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Versions: (draft-opsawg-ntf) 00 01 02

OPSAWG                                                      H. Song, Ed.
Internet-Draft                                                 Futurewei
Intended status: Informational                                    F. Qin
Expires: April 10, 2020                                     China Mobile
                                                       P. Martinez-Julia
                                                                    NICT
                                                            L. Ciavaglia
                                                                   Nokia
                                                                 A. Wang
                                                           China Telecom
                                                         October 8, 2019


                      Network Telemetry Framework
                        draft-ietf-opsawg-ntf-02

Abstract

   Network telemetry is the technology for gaining network insight and
   facilitating efficient and automated network management.  It engages
   various techniques for remote data collection, correlation, and
   consumption.  This document provides an architectural framework for
   network telemetry, motivated by the network operation challenges and
   requirements.  As evidenced by some key characteristics and industry
   practices, network telemetry covers technologies and protocols beyond
   the conventional network Operations, Administration, and Management
   (OAM).  It promises better flexibility, scalability, accuracy,
   coverage, and performance and allows automated control loops to suit
   both today's and tomorrow's network operation.  This document
   clarifies the terminologies and classifies the modules and components
   of a network telemetry system from several different perspectives.
   To the best of our knowledge, this document is the first such effort
   for network telemetry in industry standards organizations.  The
   framework and taxonomy help to set a common ground for the collection
   of related work and provide guidance for future technique and
   standard developments.

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/.





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   Internet-Drafts are draft documents valid for a maximum of six months
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   This Internet-Draft will expire on April 10, 2020.

Copyright Notice

   Copyright (c) 2019 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
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Motivation  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.1.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . .   5
     2.2.  Challenges  . . . . . . . . . . . . . . . . . . . . . . .   6
     2.3.  Glossary  . . . . . . . . . . . . . . . . . . . . . . . .   7
     2.4.  Network Telemetry . . . . . . . . . . . . . . . . . . . .   8
   3.  The Necessity of a Network Telemetry Framework  . . . . . . .  10
   4.  Network Telemetry Framework . . . . . . . . . . . . . . . . .  11
     4.1.  Data Acquiring Mechanisms and Data Types  . . . . . . . .  12
     4.2.  Data Object Modules . . . . . . . . . . . . . . . . . . .  13
       4.2.1.  Requirements and Challenges for each Module . . . . .  15
     4.3.  Function Components . . . . . . . . . . . . . . . . . . .  19
     4.4.  Existing Works Mapped in the Framework  . . . . . . . . .  21
   5.  Evolution of Network Telemetry  . . . . . . . . . . . . . . .  22
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  23
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  24
   8.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  24
   9.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  24
   10. Informative References  . . . . . . . . . . . . . . . . . . .  24
   Appendix A.  A Survey on Existing Network Telemetry Techniques  .  28
     A.1.  Management Plane Telemetry  . . . . . . . . . . . . . . .  28
       A.1.1.  Push Extensions for NETCONF . . . . . . . . . . . . .  28
       A.1.2.  gRPC Network Management Interface . . . . . . . . . .  28
     A.2.  Control Plane Telemetry . . . . . . . . . . . . . . . . .  29



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       A.2.1.  BGP Monitoring Protocol . . . . . . . . . . . . . . .  29
     A.3.  Data Plane Telemetry  . . . . . . . . . . . . . . . . . .  29
       A.3.1.  The IPFPM technology  . . . . . . . . . . . . . . . .  29
       A.3.2.  Dynamic Network Probe . . . . . . . . . . . . . . . .  30
       A.3.3.  IP Flow Information Export (IPFIX) protocol . . . . .  31
       A.3.4.  In-Situ OAM . . . . . . . . . . . . . . . . . . . . .  31
       A.3.5.  Postcard Based Telemetry  . . . . . . . . . . . . . .  31
     A.4.  External Data and Event Telemetry . . . . . . . . . . . .  31
       A.4.1.  Sources of External Events  . . . . . . . . . . . . .  32
       A.4.2.  Connectors and Interfaces . . . . . . . . . . . . . .  33
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  33

1.  Introduction

   Network visibility is the ability of management tools to see the
   state and behavior of a network.  It is essential for successful
   network operation.  Network telemetry is the process of measuring,
   correlating, recording, and distributing information about the
   behavior of a network.  Network telemetry has been considered as an
   ideal means to gain sufficient network visibility with better
   flexibility, scalability, accuracy, coverage, and performance than
   some conventional network Operations, Administration, and Management
   (OAM) techniques.

   However, so far 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
   technologies.  We then provide an architectural framework from three
   different perspectives for network telemetry.  We show how network
   telemetry can meet the current and future network operation
   requirements, and the challenges each telemetry module is facing.
   Based on the distinction of modules and function components, we can
   easily map the existing and emerging techniques and protocols into
   the framework.  At last, we outline a road-map for the evolution of
   the network telemetry system and discuss the potential security
   concerns for network telemetry.

   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,



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   this document is the first such effort for network telemetry in
   industry standards organizations.

2.  Motivation

   The term of Big data is used to describe the extremely large volume
   of data sets that can be analyzed computationally to reveal patterns,
   trends, and associations.  Network is undoubtedly a source of big
   data because of its scale and all the traffic goes through it.  It is
   easy to see that network OAM can benefit from network big data.

   Today one can easily 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
   unprecedented 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 intent-driven 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 the most efficient usage of network resources, and
   provide better services more aligned with customer requirements.
   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.  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 network configuration
   and policy change at a much smaller time slot than ever 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 network efficiently,
   timely, and flexibly.  Therefore, we need to examine the existing
   network technologies and protocols, and identify any potential
   technique and standard gaps based on the real network and device
   architectures.





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   In the remaining of this section, first 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 may assign under an umbrella term
   - network telemetry.

2.1.  Use Cases

   These use cases are 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 networks.

   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.  An intent is a high-level abstract policy which
      requires a complex translation and mapping process before being
      applied on networks.  While a policy is enforced, the compliance
      needs to be verified and monitored continuously.

   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.

   Root Cause Analysis:  Any network failure can be the cause or 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 related
      and unrelated events are overwhelming.  While machine learning
      technologies can be used for root cause analysis, it up to the
      network to sense and provide all the relevant data.

   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



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      be applied to avoid network congestion.  The long-term network
      capacity planning and topology augmentation also rely on the
      accumulated data of the network operations.

   Event Tracking and Prediction:  The visibility of user traffic path
      and performance is critical for healthy network operation.
      Numerous related network events are of interest to network
      operators.  For example, Network operators always want to learn
      where and why packets are dropped for an application flow.  They
      also want to be warned of issues in advance so proactive actions
      can be taken to avoid catastrophic consequences.

2.2.  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
   reasons:

   o  Most use cases need to continuously monitor the network and
      dynamically refine the data collection in real-time and
      interactively.  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.  An open and programmable network device is therefore
      needed.

   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.



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   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).  We require the
      data with arbitrary source, granularity, and precision which are
      beyond the capability of the existing techniques.

   o  The conventional passive measurement techniques can either consume
      too much network resources and render too much redundant data, or
      lead to inaccurate results; the conventional active measurement
      techniques can interfere with the user traffic and their results
      are indirect.  We need techniques that can collect direct and on-
      demand data from user traffic.

2.3.  Glossary

   Before further discussion, we list some key terminology and acronyms
   used in this documents.  We make an intended distinction between
   network telemetry and network OAM.

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

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

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

   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].

   IPFPM:  IP Flow Performance Measurement method, specified in
      [RFC8321].



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

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

   Network Telemetry:  Acquiring and processing network data remotely
      for network monitoring and operation.  A general term for a large
      set of network visibility techniques and protocols, with the
      characteristics defined in this document.  Network telemetry
      addresses the current network operation issues and enables smooth
      evolution toward intent-driven autonomous networks.

   NMS:  Network Management System, referring to applications that allow
      network administrators manage a network's software and hardware
      components.  It usually records data from a network's remote
      points to carry out central reporting to a system administrator.

   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
      technique.

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

   YANG FSM:  A YANG model that describes events, operations, and finite
      state machine of YANG-defined network elements.

   YANG PUSH:  A method to subscribe pushed data from remote YANG
      datastore on network devices.

2.4.  Network Telemetry

   Network telemetry has emerged as a mainstream technical term to refer
   to the newer data collection and consumption techniques,
   distinguishing itself from the convention techniques for network OAM.
   The representative techniques and protocols include IPFIX [RFC7011]
   and gPRC [grpc].  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



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   telemetry overlaps with the conventional network OAM and has a wider
   scope than it.  It is expected that network telemetry can provide the
   necessary network insight for autonomous networks and address the
   shortcomings of conventional OAM techniques.

   One difference between the network telemetry and the network OAM is
   that the network telemetry assumes machines as data consumer rather
   than human operators.  Hence, the network telemetry can directly
   trigger the automated network operation, while the conventional OAM
   tools usually help human operators to monitor and diagnose the
   networks and guide manual network operations.  The difference leads
   to very different techniques.

   Although the network telemetry techniques are just 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,
      the telemetry collector subscribes to the streaming data pushed
      from data sources in network devices.

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

   o  Normalization and Unification: Telemetry aims to address the
      overall network automation needs.  The piecemeal solutions offered
      by the conventional OAM approach are no longer suitable.  Efforts
      need to be made to normalize the data representation and unify the
      protocols.

   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:



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   o  In-Network Customization: The data 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
      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 and Action: Sometimes it is not necessary or
      feasible to gather all information to a central point so that it
      can be processed and acted upon.  It is possible for the data
      processing to be done in the network, and actions taken more
      locally and more responsively.

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

   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.

   It is worth noting that, no matter how sophisticated a network
   telemetry system is, it should not be intrusive to networks, by
   avoiding the pitfall of the "observer effect".  That is, it should
   not change the network behavior and affect the forwarding
   performance.

   Although in many cases a network telemetry system is akin to the SDN
   architecture, it is important to understand that network telemetry
   does not infer the need of any centralized data processing and
   analytics engine.  Telemetry data producers and consumers can
   perfectly work in distributed or peer-to-peer fashions instead.

3.  The Necessity of a Network Telemetry Framework

   Big data analytics and machine-learning based AI technologies are
   applied for network operation automation, relying on abundant data
   from networks.  The single-sourced and static data acquisition cannot
   meet the data requirements.  It is desirable to have a framework that
   integrates multiple telemetry approaches from different layers.  This
   allows flexible combinations for different applications.  The




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   framework would benefit application development for the following
   reasons:

   o  The future autonomous networks will require a holistic view on
      network visibility.  All the use cases and applications need to be
      supported uniformly and coherently under a single intelligent
      agent.  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
      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 impact on network experience to acquire the
      comprehensive information.

   o  Applications require network telemetry to be elastic in order to
      efficiently use the network resource and reduce the performance
      impact.  Routine network monitoring covers the entire network with
      low data sampling rate.  When issues arise or trends emerge, the
      telemetry data source can be modified and the data rate can be
      boosted.

   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
   work from different sources and working groups within the 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 clarifies the layered modules on which
   the telemetry is exerted and decomposes the telemetry system into a
   set of distinct components that the existing and future work can
   easily map to.

4.  Network Telemetry Framework

   Network telemetry techniques can be classified from multiple
   dimensions.  In this document, we provide three unique perspectives:
   data acquiring mechanisms, data objects, and function components.






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4.1.  Data Acquiring Mechanisms and Data Types

   Broadly speaking, network data can be acquired through subscription
   (push) and query (poll).  A subscriber may request data when it is
   ready.  It follows a Publish-Subscription (Pub-Sub) mode or a
   Subscription-Publish (Sub-Pub) mode.  In the Pub-Sub mode, pre-
   defined data are published and multiple qualified subscribers can
   subscribe the data.  In the Sub-Pub mode, a subscriber designates
   what data are of interest and demands the network devices to deliver
   the data when they are available.

   In contrast, a querier expects immediate feedback from network
   devices.  It is usually used in a more interactive environment.  The
   queried data may be directly extracted from some specific data
   source, or synthesized and processed from raw data.

   There are four types of data from network devices:

   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.

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

   Event-triggered Data:  The data are conditionally acquired based on
      the occurrence of some event.  An event can be modeled as a Finite
      State Machine (FSM).

   Streaming Data:  The data are continuously or periodically 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 above data types are not mutually exclusive.  For example, event-
   triggered data can be simple or complex, and streaming data can be
   event triggered.  The relationships of these data types are
   illustrated in Figure 1











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                   +--------------------------+
                   | +----------------------+ |
                   | | +-----------------+  | |
                   | | | +-------------+ |  | |
                   | | | | Simple Data | |  | |
                   | | | +-------------+ |  | |
                   | | |  Complex Data   |  | |
                   | | +-----------------+  | |
                   | | Event-triggered Data | |
                   | +----------------------+ |
                   |       Streaming Data     |
                   +--------------------------+


                     Figure 1: Data Type Relationship

   Subscription usually deals with event-triggered data and streaming
   data, and query usually deals with simple data and complex data.  It
   is easy to see that conventional OAM techniques are mostly about
   querying simple data only.  While these techniques are still useful,
   advanced network telemetry techniques pay more attention on the other
   three data types, and prefer event/streaming data subscription and
   complex data query over simple data query.

4.2.  Data Object 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 2.  Therefore, we categorize the
   network telemetry into four distinct modules with each having its own
   interface to Network Operation Applications.




















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


                Figure 2: 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 data bandwidth and latency.

   We summarize the major differences of the four modules in the
   following table.  They are mainly compared from six aspects: data
   object, data export location, data model, data encoding, telemetry
   protocol, and transport method.  Data object is the target and source
   of each module.  Because the data source varies, the data export
   location varies.  Because each data export location has different
   capability, the proper data model, encoding, and transport method
   cannot be kept the same.  As a result, the suitable telemetry
   protocol for each module can be different.  Some representative
   techniques are shown in some table blocks to highlight the technical
   diversity of these modules.  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,| gPRC,NETCONF,| IPFIX, mirror| gRPC      |
   |         | IPFIX,mirror |              |              |           |
   +---------+--------------+--------------+--------------+-----------+
   |Transport| HTTP, TCP,   | HTTP, TCP    | UDP          | HTTP,TCP  |
   |         | UDP          |              |              | UDP       |
   +---------+--------------+--------------+--------------+-----------+


              Figure 3: Comparison of the Data Object Modules

   Note that the interaction with the network operation applications can
   be indirect.  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 the data plane
   such as the interface status and statistics.  For another example,
   the control plane telemetry may need to access the Forwarding
   Information Base (FIB) in data plane.  On the other hand, an
   application may involve more than one plane simultaneously.  For
   example, an SLA compliance application may require both the data
   plane telemetry and the control plane telemetry.

4.2.1.  Requirements and Challenges for each Module







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4.2.1.1.  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.  Some legacy protocols, such as
   SNMP and Syslog, are widely used for the management plane.  However,
   these protocols are insufficient to meet the requirements of the
   future automated network operation applications.

   New management plane telemetry protocols should consider the
   following requirements:

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

   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.

   High Speed Data Transport:  In order to retain the information, a
      server needs to send a large amount of data at high frequency.
      Compact encoding formats are needed to compress the data and
      improve the data transport efficiency.  The push mode, by
      replacing the poll mode, can also reduce the interactions between
      clients and servers, which help to improve the server's
      efficiency.

4.2.1.2.  Control Plane Telemetry

   The control plane telemetry refers to the health condition monitoring
   of different network protocols, which covers 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.

   One of the most challenging problems for the control plane telemetry
   is how to correlate the 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 work to delimit and localize the issue to 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.



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   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.  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
   substantial further research.

4.2.1.3.  Data Plane Telemetry

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

   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
      functions).

   o  The network operation applications requires end-to-end visibility
      from various sources, which results in a huge volume of data.
      However, the sheer data quantity should not stress the network
      bandwidth, regardless of the data delivery approach (i.e., 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
      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
      applications.





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   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.

   The industry has agreed that the data plane programmability is
   essential to support network telemetry.  Newer data plane chips are
   all equipped with advanced telemetry features and provide flexibility
   to support customized telemetry functions.

4.2.1.3.1.  Technique Taxonomy

   There can be multiple possible dimensions to classify the data plane
   telemetry techniques.

   Active and Passive:  The active and passive methods (as well as the
      hybrid types) are well documented in [RFC7799].  The passive
      methods include TCPDUMP, IPFIX [RFC7011], sflow, and traffic
      mirror.  These methods usually have low data coverage.  The
      bandwidth cost is very high in order to improve the data coverage.
      On the other hand, the active methods include Ping, Traceroute,
      OWAMP [RFC4656], and TWAMP [RFC5357].  These methods are intrusive
      and only provide indirect network measurement results.  The hybrid
      methods, including in-situ OAM
      [I-D.brockners-inband-oam-requirements], IPFPM [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.

   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.brockners-inband-oam-requirements]).  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., IPFPM [RFC8321]).

   E2E and In-Network:  Some E2E methods start from and end at the
      network end hosts (e.g., Ping).  The other methods work in
      networks and are transparent to end hosts.  However, if needed,
      the in-network methods can be easily extended into end hosts.

   Flow, Path, and Node:  Depending on the telemetry objective, the
      methods can be flow-based (e.g., in-situ OAM
      [I-D.brockners-inband-oam-requirements]), path-based (e.g.,
      Traceroute), and node-based (e.g., IPFIX [RFC7011]).



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4.2.1.4.  External Data Telemetry

   Events that occur outside the boundaries of the network system are
   another important source of telemetry information.  Correlating both
   internal telemetry data and external events with the requirements of
   network systems, as presented in Exploiting External Event Detectors
   to Anticipate Resource Requirements for the Elastic Adaptation of
   SDN/NFV Systems [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.  Thus, the specific challenges are described as
   follows:

   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 ontology.

   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 ontology 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.

4.3.  Function Components

   At each plane, the telemetry can be further partitioned into five
   distinct components:

   Data Query, Analysis, and Storage:  This component works at the
      application layer.  On the one hand, it is responsible for issuing



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      data queries.  The queries can be for modeled data through
      configuration or custom data through programming.  The queries can
      be one shot 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.  Note that this component can
      reside in either network devices or remote controllers.

   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.

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

   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
      devices.

   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.  A probe can be statically installed or
      dynamically installed.




















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


          Figure 4: Components in the Network Telemetry Framework

4.4.  Existing Works Mapped in the Framework

   The following two tables provide a non-exhaustive list of existing
   works (mainly published in IETF and with the emphasis on the latest
   new technologies) and shows their positions in the framework.  The
   details about the mentioned work can be found in Appendix A.


















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         +-----------------+---------------+----------------+
         |                 | Query         | Subscription   |
         |                 |               |                |
         +-----------------+---------------+----------------+
         | Simple Data     | SNMP, NETCONF,|                |
         |                 | YANG, BMP,    |                |
         |                 | IOAM, PBT,gPRC|                |
         +-----------------+---------------+----------------+
         | Complex Data    | DNP, YANG FSM |                |
         |                 | gRPC, NETCONF |                |
         +-----------------+---------------+----------------+
         | Event-triggered |               | gRPC, NETCONF, |
         | Data            |               | YANG PUSH, DNP |
         |                 |               | IOAM, PBT,     |
         |                 |               | YANG FSM       |
         +-----------------+---------------+----------------+
         | Streaming Data  |               | gRPC, NETCONF, |
         |                 |               | IOAM, PBT, DNP |
         |                 |               | IPFIX, IPFPM   |
         +-----------------+---------------+----------------+


                     Figure 5: Existing Work Mapping I

       +--------------+---------------+----------------+---------------+
       |              | Management    | Control        | Forwarding    |
       |              | Plane         | Plane          | Plane         |
       +--------------+---------------+----------------+---------------+
       | data Config. | gRPC, NETCONF,| NETCONF/YANG   | NETCONF/YANG, |
       | & subscrib.  | YANG PUSH     |                | YANG FSM      |
       +--------------+---------------+----------------+---------------+
       | data gen. &  | DNP,          | DNP,           | IOAM,         |
       | processing   | YANG          | YANG           | PBT, IPFPM,   |
       |              |               |                | DNP           |
       +--------------+---------------+----------------+---------------+
       | data         | gRPC, NETCONF | BMP, NETCONF   | IPFIX         |
       | export       | YANG PUSH     |                |               |
       +--------------+---------------+----------------+---------------+


                    Figure 6: Existing Work Mapping II

5.  Evolution of Network Telemetry

   As the network is evolving towards the automated operation, network
   telemetry also undergoes several levels of evolution.





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   Level 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.

   Level 1 - Dynamic Telemetry:  The telemetry data can be dynamically
      programmed or configured at runtime, allowing a tradeoff among
      resource, performance, flexibility, and coverage.  DNP is an
      effort towards this direction.

   Level 2 - Interactive Telemetry:  The network operator can
      continuously customize the telemetry data in real time to reflect
      the network operation's visibility requirements.  At this level,
      some tasks can be automated, although ultimately human operators
      will still need to sit in the middle to make decisions.

   Level 3 - Closed-loop Telemetry:  Human operators are completely
      excluded from the control loop.  The intelligent network operation
      engine automatically issues the telemetry data request, analyzes
      the data, and updates the network operations in closed control
      loops.

   While most of the existing technologies belong to level 0 and level
   1, with the help of a clearly defined network telemetry framework, we
   can assemble the technologies to support level 2 and make solid steps
   towards level 3.

6.  Security Considerations

   Given that this document has proposed a framework for network
   telemetry and the telemetry mechanisms discussed are distinct (in
   both message frequency and traffic amount) from 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 data plane, control plane, and the management plane
   in a network, but the it is important to consider if any new threat
   vectors are now being enabled via the use of network telemetry
   procedures and mechanisms.

   Security considerations for networks that use telemetry methods may
   include:

   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
      capabilities;



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   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.

   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 discussion and development of this section will be required,
   and it is expected that this security section, and subsequent policy
   section will be developed further.

7.  IANA Considerations

   This document includes no request to IANA.

8.  Contributors

   The other contributors of this document are listed as follows.

   o  Tianran Zhou

   o  Zhenbin Li

   o  Daniel King

   o  Adrian Farrel

9.  Acknowledgments

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

10.  Informative References

   [gnmi]     "gNMI - gRPC Network Management Interface",
              <https://github.com/openconfig/reference/tree/master/rpc/
              gnmi>.




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

   [I-D.brockners-inband-oam-requirements]
              Brockners, F., Bhandari, S., Dara, S., Pignataro, C.,
              Gredler, H., Leddy, J., Youell, S., Mozes, D., Mizrahi,
              T., Lapukhov, P., and r. remy@barefootnetworks.com,
              "Requirements for In-situ OAM", draft-brockners-inband-
              oam-requirements-03 (work in progress), March 2017.

   [I-D.fioccola-ippm-multipoint-alt-mark]
              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.

   [I-D.ietf-grow-bmp-adj-rib-out]
              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.

   [I-D.ietf-grow-bmp-local-rib]
              Evens, T., Bayraktar, S., Bhardwaj, M., and P. Lucente,
              "Support for Local RIB in BGP Monitoring Protocol (BMP)",
              draft-ietf-grow-bmp-local-rib-05 (work in progress),
              August 2019.

   [I-D.ietf-netconf-udp-pub-channel]
              Zheng, G., Zhou, T., and A. Clemm, "UDP based Publication
              Channel for Streaming Telemetry", draft-ietf-netconf-udp-
              pub-channel-05 (work in progress), March 2019.

   [I-D.ietf-netconf-yang-push]
              Clemm, A. and E. Voit, "Subscription to YANG Datastores",
              draft-ietf-netconf-yang-push-25 (work in progress), May
              2019.

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

   [I-D.openconfig-rtgwg-gnmi-spec]
              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.



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   [I-D.pedro-nmrg-anticipated-adaptation]
              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.

   [I-D.song-ippm-postcard-based-telemetry]
              Song, H., Zhou, T., Li, Z., Shin, J., and K. Lee,
              "Postcard-based On-Path Flow Data Telemetry", draft-song-
              ippm-postcard-based-telemetry-05 (work in progress),
              September 2019.

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

   [I-D.zhou-netconf-multi-stream-originators]
              Zhou, T., Zheng, G., Voit, E., Clemm, A., and A. Bierman,
              "Subscription to Multiple Stream Originators", draft-zhou-
              netconf-multi-stream-originators-06 (work in progress),
              July 2019.

   [RFC1157]  Case, J., Fedor, M., Schoffstall, M., and J. Davin,
              "Simple Network Management Protocol (SNMP)", RFC 1157,
              DOI 10.17487/RFC1157, May 1990,
              <https://www.rfc-editor.org/info/rfc1157>.

   [RFC2981]  Kavasseri, R., Ed., "Event MIB", RFC 2981,
              DOI 10.17487/RFC2981, October 2000,
              <https://www.rfc-editor.org/info/rfc2981>.

   [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,
              <https://www.rfc-editor.org/info/rfc3416>.

   [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,
              <https://www.rfc-editor.org/info/rfc4656>.






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   [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,
              <https://www.rfc-editor.org/info/rfc5357>.

   [RFC6020]  Bjorklund, M., Ed., "YANG - A Data Modeling Language for
              the Network Configuration Protocol (NETCONF)", RFC 6020,
              DOI 10.17487/RFC6020, October 2010,
              <https://www.rfc-editor.org/info/rfc6020>.

   [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,
              <https://www.rfc-editor.org/info/rfc6241>.

   [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,
              <https://www.rfc-editor.org/info/rfc7011>.

   [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,
              <https://www.rfc-editor.org/info/rfc7276>.

   [RFC7540]  Belshe, M., Peon, R., and M. Thomson, Ed., "Hypertext
              Transfer Protocol Version 2 (HTTP/2)", RFC 7540,
              DOI 10.17487/RFC7540, May 2015,
              <https://www.rfc-editor.org/info/rfc7540>.

   [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,
              <https://www.rfc-editor.org/info/rfc7575>.

   [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,
              <https://www.rfc-editor.org/info/rfc7854>.





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   [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>.

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
   module.

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
   [I-D.ietf-netconf-yang-push] 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.zhou-netconf-multi-stream-originators] via UDP based publication
   channel [I-D.ietf-netconf-udp-pub-channel] 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
      characteristic is especially valuable for the fact that telemetry
      data collectors normally reside on a large variety of platforms.




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   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
   monitoring.

A.3.  Data Plane Telemetry

A.3.1.  The IPFPM 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 IPFPM [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).

   Since networks offer rich sets of network performance measurement
   data (e.g packet counters), traditional approaches run into



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   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 IPFPM [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.brockners-inband-oam-requirements], 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 (editor)
   Futurewei
   2330 Central Expressway
   Santa Clara
   USA

   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
   NICT
   4-2-1, Nukui-Kitamachi
   Koganei, Tokyo  184-8795
   Japan

   Email: pedro@nict.go.jp


   Laurent Ciavaglia
   Nokia
   Villarceaux  91460
   France

   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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