OPSAWG                                                           H. Song
Internet-Draft                                                 Futurewei
Intended status: Informational                                    F. Qin
Expires: 24 April 2022                                      China Mobile
                                                                 H. Chen
                                                           China Telecom
                                                                  J. Jin
                                                                   LG U+
                                                                 J. Shin
                                                              SK Telecom
                                                         21 October 2021


                   In-situ Flow Information Telemetry
                  draft-song-opsawg-ifit-framework-16

Abstract

   As network scale increases and network operation becomes more
   sophisticated, traditional Operation, Administration and Maintenance
   (OAM) methods, which include proactive and reactive techniques,
   running in active and passive modes, are no longer sufficient to meet
   the monitoring and measurement requirements.  Data-plane on-path
   telemetry techniques which provide high-precision flow insight and
   real-time issue notification are emerging to support suitable quality
   of experience for users and applications, and network fault or
   deficiency identification.

   Centering on the new data-plane on-path telemetry techniques, this
   document outlines a high-level framework to provide an operational
   environment that utilizes these techniques to enable the collection
   and correlation of performance measurement information from the
   network.  The framework identifies the components that are needed to
   coordinate the existing protocol tools and telemetry mechanisms, and
   addresses key deployment challenges for flow-oriented on-path
   telemetry techniques, especially in carrier networks.

   The framework is informational and intended to guide system designers
   attempting to apply the referenced techniques as well as to motivate
   further work to enhance the ecosystem.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.






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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     1.1.  Classification and Modes of On-path Telemetry . . . . . .   4
     1.2.  Requirements and Challenges . . . . . . . . . . . . . . .   6
     1.3.  Scope . . . . . . . . . . . . . . . . . . . . . . . . . .   7
     1.4.  Glossary  . . . . . . . . . . . . . . . . . . . . . . . .   8
     1.5.  Requirements Language . . . . . . . . . . . . . . . . . .   8
   2.  Architectural Concepts and Key Components . . . . . . . . . .   9
     2.1.  Typical Deployment  . . . . . . . . . . . . . . . . . . .   9
     2.2.  Key Components  . . . . . . . . . . . . . . . . . . . . .  10
       2.2.1.  Flexible Flow, Packet, and Data Selection . . . . . .  11
       2.2.2.  Flexible Data Export  . . . . . . . . . . . . . . . .  12
       2.2.3.  Dynamic Network Probe . . . . . . . . . . . . . . . .  14
       2.2.4.  On-demand Technique Selection and Integration . . . .  16
     2.3.  Relationship with Network Telemetry Framework (NTF) . . .  17
     2.4.  IFIT for Reflective Telemetry . . . . . . . . . . . . . .  17
       2.4.1.  Intelligent Multipoint Performance Monitoring . . . .  18
       2.4.2.  Intent-based Network Monitoring . . . . . . . . . . .  19
   3.  Guidance for Solution Developers  . . . . . . . . . . . . . .  20
     3.1.  Encapsulation in Transport Protocols  . . . . . . . . . .  20
     3.2.  Tunneling Support . . . . . . . . . . . . . . . . . . . .  20



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     3.3.  Deployment Automation . . . . . . . . . . . . . . . . . .  21
   4.  Security Considerations . . . . . . . . . . . . . . . . . . .  21
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  21
   6.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  21
   7.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  22
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  22
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  22
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  22
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  25

1.  Introduction

   Efficient network operation increasingly relies on high-quality data-
   plane telemetry to provide the necessary visibility.  Traditional
   Operation, Administration and Maintenance (OAM) methods, which
   include proactive and reactive techniques, running both active and
   passive modes, are no longer sufficient to meet the monitoring and
   measurement requirements when networks becomes more and more
   autonomous and application-aware.  The complexity of today's networks
   and service quality requirements demand new high-precision and real-
   time techniques.

   The ability to expedite network failure detection, fault
   localization, and recovery mechanisms, particularly in the case of
   soft failures or path degradation is expected, without causing
   service disruption.  Application-awareness requires the capacity of a
   network to maintain current information about users and application
   connections which may be used to optimize the network resource usage,
   provide differential services, and improve the quality of service.

   The emerging on-path telemetry techniques can provide high-precision
   flow insight and real-time network issue notification (e.g., jitter,
   latency, packet loss, significant bit error variations, and unequal
   load-balancing).  On-path telemetry refers to the data-plane
   telemetry techniques that directly tap and measure network traffic by
   embedding instructions or metadata into user packets.  The data
   provided by on-path telemetry are especially useful for SLA
   compliance, user experience enhancement, service path enforcement,
   fault diagnosis, and network resource optimization.  It is essential
   to recognize that existing work on this topic includes a variety of
   on-path telemetry techniques, including In-situ OAM(IOAM)
   [I-D.ietf-ippm-ioam-data], IOAM Direct Export (DEX)
   [I-D.ietf-ippm-ioam-direct-export], Marking-based Postcard-based
   Telemetry(PBT-M) [I-D.song-ippm-postcard-based-telemetry], Enhanced
   Alternate Marking (EAM) [I-D.zhou-ippm-enhanced-alternate-marking],
   and Hybrid Two Steps (HTS) [I-D.mirsky-ippm-hybrid-two-step], have
   been proposed, which can provide flow information on the entire
   forwarding path on a per-packet basis in real-time.  The



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   aforementioned on-path telemetry techniques differ from the active
   and passive OAM schemes discussed earlier in that, they directly
   modify and monitor the user packets in networks so as to achieve high
   measurement accuracy.  Formally, these on-path telemetry techniques
   can be classified as the OAM hybrid type I, since they involve
   "augmentation or modification of the stream of interest, or
   employment of methods that modify the treatment of the streams",
   according to [RFC7799].

   On-path telemetry is useful for application-aware networking
   operations not only in data center and enterprise networks but also
   in carrier networks which may cross multiple domains.  Carrier
   network operators have shown interest in utilizing such techniques
   for various purposes.  For example, it is critical for the operators
   who offer high-bandwidth, latency and loss-sensitive services such as
   video streaming and online gaming to closely monitor the relevant
   flows in real-time as the basis for any further optimizations.

   This framework document is intended to guide system designers
   attempting to use the referenced techniques as well as to motivate
   further work to enhance the telemetry ecosystem.  It highlights
   requirements and challenges, outlines vital techniques that are
   applicable, and provides examples of how these might be applied for
   critical use cases.

   The document scope is discussed in Section 1.3.

1.1.  Classification and Modes of On-path Telemetry

   The operation of on-path telemetry differs from both active OAM and
   passive OAM as defined in [RFC7799].  It does not generate any active
   probe packets or passively observes unmodified user packets.
   Instead, it modifies selected user packets in order to collect useful
   information about them.  Therefore, the operation is categorized as
   the hybrid OAM type I mode per [RFC7799].

   This hybrid type OAM can be further partitioned into two modes
   [passport-postcard].  In the passport mode, each node on the path
   adds the telemetry data to the user packets (i.e., stamp the
   passport).  The accumulated data trace is exported at a configured
   end node.  In the postcard mode, each node directly exports the
   telemetry data using an independent packet (i.e., send a postcard)
   while the user packets are intact.  It is possible to combine the two
   modes together in one solution.  We call this the hybrid mode.

   Figure 1 shows the classification of the existing on-path telemetry
   techniques.




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    +-----------+--------------+--------------+---------------+
    |  Mode     | Passport     | Postcard     | Hybrid        |
    +-----------+--------------+--------------+---------------+
    |           | IOAM Trace   | IOAM DEX     | Multicast Te- |
    | Technique | IOAM E2E     | PBT-M        | lemetry       |
    |           |              | EAM          | HTS           |
    +-----------+--------------+--------------+---------------+

            Figure 1: On-path Telemetry Technique Classification

   IOAM Trace and E2E options are described in
   [I-D.ietf-ippm-ioam-data].  EAM is described in
   [I-D.zhou-ippm-enhanced-alternate-marking].  IOAM DEX option is
   described in [I-D.ietf-ippm-ioam-direct-export].  PBT-M is described
   in [I-D.song-ippm-postcard-based-telemetry].  Multicast Telemetry is
   described in [I-D.ietf-mboned-multicast-telemetry].  HTS is described
   in [I-D.mirsky-ippm-hybrid-two-step].

   The advantages of the passport mode include:

   *  It automatically retains the telemetry data correlation along the
      entire path.  The self-describing feature eases the data
      consumption.

   *  The on-path data for a packet is only exported once so the data
      export overhead is low.

   *  Only the head and end nodes of the paths need to be configured so
      the configuration overhead is low.

   The disadvantages of the passport mode include:

   *  The telemetry data carried by user packets inflate the packet
      size, which may be undesirable or prohibitive.

   *  Approaches for encapsulating the instruction header and data in
      transport protocols need to be standardized.

   *  Carrying sensitive data along the path is vulnerable to security
      and privacy breach.

   *  If a packet is dropped on the path, the data collected are also
      lost.

   The postcard mode complements the passport mode.  The advantages of
   the postcard mode include:





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   *  Either there is no packet header overhead (e.g., PBT-M) or the
      overhead is small and fixed (e.g., IOAM DEX).

   *  The encapsulation requirement may be avoided (e.g., PBT-M).

   *  The telemetry data can be secured before export.

   *  Even if a packet is dropped on the path, the partial data
      collected are still available.

   The disadvantages of the postcard mode include:

   *  Telemetry data are spread in multiple postcards so extra effort is
      needed to correlate the data.

   *  Every node exports a postcard for a packet which increases the
      data export overhead.

   *  In case of PBT-M, every node on the path needs to be configured,
      so the configuration overhead is high.

   *  In case of IOAM DEX, the transport encapsulation requirement
      remains.

   The hybrid mode either tailors for some specific application scenario
   (e.g., Multicast Telemetry) or provides some alternative approach
   (e.g., HTS).

1.2.  Requirements and Challenges

   Although on-path telemetry is beneficial, successfully applying such
   techniques in carrier networks must consider performance,
   deployability, and flexibility.  Specifically, we need to address the
   following practical deployment challenges:

   *  C1: On-path telemetry incurs extra packet processing which may
      cause stress on the network data plane.  The potential impact on
      the forwarding performance creates an unfavorable "observer
      effect".  This will not only damages the fidelity of the
      measurement but also defies the purpose of the measurement.

   *  C2: On-path telemetry can generate a considerable amount of data
      which may claim too much transport bandwidth and inundate the
      servers for data collection, storage, and analysis.  Increasing
      the data handling capacity is technically viable but expensive.
      For example, if the technique is applied to all the traffic, one
      node may collect a few tens of bytes as telemetry data for each
      packet.  The whole forwarding path might accumulate telemetry data



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      with a size similar to or even exceeding that of the original
      packet.  Transporting the telemetry data alone is projected to
      consume almost half of the network bandwidth, plus it creates
      significant back-end data handling and storage requirements.

   *  C3: The collectible data defined currently are essential but
      limited.  As the network operation evolves to be declarative
      (intent-based) and automated, and the trends of network
      virtualization, wireline and wireless convergence, and packet-
      optical integration continue, more data is needed in an on-demand
      and interactive fashion.  Flexibility and extensibility on data
      defining, aggregation, acquisition, and filtering, must be
      considered.

   *  C4: Applying only a single underlying on-path telemetry technique
      may lead to a defective result.  For example, packet drop can
      cause the loss of the flow telemetry data and the packet drop
      location and reason remains unknown if only the In-situ OAM trace
      option is used.  A comprehensive solution needs the flexibility to
      switch between different underlying techniques and adjust the
      configurations and parameters at runtime.  Thus, system-level
      orchestration is needed.

   *  C5: If we were to apply some on-path telemetry technique in
      today's carrier operator networks, we must provide solutions to
      tailor the provider's network deployment base and support an
      incremental deployment strategy.  That is, we need to support
      established encapsulation schemes for various predominant
      protocols such as Ethernet, IPv4, IPv6, and MPLS with backward
      compatibility and properly handle various transport tunnels.

   *  C6: The development of simplified on-path telemetry primitives and
      models for configuration and queries is essential.  Telemetry
      models may be utilized via an API-based telemetry service for
      external applications, for end-to-end performance measurement and
      application performance monitoring.  The standard-based protocols
      and methods are needed for network configuration and programming,
      and telemetry data processing and export, to provide
      interoperability.

1.3.  Scope

   Following the network telemetry framework discussed in
   [I-D.ietf-opsawg-ntf], this document focuses on the on-path
   telemetry, a specific class of data-plane telemetry techniques, and
   provides a high-level framework which addresses the aforementioned
   challenges for deployment, especially in carrier operator networks.




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   This document aims to clarify the problem space, essential
   requirements, and summarizes best practices and general system design
   considerations.  This document provides some examples to show the
   novel network telemetry applications under the framework.

   As an informational document, it describes an open framework with a
   few key components.  The framework does not enforces any specific
   implementation on each component, neither does it define interfaces
   (e.g., API, protocol) between components.  The choice of underlying
   on-path telemetry techniques and other implementation details is
   determined by application implementer.  Therefore, the framework is
   not a solution specification.  It only provides a high-level overview
   and is not necessarily a mandatory recommendation for on-path
   telemetry applications.

   The standardization of the underlying techniques and interfaces
   mentioned in this document is undertaken by various working groups.
   Due to the limited scope and intended status of this document, it has
   no overlap or conflict with those works.

1.4.  Glossary

   This section defines and explains the acronyms and terms used in this
   document.

   On-path Telemetry:  Remotely acquiring performance and behavior data
      about network flows on a per-packet basis on the packet's
      forwarding path.  The term refers to a class of data-plane
      telemetry techniques, including IOAM, PBT, EAM, and HTS.  Such
      techniques may need to mark user packets, or insert instruction/
      metadata to the headers of user packets.

   IFIT:  In-situ Flow Information Telemetry is a high-level reference
      framework that shows how network data-plane monitoring and
      measurement applications can address the deployment challenges of
      the flow-oriented on-path telemetry techniques.

   Reflective Telemetry:  The telemetry functions in a dynamic and
      closed-loop fashion.  A new telemetry action is provisioned as a
      result of self-knowledge acquired through prior telemetry actions.

1.5.  Requirements Language

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



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2.  Architectural Concepts and Key Components

   To address the challenges mentioned above, a high-level framework
   which can help to build a workable and efficient on-path telemetry
   application is presented.  In-situ Flow Information Telemetry (IFIT)
   is dedicated to on-path telemetry data about user and application
   traffic flows.  It covers a class of on-path telemetry techniques and
   works a level higher than any specific underlying technique.  The
   framework is comprised of some key functional components
   (Section 2.2).  By assembling these components, IFIT supports
   reflective telemetry that enables autonomous network operations
   (Section 2.4).

2.1.  Typical Deployment

   Figure 2 shows a typical deployment scenario of on-path telemetry.

                                   Application
                      +-------------------------------------+
                      |             Controller              |
                      | +------------+        +-----------+ |
                      | | Configure  |        | Collector | |
                      | |     &      |<-------|     &     | |
                      | | Control    |        | Analyzer  | |
                      | +-----:------+        +-----------+ |
                      |       :                     ^       |
                      +-------:---------------------|-------+
                              :configuration        |telemetry data
                              :& action             |
               ...............:.....................|..........
               :             :                 :    |         :
               :   +---------:---+-------------:---++---------:---+
               :   |         :   |             :   |          :   |
               V   |         V   |             V   |          V   |
            +------+-+     +-----+--+       +------+-+     +------+-+
     packets| Head   |     | Path   |       | Path   |     | End    |
         ==>| Node   |====>| Node   |==//==>| Node   |====>| Node   |==>
            |        |     | A      |       | B      |     |        |
            +--------+     +--------+       +--------+     +--------+

            |<---          On-path Telemetry Domain             --->|

                       Figure 2: Deployment Scenario

   An on-path telemetry application can conduct some network data-plane
   monitoring and measurement tasks over a limited domain by applying
   one or more underlying techniques.  The application needs to contains
   multiple elements, including configuring the network nodes and



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   processing the telemetry data.  The application usually runs in a
   logically centralized controller which is responsible for configuring
   the network nodes in the domain, and collecting and analyzing
   telemetry data.  The configuration determines which underlying
   technique is used, what telemetry data are of interest, which flows
   and packets are concerned with, how the telemetry data are collected,
   etc.  The process can be dynamic and interactive: after the telemetry
   data processing and analyzing, the application may instruct the
   controller to modify the configuration of the nodes, which affects
   the future telemetry data collection.

   From the system-level view, it is recommended to use the standardized
   configuration and data collection interfaces, regardless of the
   underlying technique.  The specification of these interfaces and the
   implementation of the controller are out of scope for this document.

   The on-path telemetry domain encompasses the head nodes and the end
   nodes, and may cross multiple network domains.  The head nodes are
   responsible for enabling the on-path telemetry functions and the end
   nodes are responsible for terminating them.  All capable nodes in
   this domain will be capable of executing the instructed on-path
   telemetry function.  It is important to note that any application
   must, through configuration and policy, guarantee that any packet
   with on-path telemetry header and metadata will not leak out of the
   domain.

   The underlying on-path telemetry techniques covered by the IFIT
   framework can be of any modes discussed in Section 1.1.

2.2.  Key Components

   The key components of IFIT are as follows, to address the challenges
   mentioned above:

   *  Flexible flow, packet, and data selection policy, addressing the
      challenge C1 described in Section 1;

   *  Flexible data export, addressing the challenge C2;

   *  Dynamic network probe, addressing C3;

   *  On-demand technique selection and integration, addressing C4.

   Note that the challenges C5 and C6 are mostly standard related, which
   are fundamental to IFIT.  We discuss the protocol implications and
   guidance for solution developers in Section 3.





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   In the following section, we provide a detailed description of each
   component.

2.2.1.  Flexible Flow, Packet, and Data Selection

   In most cases, it is impractical to enable the data collection for
   all the flows and for all the packets in a flow due to the potential
   performance and bandwidth impact.  Therefore, a workable solution
   usually need to select only a subset of flows and flow packets to
   enable the data collection, even though this means the loss of some
   information and accuracy.

   In the data plane, the Access Control List (ACL) provides an ideal
   means to determine the subset of flow(s).  An application can set a
   sample rate or probability to a flow to allow only a subset of flow
   packets to be monitored, collect a different set of data for
   different packets, and disable or enable data collection on any
   specific network node.  An application can further allow any node to
   accept or deny the data collection process in full or partially.

   Based on these flexible mechanisms, IFIT allows applications to apply
   flexible flow and data selection policies to suit the requirements.
   The applications can dynamically change the policies at any time
   based on the network load, processing capability, focus of interest,
   and any other criteria.

2.2.1.1.  Block Diagram

               +----------------------------+
               | +----------+  +----------+ |
               | |Flow      |  |Data      | |
               | |Selection |  |Selection | |
               | +----------+  +----------+ |
               | +----------+               |
               | |Packet    |               |
               | |Selection |               |
               | +----------+               |
               +----------------------------+

            Figure 3: Flexible Flow, Packet, and Data Selection

   Figure 3 shows the block diagram of this component.  The flow
   selection block defines the policies to choose target flows for
   monitoring.  Flow has different granularity.  A basic flow is defined
   by 5-tuple IP header fields.  Flow can also be aggregated at
   interface level, tunnel level, protocol level, and so on.  The packet
   selection block defines the policies to choose packets from a target
   flow.  The policy can be either a sampling interval, a sampling



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   probability, or some specific packet signature.  The data selection
   block defines the set of data to be collected.  This can be changed
   on a per-packet or per-flow basis.

2.2.1.2.  Example: Sketch-guided Elephant Flow Selection

   Network operators are usually more interested in elephant flows which
   consume more resource and are sensitive to changes in network
   conditions.  A CountMin Sketch [CMSketch] can be used on the data
   path of the head nodes, which identifies and reports the elephant
   flows periodically.  The controller maintains a current set of
   elephant flows and dynamically enables the on-path telemetry for only
   these flows.

2.2.1.3.  Example: Adaptive Packet Sampling

   Applying on-path telemetry on all packets of selected flows can still
   be out of reach.  A sample rate should be set for these flows and
   only enable telemetry on the sampled packets.  However, the head
   nodes have no clue on the proper sampling rate.  An overly high rate
   would exhaust the network resource and even cause packet drops; An
   overly low rate, on the contrary, would result in the loss of
   information and inaccuracy of measurements.

   An adaptive approach can be used based on the network conditions to
   dynamically adjust the sampling rate.  Every node gives user traffic
   forwarding higher priority than telemetry data export.  In case of
   network congestion, the telemetry can sense some signals from the
   data collected (e.g., deep buffer size, long delay, packet drop, and
   data loss).  The controller may use these signals to adjust the
   packet sampling rate.  In each adjustment period (i.e., RTT of the
   feedback loop), the sampling rate is either decreased or increased in
   response of the signals.  An AIMD policy similar to the TCP flow
   control mechanism for the rate adjustment can be used.

2.2.2.  Flexible Data Export

   The flow telemetry data can catch the dynamics of the network and the
   interactions between user traffic and network.  Nevertheless, the
   data inevitably contain redundancy.  It is advisable to remove the
   redundancy from the data in order to reduce the data transport
   bandwidth and server processing load.









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   In addition to efficient export data encoding (e.g., IPFIX [RFC7011]
   or protobuf (https://developers.google.com/protocol-buffers/)), nodes
   have several other ways to reduce the export data by taking advantage
   of network device's capability and programmability.  Nodes can cache
   the data and send the accumulated data in batch if the data is not
   time sensitive.  Various deduplication and compression techniques can
   be applied on the batch data.

   From the application perspective, an application may only be
   interested in some special events which can be derived from the
   telemetry data.  For example, in case that the forwarding delay of a
   packet exceeds a threshold, or a flow changes its forwarding path is
   of interest, it is unnecessary to send the original raw data to the
   data collecting and processing servers.  Rather, IFIT takes advantage
   of the in-network computing capability of network devices to process
   the raw data and only push the event notifications to the subscribing
   applications.

   Such events can be expressed as policies.  An policy can request data
   export only on change, on exception, on timeout, or on threshold.

2.2.2.1.  Block Diagram

               +-------------------------------------------+
               | +-----------+ +-----------+ +-----------+ |
               | |Data       | |Data       | |Export     | |
               | |Encoding   | |Batching   | |Protocol   | |
               | +-----------+ +-----------+ +-----------+ |
               | +-----------+ +-----------+ +-----------+ |
               | |Data       | |Data       | |Data       | |
               | |Compression| |Dedup.     | |Filter     | |
               | +-----------+ +-----------+ +-----------+ |
               | +-----------+ +-----------+               |
               | |Data       | |Data       |               |
               | |Computing  | |Aggregation|               |
               | +-----------+ +-----------+               |
               +-------------------------------------------+

                       Figure 4: Flexible Data Export

   Figure 4 shows the block diagram of this component.  The data
   encoding block defines the method to encode the telemetry data.  The
   data batching block defines the size of batch data buffered at the
   device side before export.  The export protocol block defines the
   protocol used for telemetry data export.  The data compression block
   defines the algorithm to compress the raw data.  The data
   deduplication block defines the algorithm to remove the redundancy in
   the raw data.  The data filter block defines the policies to filter



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   the needed data.  The data computing block defines the policies to
   prepocess the raw data and generate some new data.  The data
   aggregation block defines the procedure to combine and synthesize the
   data.

2.2.2.2.  Example: Event-based Anomaly Monitor

   Network operators are interested in the anomalies such as path
   change, network congestion, and packet drop.  Such anomalies are
   hidden in raw telemetry data (e.g., path trace, timestamp).  Such
   anomalies can be described as events and programmed into the device
   data plane.  Only the triggered events are exported.  For example, if
   a new flow appears at any node, a path change event is triggered; if
   the packet delay exceeds a predefined threshold in a node, the
   congestion event is triggered; if a packet is dropped due to buffer
   overflow, a packet drop event is triggered.

   The export data reduction due to such optimization is substantial.
   For example, given a single 5-hop 10Gbps path, assume a moderate
   number of 1 million packets per second are monitored, and the
   telemetry data plus the export packet overhead consume less than 30
   bytes per hop.  Without such optimization, the bandwidth consumed by
   the telemetry data can easily exceed 1Gbps (more than 10% of the path
   bandwidth), When the optimization is used, the bandwidth consumed by
   the telemetry data is negligible.  Moreover, the pre-processed
   telemetry data greatly simplify the work of data analyzers.

2.2.3.  Dynamic Network Probe

   Due to limited data plane resource and network bandwidth, it is
   unlikely one can monitor all the data all the time.  On the other
   hand, the data needed by applications may be arbitrary but ephemeral.
   It is critical to meet the dynamic data requirements with limited
   resource.

   Fortunately, data plane programmability allows IFIT to dynamically
   load new data probes.  These on-demand probes are called Dynamic
   Network Probes (DNP).  DNP is the technique to enable probes for
   customized data collection in different network planes.  When working
   with an on-path telemetry technique, DNP is loaded to the data plane
   through incremental programming or configuration.  The DNP can
   effectively conduct data generation, processing, and aggregation.

   DNP introduces enough flexibility and extensibility to IFIT.  It can
   implement the optimizations for export data reduction motioned in the
   previous section.  It can also generate custom data as required by
   today and tomorrow's applications.




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2.2.3.1.  Block Diagram

               +----------------------------+
               | +----------+  +----------+ |
               | |ACL       |  |YANG      | |
               | |          |  |Model     | |
               | +----------+  +----------+ |
               | +----------+  +----------+ |
               | |Hardware  |  |Software  | |
               | |Function  |  |Function  | |
               | +----------+  +----------+ |
               +----------------------------+

                      Figure 5: Dynamic Network Probes

   Figure 5 shows the block diagram of this component.  The Access
   Control List (ACL) block is available in most hardware and it defines
   DNPs through dynamically update the ACL policies (including flow
   filtering and action).  YANG models can be dynamically deployed to
   enable different data processing and filtering functions.  Some
   hardware allows dynamically loading hardware-based functions into the
   forwarding path at runtime through mechanisms such as reserved
   pipelines and function stubs.  Dynamically loadable software
   functions can be implemented in the control processors in capable
   nodes.

2.2.3.2.  Examples

   Following are some possible DNPs that can be dynamically deployed to
   support applications.

   On-demand Flow Sketch:  A flow sketch is a compact online data
      structure (usually a variation of multi-hashing table) for
      approximate estimation of multiple flow properties.  It can be
      used to facilitate flow selection.  The aforementioned CountMin
      Sketch [CMSketch] is such an example.  Since a sketch consumes
      data plane resources, it should only be deployed when actually
      needed.

   Smart Flow Filter:  The policies that choose flows and packet
      sampling rate can change during the lifetime of an application.

   Smart Statistics:  An application may need to count flows based on
      different flow granularity or maintain hit counters for selected
      flow table entries.

   Smart Data Reduction:  DNP can be used to program the events that
      conditionally trigger data export.



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2.2.4.  On-demand Technique Selection and Integration

   With multiple underlying data collection and export techniques at its
   disposal, IFIT can flexibly adapt to different network conditions and
   different application requirements.

   For example, depending on the types of data that are of interest,
   IFIT may choose either passport or postcard mode to collect the data;
   if an application needs to track down where the packets are lost,
   switching from passport mode to postcard mode should be supported.

   IFIT can further integrate multiple data plane monitoring and
   measurement techniques together and present a comprehensive data
   plane telemetry solution.

   Based on the application requirements and the real-time telemetry
   data analysis results, new configurations and actions can be
   deployed.

2.2.4.1.  Block Diagram

               +----------------------------------------------+
               | +------------+  +-------------+  +---------+ |
               | |Application |  |Configuration|  |Telemetry| |
               | |Requirements|->|& Action     |<-|Data     | |
               | |            |  |             |  |Analysis | |
               | +------------+  +-------------+  +---------+ |
               +----------------------------------------------+
               | Passport Mode:                               |
               | +----------+   +----------+                  |
               | |IOAM E2E  |   |IOAM Trace|                  |
               | +----------+   +----------+                  |
               | Postcard Mode:                               |
               | +----------+   +----------+   +----------+   |
               | |PBT-M     |   |IOAM DEX  |   |EAM       |   |
               | +----------+   +----------+   +----------+   |
               | Hybrid Mode:                                 |
               | +----------+   +----------+                  |
               | |HTS       |   |Multicast |                  |
               | |          |   |Telemetry |                  |
               | +----------+   +----------+                  |
               +----------------------------------------------+

               Figure 6: Technique Selection and Integration

   Figure 6 shows the block diagram of this component, which lists the
   candidate on-path telemetry techniques.




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   Located in the logically centralized controller, this component makes
   all the control and configuration dynamically to the capable nodes in
   the domain which will affect the future telemetry data.  The
   configuration and action decisions are based on the inputs from the
   application requirements and the realtime telemetry data analysis
   results.  Note that here the telemetry data source is not limited to
   the data plane.  The data can come form all the sources mentioned in
   [I-D.ietf-opsawg-ntf], including external data sources.

2.3.  Relationship with Network Telemetry Framework (NTF)

   [I-D.ietf-opsawg-ntf] describes a Network Telemetry Framework (NTF).
   One dimension used by NTF to partition network telemetry techniques
   and systems is based on the three planes in networks plus external
   data sources.  IFIT fits in the category of forwarding-plane
   telemetry and deals with the specific on-path technical branch of the
   forwarding-plane telemetry.

   According to NTF, an on-path telemetry application mainly subscribes
   event-triggered or streaming data.  The key functional components of
   IFIT match the components in NTF.  "On-demand Technique Selection and
   Integration" is an application layer function, matching the "Data
   Query, Analysis, and Storage" component in NTF; "Flexible Flow,
   Packet, and Data Selection" matches the "Data Configuration and
   Subscription" component; "Flexible Data Export" matches the "Data
   Encoding and Export" component; "Dynamic Network Probe" matches the
   "Data Generation and Processing" component.

2.4.  IFIT for Reflective Telemetry

   The above components can work together to support reflective
   telemetry, as shown in Figure 7.



















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                           +---------------------+
                           |                     |
                    +------+    Applications     |<------+
                    |      |                     |       |
                    |      +---------------------+       |
                    |         Technique Selection        |
                    |         and Integration            |
                    |                                    |
                    |Flexible                   Flexible |
                    |Flow,     reflection-loop      Data |
                    |Packet,                       Export|
                    |and Data                            |
                    |Selection                      +----+----+
                    V                              +---------+|
              +----------+ Encapsulation          +---------+||
              |  Head    | and Tunneling          |  Path   |||
              |  Node    |----------------------->|  Nodes  ||+
              |          |                        |         |+
              +----------+                        +---------+
                  DNP                                DNP

                 Figure 7: IFIT-based Reflective Telemetry

   An application may pick a suite of telemetry techniques based on its
   requirements and apply an initial technique to the data plane.  It
   then configures the head nodes to decide the initial target flows/
   packets and telemetry data set, the encapsulation and tunneling
   scheme based on the underlying network architecture, and the IFIT-
   capable nodes to decide the initial telemetry data export policy.
   Based on the network condition and the analysis results of the
   telemetry data, the application can change the telemetry technique,
   the flow/data selection policy, and the data export approach in real
   time without breaking the normal network operation.  Many of such
   dynamic changes can be done through loading and unloading DNPs.

   The reflective telemetry enabled by the IFIT allows numerous new
   applications suitable for future network operation architecture.  Two
   examples are provided below.

2.4.1.  Intelligent Multipoint Performance Monitoring

   [RFC8889] describes an intelligent performance management based on
   the network condition.  The idea is to split the monitoring network
   into clusters.  The cluster partition that can be applied to every
   type of network graph and the possibility to combine clusters at
   different levels enable the so-called Network Zooming.  It allows a
   controller to calibrate the network telemetry, so that it can start
   without examining in depth and monitor the network as a whole.  In



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   case of necessity (packet loss or too high delay), an immediate
   detailed analysis can be reconfigured.  In particular, the
   controller, that is aware of the network topology, can set up the
   most suited cluster partition by changing the traffic filter or
   activate new measurement points and the problem can be localized with
   a step-by-step process.

   An application on top of the controllers can manage such mechanism,
   whose dynamic and reflective operations are supported by the IFIT
   framework.

2.4.2.  Intent-based Network Monitoring

                         User Intents
                               |
                               V          Per-packet
                         +------------+   Telemetry
                  ACL    |            |   Data
                +--------+ Controller |<--------+
                |        |            |         |
                |        +--+---------+         |
                |           |       ^           |
                |           |DNPs   |Network    |
                |           |       |Information|
                |           V       |           |
         +------+-------------------+-----------+---+
         |      |                                   |
         |      V                      +------+     |
         | +-------+                  +------+|     |
         | | Head  |                 +------+||     |
         | | Node  |                 |Path  ||+     |
         | |       |                 |Nodes |+      |
         | +-------+                 +------+       |
         +------------------------------------------+

                     Figure 8: Intent-based Monitoring

   In this example, a user can express high level intents for network
   monitoring.  The controller translates an intent and configure the
   corresponding DNPs in capable nodes which collect necessary network
   information.  Based on the real-time information feedback, the
   controller runs a local algorithm to determine the suspicious flows.
   It then deploys ACLs to the head node to initiate the high precision
   per-packet on-path telemetry for these flows.







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3.  Guidance for Solution Developers

   Having a high-level framework covering a class of related techniques
   also promotes a holistic approach for standard development and helps
   to avoid duplicated efforts and piecemeal solutions that only focus
   on a specific technique while omitting the compatibility and
   extensibility issues, which is important to a healthy ecosystem for
   network telemetry.

   A complete IFIT-based solution needs standard interfaces for
   configuration and data extraction, and standard encapsulation on
   various transport protocols.  It may also need standard API and
   primitives for application programming and deployment.  The draft
   [I-D.ietf-ippm-ioam-deployment] summarizes some current proposals on
   encapsulation and data export for IOAM.  Solution developers need to
   consider the following aspects from the protocol point of view.

3.1.  Encapsulation in Transport Protocols

   Since the introduction of IOAM, the IOAM option header encapsulation
   schemes in various network protocols have been proposed.  Similar
   encapsulation schemes need to be extended to cover the other on-path
   telemetry techniques.  Meanwhile, the encapsulation schemes for some
   popular protocols, such as MPLS and IPv4, are noticeably missing.  It
   is important to provide solutions for these protocols because they
   are still prevalent in carrier networks.  PBT-M
   [I-D.song-ippm-postcard-based-telemetry] does not introduce new
   headers to the packets so the trouble of encapsulation for a new
   header is avoided.  While there are some proposals which allow new
   header encapsulation in MPLS packets (e.g.,
   [I-D.song-mpls-extension-header]) or in IPv4 packets (e.g.,
   [I-D.herbert-ipv4-eh]), they are still in their infancy stage and
   require significant future work.  Before standards are available, in
   a confined domain, pre-standard encapsulation approaches may be
   applied.

3.2.  Tunneling Support

   In carrier networks, it is common for user traffic to traverse
   various tunnels for QoS, traffic engineering, or security.  Both the
   uniform mode and the pipe mode for tunnel support are required and
   described in [I-D.song-ippm-ioam-tunnel-mode].  With such
   flexibility, the operator can either gain a true end-to-end
   visibility or apply a hierarchical approach which isolates the
   monitoring domain between customer and provider.






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3.3.  Deployment Automation

   In addition, standard approaches that automates the function
   configuration, and capability query and advertisement, could either
   be in a centralized fashion or a distributed fashion.  The draft
   [I-D.ietf-ippm-ioam-yang] provides the YANG model for IOAM
   configuration.  Similar models needs to be defined for other
   techniques.  It is also helpful to provide standards-based approaches
   for configuration in various network environments.  For example, in
   segment routing networks, extensions to BGP or PCEP can be defined to
   distribute SR policies carrying on-path telemetry information, so
   that telemetry behavior can be enabled automatically when the SR
   policy is applied.  [I-D.chen-pce-sr-policy-ifit] proposes to extend
   PCEP policy for on-path telemetry configuration in segment routing
   networks.  [I-D.ietf-idr-sr-policy-ifit] proposes to extend BGP
   policy in segment routing networks.  Additional capability discovery
   and dissemination will be needed for other types of networks.

   To realize the potential of on-path telemetry, programming and
   deploying DNPs are important.  ForCES [RFC5810] is a standard
   protocol for network device programming, which can be used for DNP
   deployment.  Currently some related works such as
   [I-D.wwx-netmod-event-yang] and [I-D.bwd-netmod-eca-framework] have
   proposed to use YANG model to define the smart policies which can be
   used to implement DNPs.  In the future, other approaches for hardware
   and software-based functions can be development to enhance the
   programmability and flexibility.

4.  Security Considerations

   In addition to the specific security issues discussed in each
   individual document on on-path telemetry, this document considers the
   overall security issues at the system level.  This should serve as a
   guide to the on-path telemetry application developers and users.
   General security and privacy considerations for any network telemetry
   system are also discussed in [I-D.ietf-opsawg-ntf].

5.  IANA Considerations

   This document includes no request to IANA.

6.  Contributors

   Other major contributors of this document include Giuseppe Fioccola,
   Daniel King, Zhenqiang Li, Zhenbin Li, Tianran Zhou, and James
   Guichard.





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7.  Acknowledgments

   We thank Diego Lopez, Shwetha Bhandari, Joe Clarke, Adrian Farrel,
   Frank Brockners, Al Morton, Alex Clemm, Alan DeKok, Benoit Claise,
   and Warren Kumari for their constructive suggestions for improving
   this document.

8.  References

8.1.  Normative References

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

   [RFC7799]  Morton, A., "Active and Passive Metrics and Methods (with
              Hybrid Types In-Between)", RFC 7799, DOI 10.17487/RFC7799,
              May 2016, <https://www.rfc-editor.org/info/rfc7799>.

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

8.2.  Informative References

   [CMSketch] Cormode, G. and S. Muthukrishnan, "An improved data stream
              summary: the count-min sketch and its applications", 2005,
              <http://dx.doi.org/10.1016/j.jalgor.2003.12.001>.

   [I-D.bwd-netmod-eca-framework]
              Boucadair, M., Wu, Q., Wang, M., King, D., and C. Xie,
              "Framework for Use of ECA (Event Condition Action) in
              Network Self Management", Work in Progress, Internet-
              Draft, draft-bwd-netmod-eca-framework-00, 3 November 2019,
              <https://www.ietf.org/archive/id/draft-bwd-netmod-eca-
              framework-00.txt>.

   [I-D.chen-pce-sr-policy-ifit]
              Chen, H., Yuan, H., Zhou, T., Li, W., Fioccola, G., and Y.
              Wang, "PCEP SR Policy Extensions to Enable IFIT", Work in
              Progress, Internet-Draft, draft-chen-pce-sr-policy-ifit-
              02, 10 July 2020, <https://www.ietf.org/archive/id/draft-
              chen-pce-sr-policy-ifit-02.txt>.







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   [I-D.herbert-ipv4-eh]
              Herbert, T., "IPv4 Extension Headers and Flow Label", Work
              in Progress, Internet-Draft, draft-herbert-ipv4-eh-01, 2
              May 2019, <https://www.ietf.org/archive/id/draft-herbert-
              ipv4-eh-01.txt>.

   [I-D.ietf-idr-sr-policy-ifit]
              Qin, F., Yuan, H., Zhou, T., Fioccola, G., and Y. Wang,
              "BGP SR Policy Extensions to Enable IFIT", Work in
              Progress, Internet-Draft, draft-ietf-idr-sr-policy-ifit-
              02, 9 July 2021, <https://www.ietf.org/archive/id/draft-
              ietf-idr-sr-policy-ifit-02.txt>.

   [I-D.ietf-ippm-ioam-data]
              Brockners, F., Bhandari, S., and T. Mizrahi, "Data Fields
              for In-situ OAM", Work in Progress, Internet-Draft, draft-
              ietf-ippm-ioam-data-15, 3 October 2021,
              <https://www.ietf.org/archive/id/draft-ietf-ippm-ioam-
              data-15.txt>.

   [I-D.ietf-ippm-ioam-deployment]
              Brockners, F., Bhandari, S., Bernier, D., and T. Mizrahi,
              "In-situ OAM Deployment", Work in Progress, Internet-
              Draft, draft-ietf-ippm-ioam-deployment-00, 19 October
              2021, <https://www.ietf.org/archive/id/draft-ietf-ippm-
              ioam-deployment-00.txt>.

   [I-D.ietf-ippm-ioam-direct-export]
              Song, H., Gafni, B., Zhou, T., Li, Z., Brockners, F.,
              Bhandari, S., Sivakolundu, R., and T. Mizrahi, "In-situ
              OAM Direct Exporting", Work in Progress, Internet-Draft,
              draft-ietf-ippm-ioam-direct-export-07, 13 October 2021,
              <https://www.ietf.org/archive/id/draft-ietf-ippm-ioam-
              direct-export-07.txt>.

   [I-D.ietf-ippm-ioam-yang]
              Zhou, T., Guichard, J., Brockners, F., and S. Raghavan, "A
              YANG Data Model for In-Situ OAM", Work in Progress,
              Internet-Draft, draft-ietf-ippm-ioam-yang-01, 11 July
              2021, <https://www.ietf.org/archive/id/draft-ietf-ippm-
              ioam-yang-01.txt>.










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   [I-D.ietf-mboned-multicast-telemetry]
              Song, H., McBride, M., Mirsky, G., Mishra, G., Asaeda, H.,
              and T. Zhou, "Multicast On-path Telemetry Solutions", Work
              in Progress, Internet-Draft, draft-ietf-mboned-multicast-
              telemetry-01, 6 July 2021,
              <https://www.ietf.org/archive/id/draft-ietf-mboned-
              multicast-telemetry-01.txt>.

   [I-D.ietf-opsawg-ntf]
              Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
              A. Wang, "Network Telemetry Framework", Work in Progress,
              Internet-Draft, draft-ietf-opsawg-ntf-09, 13 October 2021,
              <https://www.ietf.org/archive/id/draft-ietf-opsawg-ntf-
              09.txt>.

   [I-D.mirsky-ippm-hybrid-two-step]
              Mirsky, G., Lingqiang, W., Zhui, G., and H. Song, "Hybrid
              Two-Step Performance Measurement Method", Work in
              Progress, Internet-Draft, draft-mirsky-ippm-hybrid-two-
              step-11, 8 July 2021, <https://www.ietf.org/archive/id/
              draft-mirsky-ippm-hybrid-two-step-11.txt>.

   [I-D.song-ippm-ioam-tunnel-mode]
              Song, H., Li, Z., Zhou, T., and Z. Wang, "In-situ OAM
              Processing in Tunnels", Work in Progress, Internet-Draft,
              draft-song-ippm-ioam-tunnel-mode-00, 27 June 2018,
              <https://www.ietf.org/archive/id/draft-song-ippm-ioam-
              tunnel-mode-00.txt>.

   [I-D.song-ippm-postcard-based-telemetry]
              Song, H., Mirsky, G., Filsfils, C., Abdelsalam, A., Zhou,
              T., Li, Z., Shin, J., and K. Lee, "Postcard-based On-Path
              Flow Data Telemetry using Packet Marking", Work in
              Progress, Internet-Draft, draft-song-ippm-postcard-based-
              telemetry-10, 9 July 2021,
              <https://www.ietf.org/archive/id/draft-song-ippm-postcard-
              based-telemetry-10.txt>.

   [I-D.song-mpls-extension-header]
              Song, H., Li, Z., Zhou, T., Andersson, L., and Z. Zhang,
              "MPLS Extension Header", Work in Progress, Internet-Draft,
              draft-song-mpls-extension-header-05, 10 July 2021,
              <https://www.ietf.org/archive/id/draft-song-mpls-
              extension-header-05.txt>.

   [I-D.wwx-netmod-event-yang]
              Wu, Q., Bryskin, I., Birkholz, H., Liu, X., and B. Claise,
              "A YANG Data model for ECA Policy Management", Work in



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              Progress, Internet-Draft, draft-wwx-netmod-event-yang-10,
              1 November 2020, <https://www.ietf.org/archive/id/draft-
              wwx-netmod-event-yang-10.txt>.

   [I-D.zhou-ippm-enhanced-alternate-marking]
              Zhou, T., Fioccola, G., Liu, Y., Lee, S., Cociglio, M.,
              and W. Li, "Enhanced Alternate Marking Method", Work in
              Progress, Internet-Draft, draft-zhou-ippm-enhanced-
              alternate-marking-07, 11 July 2021,
              <https://www.ietf.org/archive/id/draft-zhou-ippm-enhanced-
              alternate-marking-07.txt>.

   [passport-postcard]
              Handigol, N., Heller, B., Jeyakumar, V., Mazieres, D., and
              N. McKeown, "Where is the debugger for my software-defined
              network?", 2012,
              <https://doi.org/10.1145/2342441.2342453>.

   [RFC5810]  Doria, A., Ed., Hadi Salim, J., Ed., Haas, R., Ed.,
              Khosravi, H., Ed., Wang, W., Ed., Dong, L., Gopal, R., and
              J. Halpern, "Forwarding and Control Element Separation
              (ForCES) Protocol Specification", RFC 5810,
              DOI 10.17487/RFC5810, March 2010,
              <https://www.rfc-editor.org/info/rfc5810>.

   [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
              "Specification of the IP Flow Information Export (IPFIX)
              Protocol for the Exchange of Flow Information", STD 77,
              RFC 7011, DOI 10.17487/RFC7011, September 2013,
              <https://www.rfc-editor.org/info/rfc7011>.

   [RFC8889]  Fioccola, G., Ed., Cociglio, M., Sapio, A., and R. Sisto,
              "Multipoint Alternate-Marking Method for Passive and
              Hybrid Performance Monitoring", RFC 8889,
              DOI 10.17487/RFC8889, August 2020,
              <https://www.rfc-editor.org/info/rfc8889>.

Authors' Addresses

   Haoyu Song
   Futurewei
   2330 Central Expressway
   Santa Clara,
   United States of America

   Email: haoyu.song@futurewei.com





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

   Email: qinfengwei@chinamobile.com


   Huanan Chen
   China Telecom

   Email: chenhuan6@chinatelecom.cn


   Jaehwan Jin
   LG U+
   South Korea

   Email: daenamu1@lguplus.co.kr


   Jongyoon Shin
   SK Telecom
   South Korea

   Email: jongyoon.shin@sk.com
























Song, et al.              Expires 24 April 2022                [Page 26]