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Network Management Research Group

Document Charter Network Management RG (nmrg)
Title Network Management Research Group
Last updated 2020-03-21
State Approved
RG State Active
Send notices to (None)

The Network Management Research Group (NMRG) provides a forum for
researchers to explore new technologies for the management of the Internet.
In particular, the NMRG will work on solutions for problems that are not
yet considered well understood enough for engineering work within the IETF.

The focus of the NMRG will be on management services that interface with
the current Internet management framework. This includes communication
services between management systems, which may belong to different
management domains, as well as customer-oriented management services. The
NMRG is expected to identify and document requirements, to survey possible
approaches, to consider new architectural frameworks, to provide
specifications for proposed solutions, and to prove concepts with prototype
implementations that can be tested in large-scale real-world environments.

The IETF Operations and Management Area Directors are members of the NMRG
mailing list and invited to NMRG meetings in order to ensure free flow of
information in both directions, and to avoid duplication of work with the
various IETF working groups.

The group will report its progress through a publicly accessible web site
and presentations at IETF meetings. Specifications developed by the NMRG
will be submitted for publication as Experimental or Informational RFCs.

## Membership

Membership in the NMRG is open to all interested parties.

## Meetings

Regular working meetings are held about three to five times per year at
locations convenient to the majority of the participants. Working meetings
vary from hours-long working sessions (typically when held as part of IETF
meetings) to days-long meetings when co-located with conferences or events
related to network management.

Regular virtual meetings are also organized on a monthly or per-need basis.

## Research Activities (2017-2022)

The constant evolution of networking technologies, in scale, versatility,
and heterogeneity, generates operational complexity and demands novel
disruptive management solutions to address it.  The NMRG will prioritize
investigation of three related topics: 1) self-driving/-managing networks,
2) intent-based networking and 3) artificial intelligence in network
management.  Note: beyond these three topics, the NMRG remains open to
presentation of other topics of interest.

While the ultimate goal of self-driving/-managing networks is fully
autonomous network operations, there will be intermediate levels where the
human users remain “in the loop” and are progressively assisted and
replaced by more and more intelligent mechanisms. Interfaces between humans
and a self-driving system are important and required to allow bidirectional
communications. On one hand, the user must be able to express guidance and
its needs without having to handle the full complexity of the underlying
infrastructures. On the other hand, users must understand the decisions
which were taken and the reasons why, be informed about the future actions
the system will initiate and also be provided with recommendations. 

In this area, Intent-Based Networking (IBN) provides high-level,
user-friendly abstractions to describe business and operational goals, and
alleviates the need for the user to know and derive the technical details
on how to achieve those goals. IBN is an essential component of
self-driving networks but requires the introduction of intelligent
mechanisms to properly process intents with as little human involvement as

Certainly, some of those intelligent mechanisms can rely on advances in
(but should not be limited to) Artificial Intelligence (AI). While
different forms of AI have been used for decades in network management, the
combined progress in amount of data, computing power, AI algorithms and
flexible capabilities of networks in recent years makes highly relevant to
re-examine in depth the coupling between AI and network management. 

## Work plan

To investigate these topics, the initial set of work items comprises:

 * For Intent-Based Networking (IBN):

   1. Document the problem statement, design goals and challenges.
      Goal: describe the problem and solution spaces; identify the limits
      of current technologies and methods and derive the associated
      research challenges.

   2. Document fundamental concepts, background, and terminology.
      Goal: provide clarity and achieve a common understanding of the
      various concepts, definitions and terms of what constitutes an IBN

   3. Develop a taxonomy and document suitable means to express intents.
      Goal: categorize the different forms of intents and define what
      constitutes a “well-formed” intent; describe how an intent can be
      expressed and what can be expressed using an intent with means such
      as information models, grammars, and languages.

   4. Design and specify a common architectural framework comprising
      requirements, functions and techniques to realize an archetypal
      IBN system; describe the life-cycle and theory of operations.  
      Goal: determine the elementary functional blocks of an IBN system,
      their interactions, inputs and outputs; propose different techniques
      applicable for the different functions.

   5. Define appropriate validation scenarios and use cases describing
      concrete examples of intent expressions and functions.
      Goal: assess the quality and completeness of specifications and
      evaluate intent-based systems functionalities in experimental

   6. Develop implementations and proof of concepts.
      Goal: demonstrate the feasibility of the proposed framework and its
      functions; detect potential design flaws, and provide a basis for
      interoperability evaluations.

   7. Study the integrability and interoperability aspects of the proposed
      IBN architectural framework.
      Goal: enable the large adoption and applicability of IBN with
      existing and emerging technologies, and provide guidance on
      deployment considerations.

### For Artificial Intelligence in Network Management (AI-NM):

   1. Investigate, organize and document the major research challenges in
      AI for Network Management.
      Goal: provide a reference document which defines the different forms
      and usages of AI in network management and articulates the different
      goals, challenges, requirements and research directions.
   2. Organize and animate a series of practical Network Management AI
      Goal: promote experimental research, practical knowledge and
      validation of AI techniques to solve network management problems and
      foster exchanges and cross-participation of both AI and Network
      Management specialists.

   3. Support discussion and collaboration on techniques, (meta-)data,
      experimentations and best practises for the use and integration
      of AI with networking management approaches.
      Goal: offer a forum for the Network Management AI community to report
      on advances, developments and key results and introduce its efforts
      to the IETF.  Note: Applicability of AI techniques for IBN
      functionalities and mechanisms is an example of potential joint
      activity between the Network Management AI and IBN realms.

### For Self-Driving/-Managing Networks (SD/MN): 

   1. Support discussion to develop a common understanding of the
      problem-solution space on new architectural frameworks, articulate
      related requirements, survey and propose possible novel approaches.
      Goal: offer a venue for the Network Management community to debate on
      current Internet management frameworks and new proposals, and how to
      adapt and anticipate on needs, technologies and ecosystem evolution.

   2. Investigate and document reference models and de-facto best practises.
      Goal: describe how various realms and components, such as
      intent-based functionality, automation and zero-touch capabilities,
      or else algorithmic approaches (AI or non-AI based), compose together
      to form modern, comprehensive and coherent management solutions.