OPSAWG                                                           H. Song
Internet-Draft                                                 Futurewei
Intended status: Informational                                    F. Qin
Expires: August 23, 2021                                    China Mobile
                                                       P. Martinez-Julia
                                                            L. Ciavaglia
                                                                 A. Wang
                                                           China Telecom
                                                       February 19, 2021

                      Network Telemetry Framework


   Network telemetry is a technology for gaining network insight and
   facilitating efficient and automated network management.  It
   encompasses various techniques for remote data generation,
   collection, correlation, and consumption.  This document describes an
   architectural framework for network telemetry, motivated by
   challenges that are encountered as part of the operation of networks
   and by the requirements that ensue.  Network telemetry, as
   necessitated by best industry practices, covers technologies and
   protocols that extend beyond conventional network Operations,
   Administration, and Management (OAM).  The presented network
   telemetry framework promises flexibility, scalability, accuracy,
   coverage, and performance.  In addition, it facilitates the
   implementation of automated control loops to address both today's and
   tomorrow's network operational needs.  This document clarifies the
   terminologies and classifies the modules and components of a network
   telemetry system from several different perspectives.  The framework
   and taxonomy help to set a common ground for the collection of
   related work and provide guidance for related technique and standard

Status of This Memo

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   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   Drafts is at https://datatracker.ietf.org/drafts/current/.

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   Internet-Drafts are draft documents valid for a maximum of six months
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Glossary  . . . . . . . . . . . . . . . . . . . . . . . . . .   4
   3.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   6
     3.1.  Telemetry Data Coverage . . . . . . . . . . . . . . . . .   7
     3.2.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . .   7
     3.3.  Challenges  . . . . . . . . . . . . . . . . . . . . . . .   9
     3.4.  Network Telemetry . . . . . . . . . . . . . . . . . . . .  10
   4.  The Necessity of a Network Telemetry Framework  . . . . . . .  12
   5.  Network Telemetry Framework . . . . . . . . . . . . . . . . .  13
     5.1.  Top Level Modules . . . . . . . . . . . . . . . . . . . .  14
       5.1.1.  Management Plane Telemetry  . . . . . . . . . . . . .  17
       5.1.2.  Control Plane Telemetry . . . . . . . . . . . . . . .  17
       5.1.3.  Forwarding Plane Telemetry  . . . . . . . . . . . . .  18
       5.1.4.  External Data Telemetry . . . . . . . . . . . . . . .  20
     5.2.  Second Level Function Components  . . . . . . . . . . . .  21
     5.3.  Data Acquisition Mechanism and Type Abstraction . . . . .  22
     5.4.  Mapping Existing Mechanisms into the Framework  . . . . .  24
   6.  Evolution of Network Telemetry Applications . . . . . . . . .  25
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  26
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  27
   9.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  27
   10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  28
   11. Informative References  . . . . . . . . . . . . . . . . . . .  28
   Appendix A.  A Survey on Existing Network Telemetry Techniques  .  32

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     A.1.  Management Plane Telemetry  . . . . . . . . . . . . . . .  32
       A.1.1.  Push Extensions for NETCONF . . . . . . . . . . . . .  32
       A.1.2.  gRPC Network Management Interface . . . . . . . . . .  32
     A.2.  Control Plane Telemetry . . . . . . . . . . . . . . . . .  33
       A.2.1.  BGP Monitoring Protocol . . . . . . . . . . . . . . .  33
     A.3.  Data Plane Telemetry  . . . . . . . . . . . . . . . . . .  33
       A.3.1.  The Alternate Marking (AM) technology . . . . . . . .  33
       A.3.2.  Dynamic Network Probe . . . . . . . . . . . . . . . .  34
       A.3.3.  IP Flow Information Export (IPFIX) protocol . . . . .  35
       A.3.4.  In-Situ OAM . . . . . . . . . . . . . . . . . . . . .  35
       A.3.5.  Postcard Based Telemetry  . . . . . . . . . . . . . .  35
     A.4.  External Data and Event Telemetry . . . . . . . . . . . .  35
       A.4.1.  Sources of External Events  . . . . . . . . . . . . .  36
       A.4.2.  Connectors and Interfaces . . . . . . . . . . . . . .  37
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  37

1.  Introduction

   Network visibility is the ability of management tools to see the
   state and behavior of a network, which is essential for successful
   network operation.  Network Telemetry revolves around network data
   that can help provide insights about the current state of the
   network, including network devices, forwarding, control, and
   management planes, and that can be generated and obtained through a
   variety of techniques, including but not limited to network
   instrumentation and measurements, and that can be processed for
   purposes ranging from service assurance to network security using a
   wide variety of techniques including machine learning, data analysis,
   and correlation.  In this document, Network Telemetry refer to both
   the data itself (i.e., "Network Telemetry Data"), and the techniques
   and processes used to generate, export, collect, and consume that
   data for use by potentially automated management applications.
   Network telemetry extends beyond the conventional network Operations,
   Administration, and Management (OAM) techniques and expects to
   support better flexibility, scalability, accuracy, coverage, and

   However, the term of network telemetry lacks a solid and unambiguous
   definition.  The scope and coverage of it cause confusion and
   misunderstandings.  It is beneficial to clarify the concept and
   provide a clear architectural framework for network telemetry, so we
   can articulate the technical field, and better align the related
   techniques and standard works.

   To fulfill such an undertaking, we first discuss some key
   characteristics of network telemetry which set a clear distinction
   from the conventional network OAM and show that some conventional OAM
   technologies can be considered a subset of the network telemetry

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   technologies.  We then provide an architectural framework for network
   telemetry which includes four modules, each concerned with a
   different category of telemetry data and corresponding procedures.
   All the modules are internally structured in the same way, including
   components that allow to configure data sources with regards to what
   data to generate and how to make that available to client
   applications, components that instrument the underlying data sources,
   and components that perform the actual rendering, encoding, and
   exporting of the generated data.  We show how the network telemetry
   framework can benefit the current and future network operations.
   Based on the distinction of modules and function components, we can
   map the existing and emerging techniques and protocols into the
   framework.  The framework can also simplify the tasks for designing,
   maintaining, and understanding a network telemetry system.  At last,
   we outline the evolution stages of the network telemetry system and
   discuss the potential security concerns.

   The purpose of the framework and taxonomy is to set a common ground
   for the collection of related work and provide guidance for future
   technique and standard developments.  To the best of our knowledge,
   this document is the first such effort for network telemetry in
   industry standards organizations.

2.  Glossary

   Before further discussion, we list some key terminology and acronyms
   used in this documents.  We make an intended differentiation between
   the terms of network telemetry and OAM.  However, it should be
   understood that there is not a hard-line distinction between the two
   concepts.  Rather, network telemetry is considered as the extension
   of OAM.  It covers all the existing OAM protocols but puts more
   emphasis on the newer and emerging techniques and protocols
   concerning all aspects of network data from acquisition to

   AI:  Artificial Intelligence.  In network domain, AI refers to the
      machine-learning based technologies for automated network
      operation and other tasks.

   AM:  Alternate Marking, a flow performance measurement method,
      specified in [RFC8321].

   BMP:  BGP Monitoring Protocol, specified in [RFC7854].

   DNP:  Dynamic Network Probe, referring to programmable in-network
      sensors for network monitoring and measurement.

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   DPI:  Deep Packet Inspection, referring to the techniques that
      examines packet beyond packet L3/L4 headers.

   gNMI:  gRPC Network Management Interface, a network management
      protocol from OpenConfig Operator Working Group, mainly
      contributed by Google.  See [gnmi] for details.

   gRPC:  gRPC Remote Procedure Call, a open source high performance RPC
      framework that gNMI is based on.  See [grpc] for details.

   IPFIX:  IP Flow Information Export Protocol, specified in [RFC7011].

   IOAM:  In-situ OAM, a dataplane on-path telemetry technique.

   NETCONF:  Network Configuration Protocol, specified in [RFC6241].

   NetFlow:  A Cisco protocol for flow record collecting, described in

   Network Telemetry:  The process and instrumentation for acquiring and
      utilizing network data remotely for network monitoring and
      operation.  A general term for a large set of network visibility
      techniques and protocols, concerning aspects like data generation,
      collection, correlation, and consumption.  Network telemetry
      addresses the current network operation issues and enables smooth
      evolution toward future intent-driven autonomous networks.

   NMS:  Network Management System, referring to applications that allow
      network administrators manage a network.

   OAM:  Operations, Administration, and Maintenance.  A group of
      network management functions that provide network fault
      indication, fault localization, performance information, and data
      and diagnosis functions.  Most conventional network monitoring
      techniques and protocols belong to network OAM.

   PBT:  Postcard-Based Telemetry, a dataplane on-path telemetry

   SMIv2  Structure of Management Information Version 2, specified in

   SNMP:  Simple Network Management Protocol.  Version 1 and 2 are
      specified in [RFC1157] and [RFC3416], respectively.

   YANG:  The abbreviation of "Yet Another Next Generation".  YANG is a
      data modeling language for the definition of data sent over

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      network management protocols such as the NETCONF and RESTCONF.
      YANG is defined in [RFC6020].

   YANG ECA  A YANG model for Event-Condition-Action policies, defined
      in [I-D.wwx-netmod-event-yang].

   YANG PUSH:  A method to subscribe pushed data from remote YANG
      datastore on network devices.  Details are specified in [RFC8641]
      and [RFC8639].

3.  Background

   The term "big data" is used to describe the extremely large volume of
   data sets that can be analyzed computationally to reveal patterns,
   trends, and associations.  Networks are undoubtedly a source of big
   data because of their scale and the volume of network traffic they
   forward.  It is easy to see that network operations can benefit from
   network big data.

   Today one can access advanced big data analytics capability through a
   plethora of commercial and open source platforms (e.g., Apache
   Hadoop), tools (e.g., Apache Spark), and techniques (e.g., machine
   learning).  Thanks to the advance of computing and storage
   technologies, network big data analytics gives network operators an
   opportunity to gain network insights and move towards network
   autonomy.  Some operators start to explore the application of
   Artificial Intelligence (AI) to make sense of network data.  Software
   tools can use the network data to detect and react on network faults,
   anomalies, and policy violations, as well as predicting future
   events.  In turn, the network policy updates for planning, intrusion
   prevention, optimization, and self-healing may be applied.

   It is conceivable that an autonomic network [RFC7575] is the logical
   next step for network evolution following Software Defined Network
   (SDN), aiming to reduce (or even eliminate) human labor, make more
   efficient use of network resources, and provide better services more
   aligned with customer requirements.  Intent-based Networking (IBN)
   [I-D.irtf-nmrg-ibn-concepts-definitions] requires network visibility
   and telemetry data in order to ensure that the network is behaving as
   intended.  Although it takes time to reach the ultimate goal, the
   journey has started nevertheless.

   However, while the data processing capability is improved and
   applications are hungry for more data, the networks lag behind in
   extracting and translating network data into useful and actionable
   information in efficient ways.  The system bottleneck is shifting
   from data consumption to data supply.  Both the number of network
   nodes and the traffic bandwidth keep increasing at a fast pace.  The

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   network configuration and policy change at smaller time slots than
   before.  More subtle events and fine-grained data through all network
   planes need to be captured and exported in real time.  In a nutshell,
   it is a challenge to get enough high-quality data out of the network
   in a manner that is efficient, timely, and flexible.  Therefore, we
   need to survey the existing technologies and protocols and identify
   any potential gaps.

   In the remainder of this section, first we clarify the scope of
   network data (i.e., telemetry data) concerned in the context.  Then,
   we discuss several key use cases for today's and future network
   operations.  Next, we show why the current network OAM techniques and
   protocols are insufficient for these use cases.  The discussion
   underlines the need of new methods, techniques, and protocols which
   we assign under the umbrella term - Network Telemetry.

3.1.  Telemetry Data Coverage

   Any information that can be extracted from networks (including data
   plane, control plane, and management plane) and used to gain
   visibility or as basis for actions is considered telemetry data.  It
   includes statistics, event records and logs, snapshots of state,
   configuration data, etc.  It also covers the outputs of any active
   and passive measurements [RFC7799].  Specially, raw data can be
   processed in-network before being sent to a data consumer.  Such
   processed data is also considered telemetry data.  A classification
   of telemetry data is provided in Section 5.

3.2.  Use Cases

   The following set of use cases is essential for network operations.
   While the list is by no means exhaustive, it is enough to highlight
   the requirements for data velocity, variety, volume, and veracity in

   o  Security: Network intrusion detection and prevention systems need
      to monitor network traffic and activities and act upon anomalies.
      Given increasingly sophisticated attack vector coupled with
      increasingly severe consequences of security breaches, new tools
      and techniques need to be developed, relying on wider and deeper
      visibility into networks.

   o  Policy and Intent Compliance: Network policies are the rules that
      constraint the services for network access, provide service
      differentiation, or enforce specific treatment on the traffic.
      For example, a service function chain is a policy that requires
      the selected flows to pass through a set of ordered network
      functions.  Intent, as defined in

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      [I-D.irtf-nmrg-ibn-concepts-definitions], is a set of operational
      goal that a network should meet and outcomes that a network is
      supposed to deliver, defined in a declarative manner without
      specifying how to achieve or implement them.  An intent requires a
      complex translation and mapping process before being applied on
      networks.  While a policy or an intent is enforced, the compliance
      needs to be verified and monitored continuously, relying on
      visibility that is provided through network telemetry data, and
      any violation needs to be reported immediately.

   o  SLA Compliance: A Service-Level Agreement (SLA) defines the level
      of service a user expects from a network operator, which include
      the metrics for the service measurement and remedy/penalty
      procedures when the service level misses the agreement.  Users
      need to check if they get the service as promised and network
      operators need to evaluate how they can deliver the services that
      can meet the SLA based on realtime network telemetry data,
      including data from network measurements.

   o  Root Cause Analysis: Any network failure can be the effect of a
      sequence of chained events.  Troubleshooting and recovery require
      quick identification of the root cause of any observable issues.
      However, the root cause is not always straightforward to identify,
      especially when the failure is sporadic and the number of event
      messages, both related and unrelated to the same cause, is
      overwhelming.  While machine learning technologies can be used for
      root cause analysis, it up to the network to sense and provide the
      relevant data to feed into machine learning applications.

   o  Network Optimization: This covers all short-term and long-term
      network optimization techniques, including load balancing, Traffic
      Engineering (TE), and network planning.  Network operators are
      motivated to optimize their network utilization and differentiate
      services for better Return On Investment (ROI) or lower Capital
      Expenditures (CAPEX).  The first step is to know the real-time
      network conditions before applying policies for traffic
      manipulation.  In some cases, micro-bursts need to be detected in
      a very short time-frame so that fine-grained traffic control can
      be applied to avoid network congestion.  Long-term planning of
      network capacity and topology requires analysis of real-world
      network telemetry data that is obtained over long periods of time.

   o  Event Tracking and Prediction: The visibility into traffic path
      and performance is critical for services and applications that
      rely on healthy network operation.  Numerous related network
      events are of interest to network operators.  For example, Network
      operators want to learn where and why packets are dropped for an
      application flow.  They also want to be warned of issues in

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      advance so proactive actions can be taken to avoid catastrophic

3.3.  Challenges

   For a long time, network operators have relied upon SNMP [RFC3416],
   Command-Line Interface (CLI), or Syslog to monitor the network.  Some
   other OAM techniques as described in [RFC7276] are also used to
   facilitate network troubleshooting.  These conventional techniques
   are not sufficient to support the above use cases for the following

   o  Most use cases need to continuously monitor the network and
      dynamically refine the data collection in real-time.  The poll-
      based low-frequency data collection is ill-suited for these
      applications.  Subscription-based streaming data directly pushed
      from the data source (e.g., the forwarding chip) is preferred to
      provide enough data quantity and precision at scale.

   o  Comprehensive data is needed from packet processing engine to
      traffic manager, from line cards to main control board, from user
      flows to control protocol packets, from device configurations to
      operations, and from physical layer to application layer.
      Conventional OAM only covers a narrow range of data (e.g., SNMP
      only handles data from the Management Information Base (MIB)).
      Traditional network devices cannot provide all the necessary
      probes.  More open and programmable network devices are therefore

   o  Many application scenarios need to correlate network-wide data
      from multiple sources (i.e., from distributed network devices,
      different components of a network device, or different network
      planes).  A piecemeal solution is often lacking the capability to
      consolidate the data from multiple sources.  The composition of a
      complete solution, as partly proposed by Autonomic Resource
      Control Architecture(ARCA)
      [I-D.pedro-nmrg-anticipated-adaptation], will be empowered and
      guided by a comprehensive framework.

   o  Some of the conventional OAM techniques (e.g., CLI and Syslog)
      lack a formal data model.  The unstructured data hinder the tool
      automation and application extensibility.  Standardized data
      models are essential to support the programmable networks.

   o  Although some conventional OAM techniques support data push (e.g.,
      SNMP Trap [RFC2981][RFC3877], Syslog, and sFlow), the pushed data
      are limited to only predefined management plane warnings (e.g.,
      SNMP Trap) or sampled user packets (e.g., sFlow).  Network

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      operators require the data with arbitrary source, granularity, and
      precision which are beyond the capability of the existing

   o  The conventional passive measurement techniques can either consume
      excessive network resources and render excessive redundant data,
      or lead to inaccurate results; on the other hand, the conventional
      active measurement techniques can interfere with the user traffic
      and their results are indirect.  Techniques that can collect
      direct and on-demand data from user traffic are more favorable.

   These challenges were addressed by newer standards and techniques
   (e.g., IPFIX/Netflow, PSAMP, IOAM, and YANG-Push) and more are
   emerging.  These standards and techniques need to be recognized and
   accommodated in a new framework.

3.4.  Network Telemetry

   Network telemetry has emerged as a mainstream technical term to refer
   to the network data collection and consumption techniques.  Several
   network telemetry techniques and protocols (e.g., IPFIX [RFC7011] and
   gRPC [grpc]) have been widely deployed.  Network telemetry allows
   separate entities to acquire data from network devices so that data
   can be visualized and analyzed to support network monitoring and
   operation.  Network telemetry covers the conventional network OAM and
   has a wider scope.  It is expected that network telemetry can provide
   the necessary network insight for autonomous networks and address the
   shortcomings of conventional OAM techniques.

   Network telemetry usually assumes machines as data consumers rather
   than human operators.  Hence, the network telemetry can directly
   trigger the automated network operation, while in contrast some
   conventional OAM tools are designed and used to help human operators
   to monitor and diagnose the networks and guide manual network
   operations.  Such a proposition leads to very different techniques.

   Although new network telemetry techniques are emerging and subject to
   continuous evolution, several characteristics of network telemetry
   have been well accepted.  Note that network telemetry is intended to
   be an umbrella term covering a wide spectrum of techniques, so the
   following characteristics are not expected to be held by every
   specific technique.

   o  Push and Streaming: Instead of polling data from network devices,
      telemetry collectors subscribe to streaming data pushed from data
      sources in network devices.

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   o  Volume and Velocity: The telemetry data is intended to be consumed
      by machines rather than by human being.  Therefore, the data
      volume can be huge and the processing is optimized for the needs
      of automation in realtime.

   o  Normalization and Unification: Telemetry aims to address the
      overall network automation needs.  Efforts are made to normalize
      the data representation and unify the protocols, so to simplify
      data analysis and provide integrated analysis across heterogeneous
      devices and data sources across a network.

   o  Model-based: The telemetry data is modeled in advance which allows
      applications to configure and consume data with ease.

   o  Data Fusion: The data for a single application can come from
      multiple data sources (e.g., cross-domain, cross-device, and
      cross-layer) and needs to be correlated to take effect.

   o  Dynamic and Interactive: Since the network telemetry means to be
      used in a closed control loop for network automation, it needs to
      run continuously and adapt to the dynamic and interactive queries
      from the network operation controller.

   In addition, an ideal network telemetry solution may also have the
   following features or properties:

   o  In-Network Customization: The data that is generated can be
      customized in network at run-time to cater to the specific need of
      applications.  This needs the support of a programmable data plane
      which allows probes with custom functions to be deployed at
      flexible locations.

   o  In-Network Data Aggregation and Correlation: Network devices and
      aggregation points can work out which events and what data needs
      to be stored, reported, or discarded thus reducing the load on the
      central collection and processing points while still ensuring that
      the right information is ready to be processed in a timely way.

   o  In-Network Processing: Sometimes it is not necessary or feasible
      to gather all information to a central point to be processed and
      acted upon.  It is possible for the data processing to be done in
      network, allowing reactive actions to be taken locally.

   o  Direct Data Plane Export: The data originated from the data plane
      forwarding chips can be directly exported to the data consumer for
      efficiency, especially when the data bandwidth is large and the
      real-time processing is required.

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   o  In-band Data Collection: In addition to the passive and active
      data collection approaches, the new hybrid approach allows to
      directly collect data for any target flow on its entire forwarding
      path [I-D.song-opsawg-ifit-framework].

   It is worth noting that a network telemetry system should not be
   intrusive to normal network operations by avoiding the pitfall of the
   "observer effect".  That is, it should not change the network
   behavior and affect the forwarding performance.  Otherwise, the whole
   purpose of network telemetry is compromised.

   Although in many cases a system for network telemetry involves a
   remote data collecting and consuming entity, it is important to
   understand that there are no inherent assumptions about how a system
   should be architected.  Telemetry data producers and consumers can
   work in distributed or peer-to-peer fashions rather than assuming a
   centralized data consuming entity.  In such cases, a network node can
   be the direct consumer of telemetry data from other nodes.

4.  The Necessity of a Network Telemetry Framework

   Network data analytics and machine-learning technologies are applied
   for network operation automation, relying on abundant and coherent
   data from networks.  Data acquisition that is limited to a single
   source and static in nature will in many cases not be sufficient to
   meet an application's telemetry data needs.  As a result, multiple
   data sources, involving a variety of techniques and standards, will
   need to be integrated.  It is desirable to have a framework that
   classifies and organizes different telemetry data source and types,
   defines different components of a network telemetry system and their
   interactions, and helps coordinate and integrate multiple telemetry
   approaches across layers.  This allows flexible combinations of data
   for different applications, while normalizing and simplifying
   interfaces.  In detail, such a framework would benefit application
   development for the following reasons:

   o  Future networks, autonomous or otherwise, depend on holistic and
      comprehensive network visibility.  All the use cases and
      applications are better to be supported uniformly and coherently
      under a single intelligent agent using an integrated, converged
      mechanism and common telemetry data representations wherever
      feasible.  Therefore, the protocols and mechanisms should be
      consolidated into a minimum yet comprehensive set.  A telemetry
      framework can help to normalize the technique developments.

   o  Network visibility presents multiple viewpoints.  For example, the
      device viewpoint takes the network infrastructure as the
      monitoring object from which the network topology and device

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      status can be acquired; the traffic viewpoint takes the flows or
      packets as the monitoring object from which the traffic quality
      and path can be acquired.  An application may need to switch its
      viewpoint during operation.  It may also need to correlate a
      service and its impact on user experience to acquire the
      comprehensive information.

   o  Applications require network telemetry to be elastic in order to
      make efficient use of network resources and reduce the impact of
      processing related to network telemetry on network performance.
      For example, routine network monitoring should cover the entire
      network with a low data sampling rate.  Only when issues arise or
      critical trends emerge should telemetry data source be modified
      and telemetry data rates boosted as needed.

   o  Efficient data fusion is critical for applications to reduce the
      overall quantity of data and improve the accuracy of analysis.

   A telemetry framework collects together all of the telemetry-related
   works from different sources and working groups within IETF.  This
   makes it possible to assemble a comprehensive network telemetry
   system and to avoid repetitious or redundant work.  The framework
   should cover the concepts and components from the standardization
   perspective.  This document describes the modules which make up a
   network telemetry framework and decomposes the telemetry system into
   a set of distinct components that existing and future work can easily
   map to.

5.  Network Telemetry Framework

   The top level network telemetry framework partitions the network
   telemetry into four modules based on the telemetry data object source
   and represents their relationship.  At the next level, the framework
   decomposes each module into separate components.  Each of the modules
   follows the same underlying structure, with one component dedicated
   to the configuration of data subscriptions and data sources, a second
   component dedicated to encoding and exporting data, and a third
   component instrumenting the generation of telemetry related to the
   underlying resources.  Throughout the framework, the same set of
   abstract data acquiring mechanisms and data types are applied.  The
   two-level architecture with the uniform data abstraction helps
   accurately pinpoint a protocol or technique to its position in a
   network telemetry system or disaggregate a network telemetry system
   into manageable parts.

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5.1.  Top Level Modules

   Telemetry can be applied on the forwarding plane, the control plane,
   and the management plane in a network, as well as other sources out
   of the network, as shown in Figure 1.  Therefore, we categorize the
   network telemetry into four distinct modules with each having its own
   interface to Network Operation Applications.

                   |                              |
                   |       Network Operation      |<-------+
                   |          Applications        |        |
                   |                              |        |
                   +------------------------------+        |
                        ^      ^           ^               |
                        |      |           |               |
                        V      |           V               V
                   +-----------|---+--------------+  +-----------+
                   |           |   |              |  |           |
                   | Control Pl|ane|              |  | External  |
                   | Telemetry | <--->            |  | Data and  |
                   |           |   |              |  | Event     |
                   |      ^    V   |  Management  |  | Telemetry |
                   +------|--------+  Plane       |  |           |
                   |      V        |  Telemetry   |  +-----------+
                   | Forwarding    |              |
                   | Plane       <--->            |
                   | Telemetry     |              |
                   |               |              |

                Figure 1: Modules in Layer Category of NTF

   The rationale of this partition lies in the different telemetry data
   objects which result in different data source and export locations.
   Such differences have profound implications on in-network data
   programming and processing capability, data encoding and transport
   protocol, and required data bandwidth and latency.

   We summarize the major differences of the four modules in the
   following table.  They are compared from six angles:

   o  Data Object

   o  Data Export Location

   o  Data Model

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   o  Data Encoding

   o  Telemetry Protocol

   o  Transport Method

   Data Object is the target and source of each module.  Because the
   data source varies, the location where data is mostly conveniently
   exported also varies.  For example, forwarding plane data mainly
   originates from the fast path(e.g., forwarding chips) while control
   plane data mainly originates from the slow path (e.g., main control
   CPU).  For convenience and efficiency, it is preferred to export the
   data from locations near the source.  Because each location that can
   export data has different capability, the proper data model,
   encoding, and transport method cannot be kept the same.  For example,
   the forwarding chip has high throughput but limited capacity for
   processing complex data and maintaining states, while the main
   control CPU is capable of complex data and state processing, but has
   limited bandwidth for high throughput data.  As a result, the
   suitable telemetry protocol for each module can be different.  Some
   representative techniques are shown in the corresponding table blocks
   to highlight the technical diversity of these modules.  Note that the
   selected techniques just reflect the de-facto state of the art and
   are not exhaustive.  The key point is that one cannot expect to use a
   universal protocol to cover all the network telemetry requirements.

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   | Module  | Control      | Management   | Forwarding   | External  |
   |         | Plane        | Plane        | Plane        | Data      |
   |Object   | control      | config. &    | flow & packet| terminal, |
   |         | protocol &   | operation    | QoS, traffic | social &  |
   |         | signaling,   | state, MIB   | stat., buffer| environ-  |
   |         | RIB, ACL     |              | & queue stat.| mental    |
   |Export   | main control | main control | fwding chip  | various   |
   |Location | CPU,         | CPU          | or linecard  |           |
   |         | linecard CPU |              | CPU; main    |           |
   |         | or fwding    |              | control CPU  |           |
   |         | chip         |              | unlikely     |           |
   |Data     | YANG,        | MIB, syslog, | template,    | YANG      |
   |Model    | custom       | YANG,        | YANG,        |           |
   |         |              | custom       | custom       |           |
   |Data     | GPB, JSON,   | GPB, JSON,   | plain        | GPB, JSON |
   |Encoding | XML, plain   | XML          |              | XML, plain|
   |Protocol | gRPC,NETCONF,| gRPC,NETCONF,| IPFIX, mirror| gRPC      |
   |         | IPFIX,mirror |              |              |           |
   |Transport| HTTP, TCP,   | HTTP, TCP    | UDP          | HTTP,TCP  |
   |         | UDP          |              |              | UDP       |

              Figure 2: Comparison of the Data Object Modules

   Note that the interaction with the applications that consume network
   telemetry data can be indirect.  Some in-device data transfer is
   possible.  For example, in the management plane telemetry, the
   management plane may need to acquire data from the data plane.  Some
   of the operational states can only be derived from data plane data
   sources such as the interface status and statistics.  For another
   example, obtaining control plane telemetry data may require the
   ability access the Forwarding Information Base (FIB) of the data

   On the other hand, an application may involve more than one plane and
   interact with multiple planes simultaneously.  For example, an SLA
   compliance application may require both the data plane telemetry and
   the control plane telemetry.

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   The requirements and challenges for each module are summarized as
   follows (note that the requirements may pertain across all telemetry
   modules; however, we emphasize those that are most pronounced for a
   particular plane).

5.1.1.  Management Plane Telemetry

   The management plane of network elements interacts with the Network
   Management System (NMS), and provides information such as performance
   data, network logging data, network warning and defects data, and
   network statistics and state data.  The management plane includes
   many protocols, including some that are considered "legacy", such as
   SNMP and syslog.  Regardless the protocol, management plane telemetry
   must address the following requirements:

   o  Convenient Data Subscription: An application should have the
      freedom to choose the data export means such as the data types and
      the export frequency.

   o  Structured Data: For automatic network operation, machines will
      replace human for network data comprehension.  The schema
      languages such as YANG can efficiently describe structured data
      and normalize data encoding and transformation.

   o  High Speed Data Transport: In order to keep up with the velocity
      of information, a server needs to be able to send large amounts of
      data at high frequency.  Compact encoding formats are needed to
      compress the data and improve the data transport efficiency.  The
      subscription mode, by replacing the query mode, reduces the
      interactions between clients and servers and helps to improve the
      server's efficiency.

5.1.2.  Control Plane Telemetry

   The control plane telemetry refers to the health condition monitoring
   of different network control protocols covering Layer 2 to Layer 7.
   Keeping track of the running status of these protocols is beneficial
   for detecting, localizing, and even predicting various network
   issues, as well as network optimization, in real-time and in fine
   granularity.  Some particular challenges and issues faced by the
   control plane telemetry are as follows:

   o  One challenging problem for the control plane telemetry is how to
      correlate the End-to-End (E2E) Key Performance Indicators (KPI) to
      a specific layer's KPIs.  For example, an IPTV user may describe
      his User Experience (UE) by the video fluency and definition.
      Then in case of an unusually poor UE KPI or a service
      disconnection, it is non-trivial to delimit and pinpoint the issue

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      in the responsible protocol layer (e.g., the Transport Layer or
      the Network Layer), the responsible protocol (e.g., ISIS or BGP at
      the Network Layer), and finally the responsible device(s) with
      specific reasons.

   o  Traditional OAM-based approaches for control plane KPI measurement
      include PING (L3), Tracert (L3), Y.1731 (L2), and so on.  One
      common issue behind these methods is that they only measure the
      KPIs instead of reflecting the actual running status of these
      protocols, making them less effective or efficient for control
      plane troubleshooting and network optimization.

   o  An example of the control plane telemetry is the BGP monitoring
      protocol (BMP), it is currently used to monitoring the BGP routes
      and enables rich applications, such as BGP peer analysis, AS
      analysis, prefix analysis, security analysis, and so on.  However,
      the monitoring of other layers, protocols and the cross-layer,
      cross-protocol KPI correlations are still in their infancy (e.g.,
      the IGP monitoring is missing), which require further research.

5.1.3.  Forwarding Plane Telemetry

   An effective forwarding plane telemetry system relies on the data
   that the network device can expose.  The quality, quantity, and
   timeliness of data must meet some stringent requirements.  This
   raises some challenges to the network data plane devices where the
   first hand data originates.

   o  A data plane device's main function is user traffic processing and
      forwarding.  While supporting network visibility is important, the
      telemetry is just an auxiliary function, and it should not impede
      normal traffic processing and forwarding (i.e., the performance is
      not lowered and the behavior is not altered due to the telemetry

   o  Network operation applications require end-to-end visibility
      across various sources, which can result in a huge volume of data.
      However, the sheer data quantity should not exhaust the network
      bandwidth, regardless of the data delivery approach (i.e., whether
      through in-band or out-of-band channels).

   o  The data plane devices must provide timely data with the minimum
      possible delay.  Long processing, transport, storage, and analysis
      delay can impact the effectiveness of the control loop and even
      render the data useless.

   o  The data should be structured and labeled, and easy for
      applications to parse and consume.  At the same time, the data

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      types needed by applications can vary significantly.  The data
      plane devices need to provide enough flexibility and
      programmability to support the precise data provision for

   o  The data plane telemetry should support incremental deployment and
      work even though some devices are unaware of the system.  This
      challenge is highly relevant to the standards and legacy networks.

   Although not specific to the forwarding plane, these challenges are
   more difficult to the forwarding plane because of the limited
   resource and flexibility.  The data plane programmability is
   essential to support network telemetry.  Newer data plane forwarding
   chips are equipped with advanced telemetry features and provide
   flexibility to support customized telemetry functions.

   Technique Taxonomy: concerning about how one instruments the
   telemetry, there can be multiple possible dimensions to classify the
   forwarding plane telemetry techniques.

   o  Active, Passive, and Hybrid: This dimension concerns about the
      end-to-end measurement.  Active and passive methods (as well as
      the hybrid types) are well documented in [RFC7799].  Passive
      methods include TCPDUMP, IPFIX [RFC7011], sflow, and traffic
      mirroring.  These methods usually have low data coverage.  The
      bandwidth cost is very high in order to improve the data coverage.
      On the other hand, active methods include Ping, OWAMP [RFC4656],
      TWAMP [RFC5357], and Cisco's SLA Protocol [RFC6812].  These
      methods are intrusive and only provide indirect network
      measurement results.  Hybrid methods, including in-situ OAM
      [I-D.ietf-ippm-ioam-data], Alternate-Marking (AM) [RFC8321], and
      Multipoint Alternate Marking
      [I-D.fioccola-ippm-multipoint-alt-mark], provide a well-balanced
      and more flexible approach.  However, these methods are also more
      complex to implement.

   o  In-Band and Out-of-Band: The telemetry data, before being exported
      to some collector, can be carried in user packets.  Such methods
      are considered in-band (e.g., in-situ OAM
      [I-D.ietf-ippm-ioam-data]).  If the telemetry data is directly
      exported to some collector without modifying the user packets,
      such methods are considered out-of-band (e.g., postcard-based
      INT).  It is possible to have hybrid methods.  For example, only
      the telemetry instruction or partial data is carried by user
      packets (e.g., AM [RFC8321]).

   o  E2E and In-Network: Some E2E methods start from and end at the
      network end hosts (e.g., Ping).  The other methods work in

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      networks and are transparent to end hosts.  However, if needed,
      in-network methods can be easily extended into end hosts.

   o  Data Subject: Depending on the telemetry objective, the methods
      can be flow-based (e.g., in-situ OAM [I-D.ietf-ippm-ioam-data]),
      path-based (e.g., Traceroute), and node-based (e.g., IPFIX
      [RFC7011]).  The various data objects can be packet, flow record,
      measurement, states, and signal.

5.1.4.  External Data Telemetry

   Events that occur outside the boundaries of the network system are
   another important source of network telemetry.  Correlating both
   internal telemetry data and external events with the requirements of
   network systems, as presented in
   [I-D.pedro-nmrg-anticipated-adaptation], provides a strategic and
   functional advantage to management operations.

   As with other sources of telemetry information, the data and events
   must meet strict requirements, especially in terms of timeliness,
   which is essential to properly incorporate external event information
   to management cycles.  The specific challenges are described as

   o  The role of external event detector can be played by multiple
      elements, including hardware (e.g. physical sensors, such as
      seismometers) and software (e.g.  Big Data sources that analyze
      streams of information, such as Twitter messages).  Thus, the
      transmitted data must support different shapes but, at the same
      time, follow a common but extensible schema.

   o  Since the main function of the external event detectors is to
      perform the notifications, their timeliness is assumed.  However,
      once messages have been dispatched, they must be quickly collected
      and inserted into the control plane with variable priority, which
      will be high for important sources and/or important events and low
      for secondary ones.

   o  The schema used by external detectors must be easily adopted by
      current and future devices and applications.  Therefore, it must
      be easily mapped to current information models, such as in terms
      of YANG.

   Organizing together both internal and external telemetry information
   will be key for the general exploitation of the management
   possibilities of current and future network systems, as reflected in
   the incorporation of cognitive capabilities to new hardware and
   software (virtual) elements.

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5.2.  Second Level Function Components

   Reflecting the best current practice, the telemetry module at each
   plane is further partitioned into five distinct components:

   o  Data Query, Analysis, and Storage: This component works at the
      application layer.  It is a part of the network management system
      at the receiver side.  On the one hand, it is responsible for
      issuing data requirements.  The data of interest can be modeled
      data through configuration or custom data through programming.
      The data requirements can be queries for one-shot data or
      subscriptions for events or streaming data.  On the other hand, it
      receives, stores, and processes the returned data from network
      devices.  Data analysis can be interactive to initiate further
      data queries.  This component can reside in either network devices
      or remote controllers.  It can be centralized and distributed, and
      involve one or more instances.

   o  Data Configuration and Subscription: This component deploys data
      queries on devices.  It determines the protocol and channel for
      applications to acquire desired data.  This component is also
      responsible for configuring the desired data that might not be
      directly available form data sources.  The subscription data can
      be described by models, templates, or programs.

   o  Data Encoding and Export: This component determines how telemetry
      data is delivered to the data analysis and storage component.  The
      data encoding and the transport protocol may vary due to the data
      exporting location.

   o  Data Generation and Processing: The requested data needs to be
      captured, processed, and formatted in network devices from raw
      data sources.  This may involve in-network computing and
      processing on either the fast path or the slow path in network

   o  Data Object and Source: This component determines the monitoring
      object and original data source.  The data source usually just
      provides raw data which needs further processing.  A data source
      can be considered a probe.  Some data sources can be dynamically
      installed, while others will be more static.

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                   +----------------------------------------+ |
                   |                                        | |
                   |    Data Query, Analysis, & Storage     | |
                   |                                        | +
                   +-------+++ -----------------------------+
                           |||                   ^^^
                           |||                   |||
                           ||V                   |||
                     +-----V---------------------+------------+ |
                   +---------------------+-------+----------+ | |
                   | Data Configuration  |                  | | |
                   | & Subscription      | Data Encoding    | | |
                   | (model, template,   | & Export         | | |
                   | & program)          |                  | | |
                   +---------------------+------------------| | |
                   |                                        | | |
                   |           Data Generation              | | |
                   |           & Processing                 | | |
                   |                                        | | |
                   +----------------------------------------| | |
                   |                                        | | |
                   |       Data Object and Source           | |-+
                   |                                        |-+

          Figure 3: Components in the Network Telemetry Framework

5.3.  Data Acquisition Mechanism and Type Abstraction

   Broadly speaking, network data can be acquired through subscription
   (push) and query (poll).  A subscription is a contract between
   publisher and subscriber.  After initial setup, the subscribed data
   is automatically delivered to registered subscribers until the
   subscription expires.  Subscription can be partitioned into two sub
   modes: the Publish-Subscription (Pub-Sub) mode and the Subscription-
   Publish (Sub-Pub) mode.  In the Pub-Sub mode, a publisher publishes
   pre-defined data and any qualified subscribers can subscribe the data
   as-is.  In the Sub-Pub mode, a subscriber initiates a data request
   and sends it to a publisher; the publisher will deliver the requested
   data when available.  While for both modes, the subscribed data is
   pushed to the subscriber, the Sub-Pub mode allows subscribers to
   customize their subscriptions.

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   In contrast, query is used when a querier expects immediate and one-
   off feedback from network devices.  The queried data may be directly
   extracted from some specific data source, or synthesized and
   processed from raw data.  Query suits for interactive network
   telemetry applications.

   There are four types of data from network devices that a telemetry
   data consumer can subscribe or query:

   o  Simple Data: The data that are steadily available from some data
      store or static probes in network devices. such data can be
      specified by YANG model.

   o  Complex Data: The data need to be synthesized or processed in
      network from raw data from one or more network devices.  The data
      processing function can be statically or dynamically loaded into
      network devices.

   o  Event-triggered Data: The data are conditionally acquired based on
      the occurrence of some events.  It can be actively pushed through
      subscription or passively polled through query.  There are many
      ways to model events, including using Finite State Machine (FSM)
      or Event Condition Action (ECA) [I-D.wwx-netmod-event-yang].

   o  Streaming Data: The data are continuously generated.  It can be
      time series or the dump of databases.  The streaming data reflect
      realtime network states and metrics and require large bandwidth
      and processing power.  The streaming data are always actively
      pushed to the subscribers.

   The above data types are not mutually exclusive.  Rather, they often
   overlap.  For example, event-triggered data can be simple or complex,
   and streaming data can be simple, complex, or triggered by events.
   The relationships of these data types are illustrated in Figure 4.

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              +------>| Simple Data  |<------+
              |       +------------- +       |
              |              ^               |
              |              |               |
              |       +------+-------+       |
              |   +-->| Complex Data |<--+   |
              |   |   +--------------+   |   |
              |   |                      |   |
              |   |                      |   |
      +-------+---+----------+     +-----+---+-------+
      | Event-triggered Data |<----+ Streaming Data  |
      +----------------------+     +-----------------+

                     Figure 4: Data Type Relationship

   Subscription usually deals with event-triggered data and streaming
   data, and query usually deals with simple data and complex data.  But
   the other ways are also possible.  The conventional OAM techniques
   are mostly about querying simple data.  While these techniques are
   still useful, more advanced network telemetry techniques are designed
   mainly for event-triggered or streaming data subscription, and
   complex data query.

5.4.  Mapping Existing Mechanisms into the Framework

   The following two tables show how the existing mechanisms (mainly
   published in IETF and with the emphasis on the latest new
   technologies) are positioned in the framework.  Given the vast body
   of existing work, we cannot provide an exhaustive list, so the
   mechanisms in the tables should be considered as just examples.
   Also, some comprehensive protocols and techniques may cover multiple
   aspects or modules of the framework, so a name in a block only
   emphasizes one particular characteristic of it.  More details about
   some listed mechanisms can be found in Appendix A.

   The first table is based on the data acquisition mechanisms and data

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         |                      | Query     | Subscription |
         | Simple Data          | SNMP      | YANG         |
         | Complex Data         | DNP       | YANG PUSH    |
         | Event-triggered Data | DNP       | YANG PUSH    |
         | Streaming Data       | N/A       | gRPC         |

                     Figure 5: Existing Work Mapping I

   The second table is based on the telemetry modules and components.

        |             | Management      | Control       | Forwarding   |
        |             | Plane           | Plane         | Plane        |
        | data config.| gRPC, NETCONF,  | NETCONF/YANG  | NETCONF/YANG,|
        | & subscribe | SMIv2,YANG PUSH | YANG PUSH     | YANG PUSH    |
        | data gen. & | DNP,            | DNP,          | IOAM, PSAMP  |
        | process     | YANG            | YANG          | PBT, AM,     |
        |             |                 |               | DNP          |
        | data        | gRPC, NETCONF   | BMP, NETCONF  | IPFIX        |
        | export      | YANG PUSH       |               |              |

                    Figure 6: Existing Work Mapping II

6.  Evolution of Network Telemetry Applications

   Network telemetry is a fast evolving technical area.  As the network
   moves towards the automated operation, network telemetry applications
   undergo several stages of evolution which add new layer of
   requirements to the underlying network telemetry techniques.  Each
   stage is built upon the techniques adopted by the previous stages
   plus some new requirements.

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   Stage 0 - Static Telemetry:  The telemetry data source and type are
      determined at design time.  The network operator can only
      configure how to use it with limited flexibility.

   Stage 1 - Dynamic Telemetry:  The custom telemetry data can be
      dynamically programmed or configured at runtime without
      interrupting the network operation, allowing a tradeoff among
      resource, performance, flexibility, and coverage.  DNP is an
      effort towards this direction.

   Stage 2 - Interactive Telemetry:  The network operator can
      continuously customize and fine tune the telemetry data in real
      time to reflect the network operation's visibility requirements.
      Compared with Stage 1, the changes are frequent based on the real-
      time feedback.  At this stage, some tasks can be automated, but
      human operators still need to sit in the middle to make decisions.

   Stage 3 - Closed-loop Telemetry:  The telemetry is free from the
      interference of human operators, except for generating the
      reports.  The intelligent network operation engine automatically
      issues the telemetry data requests, analyzes the data, and updates
      the network operations in closed control loops.

   Existing technologies are ready for stage 0 and stage 1.  Individual
   stage 2 and stage 3 applications are also possible now.  However, the
   future autonomic networks may need a comprehensive operation
   management system which works at stage 2 and stage 3 to cover all the
   network operation tasks.  A well-defined network telemetry framework
   is the first step towards this direction.

7.  Security Considerations

   The complexity of network telemetry raises significant security
   implications.  For example, telemetry data can be manipulated to
   exhaust various network resources at each plane as well as the data
   consumer; falsified or tampered data can mislead the decision making
   and paralyze networks; wrong configuration and programming for
   telemetry is equally harmful.

   Given that this document has proposed a framework for network
   telemetry and the telemetry mechanisms discussed are more extensive
   (in both message frequency and traffic amount) than the conventional
   network OAM concepts, we must also reflect that various new security
   considerations may also arise.  A number of techniques already exist
   for securing the forwarding plane, the control plane, and the
   management plane in a network, but it is important to consider if any
   new threat vectors are now being enabled via the use of network
   telemetry procedures and mechanisms.

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   Security considerations for networks that use telemetry methods may

   o  Telemetry framework trust and policy model;

   o  Role management and access control for enabling and disabling
      telemetry capabilities;

   o  Protocol transport used telemetry data and inherent security

   o  Telemetry data stores, storage encryption and methods of access;

   o  Tracking telemetry events and any abnormalities that might
      identify malicious attacks using telemetry interfaces.

   o  Authentication and signing of telemetry data to make data more

   Some of the security considerations highlighted above may be
   minimized or negated with policy management of network telemetry.  In
   a network telemetry deployment it would be advantageous to separate
   telemetry capabilities into different classes of policies, i.e., Role
   Based Access Control and Event-Condition-Action policies.  Also,
   potential conflicts between network telemetry mechanisms must be
   detected accurately and resolved quickly to avoid unnecessary network
   telemetry traffic propagation escalating into an unintended or
   intended denial of service attack.

   Further study of the security issues will be required, and it is
   expected that the secuirty mechanisms and protocols are developed and
   deployed along with a network telemetry system.

8.  IANA Considerations

   This document includes no request to IANA.

9.  Contributors

   The other contributors of this document are listed as follows.

   o  Tianran Zhou

   o  Zhenbin Li

   o  Zhenqiang Li

   o  Daniel King

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   o  Adrian Farrel

   o  Alexander Clemm

10.  Acknowledgments

   We would like to thank Greg Mirsky, Randy Presuhn, Joe Clarke, Victor
   Liu, James Guichard, Uri Blumenthal, Giuseppe Fioccola, Yunan Gu,
   Parviz Yegani, Young Lee, Qin Wu, and many others who have provided
   helpful comments and suggestions to improve this document.

11.  Informative References

   [gnmi]     "gNMI - gRPC Network Management Interface",

   [grpc]     "gPPC, A high performance, open-source universal RPC
              framework", <https://grpc.io>.

              Fioccola, G., Cociglio, M., Sapio, A., and R. Sisto,
              "Multipoint Alternate Marking method for passive and
              hybrid performance monitoring", draft-fioccola-ippm-
              multipoint-alt-mark-04 (work in progress), June 2018.

              Evens, T., Bayraktar, S., Lucente, P., Mi, K., and S.
              Zhuang, "Support for Adj-RIB-Out in BGP Monitoring
              Protocol (BMP)", draft-ietf-grow-bmp-adj-rib-out-07 (work
              in progress), August 2019.

              Evens, T., Bayraktar, S., Bhardwaj, M., and P. Lucente,
              "Support for Local RIB in BGP Monitoring Protocol (BMP)",
              draft-ietf-grow-bmp-local-rib-09 (work in progress),
              January 2021.

              Brockners, F., Bhandari, S., and T. Mizrahi, "Data Fields
              for In-situ OAM", draft-ietf-ippm-ioam-data-11 (work in
              progress), November 2020.

              Zhou, T., Zheng, G., Voit, E., Graf, T., and P. Francois,
              "Subscription to Distributed Notifications", draft-ietf-
              netconf-distributed-notif-01 (work in progress), November

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              Zheng, G., Zhou, T., Graf, T., Francois, P., and P.
              Lucente, "UDP-based Transport for Configured
              Subscriptions", draft-ietf-netconf-udp-notif-01 (work in
              progress), November 2020.

              Clemm, A., Ciavaglia, L., Granville, L., and J. Tantsura,
              "Intent-Based Networking - Concepts and Definitions",
              draft-irtf-nmrg-ibn-concepts-definitions-02 (work in
              progress), September 2020.

              Kumar, A., Kolhe, J., Ghemawat, S., and L. Ryan, "gRPC
              Protocol", draft-kumar-rtgwg-grpc-protocol-00 (work in
              progress), July 2016.

              Shakir, R., Shaikh, A., Borman, P., Hines, M., Lebsack,
              C., and C. Morrow, "gRPC Network Management Interface
              (gNMI)", draft-openconfig-rtgwg-gnmi-spec-01 (work in
              progress), March 2018.

              Martinez-Julia, P., "Exploiting External Event Detectors
              to Anticipate Resource Requirements for the Elastic
              Adaptation of SDN/NFV Systems", draft-pedro-nmrg-
              anticipated-adaptation-02 (work in progress), June 2018.

              Song, H., Zhou, T., Li, Z., Mirsky, G., Shin, J., and K.
              Lee, "Postcard-based On-Path Flow Data Telemetry using
              Packet Marking", draft-song-ippm-postcard-based-
              telemetry-08 (work in progress), October 2020.

              Song, H. and J. Gong, "Requirements for Interactive Query
              with Dynamic Network Probes", draft-song-opsawg-dnp4iq-01
              (work in progress), June 2017.

              Song, H., Qin, F., Chen, H., Jin, J., and J. Shin, "In-
              situ Flow Information Telemetry", draft-song-opsawg-ifit-
              framework-13 (work in progress), October 2020.

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              WU, Q., Bryskin, I., Birkholz, H., Liu, X., and B. Claise,
              "A YANG Data model for ECA Policy Management", draft-wwx-
              netmod-event-yang-10 (work in progress), November 2020.

   [RFC1157]  Case, J., Fedor, M., Schoffstall, M., and J. Davin,
              "Simple Network Management Protocol (SNMP)", RFC 1157,
              DOI 10.17487/RFC1157, May 1990,

   [RFC2578]  McCloghrie, K., Ed., Perkins, D., Ed., and J.
              Schoenwaelder, Ed., "Structure of Management Information
              Version 2 (SMIv2)", STD 58, RFC 2578,
              DOI 10.17487/RFC2578, April 1999,

   [RFC2981]  Kavasseri, R., Ed., "Event MIB", RFC 2981,
              DOI 10.17487/RFC2981, October 2000,

   [RFC3416]  Presuhn, R., Ed., "Version 2 of the Protocol Operations
              for the Simple Network Management Protocol (SNMP)",
              STD 62, RFC 3416, DOI 10.17487/RFC3416, December 2002,

   [RFC3594]  Duffy, P., "PacketCable Security Ticket Control Sub-Option
              for the DHCP CableLabs Client Configuration (CCC) Option",
              RFC 3594, DOI 10.17487/RFC3594, September 2003,

   [RFC3877]  Chisholm, S. and D. Romascanu, "Alarm Management
              Information Base (MIB)", RFC 3877, DOI 10.17487/RFC3877,
              September 2004, <https://www.rfc-editor.org/info/rfc3877>.

   [RFC4656]  Shalunov, S., Teitelbaum, B., Karp, A., Boote, J., and M.
              Zekauskas, "A One-way Active Measurement Protocol
              (OWAMP)", RFC 4656, DOI 10.17487/RFC4656, September 2006,

   [RFC5357]  Hedayat, K., Krzanowski, R., Morton, A., Yum, K., and J.
              Babiarz, "A Two-Way Active Measurement Protocol (TWAMP)",
              RFC 5357, DOI 10.17487/RFC5357, October 2008,

   [RFC6020]  Bjorklund, M., Ed., "YANG - A Data Modeling Language for
              the Network Configuration Protocol (NETCONF)", RFC 6020,
              DOI 10.17487/RFC6020, October 2010,

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   [RFC6241]  Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
              and A. Bierman, Ed., "Network Configuration Protocol
              (NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,

   [RFC6812]  Chiba, M., Clemm, A., Medley, S., Salowey, J., Thombare,
              S., and E. Yedavalli, "Cisco Service-Level Assurance
              Protocol", RFC 6812, DOI 10.17487/RFC6812, January 2013,

   [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
              "Specification of the IP Flow Information Export (IPFIX)
              Protocol for the Exchange of Flow Information", STD 77,
              RFC 7011, DOI 10.17487/RFC7011, September 2013,

   [RFC7276]  Mizrahi, T., Sprecher, N., Bellagamba, E., and Y.
              Weingarten, "An Overview of Operations, Administration,
              and Maintenance (OAM) Tools", RFC 7276,
              DOI 10.17487/RFC7276, June 2014,

   [RFC7540]  Belshe, M., Peon, R., and M. Thomson, Ed., "Hypertext
              Transfer Protocol Version 2 (HTTP/2)", RFC 7540,
              DOI 10.17487/RFC7540, May 2015,

   [RFC7575]  Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
              Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
              Networking: Definitions and Design Goals", RFC 7575,
              DOI 10.17487/RFC7575, June 2015,

   [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>.

   [RFC7854]  Scudder, J., Ed., Fernando, R., and S. Stuart, "BGP
              Monitoring Protocol (BMP)", RFC 7854,
              DOI 10.17487/RFC7854, June 2016,

   [RFC8321]  Fioccola, G., Ed., Capello, A., Cociglio, M., Castaldelli,
              L., Chen, M., Zheng, L., Mirsky, G., and T. Mizrahi,
              "Alternate-Marking Method for Passive and Hybrid
              Performance Monitoring", RFC 8321, DOI 10.17487/RFC8321,
              January 2018, <https://www.rfc-editor.org/info/rfc8321>.

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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,

   [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>.

Appendix A.  A Survey on Existing Network Telemetry Techniques

   In this non-normative appendix, we provide an overview of some
   existing techniques and standard proposals for each network telemetry

A.1.  Management Plane Telemetry

A.1.1.  Push Extensions for NETCONF

   NETCONF [RFC6241] is one popular network management protocol, which
   is also recommended by IETF.  Although it can be used for data
   collection, NETCONF is good at configurations.  YANG Push [RFC8641]
   [RFC8639] extends NETCONF and enables subscriber applications to
   request a continuous, customized stream of updates from a YANG
   datastore.  Providing such visibility into changes made upon YANG
   configuration and operational objects enables new capabilities based
   on the remote mirroring of configuration and operational state.
   Moreover, distributed data collection mechanism
   [I-D.ietf-netconf-distributed-notif] via UDP based publication
   channel [I-D.ietf-netconf-udp-notif] provides enhanced efficiency for
   the NETCONF based telemetry.

A.1.2.  gRPC Network Management Interface

   gRPC Network Management Interface (gNMI)
   [I-D.openconfig-rtgwg-gnmi-spec] is a network management protocol
   based on the gRPC [I-D.kumar-rtgwg-grpc-protocol] RPC (Remote
   Procedure Call) framework.  With a single gRPC service definition,
   both configuration and telemetry can be covered. gRPC is an HTTP/2
   [RFC7540] based open source micro service communication framework.
   It provides a number of capabilities which are well-suited for
   network telemetry, including:

   o  Full-duplex streaming transport model combined with a binary
      encoding mechanism provided further improved telemetry efficiency.

   o  gRPC provides higher-level features consistency across platforms
      that common HTTP/2 libraries typically do not.  This

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      characteristic is especially valuable for the fact that telemetry
      data collectors normally reside on a large variety of platforms.

   o  The built-in load-balancing and failover mechanism.

A.2.  Control Plane Telemetry

A.2.1.  BGP Monitoring Protocol

   BGP Monitoring Protocol (BMP) [RFC7854] is used to monitor BGP
   sessions and intended to provide a convenient interface for obtaining
   route views.

   The BGP routing information is collected from the monitored device(s)
   to the BMP monitoring station by setting up the BMP TCP session.  The
   BGP peers are monitored by the BMP Peer Up and Peer Down
   Notifications.  The BGP routes (including Adjacency_RIB_In [RFC7854],
   Adjacency_RIB_out [I-D.ietf-grow-bmp-adj-rib-out], and Local_Rib
   [I-D.ietf-grow-bmp-local-rib] are encapsulated in the BMP Route
   Monitoring Message and the BMP Route Mirroring Message, in the form
   of both initial table dump and real-time route update.  In addition,
   BGP statistics are reported through the BMP Stats Report Message,
   which could be either timer triggered or event-driven.  More BMP
   extensions can be explored to enrich the applications of BGP

A.3.  Data Plane Telemetry

A.3.1.  The Alternate Marking (AM) technology

   The Alternate Marking method is efficient to perform packet loss,
   delay, and jitter measurements both in an IP and Overlay Networks, as
   presented in [RFC8321] and [I-D.fioccola-ippm-multipoint-alt-mark].

   This technique can be applied to point-to-point and multipoint-to-
   multipoint flows.  Alternate Marking creates batches of packets by
   alternating the value of 1 bit (or a label) of the packet header.
   These batches of packets are unambiguously recognized over the
   network and the comparison of packet counters for each batch allows
   the packet loss calculation.  The same idea can be applied to delay
   measurement by selecting ad hoc packets with a marking bit dedicated
   for delay measurements.

   Alternate Marking method needs two counters each marking period for
   each flow under monitor.  For instance, by considering n measurement
   points and m monitored flows, the order of magnitude of the packet
   counters for each time interval is n*m*2 (1 per color).

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   Since networks offer rich sets of network performance measurement
   data (e.g packet counters), traditional approaches run into
   limitations.  One reason is the fact that the bottleneck is the
   generation and export of the data and the amount of data that can be
   reasonably collected from the network.  In addition, management tasks
   related to determining and configuring which data to generate lead to
   significant deployment challenges.

   Multipoint Alternate Marking approach, described in
   [I-D.fioccola-ippm-multipoint-alt-mark], aims to resolve this issue
   and makes the performance monitoring more flexible in case a detailed
   analysis is not needed.

   An application orchestrates network performance measurements tasks
   across the network to allow an optimized monitoring and it can
   calibrate how deep can be obtained monitoring data from the network
   by configuring measurement points roughly or meticulously.

   Using Alternate Marking, it is possible to monitor a Multipoint
   Network without examining in depth by using the Network Clustering
   (subnetworks that are portions of the entire network that preserve
   the same property of the entire network, called clusters).  So in
   case there is packet loss or the delay is too high the filtering
   criteria could be specified more in order to perform a detailed
   analysis by using a different combination of clusters up to a per-
   flow measurement as described in Alternate-Marking (AM) [RFC8321].

   In summary, an application can configure end-to-end network
   monitoring.  If the network does not experiment issues, this
   approximate monitoring is good enough and is very cheap in terms of
   network resources.  However, in case of problems, the application
   becomes aware of the issues from this approximate monitoring and, in
   order to localize the portion of the network that has issues,
   configures the measurement points more exhaustively.  So a new
   detailed monitoring is performed.  After the detection and resolution
   of the problem the initial approximate monitoring can be used again.

A.3.2.  Dynamic Network Probe

   Hardware-based Dynamic Network Probe (DNP) [I-D.song-opsawg-dnp4iq]
   provides a programmable means to customize the data that an
   application collects from the data plane.  A direct benefit of DNP is
   the reduction of the exported data.  A full DNP solution covers
   several components including data source, data subscription, and data
   generation.  The data subscription needs to define the complex data
   which can be composed and derived from the raw data sources.  The
   data generation takes advantage of the moderate in-network computing
   to produce the desired data.

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   While DNP can introduce unforeseeable flexibility to the data plane
   telemetry, it also faces some challenges.  It requires a flexible
   data plane that can be dynamically reprogrammed at run-time.  The
   programming API is yet to be defined.

A.3.3.  IP Flow Information Export (IPFIX) protocol

   Traffic on a network can be seen as a set of flows passing through
   network elements.  IP Flow Information Export (IPFIX) [RFC7011]
   provides a means of transmitting traffic flow information for
   administrative or other purposes.  A typical IPFIX enabled system
   includes a pool of Metering Processes collects data packets at one or
   more Observation Points, optionally filters them and aggregates
   information about these packets.  An Exporter then gathers each of
   the Observation Points together into an Observation Domain and sends
   this information via the IPFIX protocol to a Collector.

A.3.4.  In-Situ OAM

   Traditional passive and active monitoring and measurement techniques
   are either inaccurate or resource-consuming.  It is preferable to
   directly acquire data associated with a flow's packets when the
   packets pass through a network.  In-situ OAM (iOAM)
   [I-D.ietf-ippm-ioam-data], a data generation technique, embeds a new
   instruction header to user packets and the instruction directs the
   network nodes to add the requested data to the packets.  Thus, at the
   path end, the packet's experience gained on the entire forwarding
   path can be collected.  Such firsthand data is invaluable to many
   network OAM applications.

   However, iOAM also faces some challenges.  The issues on performance
   impact, security, scalability and overhead limits, encapsulation
   difficulties in some protocols, and cross-domain deployment need to
   be addressed.

A.3.5.  Postcard Based Telemetry

   PBT [I-D.song-ippm-postcard-based-telemetry] is an alternative to
   IOAM.  PBT directly exports data at each node through an independent
   packet.  PBT solves several issues of IOAM.  It can also help to
   identify packet drop location in case a packet is dropped on its
   forwarding path.

A.4.  External Data and Event Telemetry

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A.4.1.  Sources of External Events

   To ensure that the information provided by external event detectors
   and used by the network management solutions is meaningful for the
   management purposes, the network telemetry framework must ensure that
   such detectors (sources) are easily connected to the management
   solutions (sinks).  This requires the specification of a simple
   taxonomy of detectors and match it to the connectors and/or
   interfaces required to connect them.

   Once detectors are classified in such taxonomy, their definitions are
   enlarged with the qualities and other aspects used to handle them and
   represented in the ontology and information model (e.g.  YANG).
   Therefore, differentiating several types of detectors as potential
   sources of external events is essential for the integrity of the
   management framework.  We thus differentiate the following source
   types of external events:

   o  Smart objects and sensors.  With the consolidation of the Internet
      of Things~(IoT) any network system will have many smart objects
      attached to its physical surroundings and logical operation
      environments.  Most of these objects will be essentially based on
      sensors of many kinds (e.g. temperature, humidity, presence) and
      the information they provide can be very useful for the management
      of the network, even when they are not specifically deployed for
      such purpose.  Elements of this source type will usually provide a
      specific protocol for interaction, especially one of those
      protocols related to IoT, such as the Constrained Application
      Protocol (CoAP).  It will be used by the telemetry framework to
      interact with the relevant objects.

   o  Online news reporters.  Several online news services have the
      ability to provide enormous quantity of information about
      different events occurring in the world.  Some of those events can
      impact on the network system managed by a specific framework and,
      therefore, it will be interested on getting such information.  For
      instance, diverse security reports, such as the Common
      Vulnerabilities and Exposures (CVE), can be issued by the
      corresponding authority and used by the management solution to
      update the managed system if needed.  Instead of a specific
      protocol and data format, the sources of this kind of information
      usually follow a relaxed but structured format.  This format will
      be part of both the ontology and information model of the
      telemetry framework.

   o  Global event analyzers.  The advance of Big Data analyzers
      provides a huge amount of information and, more interestingly, the
      identification of events detected by analyzing many data streams

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      from different origins.  In contrast with the other types of
      sources, which are focused in specific events, the detectors of
      this source type will detect very generic events.  For example, a
      sports event takes place and some unexpected movement makes it
      highly interesting and many people connects to sites that are
      covering such event.  The systems supporting the services that
      cover the event can be affected by such situation so their
      management solutions should be aware of it.  In contrast with the
      other source types, a new information model, format, and reporting
      protocol is required to integrate the detectors of this type with
      the management solution.

   Additional types of detector types can be added to the system but
   they will be generally the result of composing the properties offered
   by these main classes.  In any case, future revisions of the network
   telemetry framework will include the required types that cover new
   circumstances and that cannot be obtained by composition.

A.4.2.  Connectors and Interfaces

   For allowing external event detectors to be properly integrated with
   other management solutions, both elements must expose interfaces and
   protocols that are subject to their particular objective.  Since
   external event detectors will be focused on providing their
   information to their main consumers, which generally will not be
   limited to the network management solutions, the framework must
   include the definition of the required connectors for ensuring the
   interconnection between detectors (sources) and their consumers
   within the management systems (sinks) are effective.

   In some situations, the interconnection between the external event
   detectors and the management system is via the management plane.  For
   those situations there will be a special connector that provides the
   typical interfaces found in most other elements connected to the
   management plane.  For instance, the interfaces will accomplish with
   a specific information model (YANG) and specific telemetry protocol,
   such as NETCONF, SNMP, or gRPC.

Authors' Addresses

   Haoyu Song
   2330 Central Expressway
   Santa Clara

   Email: hsong@futurewei.com

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   Fengwei Qin
   China Mobile
   No. 32 Xuanwumenxi Ave., Xicheng District
   Beijing, 100032
   P.R. China

   Email: qinfengwei@chinamobile.com

   Pedro Martinez-Julia
   4-2-1, Nukui-Kitamachi
   Koganei, Tokyo  184-8795

   Email: pedro@nict.go.jp

   Laurent Ciavaglia
   Villarceaux  91460

   Email: laurent.ciavaglia@nokia.com

   Aijun Wang
   China Telecom
   Beiqijia Town, Changping District
   Beijing, 102209
   P.R. China

   Email: wangaj.bri@chinatelecom.cn

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