert Cabellos, 15 min
Marco Liebsch: Agree that NTD should answer the question ‘how
changes the behaviour of my network if THAT happens?” One point
about THAT: Do you think the enforced change or event (THAT) is more
on the load on a path, or more in the configuration of a node in a
NDT, e.g. its queue size, scheduler, etc. The latter may require
creating a new model, while the impact of adding load to one
interface/path is what an existing well trained model can help to
answer. Do you agree?
14:20 Intent-Based Network Management Automation in 5G Networks,
Jaehoon Paul Jeong, 15 min
14:35 Challenges and Opportunities in Green Networking, Alex
Clemm, 15 min
Open forum
14:50 KIRA: Scalable Zero-Touch Routing for Autonomous Control
Planes, Roland Bless, 15 min