OPSAWG                                                      H. Song, Ed.
Internet-Draft                                                 Futurewei
Intended status: Informational                                     Z. Li
Expires: April 23, 2020                                          T. Zhou
                                                                  F. Qin
                                                            China Mobile
                                                                 H. Chen
                                                           China Telecom
                                                                  J. Jin
                                                                   LG U+
                                                                 J. Shin
                                                              SK Telecom
                                                        October 21, 2019

                   In-situ Flow Information Telemetry


   For efficient network operation, most operators rely on traditional
   Operation, Administration and Maintenance (OAM) methods, which
   include proactive and reactive techniques, running in active and
   passive modes.  As networks increase in scale they become more
   susceptible to measurement accuracy and misconfiguration errors.

   With the advent of programmable data-plane emerging on-path OAM
   techniques, such as flow telemetry, provide unprecedented insight and
   fast notification of network issues (e.g., jitter, increased latency,
   packet loss, significant bit error variations, and unequal load-

   In-situ Flow Information Telemetry (iFIT) provides a method for
   efficiently applying underlying on-path flow telemetry techniques,
   applicable across various network environments.

   This document outlines an iFIT framework, which enumerates several
   critical functional components and describes how these components are
   assembled together to achieve a complete and closed-loop working
   solution for on-path flow telemetry.

Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "OPTIONAL" in this document are to be interpreted as described in BCP

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   14 [RFC2119][RFC8174] when, and only when, they appear in all
   capitals, as shown here.

Status of This Memo

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

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on April 23, 2020.

Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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Table of Contents

   1.  Requirements and Challenges . . . . . . . . . . . . . . . . .   3
   2.  iFIT Framework Overview . . . . . . . . . . . . . . . . . . .   5
     2.1.  Passport vs. Postcard . . . . . . . . . . . . . . . . . .   6
   3.  Architectural Components of iFIT  . . . . . . . . . . . . . .   7
     3.1.  Smart Flow and Data Selection . . . . . . . . . . . . . .   7
       3.1.1.  Use Case: Sketch-guided Elephant Flow Selection . . .   8
       3.1.2.  Use Case: Adaptive Packet Sampling  . . . . . . . . .   8
     3.2.  Smart Data Export . . . . . . . . . . . . . . . . . . . .   8
       3.2.1.  Use Case: On-demand Anomaly Monitor . . . . . . . . .   9
     3.3.  Dynamic Network Probe . . . . . . . . . . . . . . . . . .   9
     3.4.  Encapsulation and Tunneling . . . . . . . . . . . . . . .  10

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     3.5.  On-demand Technique Selection and Integration . . . . . .  10
     3.6.  iFIT Closed-Loop Architecture . . . . . . . . . . . . . .  10
   4.  Intelligent Closed-Loop Network Telemetry Applications  . . .  12
   5.  Summary and Future Work . . . . . . . . . . . . . . . . . . .  12
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   8.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  13
   9.  Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  13
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  13
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  13
     10.2.  Informative References . . . . . . . . . . . . . . . . .  13
     10.3.  URIs . . . . . . . . . . . . . . . . . . . . . . . . . .  15
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Requirements and Challenges

   The sheer complexity of today's networks requires radical rethinking
   of existing methods network monitoring and troubleshooting.  Current
   dynamic networks require "on-path" fault monitoring and traffic
   measurement solutions for a wide range of use cases which include
   intelligent management of existing network traffic, and better
   traffic visibility of emerging applications such as large scale
   Virtual Server (VS) mobility, fluid content distribution, and elastic
   bandwidth allocation.

   Furthermore, the ability to expedite failure detection, fault
   localization, and recovery mechanisms, particularly in the case of
   soft failures or path degradation are experienced, without causing
   extreme or obvious disruption.  This is extremely important for since
   these types of network issues are often difficult to localize with
   existing Operation, Administration and Maintenance (OAM) methods and
   reduce overall network efficiency.

   Future networks must also support application-aware networking.
   Application-aware networking is an emerging industry term and
   typically used to describe the capacity of an intelligent network to
   maintain current information about user and application connections
   that use network resources and, as a result, the operator is able to
   optimize the network resource usage and monitoring to ensure
   application and traffic optimality.

   Application-aware network operation is important for user SLA
   compliance, service path enforcement, fault diagnosis, and network
   resource optimization.  A family of on-path flow telemetry
   techniques, including In-situ OAM (IOAM)
   [I-D.brockners-inband-oam-data], Postcard Based Telemetry (PBT)
   [I-D.song-ippm-postcard-based-telemetry], In-band Flow Analyzer (IFA)
   [I-D.kumar-ippm-ifa], Enhanced Alternate Marking (EAM)

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   [I-D.zhou-ippm-enhanced-alternate-marking], and Hybrid Two Steps
   (HTS) [I-D.mirsky-ippm-hybrid-two-step], are emerging, which can
   provide flow information on the entire forwarding path on a per-
   packet basis in real time.  These on-path flow telemetry techniques
   are very different from the previous active and passive OAM schemes
   in that they directly modify the user packets.  Given the unique
   characteristics of the aforementioned techniques, we may categorize
   these on-path telemetry techniques as the hybrid OAM type III,
   supplementing the classification defined in [RFC7799].

   These techniques are invaluable for application-aware network
   operations not only in data center and enterprise networks but also
   in carrier networks which may cross multiple domains.  Carrier
   network operators have shown strong interests in utilizing such
   techniques for various purposes.  For example, it is vital for the
   operators who offer the bandwidth intensive, latency and loss
   sensitive services such as video streaming and gaming to closely
   monitor the relevant flows in real time as the indispensable first
   step for any further measure.

   However, successfully applying such techniques in carrier networks
   poses several practical challenges:

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

   o  C2: On-path flow telemetry can generate a huge amount of OAM 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, assume IOAM is applied to all the traffic.  One node
      will collect a few tens of bytes as telemetry data for each
      packets.  The whole forwarding path might accumulate a data trace
      with a size similar to the average size of the original packets.
      Exporting the telemetry data will consume almost half of the
      network bandwidth.

   o  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, network convergence, and packet-optical
      integration continue, more data will be needed in an on-demand and
      interactive fashion.  Flexibility and extensibility on data
      defining, acquisition, and filtering, must be considered.

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   o  C4: If we were to apply some on-path telemetry technique in
      today's carrier 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, and MPLS with backward compatibility and properly
      handle various transport tunnels.

   o  C5: Applying only a single underlying telemetry technique may lead
      to defective result.  For example, packet drop can cause the lost
      of the flow telemetry data and the packet drop location and reason
      remains unknown if only 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.

   o  C6: Development of simplified on-path telemetry primitives and
      models, including: telemetry data (e.g., nodes, links, ports,
      paths, flows, timestamps) query primitives.  These may be used by
      an API-based telemetry service for external applications, for
      monitoring end-to-end latency measurement of network paths and
      application latency calculation.

2.  iFIT Framework Overview

   To address these challenges, we propose a framework based on multiple
   network operators' requirements and the common industry practice,
   which can help to build a workable on-path flow telemetry solution.
   We name the framework "In-situ Flow Information Telemetry" (iFIT) to
   reflect the fact that this framework is dedicated to the on-path
   telemetry data about user/application flow experience.  As a solution
   framework, iFIT works a level higher than any specific OAM
   techniques, be it active, passive, or hybrid.  The framework is built
   up on a few architectural components.  By assembling these components
   together, a closed-loop is formed to provide a complete solution for
   a particular static, dynamic, and interactive telemetry applications.

   iFIT is an open framework.  It does not enforce any implementation
   details for each component.  Users are free to pick one or more
   underlying techniques and design their own algorithms and
   architectures to fit in each component and make all the components
   work in concert.

   The network architecture that applies iFIT is shown in Figure 1.  The
   iFIT domain is confined between the iFIT head nodes and the iFIT end
   nodes.  An iFIT domain may cross multiple network domains.  iFIT
   support two basic on-path telemetry data collection modes: passport
   mode (e.g., IOAM trace option and IFA), in which telemetry data are

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   carried in user packets and exported at the iFIT end nodes, and
   postcard mode (e.g., PBT), in which each node in the iFIT domain may
   export telemetry data through independent OAM packets.  Note that the
   boundary between the two modes can be blurry.  An application only
   need to mix the two modes.

                          |                                 |
                          |        iFIT Applications        |
                          |                                 |
                                 ^                    ^
                                 |                    |
                                 V                    |
                          +------------+        +-----+-----+
                          |            |        |           |
                          | Controller |        | Collector |
                          |            |        |           |
                          +-----:------+        +-----------+
                                :                     ^
                                :configuration        |telemetry data
                                :                     |
                 :             :                 :    |         :
                 :   +---------:---+-------------:---++---------:---+
                 :   |         :   |             :   |          :   |
                 V   |         V   |             V   |          V   |
              +------+-+     +-----+--+       +------+-+     +------+-+
    usr pkts  | iFIT   |     | Path   |       | Path   |     | iFIT   |
         ====>| Head   |====>| Node   |==//==>| Node   |====>| End    |====>
              | Node   |     | A      |       | B      |     | Node   |
              +--------+     +--------+       +--------+     +--------+

                    Figure 1: iFIT Network Architecture

2.1.  Passport vs. Postcard

   [passport-postcard] first uses the analogy of passport and postcard
   to describe how the packet trace data can be collected and exported.
   In the passport mode, each node on the path adds the telemetry data
   to the user packets.  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 while the user
   packets are intact.

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   A prominent advantage of the passport mode is that it naturally
   retains the telemetry data correlation along the entire path.  The
   passport mode also reduces the number of data export packets and the
   bandwidth consumed by the data export packets.  These can help to
   make the data collector and analyzer's work easier.  On the other
   hand, the passport mode requires more processing on the user packets
   and increases the size of user packets, which can cause various
   problems.  Some other issues are documented in

   The postcard mode provides a perfect complement to the passport mode.
   It addresses most of the issues faced by the passport mode, at a cost
   of needing extra efforts to correlate the postcard packets.

3.  Architectural Components of iFIT

   The key components of iFIT are listed as follows:

   o  Smart flow and data selection policy to address C1.

   o  Smart data export to address C2.

   o  Dynamic network probe to address C3.

   o  Encapsulation and tunneling to address C4.

   o  On-demand technique selection and integration to address C5.

   Next we provide the detailed description of each component.

3.1.  Smart Flow 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 impacts.  Therefore, a workable solution
   must select only a subset of flows and flow packets to enable the
   data collection, even though this means the loss of some information.

   In data plane, the Access Control List (ACL) provides an ideal means
   to determine the subset of flow(s).
   [I-D.song-ippm-ioam-data-validation-option] describes how one can set
   a sample rate or probability to a flow to allow only a subset of flow
   packets to be monitored, how one can collect different set of data
   for different packets, and how one can disable or enable data
   collection on any specific network node.  The document further
   introduces enhancement to IOAM to allow any node to accept or deny
   the data collection in full or partially.

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   Based on these flexible mechanisms, iFIT allows applications to apply
   smart 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.  We have developed some adaptive algorithm which can
   limit the performance impact and yet achieve the satisfactory
   telemetry data density.

3.1.1.  Use Case: Sketch-guided Elephant Flow Selection

   Network operators are usually more interested in elephant flows which
   consume more resource and are sensitive to network condition changes.
   We implement a CountMin Sketch [CMSketch] 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

3.1.2.  Use Case: Adaptive Packet Sampling

   Applying on-path telemetry on all packets of selected flows can still
   be out of reach.  We should set a sample rate 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.

   We can use an adaptive approach 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 uses 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.  We can use the AIMD policy similar to the TCP flow
   control mechanism for the rate adjustment.

3.2.  Smart 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 efficiently encode the export data (e.g., IPFIX
   [RFC7011] or protobuf [1]), iFIT can also 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

   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

3.2.1.  Use Case: On-demand 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).  We can
   describe such anomalies as events and program them 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.

3.3.  Dynamic Network Probe

   Due to the limited data plane resource, it is unlikely one can
   provide 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) [I-D.song-opsawg-dnp4iq].  DNP is the technique
   to enable probes for customized data collection in different network
   planes.  When working with IOAM or PBT, 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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   The aforementioned sketch module and anomaly triggers can all be
   implemented as DNPs so they can be loaded to or unloaded from the
   data plane dynamically based on application requirments.

3.4.  Encapsulation and Tunneling

   Since MPLS and IPv4 network are still prevalent in carrier networks.
   iFIT provides solutions to apply the on-path flow telemetry
   techniques in such 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.  In case a technique that requires a new header is
   preferred, [I-D.song-mpls-extension-header] provides a means to
   encapsulate the extra header using an MPLS extension header.  As for
   IPv4, it is possible to encapsulate the new header in an IP option.
   For example, RAO [RFC2113] can be used to indicate the presence of
   the new header.  A recent proposal [I-D.herbert-ipv4-eh] that
   introduces the IPv4 extension header may lead to a long term

   In carrier networks, it is common for user traffic to traverse
   various tunnels for QoS, traffic engineering, or security. iFIT
   supports both the uniform mode and the pipe mode for tunnel support
   as 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.

3.5.  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 IOAM or PBT to collect the data; if an
   application needs to track down where the packets are lost, it may
   switch from IOAM to PBT.

   iFIT can further integrate multiple data plane monitoring and
   measurement techniques together and present a comprehensive data
   plane telemetry solution to network operating applications.

3.6.  iFIT Closed-Loop Architecture

   Working together, the aforementioned components form a closed-loop
   for complete iFIT applications, as shown in Figure 2.

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                           |                     |
                    +------+  iFIT Applications  |<------+
                    |      |                     |       |
                    |      +---------------------+       |
                    |         Technique Selection        |
                    |         and Integration            |
                    |                                    |
                    |Smart Flow                    Smart |
                    |and Data     closed-loop      Data  |
                    |Selection                     Export|
                    |                                    |
                    |                               +----+----+
                    V                              +---------+|
              +----------+ Encapsulation          +---------+||
              |  iFIT    | and Tunneling          |  iFIT   |||
              |  Head    |----------------------->|         ||+
              |  Node    |                        |  Nodes  |+
              +----------+                        +---------+
                  DNP                                DNP

                  Figure 2: iFIT Closed-Loop Architecture

   An iFIT application would pick a suite of telemetry techniques based
   on its requirements and apply an initial technique to the data plane.
   It then configures the iFIT 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 iFIT application can change the telemetry
   technique, the flow/data selection policy, and the data export
   approach in realtime without breaking the normal network operation.
   Many of such dynamic changes can be done through loading and
   unloading DNPs.

   We should avoid confusion between this closed telemetry loop and the
   closed control loop.  The latter term is often used in the context of
   network automation.  In such a closed control loop, telemetry also
   plays an important role.  Based on the telemetry results,
   applications can automatically change the network policy or
   configuration.  In such a context, iFIT is just a part of the loop.

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4.  Intelligent Closed-Loop Network Telemetry Applications

   The closed-loop nature of the iFIT framework allows numerous new
   applications which enable future network operation architecture.

   In general, it is resource consuming to monitor continuously all the
   flows and all the paths of the network.  So a flexible and dynamic
   performance monitoring approach is desired.  Some concepts like the
   Interactive Query with Dynamic Network Probes, as previously
   described, go in this direction.

   Another example is shown in [I-D.ietf-ippm-multipoint-alt-mark],
   which that 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 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.

   To apply this mechanism an iFIT application on top of the controllers
   can manage and the iFIT closed loop allows its dynamic and flexible

5.  Summary and Future Work

   iFIT is a framework for applying on-path data plane telemetry
   techniques.  Combining with algorithmic and architectural schemes
   that fit into the framework components, iFIT framework enables a
   practical telemetry solution based on two basic on-path traffic data
   collection modes: passport and postcard.

   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 to collect useful information about them.
   Therefore, the iFIT operation can be considered the hybrid type III
   mode, which can provide more flexible and accurate network OAM.

   More challenges and corresponding solutions for iFIT may need to be
   covered.  For example, how iFIT can fit in the big picture of
   autonomous networking and support closed control loops.  A complete

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   iFIT framework should also consider the cross-domain operations.  We
   leave these topics for future revisions.

6.  Security Considerations

   No specific security issues are identified other than those have been
   discussed in the drafts on on-path flow information telemetry.

7.  IANA Considerations

   This document includes no request to IANA.

8.  Contributors

   Other major contributors of this document include Giuseppe Fioccola
   and Daniel King.

9.  Acknowledgments

   We thank Shwetha Bhandari and Joe Clarke for their constructive
   suggestions for improving this document.

10.  References

10.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,

   [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>.

10.2.  Informative References

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

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              Brockners, F., Bhandari, S., Pignataro, C., Gredler, H.,
              Leddy, J., Youell, S., Mizrahi, T., Mozes, D., Lapukhov,
              P., Chang, R., and d. daniel.bernier@bell.ca, "Data Fields
              for In-situ OAM", draft-brockners-inband-oam-data-07 (work
              in progress), July 2017.

              Herbert, T., "IPv4 Extension Headers and Flow Label",
              draft-herbert-ipv4-eh-01 (work in progress), May 2019.

              Fioccola, G., Cociglio, M., Sapio, A., and R. Sisto,
              "Multipoint Alternate Marking method for passive and
              hybrid performance monitoring", draft-ietf-ippm-
              multipoint-alt-mark-02 (work in progress), July 2019.

              Kumar, J., Anubolu, S., Lemon, J., Manur, R., Holbrook,
              H., Ghanwani, A., Cai, D., Ou, H., and L. Yizhou, "Inband
              Flow Analyzer", draft-kumar-ippm-ifa-01 (work in
              progress), February 2019.

              Mirsky, G., Lingqiang, W., and G. Zhui, "Hybrid Two-Step
              Performance Measurement Method", draft-mirsky-ippm-hybrid-
              two-step-04 (work in progress), October 2019.

              Song, H. and T. Zhou, "In-situ OAM Data Validation
              Option", draft-song-ippm-ioam-data-validation-option-02
              (work in progress), April 2018.

              Song, H., Li, Z., Zhou, T., and Z. Wang, "In-situ OAM
              Processing in Tunnels", draft-song-ippm-ioam-tunnel-
              mode-00 (work in progress), June 2018.

              Song, H., Zhou, T., Li, Z., Shin, J., and K. Lee,
              "Postcard-based On-Path Flow Data Telemetry", draft-song-
              ippm-postcard-based-telemetry-05 (work in progress),
              September 2019.

              Song, H., Li, Z., Zhou, T., and L. Andersson, "MPLS
              Extension Header", draft-song-mpls-extension-header-02
              (work in progress), February 2019.

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

              Zhou, T., Fioccola, G., Li, Z., Lee, S., Cociglio, M., and
              Z. Li, "Enhanced Alternate Marking Method", draft-zhou-
              ippm-enhanced-alternate-marking-03 (work in progress),
              July 2019.

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

   [RFC2113]  Katz, D., "IP Router Alert Option", RFC 2113,
              DOI 10.17487/RFC2113, February 1997,

   [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,

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

10.3.  URIs

   [1] https://developers.google.com/protocol-buffers/

Authors' Addresses

   Haoyu Song (editor)
   2330 Central Expressway
   Santa Clara

   Email: haoyu.song@futurewei.com

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

   Email: lizhenbin@huawei.com

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

   Email: zhoutianran@huawei.com

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

   Email: qinfengwei@chinamobile.com

   Huanan Chen
   China Telecom
   P. R. China

   Email: chenhuan6@chinatelecom.cn

   Jaewhan 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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