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Network measurement intent - one of IBN use cases
draft-yang-nmrg-network-measurement-intent-05

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This is an older version of an Internet-Draft whose latest revision state is "Expired".
Authors Danyang Chen , Hongwei Yang , Kehan Yao , Giuseppe Fioccola , Qin Wu
Last updated 2022-07-07
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draft-yang-nmrg-network-measurement-intent-05
Internet Research Task Force                                     D. Chen
Internet-Draft                                                   H. Yang
Intended status: Informational                                    K. Yao
Expires: 7 January 2023                                     China Mobile
                                                             G. Fioccola
                                                                   Q. Wu
                                                                  Huawei
                                                             6 July 2022

           Network measurement intent - one of IBN use cases
             draft-yang-nmrg-network-measurement-intent-05

Abstract

   As an important technical means to detect network state, network
   measurement has attracted more and more attention in the development
   of network.  However, the current network measurement technology has
   the problem that the measurement method and the measurement purpose
   cannot match well.  To solve this problem, this memo introduces
   network measurement intent, presents a process of scheduling the
   network resource and measurement task to meet the user or network
   operator's needs.  And it can be seen as a specific use case of
   intent based network.

Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 7 January 2023.

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Copyright Notice

   Copyright (c) 2022 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.  Code Components
   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Definitions and Acronyms  . . . . . . . . . . . . . . . . . .   3
   3.  Relationship to Existing Documents  . . . . . . . . . . . . .   4
   4.  Overview  . . . . . . . . . . . . . . . . . . . . . . . . . .   5
   5.  Concrete Examples . . . . . . . . . . . . . . . . . . . . . .   7
     5.1.  Time Accuracy Measurement . . . . . . . . . . . . . . . .   8
     5.2.  Spatial Accuracy Measurement  . . . . . . . . . . . . . .  10
   6.  Classification of NMI . . . . . . . . . . . . . . . . . . . .  12
     6.1.  Static NMI  . . . . . . . . . . . . . . . . . . . . . . .  12
     6.2.  Dynamic NMI . . . . . . . . . . . . . . . . . . . . . . .  12
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  13
     9.1.  Normative References  . . . . . . . . . . . . . . . . . .  13
     9.2.  Informative References  . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   With the rapid growth of the present network, the scale of the
   network increases, while users' service requirements for the network
   are getting strict and diversified,e.g., both loss requirements and
   throughput needs to be met simultaneously.  At the same time, network
   resources growth is hard to meet user's service requirments.  In
   order to make good of network resources and improve utilization of
   the bandwidth, it becomes necessary to understand the current running
   state of the network, and network measurement, as a technical means
   to detect the network resource change, has been paid of more
   attention.  The continuous development of network measurement
   technology has also increases higher precision of network awareness.
   However, both the traditional network measurement technology
   (e.g.,loss measurement and delay measurement defined in (RFC 2679

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   [RFC2679]RFC 2680 [RFC2680]) and the network telemetry technology RFC
   8639 [RFC8639]RFC 8641
   [RFC8641][I-D.ietf-netconf-adaptive-subscription], which has emerged
   with the development of software-defined network in recent years,
   need to consume more network resources when detecting the network
   state changes and feeding back the detection results.  Therefore, to
   some extent, the choice of network measurement methods, in addition
   to different accuracy of measurement results, will also cause
   different level of network load to the network.

   In order to balance the accuracy of network measurement results with
   the network load, it is very important to choose the appropriate
   network measurement method according to the different requirements of
   network measurement.  As a result, accurate on-demand network
   measurement technology is becoming more and more important.  At the
   same time, the development of Intent based Network (IBN) enables the
   network to be configured according to users' or network
   administrators' intent.  Therefore, we can integrate the network
   measurement with IBN, i.e., the users' or network administrators'
   perceived demand for network state is regarded as network measurement
   intent.

   Our proposed approach is to use the network measurement intent to
   achieve network performance acquisition based on user/network
   administrator intent- , verify whether network measurement results
   meet the measurement intent, and further improve the accuracy of the
   configuration in IBN.

2.  Definitions and Acronyms

   CLI: Command-line Interface.

   IBN: Intent based Network.

   Policy: A set of rules that governs the choices in behavior of a
   system.

   NMI: Network Measurement Intent, refers to based on user/network
   operator's demand for network status, and automatically collect
   network status information on demand.

   SLA: Service Level Agreement.

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3.  Relationship to Existing Documents

   As the rise of IBN, different groups have different definitions of
   The intent.  For example, ONF [ONOS] defines intent is represented as
   a list of CLI modes that allows users to pass low-level details on
   the network; and there are two active RG drafts in the NMRG right
   now, Intent-Based Networking - Concepts and Definitions,
   [I-D.irtf-nmrg-ibn-concepts-definitions] solves the problem that
   "What is an intent?"
   and[I-D.irtf-nmrg-ibn-intent-classification]solves the problem "Given
   a specific intent, how to parse/disassemble it from different
   angles?".

   Naturally, the question that needs to be solved after concept
   definition should be "How to realize an specific
   intent?".[I-D.irtf-nmrg-ibn-intent-classification]can be considered
   as the first step of realization of a given intent, however, it is
   not enough.  Some other issues should be clarified, like" whether the
   input intent is valid or not?" , "What would the IBN system do when
   the result is not acceptable?", "If the result is not acceptable,
   does human/operator interference required?"... We should take a
   specific IBN use case for illustration of the realization procedure,
   so we will take the network measurement intent as an example.

   Referring to the taxonomy of intent proposed in
   [I-D.irtf-nmrg-ibn-intent-classification], the network measurement
   intent can be classified into different categories.

      Solution: the intent could cover carrier and data center.

      Intent user type: customer.

      Intent type: customer service intent.

      Intent scope: Application, QoS.

      Network scope: Radio Access, Transport, Edge, Core.

      Abstraction: Non-technical.

      Lifecycle Requirements: transient.

   In order to integrate the NMI with the IBN, in this document we
   define the components of the NMI interactive process as follows:

   *  NMI Recognition and Acquisition

   *  NMI Translation

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   *  NMI Policy

   *  NMI Orchestration and pre-Verification

   *  Data Collection and Analytics

   *  NMI Compliance Assessment

4.  Overview

   As mentioned above, NMI refers to the on-demand measurement of the
   network state based on the user/network operators' perceived intent
   of the network state.The user/network operators' perceived intent is
   usually in the form of service level objective or service level
   expectation.  We will take the measurement of the performance of the
   network overwhelming with the network traffic as a simple example and
   present the detailed interactive process for those components defined
   in section 3.

   *  NMI Recognition and Acquisition.

      -  In this function, NMI will be recognized by "ingesting" users'
         or network operators' measurement intent.  They have the
         ability to identify the NMI of a certain network performance
         that users want to measure, such as delay, jitter, etc., and at
         the same time allow users to express the NMI of network
         performance in a variety of interactive ways to ensure the
         accuracy of the identification of the NMI.  To achieve this
         functionality, such an interaction requires the use of the
         intent-northbound interface defined in the IBN,e.g., service
         interface model in [RFC8299][RFC8466] or intent interface
         defined in [TMF1253A].

   *  NMI Translation.

      -  In this function, NMI needs to be translated into corresponding
         measurement policy, which includes but is not limited to
         network performance parameters to be measured (such as delay,
         jitter, and packet loss), time period to be measured, and
         measurement unit.  For a simple example, in the measurement of
         busy network performances, due to dynamic changes of network
         characteristics, such as daily network bandwidth utilization
         rate, the period of network busy time is not fixed.  As a
         result, NMI Policy generated by NMI Translation can determine
         the threshold when the network state is busy or the network is
         congested on the same day based on the historical data learned
         by AI.

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   *  NMI Policy

      -  In this function, NMI policy needs to be translated into
         actions and instructioninvoked against the specified network
         element.  Therefore, NMI policy generated by NMI Translation
         must be executable, that is, corresponding underlying network
         devices must be able to support policy execution.  If the
         generated policy cannot be executed by the underlying device,
         the policy needs to be adjusted.  And if the measurement
         results cannot meet the service requirements set by the users
         and network operators, the policy also needs to be adjusted.

   *  NMI Orchestration and pre-Verification.

      -  In this function, according to the previous NMI Translation and
         NMI Policy step, NMI Orchestration and pre-Verification
         determines the measurement scheme according to the measurement
         policy generated by NMI Policy, and pre-verifies whether the
         measurement scheme is feasible.

      -  Take busy time network measurement as an example, besides
         choosing of measurement schemes and assigning measurement tasks
         [RFC8639][RFC8641][I-D.ietf-netconf-adaptive-subscription][RFC8
         194][I-D.ietf-netmod-eca-policy], it also needs to determine
         whether the network is busy according to the current network
         state.  In addition, this function performs automatic network
         deployment,e.g.,using model driven network management approach
         defined in [RFC8969].

   *  Data Collection and Analytics.

      -  In NMI, data collection and analysis should be based on the
         selected measurement scheme and parameters set to be measured
         that determined in previous steps, automatically realize the
         collection on demand, and generate corresponding data analysis
         results.

   *  NMI Compliance Assessment.

      -  At the end, this function verifies whether the results meets
         the service requirement and whether the NMI is satisfied.  If
         either of the two conditions is not satisfied, the NMI should
         be modified and re-enter the NMI Policy.

   And he measurement flow diagram is shown as the following figure:

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               +                  ^
     NMI input|                  |
    +---------v-------+          |
    | NMI Recognition |          |Measurement
    |and Acquisition  |          |Results
    +--------+--------+          |Feedback
             |                   |
    +--------v--------+          |
    | NMI Translation |          |
    +--------+--------+          |
             |               +---+----- -----+
    +--------v--------+      |NMI Compliance |
    |   NMI Policy    <------+Assessment     |
    +--------+--------+      +--^------------+
             |                  |
   +---------v-----------+   +--+--------------+
   | NMI Orchestration   |   | Data Collection |
   | and pre-Verification|   | and Analytics   |
   +---------+-----------+   +--^--------------+
             |                  |
         +---v------------------+---+
         |   Network Infrastructure |
         +--------------------------+

5.  Concrete Examples

   In this section, we will take SLA measurement intent as an example to
   illustrate each step of the process.

   With the development of measurement technology in recent years,
   network measurement methods can be divided into active measurement,
   passive measurement and a hybrid measurement [RFC7799].  No matter
   which measurement technology is used, the network resource
   consumption will be influenced by the network condition and change
   over the time.e.g.,, if the transmission frequency of active
   measurement message is too fast, it will occupy too much bandwidth
   resources and affect the normal operation of actual business.  While
   if the transmission frequency is too slow, some instantaneous network
   anomalies will be missed and the network status cannot be accurately
   reflected.  Passive measurement requires real- time collection of
   actual business data.  If the sampling rate is too high, a large
   amount of data will be accumulated in a short time
   [I-D.ietf-netconf-adaptive-subscription].The analysis system for
   real-time analysis of these data needs strong processing capacity; if
   the sampling rate is too low, some network anomalies will also be
   omitted.

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   How to balance and accurately measure the network state, especially
   the abnormal network affecting the service, while occupying as little
   network bandwidth as possible, and the processing capacity of the
   data analysis system is not high, this is the function that the NMI
   scheme based on IBN should realize.

   In this section, we will consider two examples to illustrate each
   step of the process.

5.1.  Time Accuracy Measurement

   Taking network SLA performance metric -- delay measurement as an
   example, the simple schematic diagram is as follows, different
   thresholds, warning value and alert value should be set for network
   delay in advance.  When the delay value is below warning, the network
   is normal and the business is normal.  When the delay is between
   warning value and alert value, the network fluctuation is abnormal,
   but the business is normal.  When the delay exceeds the alert value,
   both the network and business are abnormal.  For delay in different
   thresholds, different measurement strategies should be adopted:

   *  When the network delay exceeds the alert value, or when the
      historical data predict that the delay will exceed the alert
      value, passive measurement requires 100% sampling of business
      data, and the transmission frequency of active measurement is
      modulated to the maximum.  At the same time, the log and alarm
      data of the whole network equipment are collected to realize the
      most fine-grained measurement of the network, locate the root
      cause of the problem and repair the network in time.

   *  When the network delay exceeds warning value but is lower than
      alert value, passive measurement samples 60% of business data, and
      the transmission message frequency of the active measurement is
      adjusted to the median value, and the running state data of some
      key devices in the network is collected synchronously.

   *  When the network delay is less than warning value, passive
      measurement data is sampled at 20%, and active measurement message
      frequency is adjusted to the lowest, and the network equipment
      running state of key nodes can be collected as needed.

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           ^ms
           |
           |
           |                         XX
           |                        X X            Sampling Rate 100%
           |                       XX X
     alert +--------------------------------------------------------+
           |                      X   X             Sampling Rate 60%
           |                     X    XX
           |                    X      X                XX
           |          XX        X      X                XXX
           |          XXX       X       X              X  X
           |         XX X      X        X             X   XX
           |         X   XX    X        X  XX   XX    X    XX
   warning +-------------------------------------------------------+
           |         X    XX  X          XX X  XX X  XX      XX
           |     XX  X     X  X          X   XX   XX X        X
           |    XX X X     X  X          X   XX    XXX         X
           |   X   XX       XXX          X         XX          X
           |   X   XX       XX           X
           |        X       XX                      Sampling Rate 20%
           |
           +----------------------------------------------------------->

   Based on the above SLA time delay index measurement, different
   thresholds adopt different measurement strategies, the concrete steps
   of SLA measurement intent are as follows:

   *  In NMI Recognition and Acquisition, SLA measurement intent is
      recognized, and business requirements and performance metrics are
      identified by interacting with users.  Then the NMI Recognition
      and Acquisition module inputs the SLA measurement intent into the
      NMI Translation module.

   *  The NMI Translation module consolidates the SLA measurement intent
      with the measurement policy in NMI Policy, and outputs the
      executable measurement policy, such as the message transmission
      frequency of active measurement, the sampling rate of passive
      measurement, the collection range of equipment running state, etc.

   *  The NMI Orchestration and pre-Verification module uses the
      measurement policy as input and for orchestration layer which is
      responsible for translating it into the specific configuration and
      execution time of each device in the tested network.  The NMI
      Orchestration and pre-Verification module verifies the
      implementation of the policy in the equipment and pre-analyzes the
      measurement results.

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   *  The Data Collection and Analysis module will collect the
      measurement data according to the configuration and execution time
      requirements of the previous step, make a simple analysis of the
      collected data (e.g.,verify the correctness of the measurement
      data), and then send the collected measurement data to the NMI
      Compliance Assessment module.  After that, the NMI Compliance
      Assessment module feedbacks the measurement results (e.g., the
      measurement results match user intent) to the user to complete the
      closed loop of the measurement task.

   *  The NMI Compliance Assessment module evaluates whether the actual
      measurement results are in line with the user's intent.  If they
      are, the results will be fed back.  If they are not, the NMI
      Policy module will be informed to adjust the policy, and then the
      measurement will be restarted.  According to the measurement
      results, the NMI Compliance Assessment module notifies the NMI
      Orchestration and pre-Verification module to modify the execution
      time of the policy in time, and at the same time updates the
      measured results to the delay history database to improve the
      accuracy of delay prediction.

5.2.  Spatial Accuracy Measurement

   The desired approach is to accurately measure the network state,
   especially when there are some issues affecting the service, but at
   the same time, reduce the resources to be employed to achieve the
   desired accuracy.

   In this regard, the Clustered Alternate-Marking framework[RFC8889]
   adds flexibility to Performance Measurement (PM), because it can
   reduce the order of magnitude of the packet counters.  This allows
   the NMI Orchestration and pre-Verification module to supervise,
   control, and manage PM in large networks.

   [RFC8889] introduces the concept of cluster partition of a network.
   The monitored network can be considered as a whole or split into
   clusters that are the smallest subnetworks (group-to-group segments),
   maintaining the packet loss property for each subnetwork.  The
   clusters can be combined in new connected subnetworks at different
   levels, forming new clusters, depending on the level of detail to
   achieve.

   The clustered performance measurement intent represents the spatial
   accuracy, that is the size of the subnetworks to consider for the
   monitoring.  It is possible to start without examining in depth and,
   in case of necessity, the "network zooming" approach can be used.

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   This approach called "network zooming" and can be performed in two
   different ways:

   1.  change the traffic filter and select more detailed flows;

   2.  activate new measurement points by defining more specified
       clusters.

   The network-zooming approach implies that some filters, rules or flow
   identifiers are changed.  But these changes must be done in a way
   that do not affect the performance.  Therefore there could be a
   transient time to wait once the new network configuration takes
   effect.  Anyway, if the performance issue is relevant, it is likely
   to last for a time much longer than the transient time.

   The concrete steps of the clustered performance measurement intent
   are as follows:

   *  In NMI Recognition and Acquisition, the clustered performance
      measurement intent is recognized.  Then the NMI Recognition and
      Acquisition module inputs the clustered performance measurement
      intent into the NMI Translation module.

   *  The NMI Translation module analyzes the clustered performance
      measurement intent and outputs the executable measurement policy,
      such as network partition and the spatial accuracy for the
      monitoring.

   *  The NMI Orchestration and pre-Verification module arranges and
      calibrates the measurement with the specific configuration to
      split the whole network into clusters at different levels.

   *  The Data Collection and Analysis module collects the measurement
      data from the different clusters, and then send these data to the
      NMI Compliance Assessment module.  It verifies the performance for
      each cluster and send the measurement results to the user.

   *  The NMI Compliance Assessment module, in case a cluster is
      experiencing a packet loss or the delay is high, notifies the NMI
      Orchestration and pre-Verification module to modify the cluster
      partition of the network for further investigation.  The network
      configuration can be immediately modified in order to perform a
      new partition of the network but only for the cluster with bad
      performance.  In this way, the problem can be localized with
      successive approximation up to a flow detailed analysis.  This is
      the so-called "closed loop" performance management.

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6.  Classification of NMI

   In this section, we divide the network measurement intent into static
   NMI and dynamic NMI according to different requirement
   characteristics.

6.1.  Static NMI

   Static NMI refers to the measurement purposes remain unchanged and is
   independent of the network state/external environment.  Static NMI
   can be translated into determined network performance indicator
   values, such as concrete delay values, network bandwidth utilization,
   throughput and so on.

   Because the static NMI can be translated into the measurement of the
   determined network performance parameters, the whole process is
   relatively simple and error-prone, and only needs to verify whether
   the measurement results meet the requirements.

6.2.  Dynamic NMI

   Dynamic NMI refers to the measurement purpose remains unchanged but
   the measurement process changes dynamically according to the network
   state/external environment.  Dynamic NMI can also be translated into
   the measurement of determined network performance parameters,
   however, the values of network performance parameters will change
   with the changes of network states and external environment.

   For example, the measurement of busy network performances mentioned
   in the previous section.  Although the corresponding network
   parameters for judging whether the network is busy are determined,
   the corresponding network parameters have different values according
   to different network states and external environments.

   Due to the dynamic nature of dynamic NMI, its processing process is
   more complex than static NMI.  It is not only necessary to verify the
   accuracy of demand analysis, but also to verify whether the final
   measurement results meet the requirements.

7.  Security Considerations

   This document introduces the network measurement intent, and uses two
   concrete examples to illustrate the process of network measurement
   intent.  On the basis of existing intent work, this document can be
   used as a use case for IBN.

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   [I-D.irtf-nmrg-ibn-concepts-definitions]provides a comprehensive
   discussion of security considerations in the context of IBN, which
   are generally applicable also to the network measurement intent
   discussed in this document.

8.  IANA Considerations

   This document has no requests to IANA.

9.  References

9.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC2679]  Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
              Delay Metric for IPPM", RFC 2679, DOI 10.17487/RFC2679,
              September 1999, <https://www.rfc-editor.org/info/rfc2679>.

   [RFC2680]  Almes, G., Kalidindi, S., and M. Zekauskas, "A One-way
              Packet Loss Metric for IPPM", RFC 2680,
              DOI 10.17487/RFC2680, September 1999,
              <https://www.rfc-editor.org/info/rfc2680>.

   [RFC7799]  Morton, A., "Active and Passive Metrics and Methods (with
              Hybrid Types In-Between)", RFC 7799, DOI 10.17487/RFC7799,
              May 2016, <https://www.rfc-editor.org/info/rfc7799>.

   [RFC8194]  Schoenwaelder, J. and V. Bajpai, "A YANG Data Model for
              LMAP Measurement Agents", RFC 8194, DOI 10.17487/RFC8194,
              August 2017, <https://www.rfc-editor.org/info/rfc8194>.

   [RFC8299]  Wu, Q., Ed., Litkowski, S., Tomotaki, L., and K. Ogaki,
              "YANG Data Model for L3VPN Service Delivery", RFC 8299,
              DOI 10.17487/RFC8299, January 2018,
              <https://www.rfc-editor.org/info/rfc8299>.

   [RFC8466]  Wen, B., Fioccola, G., Ed., Xie, C., and L. Jalil, "A YANG
              Data Model for Layer 2 Virtual Private Network (L2VPN)
              Service Delivery", RFC 8466, DOI 10.17487/RFC8466, October
              2018, <https://www.rfc-editor.org/info/rfc8466>.

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   [RFC8639]  Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
              E., and A. Tripathy, "Subscription to YANG Notifications",
              RFC 8639, DOI 10.17487/RFC8639, September 2019,
              <https://www.rfc-editor.org/info/rfc8639>.

   [RFC8641]  Clemm, A. and E. Voit, "Subscription to YANG Notifications
              for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
              September 2019, <https://www.rfc-editor.org/info/rfc8641>.

   [RFC8889]  Fioccola, G., Ed., Cociglio, M., Sapio, A., and R. Sisto,
              "Multipoint Alternate-Marking Method for Passive and
              Hybrid Performance Monitoring", RFC 8889,
              DOI 10.17487/RFC8889, August 2020,
              <https://www.rfc-editor.org/info/rfc8889>.

   [RFC8969]  Wu, Q., Ed., Boucadair, M., Ed., Lopez, D., Xie, C., and
              L. Geng, "A Framework for Automating Service and Network
              Management with YANG", RFC 8969, DOI 10.17487/RFC8969,
              January 2021, <https://www.rfc-editor.org/info/rfc8969>.

9.2.  Informative References

   [I-D.ietf-netconf-adaptive-subscription]
              Wu, Q., Song, W., Liu, P., Ma, Q., Wang, W., and Z. Niu,
              "Adaptive Subscription to YANG Notification", Work in
              Progress, Internet-Draft, draft-ietf-netconf-adaptive-
              subscription-00, 23 June 2022,
              <https://www.ietf.org/archive/id/draft-ietf-netconf-
              adaptive-subscription-00.txt>.

   [I-D.ietf-netmod-eca-policy]
              Wu, Q., Bryskin, I., Birkholz, H., Liu, X., and B. Claise,
              "A YANG Data model for ECA Policy Management", Work in
              Progress, Internet-Draft, draft-ietf-netmod-eca-policy-01,
              19 February 2021, <https://www.ietf.org/archive/id/draft-
              ietf-netmod-eca-policy-01.txt>.

   [I-D.irtf-nmrg-ibn-concepts-definitions]
              Clemm, A., Ciavaglia, L., Granville, L. Z., and J.
              Tantsura, "Intent-Based Networking - Concepts and
              Definitions", Work in Progress, Internet-Draft, draft-
              irtf-nmrg-ibn-concepts-definitions-09, 24 March 2022,
              <https://www.ietf.org/archive/id/draft-irtf-nmrg-ibn-
              concepts-definitions-09.txt>.

   [I-D.irtf-nmrg-ibn-intent-classification]
              Li, C., Havel, O., Olariu, A., Martinez-Julia, P., Nobre,
              J. C., and D. R. Lopez, "Intent Classification", Work in

Chen, et al.             Expires 7 January 2023                [Page 14]
Internet-Draft            Network Working Group                July 2022

              Progress, Internet-Draft, draft-irtf-nmrg-ibn-intent-
              classification-08, 18 May 2022,
              <https://www.ietf.org/archive/id/draft-irtf-nmrg-ibn-
              intent-classification-08.txt>.

Authors' Addresses

   Danyang Chen
   China Mobile
   Beijing
   100053
   China
   Email: chendanyang@chinamobile.com

   Hongwei Yang
   China Mobile
   Beijing
   100053
   China
   Email: yanghongwei@chinamobile.com

   Kehan Yao
   China Mobile
   Beijing
   100053
   China
   Email: yaokehan@chinamobile.com

   Giuseppe Fioccola
   Huawei
   Riesstrasse, 25
   80992 Munich
   Germany
   Email: giuseppe.fioccola@huawei.com

   Qin Wu
   Huawei
   101 Software Avenue, Yuhua District
   Nanjing
   210012
   China
   Email: bill.wu@huawei.com

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