



Network Working Group                                       J. Dong, Ed.
Internet-Draft                                       Huawei Technologies
Intended status: Informational                           M. McBride, Ed.
Expires: 11 July 2026                                          Futurewei
                                                            F. Clad, Ed.
                                                           Cisco Systems
                                                                Z. Zhang
                                                        Juniper Networks
                                                                  Y. Zhu
                                                           China Telecom
                                                                   X. Xu
                                                               R. Zhuang
                                                            China Mobile
                                                                 R. Pang
                                                            China Unicom
                                                                   H. Lu
                                                                  Y. Liu
                                                                 Tencent
                                                            L. Contreras
                                                              Telefonica
                                                               M. Durmus
                                                                Turkcell
                                                               R. Rahman
                                                                 Equinix
                                                          7 January 2026


              Fast Network Notifications Problem Statement
                 draft-dong-fantel-problem-statement-03

Abstract

   Modern networks require adaptive traffic manipulation including
   Traffic Engineering (TE), load balancing, flow control, and
   protection, to support high-throughput, low-latency, and lossless
   applications such as Artificial Intelligence (AI) /Machine Learning
   (ML) training and real-time services.  A good and timely
   understanding of network operational status, such as congestion and
   failures, can help to improve network utilization, enable the
   selection of paths with reduced latency, and enable faster response
   to critical events.  This document describes the existing problems
   and why a new set of fast network notification solutions are needed.

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
   2.  Glossary  . . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Why Fast Network Notification is Needed . . . . . . . . . . .   4
   4.  The Problem with Existing Notification Mechanisms . . . . . .   5
     4.1.  Example: AI Training Cluster with Fiber Link Failure  . .   6
       4.1.1.  Limitations of Existing Mechanisms  . . . . . . . . .   7
       4.1.2.  How Fast Network Notifications Help . . . . . . . . .   8
   5.  Fast Network Notifications Problem Statement  . . . . . . . .   9
     5.1.  Information of Fast Network Notifications . . . . . . . .   9
     5.2.  Recipients of Fast Network Notifications  . . . . . . . .  10
     5.3.  Delivery of Fast Network Notifications  . . . . . . . . .  12
     5.4.  Actions to Fast Network Notifications . . . . . . . . . .  13
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  14
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  14
   8.  Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  14
   9.  Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  14
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  15
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  15
     10.2.  Informative References . . . . . . . . . . . . . . . . .  15
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16



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1.  Introduction

   Modern network applications, ranging from AI/ML training to large-
   scale cloud services, require lossless and adaptive networks to
   ensure reliable, congestion-free data transfer within or across
   multiple data centers.  These workloads demand high throughput, low
   latency, and minimal packet loss across dynamically shifting traffic
   patterns.  To meet these requirements, networks employ mechanisms
   such as traffic engineering (TE), load balancing, flow control, and
   protection.  However, existing solutions often face limitations in
   responsiveness, coverage, and operational complexity, particularly in
   high-speed, large-scale environments.

   Modern forwarding silicon is capable of detecting congestion,
   microbursts, queue buildup and other localized impairments at fine-
   grained time scales, ranging from microseconds to sub-millisecond,
   depending on hardware capabilities and deployment requirements.
   These detection capabilities substantially outpace the time required
   for such information to be disseminated to other relevant nodes for
   their actions, creating a gap between what the detecting node can
   observe and when recipients can react.  Fast network notification
   identifies the need for complementary mechanisms that enable low-
   latency notification of network conditions, allowing actions taken in
   the data plane, control plane or management plane to more closely
   align with the capabilities of contemporary forwarding hardware.

   This document summarizes the limitations of existing mechanisms that
   prevent them being used for rapid notification of critical network
   events, including link or node failures and congestion.  It also
   identifies the need for fast network notification which is critical
   for enabling fast reaction.  In the context of this document, fast
   does not imply a single, rigid numerical time threshold.  Instead, it
   characterizes a class of mechanisms to minimize the delivery time so
   that the latency of the notification is in the order of sub-
   milliseconds or milliseconds, depending on the operational objective
   and the range of the network domain, and can be substantially shorter
   than the Round-Trip-Time (RTT) of the network traffic involved.

   [I-D.geng-fantel-fantel-gap-analysis] provides a gap analysis of
   existing solutions and where they are deficient in supporting high
   demand services.  This document describes the set of problems which
   the a network notification solution needs to address.

2.  Glossary

   BFD: Bidirectional Forwarding Detection [RFC5880]

   ECN: Explicit Congestion Notification [RFC3168]



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   FRR: Fast Re-Route [RFC4090] [RFC5714]

   IOAM: In-situ Operations, Administration, and Maintenance [RFC9197]

3.  Why Fast Network Notification is Needed

   Current network mechanisms were not designed for the responsiveness
   and scale required by todays' dynamic environments.  Techniques such
   as load balancing, protection switching, and flow control rely on
   feedback loops that are often too slow, too coarse, or too resource-
   intensive.  This results in performance bottlenecks, delayed
   recovery, and inefficiencies in large-scale AI, cloud, and WAN
   deployments.  A fast network notification mechanism could help to
   address these gaps by providing lightweight, real-time, actionable
   alerts that complement existing tools and enable faster, more
   accurate traffic manipulation decisions.

   In particular, the detection and propagation of network events (e.g.,
   failure, congestion or state change) must occur within a timeframe
   short enough to meaningfully influence traffic engineering and load-
   balancing decisions before congestion or micro-loops occur or
   develop.  In backbone or datacenter networks, this typically implies
   a target of notification delivery in the order of milliseconds, with
   some environments requiring sub-millisecond performance.  The precise
   requirement is driven by:

   *  the speed at which traffic shifts can induce overload

   *  the granularity of TE tuning (fine-grained vs.  coarse-grained)

   *  the propagation diameter of the network notification

   *  the responsiveness of the control-plane and forwarding-plane
      components

   Therefore, this document focuses on notification mechanisms capable
   of operating within these millisecond/sub-millisecond ranges, rather
   than mechanisms whose latency spans tens or hundreds of milliseconds,
   which are insufficient for preventing transient overload under rapid
   traffic transitions.











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4.  The Problem with Existing Notification Mechanisms

   Current network traffic manipulation mechanisms such as TE, load
   balancing, flow control, and protection, have deficiencies in
   providing the low-latency, high-granularity responsiveness needed in
   modern, dynamic networks, at least in part due to the lack of dynamic
   network state information.  This results in suboptimal performance,
   low reliability and delayed recovery.  Fast network notification is a
   set of solutions to address this by enabling real-time, lightweight
   notifications that enhance the responsiveness for traffic
   engineering, congestion mitigation, and failure protection.  There is
   a demonstrable need for a standardized framework to define these fast
   network notification mechanisms, requirements and integration
   strategies.

   There follows a summary of the limitations of existing notification
   mechanisms:

   *  Slow Dissemination: Existing control protocols (e.g., routing
      protocol, etc.) may be used for dissemination of dynamic network
      state information, while they usually rely on control plane based
      hop-by-hop distribution, which causes delay when the recipient is
      multiple hops away.  With modern high-throughput environments (AI/
      ML clusters, multi-DC WANs), this delay is often prohibitive.
      Explicit Congestion Notification (ECN) [RFC3168] needs congestion
      signals to be sent back to the sender, which introduces Round-
      Trip-Time (RTT) delay and can be slow if the source node is far
      away, and it relies on the source node to take action in the
      transport layer.  What is needed is a lightweight signaling method
      that can provide real-time alerts (e.g., at the sub-milliseconds
      level or in the order of a few milliseconds) on failures,
      congestion, or threshold breaches, enabling prompt actions (e.g.,
      in the range of one millisecond to tens of milliseconds) in the
      network layer.

   *  Coarse-Grained Signals: Classic ECN [RFC3168] uses a 2-bit field
      in packet header to convey the ECN capability and congestion
      indication, which inherenetly limits the information it can report
      to the receiving nodes.  What would be useful is a set of
      notifications that aren't just "on-off" state reports, but can
      also convey more information like congestion level/utilization
      information, latency spikes, queue buildup or flow
      characteristics, so that it can trigger immediate and precise
      responses like rerouting, rate adjustment, or protection switching
      for specific flows.






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   *  Local-Only Decision Making: Current load-balancing, flow-control,
      and FRR techniques often act on local information and fail to
      capture downstream or cross-domain network conditions, limiting
      their effectiveness and leading to suboptimal decisions.  For
      example, the Point of Local Repair (PLR) executing FRR makes its
      decision based on its local view of the topology and network
      status.  It may switch traffic to a backup path and cause
      cascading congestion on that path, as it lacks visibility into the
      state of the entire backup path.  Similarly, traditional load-
      balancing is based on local link utilization information, which
      may cause some paths overloaded while others remain underutilized.
      This local view of network status prevents precise and optimized
      decisions and adjustments.  It would be helpful to send fast
      network notifications to upstream nodes so that they can perform
      action based on a wider view of network conditions.

   *  Overhead and Scalability Challenges: The distribution of high-
      volume network operational status information or frequent
      signaling introduces bandwidth and processing overhead.  At scale,
      this becomes a bottleneck rather than a solution.  IOAM [RFC9197]
      and similar tools provide detailed telemetry information, but the
      collection and feedback loops are controller-centric.  They cannot
      be used to deliver lightweight, real-time alerts for immediate
      action on specific network nodes.  Carrying dynamic network state
      information in control protocols (e.g., routing protocols) also
      increases the overhead and churn of the control plane, which may
      have negative impact to the core functionality of the protocol.
      It would be useful to have solutions designed to avoid the
      overhead and churn introduced by telemetry flooding or route
      distribution, so it can adapt to large-scale networks and dynamic
      traffic patterns (e.g., AI workloads, cloud WAN bursts).

4.1.  Example: AI Training Cluster with Fiber Link Failure

   Consider a large-scale AI training job distributed across multiple
   data centers.  These clusters exchange terabits of data per second
   between Graphics Processing Unit (GPU) nodes, requiring ultra-low
   latency and high throughput to maintain synchronization.













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    +-------------------------+       +------------------------+
    |   Data Center A (GPUs)  |       |   Data Center B (GPUs) |
    +----------+--------------+       +----------+-------------+
               |                                 |
               |      --------------------       |
               |   ///   +---+             \\\   |
               | //      | R |--\             \\ |
               |///     /+---+\  \--\         \\\|
              /|/     //       \     \----\     \|\
            // |     /          \          ---\  |  \\
           | +-+-+ /            +---+  Failure +-+-+ |
          |  | R +--------------+ R +---- X ---+ R |  |
           | +---+ \            +---+         /+---+ |
            \        \          /      /-----/      //
            \\        \\ +---+ /  /---/           //
              \\\       \| R |---/              ///
                 \\      +---+                 //
                  \\\ ---------------------- ///

     Figure 1: Distributed AI Training Clusters with Fiber Link Failure

   As depicted in the above figure, a single fiber link failure event
   can disrupt the entire training run, leading to:

   *  Delays in job completion (hours to days for large models)

   *  Massive energy and compute cost waste due to resynchronization

   *  Degraded convergence accuracy if synchronization windows are
      missed

4.1.1.  Limitations of Existing Mechanisms

   Today's mechanisms provide partial solutions but are not fast or
   precise enough for these scenarios:

   *  BFD [RFC5880]: Provides fast faults detection in the bidirectional
      path between two forwarding engines.  BFD can be one of the
      detection mechanisms for link or path failures, while it is not
      used to notify the failure to nodes other than the BFD endpoints
      in the network.  BFD is preconfigured with periodic message
      exchange, while fast network notifications needs to be event-
      driven.

   *  FRR [RFC4090][RFC5714] /Route convergence: Without fast
      notification, the failure detection can take tens of milliseconds,
      followed by either local repair (FRR) or route convergence.  The
      former lacks visibility of the global network situation and thus



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      may cause congestion on the backup paths, while the latter may
      breach strict synchronization requirements of the AI/ML
      application.

   In practice, this means that by the time a fiber link failure is
   detected and recovery mechanisms are invoked, critical GPU
   synchronization barriers may already have been missed, forcing
   rollbacks or restarts of the training process.

4.1.2.  How Fast Network Notifications Help

   Fast network notification mechanisms could improve the response to
   fiber link failures and congestion in distributed AI/ML clusters:

   *  Real-Time Alerts: Nodes adjacent to the failure or congestion
      could immediately (e.g., in the order of sub-milliseconds or
      milliseconds) send lightweight notifications to nodes whose
      fowarding paths might be affected.

   *  Action-Oriented Response: Upon receiving the notification, routing
      and load balancing mechanisms could instantly shift traffic to
      backup paths or alternative DC interconnects.

   *  Granularity: Notifications could carry more detailed information
      than "link failure/congestion," e.g., indicating specific link
      utilization, queue buildup or microburst congestion, allowing
      differentiated responses to different traffic flows.

   *  Complementary: The fast notification solutions are complementary
      to BFD, FRR or Telemetry, it would bridge the time gap between
      event onset and slower control plane or telemetry-driven
      responses, and enable network-wide optimization.

   By deploying fast notifications, large AI/ML workloads can maintain
   synchronization across data centers even during transient failures or
   congestion, protecting job completion time and resource utilization.

   Existing Approach:

   *  BFD detects failure after tens of ms

   *  FRR may cause congestion on backup paths

   *  Reroute/convergence delays impact GPU sync

   *  Result: Training stalls, compute resources wasted, job completion
      delayed




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   Fast Notifications Approach:

   *  Forwarding plane detects failure at the level of sub-millisecond

   *  Fast network notification alerts upstream nodes of failure or
      congestion in real time

   *  Regional or global TE steers traffic quickly to alternate link/
      path without causing new congestion

   *  Result: Training continues with minimal disruption

5.  Fast Network Notifications Problem Statement


5.1.  Information of Fast Network Notifications

   The information carried in the fast network notifications, by the
   originating node, can be one or multiple of the following:

   *  Event Type: This can be used to indicate the type of events (e.g.
      failure, congestion, performance degradation, etc.).

   *  Location of Event: This can be used to indicate the location where
      the event occurred in the network (e.g. the identifier of the
      link, the node, or the queue, etc.).

   *  Fine-grained Network Status information: This can include
      quantifiable network metrics like link utilization, queue length,
      level of congestion, link or node delay, jitter, packet loss, etc.

   *  Path Identification information: This can be used to indicate the
      path which is affected by the event.

   *  Flow Identification information: This can include the
      identification or the 5-tuple of a flow which is affected by the
      event.

   Other information related to the network status change and need to be
   actioned in a timely manner may also be carried in the fast network
   notifications.  Thus there is a need to work on the information model
   of fast network notifications to better understand what needs to be
   carried in the notifications.








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5.2.  Recipients of Fast Network Notifications

   Fast network notifications may be consumed by two broad forms of
   recipient: (1) recipient nodes that participate directly in
   forwarding or signaling, and (2) functions and applications that
   consume notifications in order to optimize, monitor, or adapt
   behaviors as depicted in the following two tables.  Separating these
   categories clarifies which entities are physical/logical nodes versus
   which are higher-level functional consumers.

     +==================+======================+=======================+
     | Node Type        | Role                 | Example Benefit       |
     +==================+======================+=======================+
     | Adjacent Routers | Data-plane neighbors | Enable local repair   |
     | / Switches       | that forward packets | (e.g., FRR, ECMP      |
     |                  |                      | adjustments)          |
     +------------------+----------------------+-----------------------+
     | Non-Adjacent     | Remote upstream      | Accelerated awareness |
     | Routers /        | forwarding elements  | of failure/congestions|
     | Switches         |                      | on specific nodes     |
     +------------------+----------------------+-----------------------+
     | Ingress Routers  | Traffic entry points | Re-map affected flows |
     | / Switches       | of a network         | before forwarding     |
     |                  | domain               | into failed regions   |
     +------------------+----------------------+-----------------------+
     | End Hosts / Edge | Optional             | Adapt sending rate,   |
     | Nodes            | subscribers, policy- | select alternate      |
     |                  | driven               | uplinks               |
     +------------------+----------------------+-----------------------+
     |Network Controller| Optional             | Accelerated awareness |
     |                  | subscribers, policy- | of failure/congestion |
     |                  | driven               | for global TE/LB      |
     +------------------+----------------------+-----------------------+

                    Table 1: Recipient Nodes
















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   +=======================+===============+===========================+
   | Function /            | Role          | Example Benefit           |
   | Application           |               |                           |
   +=======================+===============+===========================+
   | Routing Protocols     | Control-plane | Accelerated path re-      |
   | (OSPF, IS-IS, BGP)    | convergence   | computation after failure |
   +-----------------------+---------------+---------------------------+
   | Traffic Engineering   | Centralized   | Pre-compute new paths     |
   | Element (PCE)         | optimization  | before congestion         |
   |                       |               | propagates                |
   +-----------------------+---------------+---------------------------+
   | Network Operators     | Operational   | Faster troubleshooting,   |
   | (NMS/OSS)             | visibility    | earlier alerting          |
   +-----------------------+---------------+---------------------------+
   | Telemetry /           | Monitoring    | Predictive analytics, ML- |
   | Analytics Systems     | and           | based congestion          |
   |                       | prediction    | forecasting               |
   +-----------------------+---------------+---------------------------+
   | Applications /        | Critical app  | AI workloads, financial   |
   | Services              | consumers     | apps adapt to degraded    |
   |                       |               | links                     |
   +-----------------------+---------------+---------------------------+

                 Table 2: Recipient Functions and Applications

   The tables have three columns.  The fist column lists the type or
   node or type of application/function.  The second shows the example
   of the role that the node or application/function is responsible for
   within the network that could benefit from fast network
   notifications.  The third column indicates examples of how fast
   notification could benefit the node/application/function in filling
   its role.



















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                      +-----------------------------+
                      |     Application Plane       |
                      |  - Applications / Services  |
                      |  - End Hosts / Edge Nodes   |
                      +-------------|---------------+
                                    |
                      +-------------|---------------+
                      |  Management Plane           |
                      |  - Operators (NMS/OSS)      |
                      |  - Telemetry / Analytics    |
                      +-------------|---------------+
                                    |
                      +-------------|---------------+
                      |  Control Plane              |
                      |  - Routing Protocols        |
                      |  - TE Controllers (PCE/SDN) |
                      +-------------|---------------+
                                    |
                      +-------------|----------------+
                      |  Data Plane                  |
                      |  - Adjacent Routers/Switches |
                      |  - Non-Adjacent Routers      |
                      |  - Ingress Routers           |
                      +------------------------------+

          Figure 2: Notification Recipients Across Network Planes


   As illustrated in Figure 2, the latency sensitivity of recipients
   decreases as one moves from the data plane to the application plane.
   Recipient nodes (e.g., adjacent forwarding elements, ingress routers,
   etc.) often require very quick notification, while functions and
   applications (e.g., routing protocols, analytics systems, NMS, etc.)
   may tolerate slightly longer timescales but still benefit from rapid
   awareness compared to existing mechanisms.  The range of recipients
   of the notification depends on the type of recipients, it also
   depends on what type of action is required.  The mechanism to
   determine the type and range of the recipients is something that
   needs further consideration.

5.3.  Delivery of Fast Network Notifications

   Depending on the position and number of the recipient nodes, fast
   network notifications may be sent via one of the following delivery
   modes:

   *  Unicast directly to the recipient node




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   *  Multicast to a group of recipient nodes

   *  Hop-by-hop to a series of receipt nodes along a specified path

   *  Flooding in a specified range of the network

   Additionally, recipient nodes or functions may subscribe to specific
   types of notifications based on their roles or interests.  A
   subscription-based approach enables selective delivery, reduces
   unnecessary signaling overhead, and ensures that each recipient
   receives only the information relevant to its function.  Mechanisms
   supporting both delivery and subscription must guarantee timely,
   reliable, and secure propagation of notifications.  Examples:

   *  Adjacent routers subscribing to all local failure notifications

   *  Centralized controllers subscribing only to congestion alerts
      exceeding defined thresholds

   *  Applications or analytics systems subscribing to performance
      degradation events affecting specific flows or services

   The mechanisms to support the above delivery mode needs to make sure
   the notification is always sent to the targeted recipient nodes in a
   timely manner.  It could be based on existing messaging and transport
   mechanisms, or a new protocol may be introduced.

5.4.  Actions to Fast Network Notifications

   Once a fast network notification is received, the recipient needs to
   take appropriate actions to help mitigating the event reported in the
   fast network notification.  The action can be based on the
   information carried in the fast network notification, or it can be
   based on both the information in the notification and the information
   obtained by the recipient in other ways.  The action to be performed
   by the recipient may be explicitly carried in the notification, or it
   may be implicitly determined by the type of information carried in
   the notification.  Some actions are mandatory, while some actions can
   be optional.  The possible actions in response to the notification
   can be, but not limited, to one or multiple of the following:

   *  Switches all traffic from a path to other available paths

   *  Steers specific traffic flows to alternate links or paths

   *  Modifies the load balancing ratio among a group of paths

   *  Sends the notification further to other recipients



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   Whether the actions need to be explicitly indicated in the
   notification, and if so, which ones, requires further consideration.
   It is noted that in some of the cases as described in Section 5.2,
   multiple recipients may receive the same notification, then some
   action may be taken by multiple recipients.  The sender of the fast
   network notification needs to take this into consideration if some
   coordination in the actions is needed.  The mechanism for action
   coordination is for further study and is out of the scope of this
   document.

6.  IANA Considerations

   This document has no IANA actions.

7.  Security Considerations

   Fast network notifications, if not properly authenticated and rate-
   limited, could be exploited as a vector for Denial-of-Service (DoS)
   attacks.  An attacker able to inject or flood spurious notifications
   may trigger unnecessary re-convergence, path changes or repeated
   state updates, overwhelming both recipient nodes and higher-level
   applications.  An attacker may cause the sender of fast network
   notifications overwhelmed by making some network state flapping, so
   that the node is busy with sending notifications.  Fast network
   notifications may reveal sensitive information about the network, in
   some scenarios such information may be made visible to external
   entities, either by inspecting the notifications, or by registering
   as a consumer of the notifications.  Implementations must therefore
   ensure integrity protection, origin authentication, and appropriate
   rate controls on sending and receiving fast network notification
   messages.

8.  Acknowledgement

   The authors would like to thank Alia Atlas, David Black, Jeffrey
   Haas, Tony Li, Carlos J.  Bernardos, Fan Zhang and Adrian Farrel for
   their valuable comments and discussion.

9.  Contributors

   The following people contributed substantially to the content of this
   document.









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   Zafar Ali
   Cisco
   zali@cisco.com

   Tianran Zhou
   Huawei
   zhoutianran@huawei.com

   Xuesong Geng
   Huawei
   gengxuesong@huawei.com

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,
              <https://www.rfc-editor.org/info/rfc2119>.

   [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

   [I-D.geng-fantel-fantel-gap-analysis]
              Geng, X., Huo, P., Cheng, W., Li, D., Zhu, Y., and H.
              Zhengxin, "Gap Analysis of Fast Notification for Traffic
              Engineering and Load Balancing", Work in Progress,
              Internet-Draft, draft-geng-fantel-fantel-gap-analysis-01,
              7 July 2025, <https://datatracker.ietf.org/doc/html/draft-
              geng-fantel-fantel-gap-analysis-01>.

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP",
              RFC 3168, DOI 10.17487/RFC3168, September 2001,
              <https://www.rfc-editor.org/info/rfc3168>.

   [RFC4090]  Pan, P., Ed., Swallow, G., Ed., and A. Atlas, Ed., "Fast
              Reroute Extensions to RSVP-TE for LSP Tunnels", RFC 4090,
              DOI 10.17487/RFC4090, May 2005,
              <https://www.rfc-editor.org/info/rfc4090>.

   [RFC5714]  Shand, M. and S. Bryant, "IP Fast Reroute Framework",
              RFC 5714, DOI 10.17487/RFC5714, January 2010,
              <https://www.rfc-editor.org/info/rfc5714>.



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   [RFC5880]  Katz, D. and D. Ward, "Bidirectional Forwarding Detection
              (BFD)", RFC 5880, DOI 10.17487/RFC5880, June 2010,
              <https://www.rfc-editor.org/info/rfc5880>.

   [RFC9197]  Brockners, F., Ed., Bhandari, S., Ed., and T. Mizrahi,
              Ed., "Data Fields for In Situ Operations, Administration,
              and Maintenance (IOAM)", RFC 9197, DOI 10.17487/RFC9197,
              May 2022, <https://www.rfc-editor.org/info/rfc9197>.

Authors' Addresses

   Jie Dong (editor)
   Huawei Technologies
   Email: jie.dong@huawei.com


   Mike McBride (editor)
   Futurewei
   Email: mmcbride7@gmail.com


   Francois Clad (editor)
   Cisco Systems
   Email: fclad@cisco.com


   Jeffrey Zhang
   Juniper Networks
   Email: zzhang@juniper.net


   Yongqing Zhu
   China Telecom
   Email: zhuyq8@chinatelecom.cn


   Xiaohu Xu
   China Mobile
   Email: xuxiaohu_ietf@hotmail.com


   Rui Zhuang
   China Mobile
   Email: zhuangruiyjy@chinamobile.com


   Ran Pang
   China Unicom



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   Email: pangran@chinaunicom.cn


   Hao Lu
   Tencent
   Email: vickkylu@tencent.com


   Yadong Liu
   Tencent
   Email: zeepliu@tencent.com


   Luis M. Contreras
   Telefonica
   Email: luismiguel.contrerasmurillo@telefonica.com


   Mehmet Durmus
   Turkcell
   Email: mehmet.durmus@turkcell.com.tr


   Reshad Rahman
   Equinix
   Email: reshad@yahoo.com

























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