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OPSAWG                                                      H. Song, Ed.
Internet-Draft                                                   T. Zhou
Intended status: Informational                                    ZB. Li
Expires: April 22, 2019                                           Huawei
                                                                  ZQ. Li
                                                            China Mobile
                                                       P. Martinez-Julia
                                                            L. Ciavaglia
                                                                 A. Wang
                                                           China Telecom
                                                        October 19, 2018

                      Network Telemetry Framework


   This document provides an architectural framework for network
   telemetry to meet the current and future network operation
   requirements.  The defining characteristics of network telemetry show
   a clear distinction from the conventional network Operations,
   Administration, and Management (OAM) concept; hence network telemetry
   requires new procedures, methods, and protocols.  This document
   clarifies the terminologies and classifies the categories and
   components of a network telemetry framework.  The requirements,
   challenges, existing solutions, and future directions are discussed
   for each category.  The network telemetry framework and the taxonomy
   help to set a common ground for the collection of related works and
   put future technique and standard developments into perspective.

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
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   Drafts is at https://datatracker.ietf.org/drafts/current/.

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

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   This Internet-Draft will expire on April 22, 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
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   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 . . . . . . . . . . . . . . . . . . . .   7
   3.  The Necessity of a Network Telemetry Framework  . . . . . . .   9
   4.  Network Telemetry Framework . . . . . . . . . . . . . . . . .  10
     4.1.  Existing Works Mapped in the Framework  . . . . . . . . .  12
     4.2.  Management Plane Telemetry  . . . . . . . . . . . . . . .  13
       4.2.1.  Requirements and Challenges . . . . . . . . . . . . .  13
       4.2.2.  Push Extensions for NETCONF . . . . . . . . . . . . .  14
       4.2.3.  gRPC Network Management Interface . . . . . . . . . .  14
     4.3.  Control Plane Telemetry . . . . . . . . . . . . . . . . .  15
       4.3.1.  Requirements and Challenges . . . . . . . . . . . . .  15
       4.3.2.  BGP Monitoring Protocol . . . . . . . . . . . . . . .  15
     4.4.  Data Plane Telemetry  . . . . . . . . . . . . . . . . . .  16
       4.4.1.  Requirements and Challenges . . . . . . . . . . . . .  16
       4.4.2.  Technique Taxonomy  . . . . . . . . . . . . . . . . .  17
       4.4.3.  The IPFPM technology  . . . . . . . . . . . . . . . .  17
       4.4.4.  Dynamic Network Probe . . . . . . . . . . . . . . . .  19
       4.4.5.  IP Flow Information Export (IPFIX) protocol . . . . .  19
       4.4.6.  In-Situ OAM . . . . . . . . . . . . . . . . . . . . .  19
     4.5.  External Data and Event Telemetry . . . . . . . . . . . .  20
       4.5.1.  Requirements and Challenges . . . . . . . . . . . . .  20
   5.  Evolution of Network Telemetry  . . . . . . . . . . . . . . .  21
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  21
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  21

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   8.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  21
   9.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  22
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  22
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  22
     10.2.  Informative References . . . . . . . . . . . . . . . . .  22
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  25

1.  Introduction

   Network visibility is essential for network operation.  Network
   telemetry has been widely accepted as the ideal mean to gain full
   network visibility.  However, there are still confusion and
   misunderstandings about the connotation of network telemetry.  We
   need an unambiguous understanding of the concept so we can better
   align the related technology and standard developments.

   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.  The requirements, challenges, existing
   solutions, and future directions are discussed for each framework
   category.  At last, we outline a roadmap for the evolution of the
   network telemetry system.

   The network telemetry framework and the taxonomy help to set a common
   ground for the collection of related works and put future technique
   and standard developments into perspective.

1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "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

   The advance of Artifical Intelligence (AI), and specifically Machine
   Learning (ML), technologies gives networks an unprecedented
   opportunity to realize network autonomy with closed control loops.
   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

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   customer requirements.  Although we still have a long way to reach
   the ultimate goal, the machine automation journey has started

   The storage and computing technologies are already mature enough to
   be able to retain and process a huge amount of data and make real-
   time inference.  Tools based on machine learning technologies and big
   data analytics are powerful in detecting and reacting on network
   faults, anomalies, and policy violations.  In turn, the network
   policy updates for planning, intrusion prevention, optimization, and
   self-healing may be applied.  Tools exist that will profile,
   classify, and predict future events based on historical data trends.
   However, to increase the accuracy of these preditive capabilities,
   and better support autonomous networking, improvements must be made.
   The current network architecture, protocol suite, and system design
   are not ready yet to provide enough quality data.

   In the remaining of this section, first we identify the key network
   operation use cases that network operators need the most.  These use
   cases are also the essential functions of the future autonomous
   networks.  Next, we show why the current network OAM techniques and
   protocols are not sufficient to meet the requirements of 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

   The use cases highlighted use data extracted from the network data
   plane, as well as control plane and management plane.

   Intent and Policy Compliance:  Network policies are the rules that
      constraint the services for network access, provide differentiate
      within a service, 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 network functions in
      order.  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.

   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.

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   Root Cause Analysis:  Network failure often involves a sequence of
      chained events and the source of the failure is not
      straightforward to identify, especially when the failure is
      sporadic.  While machine learning or other data analytics
      technologies can be used for root cause analysis, it up to the
      network to provide all the relevant data for analysis.

   Load Balancing, Traffic Engineering, and Network Planning:  Network
      operators are motivated to optimize their network utilization for
      better ROI or lower CAPEX, as well as differentiation across
      services and/or users of a given service.  The first step is to
      know the real-time network conditions before applying policies to
      steer the user traffic or adjust the load balancing algorithm.  In
      some cases network micro-bursts need to be detected in a very
      short time-frame so that fine-grained traffic control can be
      applied to avoid possible network congestion.  The long-term
      network capacity planning and topology augmentation also rely on
      the accumulated data of the network operation.

   Event Tracking and Prediction:  Network path and performance
      visibility is critical for healthy network operation.  Numerous
      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 while proactive action may still be taken before an issue
      becomes a catastrophic problem, such as a component failure.

2.2.  Challenges

   The conventional OAM techniques, as described in [RFC7276], 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.  Streaming data directly pushed
      from the data source is preferred.

   o  Various data is needed from any place ranging from the packet
      processing engine to the QoS traffic manager.  Traditional data
      plane devices cannot provide the necessary probes.  An open and
      programmable data plane is therefore needed.

   o  Many application scenarios need to correlate data from multiple
      sources (e.g., from distributed nodes or from different network
      plane).  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

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      Control Architecture(ARCA)
      [I-D.pedro-nmrg-anticipated-adaptation], will be empowered and
      guided by a comprehensive framework.

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

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.  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

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   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

2.4.  Network Telemetry

   For a long time, network operators have relied upon SNMP [RFC3416] or
   Command-Line Interface (CLI) to monitor the network.  SNMP and CLI
   can access limited Management Information Base (MIB) information from
   the mangement plane.  Most existing implementatons are mainly poll-
   based and supports low data rate with low timing accuracy.  Such
   issues make SNMP and CLI insufficient for today and tomorrow's
   network operations.

   Network telemetry has emerged as a mainstream technical term to refer
   to the newer techniques of data collection and consumption,
   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].  SNMP is also envolving to
   support event notifications [RFC2981][RFC3877].  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 key difference between the network telemetry and the network OAM
   is that the network telemetry assumes an intelligent machine in the
   center of a closed control loop, while the network OAM assumes the
   human network operators in the middle of an open control loop.  The
   network telemetry can directly trigger the automated network
   operation; The conventional OAM tools only help human operators to
   monitor and diagnose the networks and guide manual network
   operations.  The different assumptions lead to very different

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   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 the data source 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

   o  Model-based: The data is model-based 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.

   The ideal network telemetry solution should also support the
   following features:

   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

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   o  Non-intrusive: The telemetry system should not fall into the trap
      of the "observer effect".  That is, it should not change the
      network behavior or 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

   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

   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, this 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.

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   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 data 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.

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

        Figure 1: 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

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   require both the data plane telemetry and the control plane

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

   Data Source:  Determine where the original data is acquired.  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.

   Data Subscription:  Determine the protocol and channel for
      applications to acquire desired data.  Data subscription is also
      responsible to define the desired data that might not be directly
      available form data sources.  The subscription data can be
      described by a model.  The model can be statically installed or
      dynamically installed.

   Data Generation:  The original data needs to be processed, encoded,
      and formatted in network devices to meet application subscription
      requirements.  This may involve in-network computing and
      processing on either the fast path or the slow path in network

   Data Export:  Determine how the ready data are delivered to

   Data Analysis and Storage:  In this final step, data is consumed by
      applications or stored for future reference.  Data analysis can be
      interactive.  It may initiate further data subscription.

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                   |                              |
                   |    Data Analysis/Storage     |
                   |                              |
                           |               ^
                           |               |
                           V               |
                   |               |              |
                   | Data          | Data         |
                   | Subscription  | Export       |
                   |               |              |
                   |                              |
                   |       Data Generation        |
                   |                              |
                   |                              |
                   |       Data Source            |
                   |                              |

          Figure 2: 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.

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            |           | Management   | Control       | Data         |
            |           | Plane        | Plane         | Plane        |
            |           | YANG Data    | Control Proto.| Flow/Packet  |
            | Data      | Store        | Network State | Statistics   |
            | Source    |              |               | States       |
            |           |              |               | DPI          |
            |           | gRPC         | NETCONF/YANG  | NETCONF/YANG |
            | Data      | YANG PUSH    | BGP           | YANG FSM     |
            | Subscribe |              |               |              |
            |           |              |               |              |
            |           | Soft DNP     | Soft DNP      | In-situ OAM  |
            | Data      |              |               | IPFPM        |
            | Generation|              |               | Hard DNP     |
            |           |              |               |              |
            |           | gRPC         | BMP           | IPFIX        |
            | Data      | YANG PUSH    |               | UDP          |
            | Export    | UDP          |               |              |
            |           |              |               |              |

                          Figure 3: Existing Work

4.2.  Management Plane Telemetry

4.2.1.  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.

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   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

4.2.2.  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.

4.2.3.  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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4.3.  Control Plane Telemetry

4.3.1.  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

   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.

4.3.2.  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

4.4.  Data Plane Telemetry

4.4.1.  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

   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

   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.

4.4.2.  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]).

4.4.3.  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

   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.

4.4.4.  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.

4.4.5.  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.

4.4.6.  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.

4.5.  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.

4.5.1.  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

   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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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

   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


7.  IANA Considerations

   This document includes no request to IANA.

8.  Contributors

   The other major contributors of this document are listed as follows.

   o  Daniel King

   o  Yunan Gu

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9.  Acknowledgments

   We would like to thank Adrian Farrel, Randy Presuhn, Victor Liu,
   James Guichard, Uri Blumenthal, Giuseppe Fioccola, and many others
   who have provided helpful comments and suggestions to improve this

10.  References

10.1.  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,

   [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>.

10.2.  Informative References

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

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

              Evens, T., Bayraktar, S., Lucente, P., Mi, K., and S.
              Zhuang, "Support for Adj-RIB-Out in BGP Monitoring
              Protocol (BMP)", draft-ietf-grow-bmp-adj-rib-out-02 (work
              in progress), September 2018.

              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.

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              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.

              Clemm, A., Voit, E., Prieto, A., Tripathy, A., Nilsen-
              Nygaard, E., Bierman, A., and B. Lengyel, "YANG Datastore
              Subscription", draft-ietf-netconf-yang-push-19 (work in
              progress), September 2018.

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

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

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

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

              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.

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

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

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   [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,

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

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

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

   [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,

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

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

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

   [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>.

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

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

Authors' Addresses

   Haoyu Song (editor)
   2330 Central Expressway
   Santa Clara

   Email: haoyu.song@huawei.com

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

   Email: zhoutianran@huawei.com

   Zhenbin Li
   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

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

   Email: pedro@nict.go.jp

   Laurent Ciavaglia
   Villarceaux  91460

   Email: laurent.ciavaglia@nokia.com

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

   Email: wangaj.bri@chinatelecom.cn

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