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<rfc category="info" xmlns:xi="http://www.w3.org/2001/XInclude" docName="draft-ietf-opsawg-ntf-13" ipr="trust200902"> number="9232" ipr="trust200902" obsoletes="" updates="" submissionType="IETF" category="info" consensus="true" xml:lang="en" tocInclude="true" tocDepth="3" symRefs="true" sortRefs="true" version="3">

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  <front>
    <title abbrev="Network Telemetry Framework">Network Telemetry Framework</title>
    <seriesInfo name="RFC" value="9232"/>
    <author fullname="Haoyu Song" initials="H." surname="Song">
      <organization>Futurewei</organization>
      <address>
        <postal>
          <street/>
          <city/>
<country>USA</country>
          <country>United States of America</country>
        </postal>
        <email>haoyu.song@futurewei.com</email>
      </address>
    </author>
    <author fullname="Fengwei Qin" initials="F." surname="Qin">
      <organization>China Mobile</organization>
      <address>
        <postal>
          <street/>
          <city/>
<country>P.R. China</country>
          <country>China</country>
        </postal>
        <email>qinfengwei@chinamobile.com</email>
      </address>
    </author>
    <author fullname="Pedro Martinez-Julia" initials="P." surname="Martinez-Julia">
      <organization>NICT</organization>
      <address>
        <postal>
          <street/>
          <city/>
          <country>Japan</country>
        </postal>
        <email>pedro@nict.go.jp</email>
      </address>
    </author>
    <author fullname="Laurent Ciavaglia" initials="L." surname="Ciavaglia">
      <organization>Rakuten Mobile</organization>
      <address>
        <postal>
          <street/>
          <city/>
          <country>France</country>
        </postal>
        <email>laurent.ciavaglia@rakuten.com</email>
      </address>
    </author>
    <author fullname="Aijun Wang" initials="A." surname="Wang">
      <organization>China Telecom</organization>
      <address>
        <postal>
          <street/>
          <city/>
<country>P.R. China</country>
          <country>China</country>
        </postal>
<email>wangaj.bri@chinatelecom.cn</email>
        <email>wangaj3@chinatelecom.cn</email>
      </address>
    </author>
    <date day="3" month="December" year="2021"/>
<area>Operation year="2022" month="May" />

    <area>Operations and Management Area</area>
    <workgroup>OPSAWG</workgroup>
<!--  -->
<keyword>Telemetry, OAM</keyword>

<keyword>Telemetry</keyword>
<keyword>OAM</keyword>

    <abstract>
      <t>Network telemetry is a technology for gaining network insight and facilitating efficient and automated network management. It encompasses various techniques for remote data generation, collection, correlation, and consumption. This document describes an architectural framework for network telemetry, motivated by challenges that are encountered as part of the operation of networks and by the requirements that ensue.
      This document clarifies the terminologies terminology and classifies the modules and components of a network telemetry system from different perspectives. The framework and taxonomy help to set a common ground for the collection of related work and provide guidance for related technique and standard developments.</t>
    </abstract>
  </front>
  <middle>
    <section title="Introduction"> numbered="true" toc="default">
      <name>Introduction</name>
      <t> Network visibility is the ability of management tools to see the state and behavior of a network, which is essential for successful network operation. Network Telemetry telemetry revolves around network data that 1) can help provide insights about the current state of the network, including network devices, forwarding, control, and management planes, and that planes; 2) can be generated and obtained through a variety of techniques, including but not limited to network instrumentation and measurements, measurements; and that 3) can be processed for purposes ranging from service assurance to network security using a wide variety of data analytical techniques. In this document, Network Telemetry refer network telemetry refers to both the data itself (i.e., "Network Telemetry Data"), Data") and the techniques and processes used to generate, export, collect, and consume that data for use by potentially automated management applications. Network telemetry extends beyond the classical network Operations, Administration, and Management (OAM) techniques and expects to support better flexibility, scalability, accuracy, coverage, and performance.</t>
      <t> However, the term "network telemetry" lacks an unambiguous definition. The scope and coverage of it cause confusion and misunderstandings. It is beneficial to clarify the concept and provide a clear architectural framework for network telemetry, so we can articulate the technical field, field and better align the related techniques and standard works.</t>
      <t>To fulfill such an undertaking, we first discuss some key characteristics of network telemetry which that set a clear distinction from the conventional network OAM and show that some conventional OAM technologies can be considered a subset of the network telemetry technologies. We then provide an architectural framework for network telemetry which that includes four modules, each concerned associated with a different category of telemetry data and corresponding procedures. All the modules are internally structured in the same way, including components that allow the operator to configure data sources in regard to what data to generate and how to make that available to client applications, components that instrument the underlying data sources, and components that perform the actual rendering, encoding, and exporting of the generated data. We show how the network telemetry framework can benefit the current and future network operations. Based on the distinction of modules and function components, we can map the existing and emerging techniques and protocols into the framework. The framework can also simplify designing, maintaining, and understanding a network telemetry system. In addition, we outline the evolution stages of the network telemetry system and discuss the potential security concerns. </t>

      <t> The purpose of the framework and taxonomy is to set a common ground for the collection of related work and provide guidance for future technique and standard developments. To the best of our knowledge, this document is the first such effort for network telemetry in industry standards organizations. This document does not define specific technologies.</t>
<!--

<section title="Requirements Language">
          <t>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 <xref target="RFC2119"></xref><xref target="RFC8174"></xref> when, and only when, they appear in all
          capitals, as shown here.</t>
      </section>
 -->

<section title="Applicability Statement"> numbered="true" toc="default">
        <name>Applicability Statement</name>
        <t>Large-scale network data collection is a major threat to user privacy and may be indistinguishable from pervasive monitoring <xref target="RFC7258" />. format="default"/>.  The network telemetry framework presented in this document must not be applied to generating, exporting, collecting, analyzing, or retaining individual user data or any data that can identify end users or characterize their behavior without consent. Based on this principle, the network telemetry framework is not applicable to networks whose endpoints represent individual users, such as general-purpose access networks. </t>
      </section>
      <section title="Glossary"> numbered="true" toc="default">
        <name>Glossary</name>
        <t>Before further discussion, we list some key terminology and acronyms abbreviations used in this document. We make There is an intended differentiation between the terms of network telemetry and OAM. However, it should be understood that there is not a hard-line distinction between the two concepts. Rather, network telemetry is considered as an extension of OAM. It covers all the existing OAM protocols but puts more emphasis on the newer and emerging techniques and protocols concerning all aspects of network data from acquisition to consumption.</t>
<t>
<list style="hanging">
<t hangText="AI:">
        <dl newline="false" spacing="normal" indent="12">
          <dt>AI:</dt>
          <dd> Artificial Intelligence. In the network domain, AI refers to the machine-learning based machine-learning-based technologies for automated network operation and other tasks.</t>
<t hangText="AM:"> tasks.</dd>
          <dt>AM:</dt>
          <dd> Alternate Marking, a Marking. A flow performance measurement method, as specified in <xref target="RFC8321"/>. </t>
<t hangText="BMP:"> BGP target="RFC8321" format="default"/>. </dd>
          <dt>BMP:</dt>
          <dd>BGP Monitoring Protocol, specified Protocol. Specified in <xref target="RFC7854"/>. </t>
<t hangText="DPI:"> Deep target="RFC7854" format="default"/>. </dd>
          <dt>DPI:</dt>
          <dd>Deep Packet Inspection, referring Inspection. Refers to the techniques that examines packet examine packets beyond packet L3/L4 headers. </t>
<t hangText="gNMI:"> gRPC </dd>
          <dt>gNMI:</dt>
          <dd>gRPC Network Management Interface, a Interface. A network management protocol from the OpenConfig Operator Working Group, mainly contributed by Google. See <xref target="gnmi"/> target="gnmi" format="default"/> for details. </t>
<t hangText="GPB:"> Google </dd>
          <dt>GPB:</dt>
          <dd>Google Protocol Buffer, an Buffer. An extensible mechanism for serializing structured data. See <xref target="gpb" /> format="default"/> for details. </t>
<t hangText="gRPC:"> gRPC </dd>
          <dt>gRPC:</dt>
          <dd>gRPC Remote Procedure Call, an open source high performance Call. An open-source high-performance RPC framework that gNMI is based on. See <xref target="grpc"/> target="grpc" format="default"/> for details. </t>
<t hangText="IPFIX:"> IP </dd>
          <dt>IPFIX:</dt>
          <dd>IP Flow Information Export Protocol, specified Protocol. Specified in <xref target="RFC7011"/>. </t>
<t hangText="IOAM:"> <xref target="I-D.ietf-ippm-ioam-data">In-situ OAM</xref>, a dataplane target="RFC7011" format="default"/>. </dd>
          <dt>IOAM:</dt>
          <dd>
            <xref target="RFC9197" format="default">In situ OAM</xref>. A data plane on-path telemetry technique. </t>
<t hangText="JSON:"> </dd>
          <dt>JSON:</dt>
          <dd>JavaScript Object Notation. An open standard file format and data interchange format that uses human-readable text to store and transmit data objects, as specified in <xref target="RFC8259" />. </t>
<t hangText="MIB:"> Management format="default"/>. </dd>
          <dt>MIB:</dt>
          <dd>Management Information Base, a Base. A database used for managing the entities in a network. </t>
<t hangText="NETCONF:"> Network </dd>
          <dt>NETCONF:</dt>
          <dd>Network Configuration Protocol, specified Protocol. Specified in <xref target="RFC6241"/>. </t>
<t hangText="NetFlow:"> A target="RFC6241" format="default"/>. </dd>
          <dt>NetFlow:</dt>
          <dd>A Cisco protocol used for flow record collecting, as described in <xref target="RFC3954"/>. </t>
<t hangText="Network Telemetry:"> The target="RFC3954" format="default"/>. </dd>
          <dt>Network Telemetry:</dt>
          <dd>The process and instrumentation for acquiring and utilizing network data remotely for network monitoring and operation. A general term for a large set of network visibility techniques and protocols, concerning aspects like data generation, collection, correlation, and consumption. Network telemetry addresses the current network operation issues and enables smooth evolution toward future intent-driven autonomous networks.</t>
<t hangText="NMS:"> Network networks.</dd>
          <dt>NMS:</dt>
          <dd>Network Management System, referring System. Refers to applications that allow network administrators to manage a network. </t>
<t hangText="OAM:"> Operations, </dd>
          <dt>OAM:</dt>
          <dd>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.</t>
<t hangText="PBT:"> Postcard-Based Telemetry, a dataplane OAM.</dd>

          <dt>PBT:</dt>
          <dd>Postcard-Based Telemetry. A data plane on-path telemetry technique. A representative technique is described in <xref target="I-D.ietf-ippm-ioam-direct-export"/>. </t>
<t hangText="RESTCONF:"> target="IPPM-IOAM-DIRECT-EXPORT" format="default"/>. </dd>
          <dt>RESTCONF:</dt>
          <dd> An HTTP-based protocol that provides a programmatic interface for accessing data defined in YANG, using the datastore concepts defined in NETCONF, as specified in <xref target="RFC8040"/>. </t>
<t hangText="SMIv2:"> Structure target="RFC8040" format="default"/>. </dd>
          <dt>SMIv2:</dt>
          <dd>Structure of Management Information Version 2, defining 2. Defines MIB objects, as specified in <xref target="RFC2578"/>. </t>
<t hangText="SNMP:"> Simple target="RFC2578" format="default"/>. </dd>
          <dt>SNMP:</dt>
          <dd>Simple Network Management Protocol. Version Versions 1, 2, and 3 are specified in <xref target="RFC1157"/>, target="RFC1157" format="default"/>, <xref target="RFC3416"/>, target="RFC3416" format="default"/>, and <xref target="RFC3411"/>, target="RFC3411" format="default"/>, respectively. </t>
<t hangText="XML:"> Extensible </dd>
          <dt>XML:</dt>
          <dd>Extensible Markup Language is a Language. A markup language for data encoding that is both human-readable human readable and machine-readable, machine readable, as specified by W3C <xref target="xml" />. </t>
<t hangText="YANG:"> YANG target="W3C.REC-xml-20081126" format="default"/>. </dd>
          <dt>YANG:</dt>
          <dd>YANG is a data modeling language for the definition of data sent over network management protocols such as the NETCONF and RESTCONF. YANG is defined in <xref target="RFC6020"/> target="RFC6020" format="default"/> and <xref target="RFC7950"/>. </t>
<t hangText="YANG ECA:"> A target="RFC7950" format="default"/>. </dd>
          <dt>YANG ECA:</dt>
          <dd>A YANG model for Event-Condition-Action policies, as defined in <xref target="I-D.wwx-netmod-event-yang"/>. </t>
<t hangText="YANG-Push:"> target="I-D.ietf-netmod-eca-policy" format="default"/>. </dd>
          <dt>YANG-Push:</dt>
          <dd> A mechanism that allows subscriber applications to request a stream of updates from a YANG datastore on a network device. Details are specified in <xref target="RFC8641"/> target="RFC8639" format="default"/> and <xref target="RFC8639"/>. </t>
</list>
</t> target="RFC8641" format="default"/>. </dd>
        </dl>
      </section>
    </section>
    <section title="Background"> numbered="true" toc="default">
      <name>Background</name>
      <t>The term "big data" is used to describe the extremely large volume of data sets that can be analyzed computationally to reveal patterns, trends, and associations. Networks are undoubtedly a source of big data because of their scale and the volume of network traffic they forward. When a network's endpoints do not represent individual users (e.g. (e.g., in industrial, datacenter, data-center, and infrastructure contexts), network operations can often benefit from large-scale data collection without breaching user privacy.</t>
<t>Today
      <t>Today, one can access advanced big data analytics capability through a plethora of commercial and open source open-source platforms (e.g., Apache Hadoop), tools (e.g., Apache Spark), and techniques (e.g., machine learning). Thanks to the advance of computing and storage technologies, network big data analytics gives give network operators an opportunity to gain network insights and move towards network autonomy. Some operators start to explore the application of Artificial Intelligence (AI) to make sense of network data. Software tools can use the network data to detect and react on network faults, anomalies, and policy violations, as well as predicting predict future events. In turn, the network policy updates for planning, intrusion prevention, optimization, and self-healing may be applied.</t>
      <t>It is conceivable that an <xref target="RFC7575"> target="RFC7575" format="default"> autonomic network </xref> is the logical next step for network evolution following Software Defined Software-Defined Networking (SDN), aiming which aims to reduce (or even eliminate) human labor, make more efficient use of network resources, and provide better services more aligned with customer requirements. The IETF ANIMA working group Working Group is dedicated to developing and maintaining protocols and procedures for automated network management and control of professionally-managed professionally managed networks. The related technique of <xref target="I-D.irtf-nmrg-ibn-concepts-definitions">Intent-based target="I-D.irtf-nmrg-ibn-concepts-definitions" format="default">Intent-Based Networking (IBN)</xref> requires network visibility and telemetry data in order to ensure that the network is behaving as intended. </t>
      <t>However, while the data processing capability is improved and applications require more data to function better, the networks lag behind in extracting and translating network data into useful and actionable information in efficient ways. The system bottleneck is shifting from data consumption to data supply. Both the number of network nodes and the traffic bandwidth keep increasing at a fast pace. The network configuration and policy change at smaller time slots than before. More subtle events and fine-grained data through all network planes need to be captured and exported in real time. In a nutshell, it is a challenge to get enough high-quality data out of the network in a manner that is efficient, timely, and flexible. Therefore, we need to survey the existing technologies and protocols and identify any potential gaps.</t>
      <t>In the remainder of this section, first we first clarify the scope of network data (i.e., telemetry data) relevant in this document. Then, we discuss several key use cases for today's and future network operations. operations of today and the future. 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, as well as the extensions of existing ones, which we assign under the umbrella term - Network Telemetry. "Network Telemetry". </t>
      <section title="Telemetry numbered="true" toc="default">
        <name>Telemetry Data Coverage"> Coverage</name>
        <t>Any information that can be extracted from networks (including the data plane, control plane, and management plane) and used to gain visibility or as a basis for actions is considered telemetry data. It includes statistics, event records and logs, snapshots of state, configuration data, etc. It also covers the outputs of any active and passive measurements <xref target="RFC7799"/>. target="RFC7799" format="default"/>. In some cases, raw data is processed in network before being sent to a data consumer. Such processed data is also considered telemetry data. The value of telemetry data varies. In some cases, if the cost is acceptable, less but higher quality higher-quality data are preferred rather than lots a lot of low quality low-quality data. A classification of telemetry data is provided in <xref target="framework"/>. target="framework" format="default"/>. To preserve the privacy of end-users, end users, no user packet content should be collected.  Specifically, the data objects generated, exported, and collected by a network telemetry application should not include any packet payload from traffic associated with end-users end-user systems. </t>
      </section>
      <section title="Use Cases"> numbered="true" toc="default">
        <name>Use Cases</name>
        <t>The following set of use cases is essential for network operations. While the list is by no means exhaustive, it is enough to highlight the requirements for data velocity, variety, volume, and veracity, the attributes of big data, in networks. </t>
<t>
<list style="symbols">
<t>
        <ul spacing="normal">
          <li> Security: Network intrusion detection and prevention systems need to monitor network traffic and activities and act upon anomalies. Given increasingly sophisticated attack vectors coupled with increasingly severe consequences of security breaches, new tools and techniques need to be developed, relying on wider and deeper visibility into networks. The ultimate goal is to achieve security with no, or only minimal, human intervention, intervention and without disrupting legitimate traffic flows. </t>
<t> Policy </li>
          <li>Policy and Intent Compliance: Network policies are the rules that constrain the services for network access, provide service differentiation, or enforce specific treatment on the traffic. For example, a service function chain is a policy that requires the selected flows to pass through a set of ordered network functions. Intent, as defined in <xref target="I-D.irtf-nmrg-ibn-concepts-definitions"/>, target="I-D.irtf-nmrg-ibn-concepts-definitions" format="default"/>, is a set of operational goals that a network should meet and outcomes that a network is supposed to deliver, defined in a declarative manner without specifying how to achieve or implement them. An intent requires a complex translation and mapping process before being applied on networks. While a policy or intent is enforced, the compliance needs to be verified and monitored continuously by relying on visibility that is provided through network telemetry data. Any violation must be reported immediately, immediately - this will alert the network
   administrator to the policy or intent violation and will potentially resulting
   result in updates to how the policy or intent is applied in the network to
   ensure that it remains in force, or otherwise alerting the network administrator to the policy or intent violation. </t>
<t> SLA force. </li>
          <li>SLA Compliance: A Service-Level Service Level Agreement (SLA) is a service contract between a service provider and a client, which include includes 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 promised, and network operators need to evaluate how they can deliver services that can meet the SLA based on realtime real-time network telemetry data, including data from network measurements.</t>
<t> Root measurements.</li>
          <li>Root Cause Analysis: Many network failure failures can be the effect of a sequence of chained events. Troubleshooting and recovery require quick identification of the root cause of any observable issues. However, the root cause is not always straightforward to identify, especially when the failure is sporadic and the number of event messages, both related and unrelated to the same cause, is overwhelming. While technologies such as machine learning can be used for root cause analysis, it is up to the network to sense and provide the relevant diagnostic data which that are either actively fed into, into or passively retrieved by, by the root cause analysis applications.</t>
<t> Network applications.</li>
          <li>Network Optimization: This covers all short-term and long-term network optimization techniques, including load balancing, Traffic Engineering (TE), and network planning. Network operators are motivated to optimize their network utilization and differentiate services for better Return On on Investment (ROI) or lower Capital Expenditures Expenditure (CAPEX). The first step is to know the real-time network conditions before applying policies for traffic manipulation. In some cases, micro-bursts microbursts need to be detected in a very short time-frame time frame so that fine-grained traffic control can be applied to avoid network congestion. Long-term planning of network capacity and topology requires analysis of real-world network telemetry data that is obtained over long periods of time.</t>
<t> Event time.</li>
          <li>Event Tracking and Prediction: The visibility into traffic path and performance is critical for services and applications that rely on healthy network operation. Numerous related network events are of interest to network operators. For example, Network network operators 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. </t>
</list>
</t> </li>
        </ul>
      </section>
      <section title="Challenges"> numbered="true" toc="default">
        <name>Challenges</name>
        <t>For a long time, network operators have relied upon <xref target="RFC3416">SNMP</xref>, target="RFC3416" format="default">SNMP</xref>, Command-Line Interface (CLI), or <xref target="RFC5424">Syslog</xref> target="RFC5424" format="default">Syslog</xref> to monitor the network. Some other OAM techniques as described in <xref target="RFC7276"/> target="RFC7276" format="default"/> are also used to facilitate network troubleshooting. These conventional techniques are not sufficient to support the above use cases for the following reasons: </t>
<t>
<list style="symbols">
<t>Most
        <ul spacing="normal">
          <li>Most use cases need to continuously monitor the network and dynamically refine the data collection in real-time. real time. Poll-based low-frequency data collection is ill-suited for these applications. Subscription-based streaming data directly pushed from the data source (e.g., the forwarding chip) is preferred to provide sufficient data quantity and precision at scale.</t>
<t>Comprehensive scale.</li>
          <li>Comprehensive data is needed, ranging from packet processing engines to traffic manager, from managers, line cards to main control board, from boards, user flows to control protocol packets, from device configurations to operations, and from physical layer layers to application layer. layers. Conventional OAM only covers a narrow range of data (e.g., SNMP only handles data from the Management Information Base (MIB)). Classical network devices cannot provide all the necessary probes. More open and programmable network devices are therefore needed.</t>
<t>Many needed.</li>
          <li>Many application scenarios need to correlate network-wide data from multiple sources (i.e., from distributed network devices, different components of a network device, or different network planes). A piecemeal solution is often lacking the capability to consolidate the data from multiple sources. The composition of a complete solution, as partly proposed by <xref target="I-D.pedro-nmrg-anticipated-adaptation">Autonomic target="NMRG-ANTICIPATED-ADAPTATION" format="default">Autonomic Resource Control Architecture(ARCA)</xref>, Architecture (ARCA)</xref>, will be empowered and guided by a comprehensive framework. </t>
<t>Some </li>
          <li>Some conventional OAM techniques (e.g., CLI and Syslog) lack a formal data model. The unstructured data hinder the tool automation and application extensibility. Standardized data models are essential to support the programmable networks. </t>
<t>Although </li>

	  <li>Although some conventional OAM techniques support data push (e.g., <xref target="RFC2981">SNMP target="RFC2981" format="default">SNMP Trap</xref><xref target="RFC3877"/>, target="RFC3877" format="default"/>, Syslog, and <xref target="RFC3176">sFlow</xref>), target="RFC3176" format="default">sFlow</xref>), the pushed data are limited to only predefined management plane warnings (e.g., SNMP Trap) or sampled user packets (e.g., sFlow). Network operators require the data with arbitrary source, granularity, and precision precision, which are is beyond the capability of the existing techniques. </t>
<t>The conventional </li>
          <li>Conventional passive measurement techniques can either consume excessive network resources and produce excessive redundant data, data or lead to inaccurate results; on the other hand, the conventional active measurement techniques can interfere with the user traffic traffic, and their results are indirect. Techniques that can collect direct and on-demand data from user traffic are more favorable.</t>
</list>
</t> favorable.</li>
        </ul>
        <t>These challenges were addressed by newer standards and techniques (e.g., IPFIX/Netflow, Packet Sampling (PSAMP), IOAM, and YANG-Push) YANG-Push), and more are emerging. These standards and techniques need to be recognized and accommodated in a new framework.</t>
      </section>
      <section title="Network Telemetry"> numbered="true" toc="default">
        <name>Network Telemetry</name>
        <t>Network telemetry has emerged as a mainstream technical term to refer to the network data collection and consumption techniques. Several network telemetry techniques and protocols (e.g., <xref target="RFC7011">IPFIX</xref> target="RFC7011" format="default">IPFIX</xref> and <xref target="grpc">gRPC</xref>) target="grpc" format="default">gRPC</xref>) have been widely deployed. Network telemetry allows separate entities to acquire data from network devices so that data can be visualized and analyzed to support network monitoring and operation. Network telemetry covers the conventional network OAM and has a wider scope. For instance, it is expected that network telemetry can provide the necessary network insight for autonomous networks and address the shortcomings of conventional OAM techniques. </t>
        <t>Network telemetry usually assumes machines as data consumers rather than human operators. Hence, the network telemetry can directly trigger the automated network operation, while in contrast contrast, some conventional OAM tools were designed and used to help human operators to monitor and diagnose the networks and guide manual network operations. Such a proposition leads to very different techniques. </t>
        <t>Although new network telemetry techniques are emerging and subject to continuous evolution, several characteristics of network telemetry have been well accepted. Note that network telemetry is intended to be an umbrella term covering a wide spectrum of techniques, so the following characteristics are not expected to be held by every specific technique.</t>
<t>
<list style="symbols">
<t>Push
        <ul spacing="normal">
          <li>Push and Streaming: Instead of polling data from network devices, telemetry collectors subscribe to streaming data pushed from data sources in network devices.</t>
<t>Volume devices.</li>
          <li>Volume and Velocity: The telemetry Telemetry data is intended to be consumed by machines rather than by human being. beings. Therefore, the data volume can be huge huge, and the processing is optimized for the needs of automation in realtime.</t>
<t>Normalization real time.</li>
          <li>Normalization and Unification: Telemetry aims to address the overall network automation needs. Efforts are made to normalize the data representation and unify the protocols, so as to simplify data analysis and provide integrated analysis across heterogeneous devices and data sources across a network.</t>
<t>Model-based: The telemetry network.</li>
          <li>Model-Based: Telemetry data is modeled in advance advance, which allows applications to configure and consume data with ease. </t>
<t>Data </li>

          <li>Data Fusion: The data for a single application can come from multiple data sources (e.g., cross-domain, cross-device, and cross-layer) that are based on a common naming/ID name/ID and needs need to be correlated to take effect.</t>
<t>Dynamic effect.</li>
          <li>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. </t>
</list>
</t> </li>
        </ul>
        <t>In addition, an ideal network telemetry solution may also have the following features or properties:</t>
<t>
<list style="symbols">
<t>In-Network
        <ul spacing="normal">
          <li>In-Network Customization: The data that is generated can be customized in network at run-time runtime to cater to the specific need of applications. This needs the support of a programmable data plane plane, which allows probes with custom functions to be deployed at flexible locations. </t>
<t>In-Network </li>
          <li>In-Network Data Aggregation and Correlation: Network devices and aggregation points can work out which events and what data needs to be stored, reported, or discarded discarded, thus reducing the load on the central collection and processing points while still ensuring that the right information is ready to be processed in a timely way.</t>
<t>In-Network way.</li>
          <li>In-Network Processing: Sometimes it is not necessary or feasible to gather all information to a central point to be processed and acted upon. It is possible for the data processing to be done in network, allowing reactive actions to be taken locally.</t>
<t>Direct locally.</li>
          <li>Direct Data Plane Export: The data originated from the data plane forwarding chips can be directly exported to the data consumer for efficiency, especially when the data bandwidth is large and the real-time processing is required. </t>
<t>In-band </li>
          <li>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 <xref target="I-D.song-opsawg-ifit-framework"/>. </t>
</list>
</t> target="I-D.song-opsawg-ifit-framework" format="default"/>. </li>
        </ul>
        <t>It is worth noting that a network telemetry system should not be intrusive to normal network operations by avoiding the pitfall of the "observer effect". That is, it should not change the network behavior and affect the forwarding performance. Moreover, high-volume telemetry traffic may cause network congestion unless proper isolation or traffic engineering techniques are in place, or congestion control mechanisms ensure that telemetry traffic backs off if it exceeds the network capacity. <xref target="RFC8084" /> format="default"/> and <xref target="RFC8085" /> format="default"/> are relevant Best Current Practices (BCP) (BCPs) in this space.</t>
        <t>Although in many cases a system for network telemetry involves a remote data collecting and consuming entity, it is important to understand that there are no inherent assumptions about how a system should be architected. While a network architecture with a centralized controller (e.g., SDN) seems to be a natural fit for network telemetry, network telemetry can work in distributed fashions as well. For example, telemetry data producers and consumers  can have a peer-to-peer relationship, in which a network node can be the direct consumer of telemetry data from other nodes. </t>
      </section>
      <section title="The numbered="true" toc="default">
        <name>The Necessity of a Network Telemetry Framework"> Framework</name>
        <t>Network data analytics (e.g., machine learning) is applied for network operation automation, relying on abundant and coherent data from networks. Data acquisition that is limited to a single source and static in nature will in many cases not be sufficient to meet an application's telemetry data needs. As a result, multiple data sources, involving a variety of techniques and standards, will need to be integrated. It is desirable to have a framework that classifies and organizes different telemetry data source sources and types, defines different components of a network telemetry system and their interactions, and helps coordinate and integrate multiple telemetry approaches across layers. This allows flexible combinations of data for different applications, while normalizing and simplifying interfaces. In detail, such a framework would benefit the development of network operation applications for the following reasons:</t>
<t>
<list style="symbols">
<t>Future
        <ul spacing="normal">
          <li>Future networks, autonomous or otherwise, depend on holistic and comprehensive network visibility. The use Use cases and applications are better to be when supported uniformly and coherently using an integrated, converged mechanism and common telemetry data representations wherever feasible. Therefore, the protocols and mechanisms should be consolidated into a minimum yet comprehensive set. A telemetry framework can help to normalize the technique developments.</t>
<t>Network developments.</li>
          <li>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; acquired, and the traffic viewpoint takes the flows or packets as the monitoring object from which the traffic quality and path can be acquired. An application may need to switch its viewpoint during operation. It may also need to correlate a service and its impact on user experience (UE) to acquire the comprehensive information.</t>
<t>Applications information.</li>
          <li>Applications require network telemetry to be elastic in order to make efficient use of network resources and reduce the impact of processing related to network telemetry on network performance. For example, routine network monitoring should cover the entire network with a low data sampling rate. Only when issues arise or critical trends emerge should telemetry data sources be modified and telemetry data rates be boosted as needed.</t>
<t>Efficient needed.</li>
          <li>Efficient data aggregation is critical for applications to reduce the overall quantity of data and improve the accuracy of analysis.</t>
</list>
</t>
<t> A analysis.</li>
        </ul>
        <t>A telemetry framework collects together all the telemetry-related works from different sources and working groups within the IETF. This makes it possible to assemble a comprehensive network telemetry system and to avoid repetitious or redundant work. The framework should cover the concepts and components from the standardization perspective. This document describes the modules which that make up a network telemetry framework and decomposes the telemetry system into a set of distinct components that existing and future work can easily map to.</t>
      </section>
    </section>
    <section anchor="framework" title="Network numbered="true" toc="default">
      <name>Network Telemetry Framework"> Framework</name>
      <t> The top level top-level network telemetry framework partitions the network telemetry into four modules based on the telemetry data object source and represents their relationship. Once the network operation applications acquire the data from these modules, they can apply data analytics and take actions. At the next level, the framework decomposes each module into separate components. Each of the these modules follows the same underlying structure, with one component dedicated to the configuration of data subscriptions and data sources, a second component dedicated to encoding and exporting data, and a third component instrumenting the generation of telemetry related to the underlying resources. Throughout the framework, the same set of abstract data acquiring data-acquiring mechanisms and data types (<xref target="sec:type"/>) target="sec_type" format="default"/>) are applied. The two-level architecture with the uniform data abstraction helps accurately pinpoint a protocol or technique to its position in a network telemetry system or disaggregate disaggregates a network telemetry system into manageable parts.</t>
      <section title="Top Level Modules"> numbered="true" toc="default">
        <name>Top-Level Modules</name>
        <t> Telemetry can be applied on the forwarding plane, the control plane, and the management plane in a network, as well as on other sources out of the network, as shown in <xref target="figure_1"/>. target="figure_1" format="default"/>. Therefore, we categorize the network telemetry into four distinct modules (management plane, control plane, forwarding plane, and external data and event telemetry) with each having its own interface to Network Operation Applications.</t>
<t> network operation applications.</t>
        <figure anchor="figure_1" title="Modules anchor="figure_1">
          <name>Modules in Layer Category of NTF">
<artwork><![CDATA[ the Network Telemetry Framework</name>
          <artwork name="" type="" align="left" alt=""><![CDATA[
                +------------------------------+
                |                              |
                |       Network Operation      |<-------+
                |          Applications        |        |
                |                              |        |
                +------------------------------+        |
                        ^          ^       ^            |
                        |          |       |            |
                        V          V       |            V
                +--------------+-----------|---+  +-----------+
                |              | Control   |   |  |           |
                |              | Plane     |   |  | External  |
                |            <--->         |   |  | Data and  |
                |              | Telemetry |   |  | Event     |
                |  Management  |       ^   V   |  | Telemetry |
                |  Plane       +-------|-------+  |           |
                |  Telemetry   |       V       |  +-----------+
                |              | Forwarding    |
                |              | Plane         |
                |            <--->             |
                |              | Telemetry     |
                |              |               |
                +--------------+---------------+
]]></artwork>
        </figure>
</t>
        <t>The rationale of this partition lies in the different telemetry data objects which that result in different data source sources and export locations. Such differences have profound implications on in-network data programming and processing capability, data encoding and the transport protocol, and required data bandwidth and latency. Data can be sent directly, directly or proxied via the control and management planes. There are advantages/disadvantages to both approaches.</t>
        <t>Note that in some cases cases, the network controller itself may be the source of telemetry data that is unique to it or derived from the telemetry data collected from the network elements. Some of the principles and taxonomy specific to the control plane and management plane telemetry could also be applied to the controller when it is required to provide the telemetry data to Network Operation Applications network operation applications hosted outside. The scope of the this document is focused on the network elements telemetry telemetry, and further details related to controllers are thus out of scope. </t>
        <t>We summarize the major differences of the four modules in the following table. <xref target="table_1"/>. They are compared from six angles:</t>
<t>
<list style="symbols">
<t>Data Object</t>
<t>Data
        <ul spacing="normal">
          <li>Data Object</li>
          <li>Data Export Location</t>
<t>Data Model</t>
<t>Data Encoding</t>
<t>Telemetry Location</li>
          <li>Data Model</li>
          <li>Data Encoding</li>
          <li>Telemetry Application Protocol</t>
<t>Data Protocol</li>
          <li>Data Transport Method</t>
</list>
</t> Method</li>
        </ul>
        <t>Data Object is the target and source of each module. Because the data source varies, the location where data is mostly conveniently exported also varies. For example, forwarding plane data mainly originates as data exported from the forwarding Application-Specific Integrated Circuits (ASICs), while control plane data mainly originates from the protocol daemons running on the control CPU(s). For convenience and efficiency, it is preferred to export the data off the device from locations near the source. Because the locations that can export data have different capabilities, different choices of data model, models, encoding, and transport method methods are made to balance the performance and cost. For example, the forwarding chip has high throughput but limited capacity for processing complex data and maintaining state, while the main control CPU is capable of complex data and state processing, processing but has limited bandwidth for high throughput data. As a result, the suitable telemetry protocol for each module can be different. Some representative techniques are shown in the corresponding table blocks to highlight the technical diversity of these modules. Note that the selected techniques just reflect the de facto state of the art and are by no means exhaustive (e.g., IPFIX can also be implemented over TCP and SCTP, but that is not recommended for the forwarding plane). The key point is that one cannot expect to use a universal protocol to cover all the network telemetry requirements. </t>
<t>
<figure anchor="figure_2" title="Comparison

<table anchor="table_1">
  <name>Comparison of the Data Object Modules">
<artwork><![CDATA[
+-----------+-------------+-------------+--------------+----------+
| Module    |Management   |Control      |Forwarding    |External  |
|           |Plane        |Plane        |Plane         |Data      |
+-----------+-------------+-------------+--------------+----------+
|Object     |config. &    |control      |flow & Modules</name>
  <thead>
    <tr>
      <th>Module</th>
      <th>Management Plane</th>
      <th>Control Plane</th>
      <th>Forwarding Plane</th>
      <th>External Data</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Object</td>
      <td>configuration and operation state</td>
      <td>control protocol and signaling, RIB</td>
      <td>flow and packet |terminal, |
|           |operation    |protocol &   |QoS, QoS, traffic  |social &  |
|           |state        |signaling,   |stat., stat., buffer |environ-  |
|           |             |RIB          |& and queue stat.,|mental    |
|           |             |             |ACL, FIB      |          |
+-----------+-------------+-------------+--------------+----------+
|Export     |main stat., FIB, Access Control List (ACL)</td>
      <td>terminal, social, and environmental</td>
    </tr>
    <tr>
      <td>Export Location</td>
      <td>main control |main CPU</td>
      <td>main control |fwding CPU, linecard CPU, or forwarding chip</td>
      <td>forwarding chip   |various   |
|Location   |CPU          |CPU,         |or or linecard   |          |
|           |             |linecard CPU |CPU; CPU; main     |          |
|           |             |or forwarding|control control CPU   |          |
|           |             |chip         |unlikely      |          |
+-----------+-------------+-------------+--------------+----------+
|Data       |YANG, unlikely</td>
      <td>various</td>
    </tr>
    <tr>
      <td>Data Model</td>
      <td>YANG, MIB,   |YANG,        |YANG          |YANG,     |
|Model      |syslog       |custom       |custom,       |custom    |
+-----------+-------------+-------------+--------------+----------+
|Data       |GPB, syslog</td>
      <td>YANG, custom</td>
      <td>YANG, custom</td>
      <td>YANG, custom</td>
    </tr>
    <tr>
      <td>Data Encoding</td>
      <td>GPB, JSON,   |GPB, XML</td>
      <td>GPB, JSON,   |plain text    |GPB, JSON |
|Encoding   |XML          |XML,         |              |XML, plain|
|           |             |plain text   |              |text      |
+-----------+-------------+-------------+--------------+----------+
|Application|gRPC,NETCONF,|gRPC,NETCONF,|IPFIX, traffic|gRPC      |
|Protocol   |RESTCONF     |IPFIX,traffic|mirroring,    |          |
|           |             |mirroring    |gRPC, NETFLOW |          |
+-----------+-------------+-------------+--------------+----------+
|Data       |HTTP(S), TCP |HTTP(S), TCP,|UDP           |HTTP(S),  |
|Transport  |             |UDP          |              |TCP, UDP  |
+-----------+-------------+-------------+--------------+----------+
      ]]>
</artwork>
</figure>
</t> XML, plain text</td>
      <td>plain text</td>
      <td>GPB, JSON, XML, plain text</td>
    </tr>
     <tr>
      <td>Application Protocol</td>
      <td>gRPC, NETCONF, RESTCONF</td>
      <td>gRPC, NETCONF, IPFIX, traffic mirroring</td>
      <td>IPFIX, traffic mirroring, gRPC, NETFLOW</td>
      <td>gRPC</td>
     </tr>
      <tr>
      <td>Data Transport</td>
      <td>HTTP(S), TCP</td>
      <td>HTTP(S), TCP, UDP</td>
      <td>UDP</td>
      <td>HTTP(S), TCP, UDP</td>
    </tr>
  </tbody>
</table>

        <t>Note that the interaction with the applications that consume network telemetry data can be indirect. Some in-device data transfer is possible. For example, in the management plane telemetry, the management plane will need to acquire data from the data plane. Some operational states can only be derived from data plane data sources such as the interface status and statistics. As another example, obtaining control plane telemetry data may require the ability to access the Forwarding Information Base (FIB) of the data plane.</t>
        <t>On the other hand, an application may involve more than one plane and interact with multiple planes simultaneously. For example, an SLA compliance application may require both the data plane telemetry and the control plane telemetry.</t>
        <t>The requirements and challenges for each module are summarized as follows (note that the requirements may pertain across all telemetry modules; however, we emphasize those that are most pronounced for a particular plane).</t>
        <section title="Management numbered="true" toc="default">
          <name>Management Plane Telemetry"> Telemetry</name>
          <t>The management plane of network elements interacts with the Network Management System (NMS), (NMS) and provides information such as performance data, network logging data, network warning and defects data, and network statistics and state data. The management plane includes many protocols, including the classical SNMP and syslog. Regardless the protocol, management plane telemetry must address the following requirements:</t>
<t>
<list style="symbols">
<t>Convenient
          <ul spacing="normal">
            <li>Convenient Data Subscription: An application should have the freedom to choose which data is exported (see section 4.3) <xref target="sec_type" format="default"/>) and the means and frequency of how that data is exported (e.g., on-change or periodic subscription).</t>
<t>Structured subscription).</li>
            <li>Structured Data: For automatic network operation, machines will replace human humans for network data comprehension. Data modeling languages, such as YANG, can efficiently describe structured data and normalize data encoding and transformation.</t>
<t>High Speed transformation.</li>
            <li>High-Speed Data Transport: In order to keep up with the velocity of information, a data source needs to be able to send large amounts of data at high frequency. Compact encoding formats or data compression schemes are needed to reduce the quantity of data and improve the data transport efficiency. The subscription mode, by replacing the query mode, reduces the interactions between clients and servers and helps to improve the data source's efficiency.</t>
<t>Network efficiency.</li>

            <li>Network Congestion Avoidance: The application must protect the
   network from congestion by with congestion control mechanisms or or,
   at least minimum, with circuit breakers. <xref target="RFC8084" /> format="default"/> and <xref target="RFC8085" /> format="default"/> provide some solutions in this space.</t>
</list>
</t> space.</li>
          </ul>
        </section>
        <section title="Control numbered="true" toc="default">
          <name>Control Plane Telemetry"> Telemetry</name>
          <t>The control plane telemetry refers to the health condition monitoring of different network control protocols at all layers of the protocol stack. Keeping track of the operational status of these protocols is beneficial for detecting, localizing, and even predicting various network issues, as well as for network optimization, in real-time real time and with fine granularity. Some particular challenges and issues faced by the control plane telemetry are as follows: </t>
<t>
<list style="symbols">
<t>One challenging problem for the control plane telemetry is how

          <ul spacing="normal">
            <li>How to correlate the End-to-End (E2E) Key Performance Indicators (KPI) (KPIs) to a specific layer's KPIs. For example, IPTV users may describe their User Experience (UE) UE by the video smoothness and definition. Then in case of an unusually poor UE KPI or a service disconnection, it is non-trivial to delimit and pinpoint the issue in the responsible protocol layer (e.g., the Transport Layer transport layer or the Network Layer), network layer), the responsible protocol (e.g., ISIS IS-IS or BGP at the Network Layer), network layer), and finally the responsible device(s) with specific reasons. </t>
<t> </li>
            <li> Conventional OAM-based approaches for control plane KPI measurement measurement, which include Ping (L3), Traceroute (L3), <xref target="y1731">Y.1731</xref> target="y1731" format="default">Y.1731</xref> (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. </t>
<t> An example of the control plane telemetry </li>
            <li> How more research is needed for the BGP monitoring protocol (BMP). It BMP is an example of the control plane telemetry; it is currently used for monitoring the BGP routes and enables rich applications, such as BGP peer analysis, AS Autonomous System (AS) analysis, prefix analysis, and security analysis. However, the monitoring of other layers, protocols protocols, and the cross-layer, cross-protocol KPI correlations are still in their infancy (e.g., IGP monitoring is not as extensive as BMP), which require requires further research. </t> </li>
          </ul>
		  <t> The Note that the requirement and solutions for network congestion avoidance are also applicable to the control plane telemetry. </t>
</list>
</t>
        </section>
        <section title="Forwarding numbered="true" toc="default">
          <name>Forwarding Plane Telemetry"> Telemetry</name>
          <t>An effective forwarding plane telemetry system relies on the data that the network device can expose. The quality, quantity, and timeliness of data must meet some stringent requirements. This raises some challenges to for the network data plane devices where the first-hand data originates.</t>
<t>
<list style="symbols">
<t>A
          <ul spacing="normal">
            <li>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 strive to not impede normal traffic processing and forwarding (i.e., the forwarding behavior should not be altered altered, and the trade-off between forwarding performance and telemetry should be well-balanced).</t>
<t>Network well-balanced).</li>
            <li>Network operation applications require end-to-end visibility across various sources, which can result in a huge volume of data. However, the sheer quantity of data must not exhaust the network bandwidth, regardless of the data delivery approach (i.e., whether through in-band or out-of-band channels).</t>
<t>The channels).</li>
            <li>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.</t>
<t>The useless.</li>
            <li>The data should be structured and structured, 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.</t>
<t>The applications.</li>
            <li>The data plane telemetry should support incremental deployment and work even though some devices are unaware of the system.</t>
<t>The system.</li>
            <li>The requirement and solutions for network congestion avoidance are also applicable to the forwarding plane telemetry.</t>
</list>
</t> telemetry.</li>
          </ul>
          <t>Although not specific to the forwarding plane, these challenges are more difficult to for the forwarding plane because of the limited resource resources and flexibility. Data plane programmability is essential to support network telemetry. Newer data plane forwarding chips are equipped with advanced telemetry features and provide flexibility to support customized telemetry functions. </t>
          <t>Technique Taxonomy: concerning about This pertains to how one instruments the telemetry, telemetry; there can be multiple possible dimensions to classify the forwarding plane telemetry techniques.</t>
<t>
<list style="symbols">
<t>
          <ul spacing="normal">
            <li> Active, Passive, and Hybrid: This dimension concerns about pertains to the end-to-end measurement. Active and passive methods (as well as the hybrid types) are well documented in <xref target="RFC7799"/>. target="RFC7799" format="default"/>. Passive methods include TCPDUMP, <xref target="RFC7011">IPFIX</xref>, target="RFC7011" format="default">IPFIX</xref>, sFlow, and traffic mirroring. These methods usually have low data coverage. The bandwidth cost is very high in order to improve the data coverage. On the other hand, active methods include Ping, the <xref target="RFC4656">OWAMP</xref>, target="RFC4656" format="default">One-Way Active Measurement Protocol (OWAMP)</xref>, the <xref target="RFC5357">TWAMP</xref>, target="RFC5357" format="default">Two-Way Active Measurement Protocol (TWAMP)</xref>, the <xref target="RFC8762">STAMP</xref>, target="RFC8762" format="default">Simple Two-way Active Measurement Protocol (STAMP)</xref>, and <xref target="RFC6812">Cisco's target="RFC6812" format="default">Cisco's SLA Protocol</xref>. These methods are intrusive and only provide indirect network measurements. Hybrid methods, including <xref target="I-D.ietf-ippm-ioam-data">in-situ OAM</xref>, target="RFC9197" format="default">IOAM</xref>, <xref target="RFC8321">Alternate-Marking target="RFC8321" format="default">Alternate Marking (AM)</xref>, and <xref target="RFC8889">Multipoint target="RFC8889" format="default">Multipoint Alternate Marking</xref>, provide a well-balanced and more flexible approach. However, these methods are also more complex to implement.</t>
<t> implement.</li>
            <li> In-Band and Out-of-Band: Telemetry data carried in user packets before being exported to a data collector is considered in-band (e.g., <xref target="I-D.ietf-ippm-ioam-data">in-situ OAM</xref>). target="RFC9197" format="default">IOAM</xref>). Telemetry data that is directly exported to a data collector without modifying user packets is considered out-of-band (e.g., the postcard-based approach described in <xref target="pbt" />). format="default"/>). It is also possible to have hybrid methods, where only the telemetry instruction or partial data is carried by user packets (e.g., <xref target="RFC8321">AM</xref>). </t>
<t> target="RFC8321" format="default">AM</xref>). </li>
            <li> End-to-End and In-Network: End-to-End End-to-end methods start from, and end at, the network end hosts (e.g., Ping). In-Network In-network methods work in networks and are transparent to end hosts. However, if needed, In-Network in-network methods can be easily extended into end hosts. </t>
<t> </li>
            <li> Data Subject: Depending on the telemetry objective, the methods can be flow-based flow based (e.g., <xref target="I-D.ietf-ippm-ioam-data">in-situ OAM</xref>), path-based target="RFC9197" format="default">IOAM</xref>), path based (e.g., Traceroute), and node-based node based (e.g., <xref target="RFC7011">IPFIX</xref>). target="RFC7011" format="default">IPFIX</xref>). The various data objects can be packet, flow record, measurement, states, and signal.</t>
</list>
</t> signal.</li>
          </ul>
        </section>
        <section title="External numbered="true" toc="default">
          <name>External Data Telemetry"> Telemetry</name>
          <t>Events that occur outside the boundaries of the network system are another important source of network telemetry. Correlating both internal telemetry data and external events with the requirements of network systems, as presented in <xref target="I-D.pedro-nmrg-anticipated-adaptation"/>, target="NMRG-ANTICIPATED-ADAPTATION" format="default"/>, provides a strategic and functional advantage to management operations. </t>
          <t>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 into network management applications. The specific challenges are described as follows:</t>
<t>
<list style="symbols">
<t>The
          <ul spacing="normal">
            <li>The role of the external event detector can be played by multiple elements, including hardware (e.g., physical sensors, such as seismometers) and software (e.g., Big Data big data sources that can analyze streams of information, such as Twitter messages). Thus, the transmitted data must support different shapes but, at the same time, follow a common but extensible schema. </t>
<t>Since </li>
            <li>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 is higher for important sources and events and lower for secondary ones. </t>
<t>The </li>
            <li>The schema used by external detectors must be easily adopted by current and future devices and applications. Therefore, it must be easily mapped to current data models, such as in terms of YANG. </t>
<t>As </li>
            <li>As the communication with external entities outside the boundary of a provider network may be realized over the Internet, the risk of congestion is even more relevant in this context and proper counter-measures countermeasures must be taken. Solutions such as network transport circuit breakers are needed as well.</t>
</list>
</t> well.</li>
          </ul>
          <t>Organizing both internal and external telemetry information together 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. </t>
        </section>
      </section>
      <section title="Second Level numbered="true" toc="default">
        <name>Second-Level Function Components"> Components</name>
        <t>The telemetry module at each plane can be further partitioned into five distinct conceptual components:</t>
<t>
<list style="symbols">
<t>
        <ul spacing="normal">
          <li> Data Query, Analysis, and Storage: This component works at the network operation application block in <xref target="figure_1"/>. target="figure_1" format="default"/>. It is normally a part of the network management system at the receiver side. On the one hand, it is responsible for issuing data requirements. The data of interest can be modeled data through configuration or custom data through programming. The data requirements can be queries for one-shot data or subscriptions for events or streaming data. On the other hand, it receives, stores, and processes the returned data from network devices. Data analysis can be interactive to initiate further data queries. This component can reside in either network devices or remote controllers. It can be centralized and distributed, distributed and involve one or more instances.</t>
<t> instances.</li>
          <li> Data Configuration and Subscription: This component manages 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 from data sources. The subscription data can be described by models, templates, or programs. </t>
<t> </li>
          <li> Data Encoding and Export: This component determines how telemetry data is delivered to the data analysis and storage component with access control. The data encoding and the transport protocol may vary due to the data export location.</t>
<t> location.</li>
          <li> Data Generation and Processing: The requested data needs to be captured, filtered, 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.</t>
<t> devices.</li>
          <li> Data Object and Source: This component determines the monitoring objects and original data sources provisioned in the device. A data source usually just provides raw data which that needs further processing. Each data source can be considered a probe. Some data sources can be dynamically installed, while others will be more static.</t>
</list>
</t>
<t> static.</li>
        </ul>
        <figure anchor="figure_3" title="Components anchor="figure_3">
          <name>Components in the Network Telemetry Framework">
<artwork><![CDATA[ Framework</name>
          <artwork name="" type="" align="left" alt=""><![CDATA[
                  +----------------------------------------+
                +----------------------------------------+ |
                |                                        | |
                |    Data Query, Analysis, & Storage     | |
                |                                        | +
                +-------+++ -----------------------------+
                        |||                   ^^^
                        |||                   |||
                        ||V                   |||
                     +--+V--------------------+++------------+
                  +-----V---------------------+------------+ |
                +---------------------+-------+----------+ | |
                | Data Configuration  |                  | | |
                | & Subscription      | Data Encoding    | | |
                | (model, template,   | & Export         | | |
                |  & program)         |                  | | |
                +---------------------+------------------| | |
                |                                        | | |
                |           Data Generation              | | |
                |           & Processing                 | | |
                |                                        | | |
                +----------------------------------------| | |
                |                                        | | |
                |       Data Object and Source           | |-+
                |                                        |-+
                +----------------------------------------+

]]>
</artwork>
]]></artwork>
        </figure>
</t>
      </section>
      <section anchor="sec:type" title="Data anchor="sec_type" numbered="true" toc="default">
        <name>Data Acquisition Mechanism and Type Abstraction"> Abstraction</name>
        <t>Broadly speaking, network data can be acquired through subscription (push) and query (poll). A subscription is a contract between publisher and subscriber. After initial setup, the subscribed data is automatically delivered to registered subscribers until the subscription expires.
	There are two variations of subscription. The subscriptions can be either pre-defined, predefined, or the subscribers are allowed to configure and tailor the published data to their specific needs.</t>
        <t>In contrast, queries are used when a client expects immediate and one-off feedback from network devices. The queried data may be directly extracted from some specific data source, source or synthesized and processed from raw data. Queries work well for interactive network telemetry applications. </t>
        <t>In general, data can be pulled (i.e., queried) whenever needed, but in many cases, pushing the data (i.e., subscription) is more efficient, and it can reduce the latency of a client detecting a change. From the data consumer point of view, there are four types of data from network devices that a telemetry data consumer can subscribe or query:</t>
<t>
<list style="symbols">
<t>
        <ul spacing="normal">
          <li> Simple Data: The data Data that are steadily available from some datastore or static probes in network devices.</t>
<t> devices.</li>
          <li> Derived Data: The data Data that need to be synthesized or processed in the network from raw data from one or more network devices. The data processing function can be statically or dynamically loaded into network devices.</t>
<t> devices.</li>
          <li> Event-triggered Data: The data Data that are conditionally acquired based on the occurrence of some events. An example of event-triggered data could be an interface changing operational state between up and down. Such data can be actively pushed through subscription or passively polled through query. There are many ways to model events, including using Finite State Machine (FSM) or <xref target="I-D.wwx-netmod-event-yang">Event target="I-D.ietf-netmod-eca-policy" format="default">Event Condition Action (ECA)</xref>. </t>
<t> </li>
          <li> Streaming Data: The data Data that are continuously generated. It can be a time series or the dump of databases. For example, an interface packet counter is exported every second. The streaming data reflect realtime real-time network states and metrics and require large bandwidth and processing power. The streaming data are always actively pushed to the subscribers.</t>
</list>
</t> subscribers.</li>
        </ul>
        <t>The above telemetry data types are not mutually exclusive. Rather, they are often composite. Derived data is composed of simple data; Event-triggered event-triggered data can be simple or derived; and streaming data can be based on some recurring event. The relationships of these data types are illustrated in <xref target="figure_0"/>. target="figure_0" format="default"/>. </t>
<t>
        <figure anchor="figure_0" title="Data anchor="figure_0">
          <name>Data Type Relationship">
<artwork><![CDATA[ Relationship</name>
          <artwork name="" type="" align="left" alt=""><![CDATA[
   +----------------------+     +-----------------+
   | Event-triggered Event-Triggered Data |<----+ Streaming Data  |
   +-------+---+----------+     +-----+---+-------+
           |   |                      |   |
           |   |                      |   |
           |   |   +--------------+   |   |
           |   +-->| Derived Data |<--+   |
           |       +------+------ +       |
           |              |               |
           |              V               |
           |       +--------------+       |
           +------>| Simple Data  |<------+
                   +--------------+
]]>
</artwork>
]]></artwork>
        </figure>
</t>
        <t>Subscription usually deals with event-triggered data and streaming data, and query usually deals with simple data and derived data. But the other ways are also possible. Advanced network telemetry techniques are designed mainly for event-triggered or streaming data subscription, subscription and derived data query.</t>
      </section>
      <section title="Mapping numbered="true" toc="default">
        <name>Mapping Existing Mechanisms into the Framework"> Framework</name>
        <t>The following table shows how the existing mechanisms (mainly published in IETF and with the emphasis on the latest new technologies) are positioned in the framework. Given the vast body of existing work, we cannot provide an exhaustive list, so the mechanisms in the tables should be considered as just examples. Also, some comprehensive protocols and techniques may cover multiple aspects or modules of the framework, so a name in a block only emphasizes one particular characteristic of it. More details about some listed mechanisms can be found in Appendix A.</t>
<t>
<figure anchor="figure_5" title="Existing

<table anchor="table_2">
  <name>Existing Work Mapping">
<artwork><![CDATA[
     +-------------+-----------------+---------------+--------------+
     |             | Management      | Control       | Forwarding   |
     |             | Plane           | Plane         | Plane        |
     +-------------+-----------------+---------------+--------------+
     | data config.| gNMI, NETCONF,  | gNMI, NETCONF,| Mapping</name>
  <thead>
    <tr>
      <th></th>
      <th>Management Plane</th>
      <th>Control Plane</th>
      <th>Forwarding Plane</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>data configuration and subscribe</td>
      <td>gNMI, NETCONF,     |
     | & subscribe | RESTCONF, SNMP, | YANG-Push</td>
      <td>gNMI, NETCONF, RESTCONF,     | YANG-Push</td>
      <td>NETCONF, RESTCONF,    |
     |             | YANG-Push       | YANG-Push     | YANG-Push    |
     +-------------+-----------------+---------------+--------------+
     | data gen. & | MIB,            | YANG          | IOAM, PSAMP  |
     | process     | YANG            |               | YANG-Push</td>
    </tr>
    <tr>
      <td>data generation and process</td>
      <td>MIB, YANG</td>
      <td>YANG</td>
      <td>IOAM, PSAMP, PBT, AM,     |
     +-------------+-----------------+---------------+--------------+
     | data encode.| gRPC, AM</td>
    </tr>
    <tr>
      <td>data encoding and export</td>
      <td>gRPC, HTTP, TCP | BMP, TCP      | IPFIX, UDP   |
     | & export    |                 |               |              |
     +-------------+-----------------+---------------+--------------+

]]>
</artwork>
</figure>
</t> TCP</td>
      <td>BMP, TCP</td>
      <td>IPFIX, UDP</td>
    </tr>
  </tbody>
</table>
        <t>Although the framework is generally suitable for any network environments, the multi-domain telemetry has some unique challenges which that deserve further architectural consideration, which is out of the scope of this document.</t>
      </section>
    </section>
    <section anchor="level" title="Evolution numbered="true" toc="default">
      <name>Evolution of Network Telemetry Applications"> Applications</name>
      <t>Network telemetry is an evolving technical area. As the network moves towards the automated operation, network telemetry applications undergo several stages of evolution evolution, which add a new layer of requirements to the underlying network telemetry techniques. Each stage is built upon the techniques adopted by the previous stages plus some new requirements.</t>
<t>
<list style="hanging">
<t hangText="Stage
      <dl newline="false" spacing="normal">
        <dt>Stage 0 - Static Telemetry:"> Telemetry:</dt>
        <dd> The telemetry data source and type are determined at design time. The network operator can only configure how to use it with limited flexibility. </t>
<t hangText="Stage </dd>
        <dt>Stage 1 - Dynamic Telemetry:"> Telemetry:</dt>
        <dd> The custom telemetry data can be dynamically programmed or configured at runtime without interrupting the network operation, allowing a trade-off among resource, performance, flexibility, and coverage. </t>
<t hangText="Stage coverage.</dd>
        <dt>Stage 2 - Interactive Telemetry:"> Telemetry:</dt>
        <dd> The network operator can continuously customize and fine tune the telemetry data in real time to reflect the network operation's visibility requirements. Compared with Stage 1, the changes are frequent based on the real-time feedback. At this stage, some tasks can be automated, but human operators still need to sit in the middle to make decisions. </t>
<t hangText="Stage </dd>
        <dt>Stage 3 - Closed-loop Telemetry:"> Closed-Loop Telemetry:</dt>
        <dd> The telemetry is free from the interference of human operators, except for generating the reports. The intelligent network operation engine automatically issues the telemetry data requests, analyzes the data, and updates the network operations in closed control loops. </t>
</list>
</t> </dd>
      </dl>
      <t>Existing technologies are ready for stage Stages 0 and stage 1. Individual stage applications for Stages 2 and stage 3 applications are also possible now. However, the future autonomic networks may need a comprehensive operation management system which that works at stage Stages 2 and stage 3 to cover all the network operation tasks. A well-defined network telemetry framework is the first step towards this direction. </t>
    </section>
    <section anchor="Security" title="Security Considerations"> numbered="true" toc="default">
      <name>Security Considerations</name>
      <t>The complexity of network telemetry raises significant security implications. For example, telemetry data can be manipulated to exhaust various network resources at each plane as well as the data consumer; falsified or tampered data can mislead the decision-making process and paralyze networks; and wrong configuration and programming for telemetry is equally harmful. The telemetry data is highly sensitive, which exposes a lot of information about the network and its configuration. Some of that information can make designing attacks against the network much easier (e.g., exact details of what software and patches have been installed), installed) and allows an attacker to determine whether a device may be subject to unprotected security vulnerabilities.</t>

      <t>Given that this document has proposed a framework for network telemetry and the telemetry mechanisms discussed are more extensive (in both message frequency and traffic amount) than the conventional network OAM concepts, we must also reflect anticipate that various new security considerations that may also arise. A number of techniques already exist for securing the forwarding plane, the control plane, and the management plane in a network, but it is important to consider if any new threat vectors are now being enabled via the use of network telemetry procedures and mechanisms. </t>
      <t>This document proposes a conceptual architectural for collecting, transporting, and analyzing a wide variety of data sources in support of network applications. The protocols, data formats, and configurations chosen to implement this framework will dictate the specific security considerations. These considerations may include:</t>
<t>
<list style="symbols">
<t>Telemetry
      <ul spacing="normal">
        <li>Telemetry framework trust and policy model;</t>
<t>Role models;</li>
        <li>Role management and access control for enabling and disabling telemetry capabilities;</t>
<t>Protocol capabilities;</li>
        <li>Protocol transport used for telemetry data and its inherent security capabilities;</t>
<t>Telemetry capabilities;</li>
        <li>Telemetry data stores, storage encryption, methods of access, and retention practices;</t>
<t>Tracking practices;</li>
        <li>Tracking telemetry events and any abnormalities that might identify malicious attacks using telemetry interfaces.</t>
<t>Authentication interfaces.</li>
        <li>Authentication and integrity protection of telemetry data to make data more trustworthy. </t>
<t>Segregating trustworthy; and </li>
        <li>Segregating the telemetry data traffic from the data traffic carried over the network (e.g., historically management access and management data may be carried via an independent management network).</t>
</list>
</t> network).</li>
      </ul>
      <t>Some security considerations highlighted above may be minimized or negated with policy management of network telemetry. In a network telemetry deployment deployment, it would be advantageous to separate telemetry capabilities into different classes of policies, i.e., Role Based Role-Based Access Control and Event-Condition-Action policies. Also, potential conflicts between network telemetry mechanisms must be detected accurately and resolved quickly to avoid unnecessary network telemetry traffic propagation escalating into an unintended or intended denial of service denial-of-service attack.</t>
      <t>Further study of the security issues will be required, and it is expected that the security mechanisms and protocols are developed and deployed along with a network telemetry system.</t>
    </section>
    <section anchor="IANA" title="IANA Considerations"> numbered="true" toc="default">
      <name>IANA Considerations</name>
      <t>This document includes has no request to IANA.</t>
</section>
<section anchor="Contributors" title="Contributors">
<t> The other contributors of this document are Tianran Zhou, Zhenbin Li, Zhenqiang Li, Daniel King, Adrian Farrel, and Alexander Clemm </t>
</section>
<section anchor="Acknowledgments" title="Acknowledgments">
<t>We would like to thank Rob Wilton, Greg Mirsky, Randy Presuhn, Joe Clarke, Victor Liu, James Guichard, Uri Blumenthal, Giuseppe Fioccola, Yunan Gu, Parviz Yegani, Young Lee, Qin Wu, Gyan Mishra, Ben Schwartz, Alexey Melnikov, Michael Scharf, Dhruv Dhody, Martin Duke, Roman Danyliw, Warren Kumari, Sheng Jiang, Lars Eggert, Eric Vyncke, Jean-Michel Combes, Erik Kline, Benjamin Kaduk, and many others who have provided helpful comments and suggestions to improve this document.</t> IANA actions.</t>
    </section>

  </middle>
  <back>

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      <author fullname="Haoyu Song" role="editor">
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      <author fullname="Jun Gong">
	 <organization>Huawei Technologies Co., Ltd</organization>
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      <author fullname="Tianran Zhou">
	 <organization>Huawei</organization>
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	 <organization>Huawei</organization>
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      <author fullname="Frank Brockners">
	 <organization>Cisco</organization>
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      <author fullname="Shwetha Bhandari" role="editor">
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      <author fullname="Ramesh Sivakolundu">
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<xi:include href="https://datatracker.ietf.org/doc/bibxml3/reference.I-D.song-opsawg-ifit-framework.xml"/>

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<xi:include href="https://datatracker.ietf.org/doc/bibxml3/reference.I-D.irtf-nmrg-ibn-concepts-definitions.xml"/>

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<xi:include href="https://datatracker.ietf.org/doc/bibxml3/reference.I-D.ietf-netmod-eca-policy.xml"/>

      <reference anchor="gpb" target="https://developers.google.com/protocol-buffers">
        <front>
<title>Google Protocol
          <title>Protocol Buffers</title>
<author/>
          <author><organization>Google Developers</organization></author>
          <date/>
        </front>
      </reference>

      <reference anchor="grpc" target="https://grpc.io">
        <front>
<title>gPPC,
          <title>gPPC: A high performance, open-source open source universal RPC framework</title>
<author/>
          <author><organization>gRPC</organization></author>
          <date/>
        </front>
      </reference>

     <reference anchor="gnmi" target="https://github.com/openconfig/reference/tree/master/rpc/gnmi"> target="https://datatracker.ietf.org/meeting/98/materials/slides-98-rtgwg-gnmi-intro-draft-openconfig-rtgwg-gnmi-spec-00">
        <front>
<title>gNMI - gRPC
          <title>gRPC Network Management Interface</title>
<author/>
<date/>
           <author initials="R." surname="Shakir" fullname="Rob Shakir">
              <organization/>
           </author>
	    <author initials="A." surname="Shaikh" fullname="Anees Shaikh">
              <organization/>
            </author>
	      <author initials="P." surname="Borman" fullname="Paul Borman">
              <organization/>
              </author>
	      <author initials="M." surname="Hines" fullname="Marcus Hines">
              <organization/>
              </author>
	      <author initials="C." surname="Lebsack" fullname="Carl Lebsack">
              <organization/>
              </author>
	      <author initials="C." surname="Marrow" fullname="Chris Morrow">
              <organization/>
           </author>
          <date month="March" year="2017"/>
        </front>
	<refcontent>IETF 98</refcontent>
      </reference>

  <reference anchor="xml" target="https://www.w3.org/TR/2008/REC-xml-20081126/"> anchor="W3C.REC-xml-20081126" target="https://www.w3.org/TR/2008/REC-xml-20081126">
          <front>
            <title>Extensible Markup Language (XML) 1.0 (Fifth Edition)</title>
<author/>
<date/>
            <author initials="T." surname="Bray" fullname="Tim Bray">
              <organization showOnFrontPage="true"/>
            </author>
            <author initials="J." surname="Paoli" fullname="Jean Paoli">
              <organization showOnFrontPage="true"/>
            </author>
            <author initials="M." surname="Sperberg-McQueen" fullname="Michael Sperberg-McQueen">
              <organization showOnFrontPage="true"/>
            </author>
            <author initials="E." surname="Maler" fullname="Eve Maler">
              <organization showOnFrontPage="true"/>
            </author>
            <author initials="F." surname="Yergeau" fullname="Francois Yergeau">
              <organization showOnFrontPage="true"/>
            </author>
            <date month="November" year="2008"/>
          </front>
         <refcontent>World Wide Web Consortium Recommendation REC-xml-20081126</refcontent>
        </reference>

      <reference anchor="y1731" target="https://www.itu.int/rec/T-REC-Y.1731/en">
        <front>
<title>ITU-T Y.1731: OAM Functions
          <title>Operations, administration and Mechanisms maintenance (OAM) functions and
              mechanisms for Ethernet based networks, 2015</title>
<author/>
<date/> Ethernet-based networks</title>
          <author><organization>ITU-T</organization></author>
          <date month="August" year="2015"/>
        </front>
        <seriesInfo name="ITU-T Recommendation" value="G.8013/Y.1731"/>
      </reference>
    </references>

    <section title="A numbered="true" toc="default">
      <name>A Survey on Existing Network Telemetry Techniques"> Techniques</name>
      <t>In this non-normative appendix, we provide an overview of some existing techniques and standard proposals for each network telemetry module.</t>
      <section title="Management numbered="true" toc="default">
        <name>Management Plane Telemetry"> Telemetry</name>
        <section title="Push numbered="true" toc="default">
          <name>Push Extensions for NETCONF"> NETCONF</name>
          <t><xref target="RFC6241">NETCONF</xref> target="RFC6241" format="default">NETCONF</xref> is a popular network management protocol recommended by IETF. Its core strength is for managing configuration, but it can also be used for data collection. <xref target="RFC8641">YANG-Push</xref> target="RFC8639" format="default">YANG-Push</xref> <xref target="RFC8639"/> target="RFC8641" format="default"/> 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, a <xref target="I-D.ietf-netconf-distributed-notif">distributed target="I-D.ietf-netconf-distributed-notif" format="default">distributed data collection mechanism</xref> via a <xref target="I-D.ietf-netconf-udp-notif">UDP based target="I-D.ietf-netconf-udp-notif" format="default">UDP-based publication channel</xref> provides enhanced efficiency for the NETCONF based NETCONF-based telemetry.</t>
        </section>
        <section title="gRPC numbered="true" toc="default">
          <name>gRPC Network Management Interface"> Interface</name>
          <t><xref target="gnmi">gRPC target="gnmi" format="default">gRPC Network Management Interface (gNMI)</xref> is a network management protocol based on the <xref target="grpc">gRPC</xref> RPC (Remote target="grpc" format="default">gRPC</xref> Remote Procedure Call) Call (RPC) framework. With a single gRPC service definition, both configuration and telemetry can be covered. gRPC is an <xref target="RFC7540">HTTP/2</xref>-based open-source micro-service communication framework. framework based on <xref target="RFC7540" format="default">HTTP/2</xref>. It provides a number of capabilities which that are well-suited for network telemetry, including: </t>
<t>
<list style="symbols">
<t>Full-duplex
          <ul spacing="normal">
            <li>A full-duplex streaming transport model model; when combined with a binary encoding mechanism mechanism, it provides good telemetry efficiency.</t>
<t>gRPC provides efficiency.</li>
            <li>A higher-level features feature consistency across platforms that common HTTP/2 libraries typically do not. not provide. This characteristic is especially valuable for the fact that telemetry data collectors normally reside on a large variety of platforms.</t>
<t>The platforms.</li>
            <li>A built-in load-balancing and failover mechanism.</t>
</list>
</t> mechanism.</li>
          </ul>
        </section>
      </section>
      <section title="Control numbered="true" toc="default">
        <name>Control Plane Telemetry"> Telemetry</name>
        <section title="BGP numbered="true" toc="default">
          <name>BGP Monitoring Protocol"> Protocol</name>
          <t><xref target="RFC7854">BGP Monitoring Protocol (BMP)</xref> target="RFC7854" format="default">BMP</xref> is used to monitor BGP sessions and is intended to provide a convenient interface for obtaining route views. </t>
<t>The BGP
          <t>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. notifications. The BGP routes (including <xref target="RFC7854"> Adjacency_RIB_In target="RFC7854" format="default"> Adj_RIB_In </xref>, <xref target="RFC8671"> Adjacency_RIB_out</xref>, target="RFC8671" format="default"> Adj_RIB_out</xref>, and <xref target="I-D.ietf-grow-bmp-local-rib">Local_Rib</xref>) target="RFC9069" format="default">local RIB</xref>) are encapsulated in the BMP Route Monitoring Message and the BMP Route Mirroring Message, providing both an initial table dump and real-time route updates. In addition, BGP statistics are reported through the BMP Stats Report Message, which could be either timer triggered or event-driven. Future BMP extensions could further enrich BGP monitoring applications.
</t>
        </section>
      </section>
      <section title="Data numbered="true" toc="default">
        <name>Data Plane Telemetry"> Telemetry</name>
        <section title="The Alternate Marking numbered="true" toc="default">
          <name>Alternate-Marking (AM) technology"> Technology</name>
          <t>The Alternate Marking Alternate-Marking method enables efficient measurements of packet loss, delay, and jitter both in IP and Overlay Networks, as presented in <xref target="RFC8321"/> target="RFC8321" format="default"/> and <xref target="RFC8889"/>. target="RFC8889" format="default"/>. </t>
          <t>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 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.</t>
<t>Alternate Marking
          <t>The 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).</t>
          <t>Since networks offer rich sets of network performance measurement data (e.g., packet counters), conventional approaches run into limitations. 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.</t>
          <t>The Multipoint Alternate Marking Alternate-Marking approach, described in <xref target="RFC8889"/>, target="RFC8889" format="default"/>, aims to resolve this issue and make the performance monitoring more flexible in case a detailed analysis is not needed. </t>
          <t>An application orchestrates network performance measurements measurement tasks across the network to allow for optimized monitoring.  The application can choose how roughly or precisely to configure measurement points depending on the application's requirements.</t>
          <t>Using Alternate Marking, it is possible to monitor a Multipoint Network without in depth in-depth examination 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 the case that where there is packet loss or the delay is too high then high, the specific filtering criteria could be applied to gather a more detailed analysis by using a different combination of clusters up to a per-flow measurement as described in the Alternate-Marking document <xref target="RFC8321">Alternate-Marking (AM)</xref>. target="RFC8321" format="default"/>. </t>
          <t>In summary, an application can configure end-to-end network monitoring. If the network does not experience issues, this approximate monitoring is good enough and is very cheap in terms of network resources. However, in case of problems, the application becomes aware of the issues from this approximate monitoring and, in order to localize the portion of the network that has issues, configures the measurement points more extensively, allowing more detailed monitoring to be performed. After the detection and resolution of the problem, the initial approximate monitoring can be used again.</t>
        </section>
        <section title="Dynamic numbered="true" toc="default">
          <name>Dynamic Network Probe">
<t>Hardware-based Probe</name>

          <t>A hardware-based <xref target="I-D.song-opsawg-dnp4iq">Dynamic target="OPSAWG-DNP4IQ" format="default">Dynamic Network Probe (DNP)</xref> proposes 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 derived data which that 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.</t>
          <t>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. runtime. The programming API Application Programming Interface (API) is yet to be defined.</t>
        </section>
        <section title="IP numbered="true" toc="default">
          <name>IP Flow Information Export (IPFIX) Protocol"> Protocol</name>
          <t>Traffic on a network can be seen as a set of flows passing through network elements.
<xref target="RFC7011">IP Flow Information Export (IPFIX) target="RFC7011" format="default">IPFIX </xref>
provides a means of transmitting traffic flow information for administrative or other purposes. A typical IPFIX enabled IPFIX-enabled system includes a pool of Metering Processes that collects data packets at one or more Observation Points, optionally filters them 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.</t>
        </section>
        <section title="In-Situ OAM"> numbered="true" toc="default">
          <name>In Situ OAM</name>
          <t>Classical passive and active monitoring and measurement techniques are either inaccurate or resource-consuming. resource consuming. It is preferable to directly acquire data associated with a flow's packets when the packets pass through a network. <xref target="I-D.ietf-ippm-ioam-data">In-situ OAM (iOAM)</xref>, target="RFC9197" format="default">IOAM</xref>, a data generation technique, embeds a new instruction header to user packets packets, and the instruction directs the network nodes to add the requested data to the packets. Thus, at the path path's end, the packet's experience gained on the entire forwarding path can be collected. Such firsthand data is invaluable to many network OAM applications.</t>
          <t>However, iOAM 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.</t>
        </section>
        <section anchor="pbt" title="Postcard Based Telemetry"> numbered="true" toc="default">
          <name>Postcard-Based Telemetry</name>
          <t>The postcard-based telemetry, as embodied in <xref target="I-D.ietf-ippm-ioam-direct-export">IOAM DEX</xref> target="IPPM-IOAM-DIRECT-EXPORT" format="default">IOAM Direct Export (DEX)</xref> and <xref target="I-D.song-ippm-postcard-based-telemetry">IOAM target="I-D.song-ippm-postcard-based-telemetry" format="default">IOAM Marking</xref>, is a complementary technique to the passport-based IOAM. IOAM <xref target="RFC9197" format="default"/>. PBT directly exports data at each node through an independent packet. At the cost of higher bandwidth overhead and the need for data correlation, PBT shows several unique advantages. It can also help to identify packet drop location in case a packet is dropped on its forwarding path.</t>
        </section>
        <section title="Existing numbered="true" toc="default">
          <name>Existing OAM for Specific Data Planes"> Planes</name>
          <t>
Various data planes raise unique OAM requirements. IETF has published OAM technique and framework documents (e.g., <xref target="RFC8924" /> format="default"/> and <xref target="RFC5085" />) format="default"/>) targeting different data planes such as Multi-Protocol Multiprotocol Label Switching (MPLS), L2 Virtual Private Network (L2-VPN), (VPN), Network Virtualization Overlays over Layer 3 (NVO3), Virtual Extensible LAN (VXLAN), Bit Indexed Index Explicit Replication (BIER), Service Function Chaining (SFC), Segment Routing (SR), and Deterministic Networking (DETNET). The aforementioned data plane telemetry techniques can be used to enhance the OAM capability on such data planes.
</t>
        </section>
      </section>
      <section title="External numbered="true" toc="default">
        <name>External Data and Event Telemetry"> Telemetry</name>
        <section title="Sources numbered="true" toc="default">
          <name>Sources of External Events"> Events</name>
          <t>To ensure that the information provided by external event detectors and used by the network management solutions is meaningful for 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 list of potential external data sources that could be of interest in network management and match matching it to the connectors and/or interfaces required to connect them.</t>
          <t>Categories of external event sources that may be of interest to network management include::</t>
<t>
<list style="symbols">
<t>Smart include:</t>
          <ul spacing="normal">
            <li>Smart objects and sensors. With the consolidation of the Internet of Things~(IoT) 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 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 the protocols related to IoT, such as the Constrained Application Protocol (CoAP).</t>
<t>Online (CoAP).</li>
            <li>Online news reporters. Several online news services have the ability to provide an enormous quantity of information about different events occurring in the world. Some of those events can have an impact on the network system managed by a specific framework and, framework; therefore, such information may be of interest to the management solution. For instance, diverse security reports, such as the Common Vulnerabilities and Exposures (CVE), (CVEs), can be issued by the corresponding authority and used by the management solution to update the managed system 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.</t>
<t>Global framework.</li>
            <li>Global event analyzers. The advance of Big Data big data analyzers provides a huge amount of information and, more interestingly, the identification of events detected by analyzing many data streams from different origins. In contrast with the other types of sources, which are focused on specific events, the detectors of this source type will detect generic events. For example, during a sport event sports event, some unexpected movement makes it fascinating fascinating, and many people connect to sites that are reporting on the event. The underlying networks 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.</t>
</list>
</t> solution.</li>
          </ul>
          <t>Additional types of detector types can be added to the system, but generally they will be generally the result of composing the properties offered by these main classes.</t>
        </section>
        <section title="Connectors numbered="true" toc="default">
          <name>Connectors and Interfaces"> Interfaces</name>
          <t>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.</t>
          <t>In some situations, the interconnection between the external event detectors and the management system is via the management plane. For those situations 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 could accomplish this with a specific data model (YANG) and specific telemetry protocol, such as NETCONF, YANG-Push, or gRPC.</t>
        </section>
    <section anchor="Acknowledgments" numbered="false" toc="default">
      <name>Acknowledgments</name>
      <t>We would like to thank <contact fullname="Rob Wilton"/>, <contact fullname="Greg Mirsky"/>, <contact fullname="Randy Presuhn"/>, <contact fullname="Joe Clarke"/>, <contact fullname="Victor Liu"/>, <contact fullname="James Guichard"/>, <contact fullname="Uri Blumenthal"/>, <contact fullname="Giuseppe Fioccola"/>, <contact fullname="Yunan Gu"/>, <contact fullname="Parviz Yegani"/>, <contact fullname="Young Lee"/>, <contact fullname="Qin Wu"/>, <contact fullname="Gyan Mishra"/>, <contact fullname="Ben Schwartz"/>, <contact fullname="Alexey Melnikov"/>, <contact fullname="Michael Scharf"/>, <contact fullname="Dhruv Dhody"/>, <contact fullname="Martin Duke"/>, <contact fullname="Roman Danyliw"/>, <contact fullname="Warren Kumari"/>, <contact fullname="Sheng Jiang"/>, <contact fullname="Lars Eggert"/>, <contact fullname="Éric Vyncke"/>, <contact fullname="Jean-Michel Combes"/>, <contact fullname="Erik Kline"/>, <contact fullname="Benjamin Kaduk"/>, and many others who have provided helpful comments and suggestions to improve this document.</t>
    </section>
 <section anchor="Contributors" numbered="false" toc="default">
      <name>Contributors</name>
      <t> The other contributors of this document are <contact fullname="Tianran Zhou"/>, <contact fullname="Zhenbin Li"/>, <contact fullname="Zhenqiang Li"/>, <contact fullname="Daniel King"/>, <contact fullname="Adrian Farrel"/>, and <contact fullname="Alexander Clemm"/>.</t>
    </section>
      </section>
    </section>
  </back>
</rfc>