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

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OPSAWG                                                      H. Song, Ed.
Internet-Draft                                                   T. Zhou
Intended status: Informational                                    ZB. Li
Expires: June 17, 2019                                            Huawei
                                                                  ZQ. Li
                                                            China Mobile
                                                       P. Martinez-Julia
                                                                    NICT
                                                            L. Ciavaglia
                                                                   Nokia
                                                                 A. Wang
                                                           China Telecom
                                                       December 14, 2018

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

Abstract

   This document provides an architectural framework for network
   telemetry to address the current and future network operation
   challenges and requirements.  The defining characteristics of network
   telemetry show a clear distinction from the conventional network
   Operations, Administration, and Management (OAM).  Network telemetry
   promises better scalability, accuracy, coverage, and performance and
   allows automated control loops to suit both today's and tomorrow's
   network operation requirements.  This document clarifies the
   terminologies and classifies the modules and components of a network
   telemetry system.  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

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   provisions of BCP 78 and BCP 79.

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   This Internet-Draft will expire on June 17, 2019.

Copyright Notice

   Copyright (c) 2018 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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   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   3
   2.  Motivation  . . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.2.  Challenges  . . . . . . . . . . . . . . . . . . . . . . .   5
     2.3.  Glossary  . . . . . . . . . . . . . . . . . . . . . . . .   6
     2.4.  Network Telemetry . . . . . . . . . . . . . . . . . . . .   8
   3.  The Necessity of a Network Telemetry Framework  . . . . . . .   9
   4.  Network Telemetry Framework . . . . . . . . . . . . . . . . .  10
     4.1.  Existing Works Mapped in the Framework  . . . . . . . . .  14
   5.  Evolution of Network Telemetry  . . . . . . . . . . . . . . .  15
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  16
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  16
   8.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  16
   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  16
     9.1.  Normative References  . . . . . . . . . . . . . . . . . .  16
     9.2.  Informative References  . . . . . . . . . . . . . . . . .  16
   Appendix A.  A Survey on Existing Network Telemetry Techniques  .  19
     A.1.  Management Plane Telemetry  . . . . . . . . . . . . . . .  19
       A.1.1.  Requirements and Challenges . . . . . . . . . . . . .  19
       A.1.2.  Push Extensions for NETCONF . . . . . . . . . . . . .  20
       A.1.3.  gRPC Network Management Interface . . . . . . . . . .  20
     A.2.  Control Plane Telemetry . . . . . . . . . . . . . . . . .  21
       A.2.1.  Requirements and Challenges . . . . . . . . . . . . .  21
       A.2.2.  BGP Monitoring Protocol . . . . . . . . . . . . . . .  21
     A.3.  Data Plane Telemetry  . . . . . . . . . . . . . . . . . .  22
       A.3.1.  Requirements and Challenges . . . . . . . . . . . . .  22
       A.3.2.  Technique Taxonomy  . . . . . . . . . . . . . . . . .  23
       A.3.3.  The IPFPM technology  . . . . . . . . . . . . . . . .  23

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       A.3.4.  Dynamic Network Probe . . . . . . . . . . . . . . . .  25
       A.3.5.  IP Flow Information Export (IPFIX) protocol . . . . .  25
       A.3.6.  In-Situ OAM . . . . . . . . . . . . . . . . . . . . .  25
     A.4.  External Data and Event Telemetry . . . . . . . . . . . .  26
       A.4.1.  Requirements and Challenges . . . . . . . . . . . . .  26
       A.4.2.  Sources of External Events  . . . . . . . . . . . . .  27
       A.4.3.  Connectors and Interfaces . . . . . . . . . . . . . .  28
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  28

1.  Introduction

   Network visibility is essential for network operation.  Network
   telemetry has been widely considered as an ideal mean to gain
   sufficient network visibility with better scalability, accuracy,
   coverage, and performance than conventional OAM technologies.
   However, confusion and misunderstandings about the network telemetry
   remain (e.g., the scope and coverage of the term).  We need an
   unambiguous concept and a clear architectural framework for network
   telemetry so we can better align the related technology and standard
   work.

   First, we show some key characteristics of network telemetry which
   set a clear distinction from the conventional network OAM.  We then
   provide an architectural framework for network telemetry to meet the
   current and future network operation requirements.  Following the
   framework, we classify the components of a network telemetry system
   so we can esily map the exising and emerging techniques and protocols
   into the framework.  At last, we outline a roadmap for the evolution
   of the network telemetry system.

   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.

1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119][RFC8174] when, and only when, they appear in all
   capitals, as shown here.

2.  Motivation

   Thanks to the advance of the computing and storage technologies,
   today's big data analytics and machine learning-based Artifical
   Intelligence (AI) give network operators an unprecedented opportunity
   to gain network insights and move towards network autonomy.  Software

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   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 autonomous network 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 use 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, 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 frame 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
   challenging 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 gaps based on the real network and device architectures.

   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 for 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
   of data velocity, variety, and volume.

   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 intents 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.

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   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 casue 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 ROI or lower 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.  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
   faciliate 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

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      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 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) are
      lack of 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.

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   AI:  Artificial Intelligence.  Use machine-learning based
      technologies to automate network operation.

   BMP:  BGP Monitoring Protocol

   DNP:  Dynamic Network Probe

   DPI:  Deep Packet Inspection

   gNMI:  gRPC Network Management Interface

   gRPC:  gRPC Remote Procedure Call

   IDN:  Intent-Driven Network

   IPFIX:  IP Flow Information Export Protocol

   IPFPM:  IP Flow Performance Measurement

   IOAM:  In-situ OAM

   NETCONF:  Network Configuration Protocol

   Network Telemetry:  A general term for a new brood of network
      visibility techniques and protocols, with the characteristics
      defined in this document.  Network telemetry enables smooth
      evolution toward intent-driven autonomous networks.

   NMS:  Network Management System

   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.

   SNMP:  Simple Network Management Protocol

   YANG:  A data modeling language for NETCONF

   YANG FSM:  A YANG model to define device side finite state machine

   YANG PUSH:  A method to subscribe pushed data from remote YANG
      datastore

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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 form the convention techniques for network OAM.
   The representative techniques and protocols include IPFIX [RFC7011]
   and gPRC [I-D.kumar-rtgwg-grpc-protocol].  It is expected that
   network telemetry can provide the necessary network visibility for
   autonomous networks, address the shortcomings of conventional OAM
   techniques, and allow for the emergence of new techniques bearing
   certain characteristics.

   One difference between the network telemetry and the network OAM is
   that the network telemetry assumes machines as data consumer, while
   the conventional network OAM assumes human operators.  Hence, the
   network telemetry can directly trigger the automated network
   operation, but the conventional OAM tools only 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 defining characteristics of
   network telemetry have been well accepted:

   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 machine rather than by a human.  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

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      run continuously and adapt to the dynamic and interactive queries
      from the network operation controller.

   Note that a technique does not need to have all the above
   characterisitics to be qualified as telemetry.  An ideal network
   telemetry solution may also have the following features or
   properities:

   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  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.

   o  Non-intrusive: The telemetry system should avoid the pitfall of
      the "observer effect".  That is, it should not change the network
      behavior and affect the forwarding performance.

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
   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

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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 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.

   So far, some telemetry related work has been done within IETF.
   However, the work is fragmented and scattered in different working
   groups.  The lack of coherence makes it difficult to assemble a
   comprehensive network telemetry system and causes repetitive and
   redundant work.

   A formal network telemetry framework is needed for constructing a
   working system.  The framework should cover the concepts and
   components from the standardization perspective.  This document
   clarifies the layers 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

   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.

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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 1: Layer Category of the Network Telemetry Framework

   The rationale of this partition lies in the different telemtry 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
   followng table:

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   +---------+--------------+--------------+--------------+----------+
   | Module  | Control      | Management   | Forwarding   | External |
   |         | Plane        | Plane        | Plane        | Data     |
   +---------+--------------+--------------+--------------+----------+
   |Object   | control      | config. &    | flow & packet| terminal,|
   |         | protocol &   | operation    | QoS, traffic | social & |
   |         | signailing,  | 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     |          |
   +---------+--------------+--------------+--------------+----------+
   |Model    | YANG,        | MIB, syslog, | template,    | TBD      |
   |         | custom       | YANG,        | YANG,        |          |
   |         |              | custom       | custom       |          |
   +---------+--------------+--------------+--------------+----------+
   |Encoding | GPB, JSON,   | GPB, JSON,   | plain        | TBD      |
   |         | XML, plain   | XML          |              |          |
   +---------+--------------+--------------+--------------+----------+
   |Protocol | gRPC,NETCONF,| gPRC,NETCONF,| IPFIX, mirror| TBD      |
   |         | IPFIX,mirror |              |              |          |
   +---------+--------------+--------------+--------------+----------+
   |Transport| HTTP, TCP,   | HTTP, TCP    | UDP          | TCP, UDP |
   |         | UDP          |              |              |          |
   +---------+--------------+--------------+--------------+----------+

        Figure 2: Layer Category of the Network Telemetry Framework

   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 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.

   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 responisble 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.

   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 3: Components in the Network Telemetry Framework

   Since most existing standard-related work belongs to the first four
   components, in the remainder of the document, we focus on these
   components only.

4.1.  Existing Works Mapped in the Framework

   The following table provides 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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       +--------------+---------------+----------------+---------------+
       |              | Management    | Control        | Forwardidng   |
       |              | Plane         | Plane          | Plane         |
       +--------------+---------------+----------------+---------------+
       | data Config. | gRPC, NETCONF,| NETCONF/YANG   | NETCONF/YANG, |
       | & subscrib.  | YANG PUSH     |                | YANG FSM      |
       +--------------+---------------+----------------+---------------+
       | data gen. &  | DNP,          | DNP,           | In-situ OAM,  |
       | processing   | YANG          | YANG           | PBT, IPFPM,   |
       |              |               |                | DNP           |
       +--------------+---------------+----------------+---------------+
       | data         | gRPC, NETCONF | BMP, NETCONF   | IPFIX         |
       | export       | YANG PUSH     |                |               |
       +--------------+---------------+----------------+---------------+

                          Figure 4: Existing Work

5.  Evolution of Network Telemetry

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

   Level 0 - Static Telemetry:  The telemetry data is 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.

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6.  Security Considerations

   TBD

7.  IANA Considerations

   This document includes no request to IANA.

8.  Acknowledgments

   We would like to thank Adrian Farrel, Randy Presuhn, Vi-->ctor Liu,
   James Guichard, Uri Blumenthal, Giuseppe Fioccola, Daniel King, Yunan
   Gu, and many others who have provided helpful comments and
   suggestions to improve this document.

9.  References

9.1.  Normative References

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

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

9.2.  Informative References

   [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-02 (work
              in progress), September 2018.

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   [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-02 (work in progress),
              September 2018.

   [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-04 (work in progress), October 2018.

   [I-D.ietf-netconf-yang-push]
              Clemm, A., Voit, E., Prieto, A., Tripathy, A., Nilsen-
              Nygaard, E., Bierman, A., and B. Lengyel, "Subscription to
              YANG Datastores", draft-ietf-netconf-yang-push-20 (work in
              progress), October 2018.

   [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.

   [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-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-03 (work in progress),
              October 2018.

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   [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>.

   [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>.

   [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>.

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   [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>.

   [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>.

   [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

   We provide an overview of the challenges and existing solutions for
   each network telemetry module.

A.1.  Management Plane Telemetry

A.1.1.  Requirements and Challenges

   The management plane of the network element 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
   are widely used for the management plane, such as SNMP and Syslog.
   However, these protocols are insufficient to meet the requirements of
   the automatic 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.

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   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.

A.1.2.  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.1.3.  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.

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

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A.2.  Control Plane Telemetry

A.2.1.  Requirements and Challenges

   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.

   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.

A.2.2.  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

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   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.  Requirements and Challenges

   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.

   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

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   all equipped with advanced telemetry features and provide flexibility
   to support customized telemetry functions.

A.3.2.  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]).

A.3.3.  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

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   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
   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

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   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.3.4.  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 custom 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.

   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.3.5.  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.3.6.  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.

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   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.4.  External Data and Event 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.

A.4.1.  Requirements and Challenges

   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.

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A.4.2.  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.4.3.  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 w ith
   a specific information model (YANG) and specific telemetry protocol,
   such as NETCONF, SNMP, or gRPC.

Authors' Addresses

   Haoyu Song (editor)
   Huawei
   2330 Central Expressway
   Santa Clara
   USA

   Email: haoyu.song@huawei.com

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   Tianran Zhou
   Huawei
   156 Beiqing Road
   Beijing, 100095
   P.R. China

   Email: zhoutianran@huawei.com

   Zhenbin Li
   Huawei
   156 Beiqing Road
   Beijing, 100095
   P.R. China

   Email: lizhenbin@huawei.com

   Zhenqiang Li
   China Mobile
   No. 32 Xuanwumenxi Ave., Xicheng District
   Beijing, 100032
   P.R. China

   Email: lizhenqiang@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

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

   Email: wangaj.bri@chinatelecom.cn

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