OPSAWG                                                           H. Song
Internet-Draft                                                 Futurewei
Intended status: Informational                                    F. Qin
Expires: 26 August 2022                                     China Mobile
                                                                 H. Chen
                                                           China Telecom
                                                                  J. Jin
                                                                   LG U+
                                                                 J. Shin
                                                              SK Telecom
                                                        22 February 2022

           A Framework for In-situ Flow Information Telemetry


   As network scale increases and network operation becomes more
   sophisticated, existing Operation, Administration, and Maintenance
   (OAM) methods are no longer sufficient to meet the monitoring and
   measurement requirements.  Emerging data-plane on-path telemetry
   techniques which provide high-precision flow insight and which issue
   notifications in real time can supplement existing proactive and
   reactive methods that run in active and passive modes.  These new
   approaches are collectively known as in-situ flow information
   telemetry (IFIT).  They enable quality of experience for users and
   applications, and identification of network faults and deficiencies.

   This document outlines a high-level framework for IFIT to collect and
   correlate performance measurement information from the network.  It
   identifies the components that coordinate existing protocol tools and
   telemetry mechanisms, and addresses deployment challenges for flow-
   oriented on-path telemetry techniques, especially in carrier

   The document is a guide for system designers applying the referenced
   techniques.  It is also intended to motivate further work to enhance
   the OAM 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 . . . . . . . . . . . . . . . . . . . . . . . . . .   8
     1.4.  Relationship with Network Telemetry Framework (NTF) . . .   8
     1.5.  Glossary  . . . . . . . . . . . . . . . . . . . . . . . .   9
   2.  Architectural Concepts and Key Components . . . . . . . . . .   9
     2.1.  Reference Deployment  . . . . . . . . . . . . . . . . . .   9
     2.2.  Key Components  . . . . . . . . . . . . . . . . . . . . .  11
       2.2.1.  Flexible Flow, Packet, and Data Selection . . . . . .  11
       2.2.2.  Flexible Data Export  . . . . . . . . . . . . . . . .  13
       2.2.3.  Dynamic Network Probe . . . . . . . . . . . . . . . .  15
       2.2.4.  On-demand Technique Selection and Integration . . . .  17
     2.3.  IFIT for Reflective Telemetry . . . . . . . . . . . . . .  18
       2.3.1.  Intelligent Multipoint Performance Monitoring . . . .  19
       2.3.2.  Intent-based Network Monitoring . . . . . . . . . . .  19
   3.  Guidance for Solution Developers  . . . . . . . . . . . . . .  20
     3.1.  Encapsulation in Transport Protocols  . . . . . . . . . .  20
     3.2.  Tunneling Support . . . . . . . . . . . . . . . . . . . .  21
     3.3.  Deployment Automation . . . . . . . . . . . . . . . . . .  21

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   4.  Security Considerations . . . . . . . . . . . . . . . . . . .  22
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  22
   6.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  22
   7.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  22
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  22
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  22
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  23
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  27

1.  Introduction

   Efficient network operation increasingly relies on high-quality data-
   plane telemetry to provide the necessary visibility into the behavior
   of traffic flows and network resources.  Existing 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 autonomous
   [RFC8993] and application-aware [I-D.li-apn-framework].  The
   complexity of today's networks and service quality requirements
   demand new high-precision and real-time OAM 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, and it must not cause
   service disruption.  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
   (OPT) refers to 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 verifying Service Level Agreement (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-Step
   (HTS) [I-D.mirsky-ippm-hybrid-two-step], have been developed or
   proposed.  These techniques can provide flow information on the
   entire forwarding path on a per-packet basis in real-time.  The
   aforementioned on-path telemetry techniques differ from the active
   and passive OAM schemes 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

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   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].  We name these
   techniques as "In-situ Flow Information Telemetry" (IFIT).

   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.  The techniques
   can provide benefits for carrier network operators in various
   scenarios.  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 important 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 IFIT differs from both active OAM and passive OAM as
   defined in [RFC7799].  It does not generate any active probe packets
   or passively observe 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 method per [RFC7799].

   This hybrid OAM type I method can be further partitioned into two
   modes [passport-postcard].  In the passport mode, each node on the
   path can add telemetry data to the user packets (i.e., stamps 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., sends a postcard)
   while the user packets are unmodified.  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 on-path telemetry

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

            Figure 1: On-path Telemetry Technique Classification

   IOAM Trace and E2E options are described in

   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

   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 simplifies the data

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

   *  Only the head and tail nodes of the paths need to be configured
      for header insertion and removal, so the configuration overhead is

   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.

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

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

   *  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

   The hybrid mode either tailors for some specific application scenario
   (e.g., Multicast Telemetry) or provides some alternative approach
   (e.g., HTS).  A postcard can be sent per segment of a path or the
   telemetry data can be carried in a companion packet following each
   monitored use packet.  The hybrid mode combines the advantages of
   both the passport mode and the postcard mode, but it may incur extra
   processing complexity.

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

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      effect" (where the actions of performing on-path telemetry may
      change the behavior of the traffic being measured).  This will not
      only damage the fidelity of the measurement, but also defy 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.  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 with a
      size similar to or even exceeding that of the original packet.

   *  C3: The collectible data defined currently are essential but
      limited.  This, in turn, limits the management and operational
      techniques that can be applied.  Flexibility and extensibility of
      data definition, aggregation, acquisition, and filtering, must be

   *  C4: Applying only a single underlying on-path telemetry technique
      may miss some important events or lead to incorrect results.  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: We must provide solutions to support an incremental deployment
      strategy.  That is, we need to support established encapsulation
      schemes for various predominant protocols such as Ethernet, 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.  Standard-based protocols and
      methods are needed for network configuration and programming, and
      telemetry data pre-processing and export, to provide

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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 challenges for
   deployment listed in Section 1.2, especially in carrier networks.

   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 enforce 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 the 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.  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 (i.e., control
   plane, management plane, and forwarding plane) and 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
   to event-triggered or streaming data.  The key functional components
   of IFIT described in Section 2.2 match the general components in NTF
   with more specific functions.  "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.

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1.5.  Glossary

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

   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 into 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 reflective 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.

2.  Architectural Concepts and Key Components

   To address the challenges mentioned in Section 1.2, 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 at 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 2.3).

2.1.  Reference Deployment

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

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                  On-path Telemetry Application
               |            Controller            |
               | +------------+     +-----------+ |
               | | Configure  |     | Collector | |
               | |     &      |<----|     &     | |
               | | Control    |     | Analyzer  | |
               | +-----:------+     +-----------+ |
               |       :                  ^       |
                       :configuration     |telemetry data
                       :& action          |
          :           :               :   |        :
          :   +-------:---+-----------:---+--------:---+
          :   |       :   |           :   |        :   |
          V   |       V   |           V   |        V   |
       +------+-+   +-----+--+     +------+-+   +------+-+  +--------+
packets| Head   |   | Path   |     | Path   |   | Tail   |  |Node out|
     =>| Node   |==>| Node   |=//=>| Node   |==>| Node   |=>|of OPT  |=>
       |        |   | A      |     | Z      |   |        |  |domain  |
       +--------+   +--------+     +--------+   +--------+  +--------+

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

                    Figure 2: Deployment Scenario

   An on-path telemetry application can conduct network data-plane
   monitoring and measurement tasks over a limited domain [RFC8799] by
   applying one or more underlying techniques.  The application contains
   multiple elements, including configuring the network nodes and
   processing the telemetry data.  The application usually uses a
   logically centralized controller 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

   From the system-level view, it is recommended to use 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.

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   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

   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 to address the challenges listed in
   Section 1.2 are as follows.  The components are described in more
   detail in the sections that follow.

   *  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, and
   are fundamental to IFIT.  We discuss the protocol implications and
   guidance for solution developers in Section 3.

2.2.1.  Flexible Flow, Packet, and Data Selection

   In most cases, it is impractical to enable 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 on
   which to enable data collection, even though this means the loss of
   some information and accuracy.

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   In the data plane, a flow filter like those used for an Access
   Control List (ACL) provides an ideal means to determine the subset of
   flows.  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 their 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.  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
   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.

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Internet-Draft                    IFIT                     February 2022  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.  Example: Adaptive Packet Sampling

   Applying on-path telemetry on all packets of the selected flows can
   still be out of reach.  A sample rate should be set for these flows
   and telemetry should only be enabled 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 Additive Increase/Multiplicative
   Decrease (AIMD) policy similar to the TCP flow control mechanism for
   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 may contain redundancy.  It is advisable to remove the
   redundancy from the data in order to reduce the data transport
   bandwidth and server processing load.

   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 batches if the data is not
   time sensitive.  Various deduplication and compression techniques can
   be applied on the batched data.

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   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 the 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.  A policy can request data
   export only on change, on exception, on timeout, or on threshold.  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
   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

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Internet-Draft                    IFIT                     February 2022  Example: Event-based Anomaly Monitor

   Network operators are interested in 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

   Fortunately, data plane programmability allows new data probles to be
   dynamically loaded.  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 into the data
   plane through incremental programming or configuration.  The DNP can
   effectively conduct data generation, processing, and aggregation.

   DNP introduces 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's and tomorrow's applications.  Block Diagram

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               | +----------+  +----------+ |
               | |Active    |  |YANG      | |
               | |Packet    |  |Model     | |
               | |Filter    |  |          | |
               | +----------+  +----------+ |
               | +----------+  +----------+ |
               | |Hardware  |  |Software  | |
               | |Function  |  |Function  | |
               | +----------+  +----------+ |

                      Figure 5: Dynamic Network Probes

   Figure 5 shows the block diagram of this component.  The active
   packet filter block is available in most hardware and it defines DNPs
   through dynamically update the packet filtering policies (including
   flow selection 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.  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

   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.  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.  IFIT for Reflective Telemetry

   The components described in Section 2.2 can work together to support
   reflective telemetry, as shown in Figure 7.

                           |  On-path Telemetry  |
                    +------+    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.

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   The reflective telemetry enabled by the IFIT allows numerous new
   applications.  Two examples are provided below.

2.3.1.  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
   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 suitable 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

2.3.2.  Intent-based Network Monitoring

                        1.User Intents
                               V         5.Per-packet
                4.Packet +------------+   Telemetry
                  Filter |            |   Data
                +--------+ Controller |<--------+
                |        |            |         |
                |        +--+---------+         |
                |           |       ^           |
                |         2.|DNPs 3.|Network    |
                |           |       |Information|
                |           V       |           |
         |      |                                   |
         |      V                      +------+     |
         | +-------+                  +------+|     |
         | | Head  |                 +------+||     |
         | | Node  |                 |Path  ||+     |
         | |       |                 |Nodes |+      |
         | +-------+                 +------+       |

                     Figure 8: Intent-based Monitoring

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   In this example, a user can express high level intents for network
   monitoring.  The controller translates an intent and configures 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 specific packet filters to the head node to initiate
   the high precision per-packet on-path telemetry for these flows.

3.  Guidance for Solution Developers

   Having a high-level framework covering a class of related techniques
   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

   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.
   [I-D.ietf-ippm-ioam-deployment] summarizes some techniques for
   encapsulation and data export for IOAM.  Solution developers need to
   consider the aspects set out in the following subsections.

3.1.  Encapsulation in Transport Protocols

   Since the introduction of IOAM, the IOAM option header encapsulation
   schemes in various network protocols have been defined (e.g.,
   [I-D.ietf-ippm-ioam-ipv6-options]).  Similar encapsulation schemes
   are needed to cover the other on-path telemetry techniques.
   Meanwhile, the on-path telemetry header/data encapsulation schemes in
   some popular protocols, such as MPLS and SRv6, are also needed.
   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 SRv6 packets (e.g.,
   [I-D.song-spring-siam]), they are still in their infancy stage and
   require further work.  Before standards are available, in a confined
   domain, pre-standard encapsulation approaches may be applied.

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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].  The uniform mode
   treats the nodes in a tunnel uniformly as the nodes outside of the
   tunnel on a path.  In contrast, the pipe mode abstracts all the nodes
   between the tunnel ingress and egress as a circuit so no nodes in the
   tunnel is visible to the nodes outside of the tunnel.  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.

3.3.  Deployment Automation

   Standard approaches that automate the function configuration, and
   capability query and advertisement, could either be deployed in a
   centralized fashion or a distributed fashion.  The draft
   [I-D.ietf-ippm-ioam-yang] provides a 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 (SR) networks, extensions to BGP or Path Computation
   Element Communication Protocol (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] defines extensions to PCEP to configure
   SR policies for on-path telemetry.  [I-D.ietf-idr-sr-policy-ifit]
   defines extensions to BGP for the same purpose.  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 models 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.

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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].

   Since the on-path telemetry techniques work on the network forwarding
   plane, the IFIT framework poses some security risks.  The important
   and sensitive information about a network could be exposed to an
   attacker.  Further, the on-path telemetry data might swamp various
   parts of the network, leading to a possible DoS attack.

   Fortunately, security measures can be enforced on various parts of
   the framework to mitigate such threats.  For example, the
   configuration can filter and rate limit the monitored traffic;
   encryption and authentication can be applied on the exported
   telemetry data; different underlying techniques can be chosen to
   adapt to the different network conditions.

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

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,

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   [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>.

   [RFC8799]  Carpenter, B. and B. Liu, "Limited Domains and Internet
              Protocols", RFC 8799, DOI 10.17487/RFC8799, July 2020,

8.2.  Informative References

   [CMSketch] Cormode, G. and S. Muthukrishnan, "An improved data stream
              summary: the count-min sketch and its applications", 2005,

              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,

              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-

              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-

              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-
              03, 10 January 2022, <https://www.ietf.org/archive/id/

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              Brockners, F., Bhandari, S., and T. Mizrahi, "Data Fields
              for In-situ OAM", Work in Progress, Internet-Draft, draft-
              ietf-ippm-ioam-data-17, 13 December 2021,

              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-

              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,

              Bhandari, S. and F. Brockners, "In-situ OAM IPv6 Options",
              Work in Progress, Internet-Draft, draft-ietf-ippm-ioam-
              ipv6-options-07, 6 February 2022,

              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-03, 25 January
              2022, <https://www.ietf.org/archive/id/draft-ietf-ippm-

              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-02, 4 January 2022,

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              Song, H., Qin, F., Martinez-Julia, P., Ciavaglia, L., and
              A. Wang, "Network Telemetry Framework", Work in Progress,
              Internet-Draft, draft-ietf-opsawg-ntf-13, 3 December 2021,

              Li, Z., Peng, S., Voyer, D., Li, C., Liu, P., Cao, C.,
              Mishra, G., Ebisawa, K., Previdi, S., and J. N. Guichard,
              "Application-aware Networking (APN) Framework", Work in
              Progress, Internet-Draft, draft-li-apn-framework-04, 25
              October 2021, <https://www.ietf.org/archive/id/draft-li-

              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-12, 26 January 2022,

              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,

              Song, H., Mirsky, G., Filsfils, C., Abdelsalam, A., Zhou,
              T., Li, Z., Shin, J., and K. Lee, "In-Situ OAM Marking-
              based Direct Export", Work in Progress, Internet-Draft,
              draft-song-ippm-postcard-based-telemetry-11, 15 November
              2021, <https://www.ietf.org/archive/id/draft-song-ippm-

              Song, H., Li, Z., Zhou, T., Andersson, L., and Z. Zhang,
              "MPLS Extension Header", Work in Progress, Internet-Draft,
              draft-song-mpls-extension-header-06, 10 January 2022,

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              Song, H. and T. Pan, "SRv6 In-situ Active Measurement",
              Work in Progress, Internet-Draft, draft-song-spring-siam-
              02, 6 December 2021, <https://www.ietf.org/archive/id/

              Wu, Q., Bryskin, I., Birkholz, H., Liu, X., and B. Claise,
              "A YANG Data model for ECA Policy Management", Work in
              Progress, Internet-Draft, draft-wwx-netmod-event-yang-10,
              1 November 2020, <https://www.ietf.org/archive/id/draft-

              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-08, 4 January 2022,

              Handigol, N., Heller, B., Jeyakumar, V., Mazieres, D., and
              N. McKeown, "Where is the debugger for my software-defined
              network?", 2012,

   [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,

   [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,

   [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,

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   [RFC8993]  Behringer, M., Ed., Carpenter, B., Eckert, T., Ciavaglia,
              L., and J. Nobre, "A Reference Model for Autonomic
              Networking", RFC 8993, DOI 10.17487/RFC8993, May 2021,

Authors' Addresses

   Haoyu Song
   2330 Central Expressway
   Santa Clara,
   United States of America
   Email: haoyu.song@futurewei.com

   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

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