RTGWG Working Group                                            W. Cheng
Internet Draft                                             China Mobile
Intended status: Informational                                   C. Lin
Expires: May 10, 2025                              New H3C Technologies
                                                                W. Wang
                                                           China Mobile
                                                                  B. Xu
                                                           China Unicom
                                                       November 4, 2024


                   Reliability in AI Networks Gap Analysis, Problem
                        Statement, and Requirements
            draft-cheng-rtgwg-ai-network-reliability-problem-02


Abstract

   This document provides the gap analysis of existing reliability
   mechanism in AI networks, describes the fundamental problems, and
   defines the requirements for technical improvements.

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   This Internet-Draft will expire on May 10, 2025.

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   respect to this document. Code Components extracted from this
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Table of Contents


   1. Introduction...................................................2
      1.1. Requirements Language.....................................3
      1.2. Terminology...............................................3
   2. Existing Mechanisms............................................4
      2.1. Routing Convergence in AI network.........................4
      2.2. Spine-Leaf topology.......................................5
      2.3. Dragonfly topology........................................6
   3. Gap Analysis...................................................8
      3.1. Fault detection Timing....................................8
      3.2. Notifications Event Propagation Timing....................9
      3.3. Fault switchover Timing...................................9
   4. Problem Statement..............................................9
   5. Requirements for AI network Mechanisms........................10
   6. Security Considerations.......................................11
   7. IANA Considerations...........................................11
   8. References....................................................11
      8.1. Normative References.....................................11
      8.2. Informative References...................................11
   Authors' Addresses...............................................12



1. Introduction

   AI training places higher demands on network reliability for the
   following reasons:

     Large-scale data transmission: AI training requires a significant
      amount of data for model training. These data often need to be
      obtained from distributed storage systems or cloud platforms and
      transmitted to the training servers. A highly reliable network
      ensures stable data transmission, preventing data loss or
      transmission errors.

     Long training duration: AI model training typically takes hours or
      even days. During this process, the network connection should
      remain stable to ensure that the training process is not
      interrupted or terminated. Any network interruptions or failures
      can lead to training interruptions, requiring the process to be
      restarted and wasting time and resources.



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     High bandwidth requirements: AI training demands high network
      bandwidth. Operations such as large-scale data transmission, model
      parameter updates, and gradient calculations require fast and
      stable network connections to ensure efficient training. Network
      unreliability or low bandwidth can result in slower training
      speeds and impact training effectiveness and efficiency.

     Distributed training: To accelerate training speed and improve
      model performance, AI training often employs distributed training
      methods that distribute computational tasks to multiple servers
      for parallel computing. This requires a highly reliable network to
      ensure data synchronization and communication in distributed
      training, ensuring model consistency and accuracy.

   In summary, AI training places higher demands on network
   reliability, requiring stable data transmission, fast bandwidth, and
   stable connections to ensure smooth training processes and reliable
   results.

   To ensure uninterrupted tasks during large-scale model training, it
   is crucial to address hardware failures. Take, for instance, a
   cluster that can accommodate 16,000 cards, with almost 100,000
   optical modules. Considering the quality of actual hardware, let's
   assume that the Mean Time Between Failures (MTBF) of a single module
   is 10 million hours. MTBF denotes the average usage time of a
   hardware device prior to malfunction. However, with a large number
   of modules, even with a MTBF of 10 million hours, an average failure
   may display every four days approximately. In this situation, even
   low probability events become highly likely, considering the large
   number of modules involved. Therefore, AI networks concentrate on
   developing faster recovery capabilities from hardware failures.

   This document provides the gap analysis of existing reliability
   mechanism in AI networks, describes the fundamental problems, and
   defines the requirements for technical improvements.



1.1. Requirements Language

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

1.2. Terminology

   Routing: The path or strategy that data packets take to transmit
   through the network.

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   Topology: The physical and logical layout structure of the network.

   Routing algorithm: The algorithm that determines the path or
   strategy for data packets to transmit through the network.

2. Existing Mechanisms

2.1. Routing Convergence in AI network

   This section briefly introduces the existing routing convergence
   mechanisms in AI networks.

   Traditional network failures rely on the control plane for detection
   and propagation of faults. The control plane then performs route
   convergence or uses Fast Reroute (FRR) mechanisms to quickly switch
   to backup paths. The convergence time for traditional network
   failures is typically around 50ms, and it is influenced by the
   working mechanism.

   The following are several fast convergence methods,

   The methods for link fault detection:

      Bidirectional Forwarding Detection (BFD): BFD is used for fast
      fault detection. It provides a lightweight mechanism for quickly
      detecting faults and triggering a convergence process.

   The methods for responding to local link faults and performing
   switchover.

      Equal-Cost Multipath (ECMP): ECMP allows for fast fault switching
      by distributing traffic across multiple equal-cost paths. In the
      event of a failure on one path, traffic can be quickly redirected
      to an alternate path.

      Fast Reroute (FRR): FRR is a mechanism that enables rapid
      switching to precomputed backup paths upon failure detection. It
      reduces the convergence time by bypassing the traditional control
      plane route convergence process.

   The methods for responding to remote link faults and performing
   switchover.

      BGP PIC (Prefix Independent Convergence): BGP PIC is a technique
      for fast iterative switching during network failures.






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2.2. Spine-Leaf topology

          +---------+                         +---------+
          |   R11   |                         |   R12   |
          +-#--#-#--+                         +#---#--#-+
            |  | |                             |   |  |
            |  | |                             |   |  |
            |  | +-----------------------------)-+ |  |
            |  |                               | | |  |
            |  |   +---------------------------+ | |  |
            |  |   |                             | |  |
            |  +---)----------+     +------------)-+  |
            |      |          |     |            |    |
          +-#------#+       +-#-----#-+       +--#----#-+
          |  R21    |       |  R22    |       |   R23   |
          +-#------#+       +-#------#+       +-#------#+
            |      |          |      |          |      |
          +-#+   +-#+       +-#+   +-#+       +-#+   +-#+
          |H1|   |H2|       |H3|   |H4|       |H5|   |H6|
          +--+   +--+       +--+   +--+       +--+   +--+
                  Figure 1: Spine-Leaf network diagram

   In the commonly used Spine-Leaf topology for AI, there are two paths
   for communication between H1 and H5. The first path is
   R21->R11->R23, and the second path is R21->R12->R23. These two paths
   form ECMP (Equal Cost Multi-Path) paths, enabling load balancing of
   traffic.

          +---------+                         +---------+
          |   R11   |                         |   R12   |
          +-#--#-#--+                         +#---#--#-+
            |  | |                             |   |  |
            |  | |                             |   |  |
            |  | +-----------------------------)-+ |  |
            |  |                               | | |  |
            |  |   +---------------------------+ | |  |
            |  |   |                             | |  |
      Fail  x  +---)----------+     +------------)-+  |
            |      |          |     |            |    |
          +-#------#+       +-#-----#-+       +--#----#-+
          |  R21    |       |  R22    |       |   R23   |
          +-#------#+       +-#------#+       +-#------#+
            |      |          |      |          |      |
          +-#+   +-#+       +-#+   +-#+       +-#+   +-#+
          |H1|   |H2|       |H3|   |H4|       |H5|   |H6|
          +--+   +--+       +--+   +--+       +--+   +--+
                  Figure 2: Local Link Failure

   If a link failure occurs between R21 and R11, it is considered a
   local link failure for R21. Existing detection techniques such as

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   BFD can quickly identify this type of failure. When a local link
   failure (R21->R11->R23) is detected on one of the ECMP paths, the
   other equivalent path (R21->R12->R23) will be used for traffic
   forwarding. The duration of this process is mainly dependent on the
   time taken to detect the link failure.

          +---------+                         +---------+
          |   R11   |                         |   R12   |
          +-#--#-#--+                         +#---#--#-+
            |  | x fail                        |   |  |
            |  | |                             |   |  |
            |  | +-----------------------------)-+ |  |
            |  |                               | | |  |
            |  |   +---------------------------+ | |  |
            |  |   |                             | |  |
            |  +---)----------+     +------------)-+  |
            |      |          |     |            |    |
          +-#------#+       +-#-----#-+       +--#----#-+
          |  R21    |       |  R22    |       |   R23   |
          +-#------#+       +-#------#+       +-#------#+
            |      |          |      |          |      |
          +-#+   +-#+       +-#+   +-#+       +-#+   +-#+
          |H1|   |H2|       |H3|   |H4|       |H5|   |H6|
          +--+   +--+       +--+   +--+       +--+   +--+
                  Figure 3: Remote Link Failure

   If a link failure occurs between R11 and R23, this failure is
   considered a remote link failure for R21.

   R11 propagates the link failure to R21 through IGP link state
   updates or BGP route withdrawal.

   In the case of a remote link failure switchover, the process is
   mainly delayed by the propagation of fault information and the
   response switching of the remote link failure.

2.3. Dragonfly topology

   Dragonfly is another widely used topology for AI training.











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            N2 N N N N N    N N N N N N     N N N N N N
             | | | | | |    | | | | | |     | | | | | |
            ++-+-+-+-+-++  ++-+-+-+-+-++   ++-+-+-+-+-++
            |     G1    |  |     G2    |...|    G8     |
            +-+---+----++  ++----+----++   ++---+----+-+
              |   |    |    |    |    |     |   |    |
              |   |    +----+    |    +-----+   |    |
              |   +--------------)--------------+    |
            +-+------------------+-------------------+-+
            |   +------------------------------+       |
            |   |                              |   G0  |
            | +-+-+          +---+           +-+-+     |
            | |R0 +----------+R1 +-----------+ R2|     |
            | ++-++          ++-++           ++-++     |
            |  | |            | |             | |      |
            +--)-)------------)-)-------------)-)------+
               | |            | |             | |
              N1 N            N N             N N
                        Figure 4: DragonFly network diagram

   As shown in the diagram, N1 is connected to R0 in Group 0, and N2 is
   connected to the router in Group 1. The Inter-Group Link between
   Group 0 and Group 1 is assumed to be connected through R2. The
   traffic from N1 to N2 first goes through the Intra-Group Link from
   R0 to R2, then it is sent through the Inter-Group Link to Group1,
   and finally, it is forwarded to N2 via the Inter-Group Link in Group
   1.

            N2 N N N N N    N N N N N N     N N N N N N
             | | | | | |    | | | | | |     | | | | | |
            ++-+-+-+-+-++  ++-+-+-+-+-++   ++-+-+-+-+-++
            |     G1    |  |     G2    |...|    G8     |
            +-+---+----++  ++----+----++   ++---+----+-+
              |   |    |    |    |    |     |   |    |
              |   |    +----+    |    +-----+   |    |
              |   +--------------)--------------+    |
            +-+------------------+-------------------+-+
            |   +------------------------------+       |
            |   x fail                         |   G0  |
            | +-+-+          +---+           +-+-+     |
            | |R0 +----------+R1 +-----------+ R2|     |
            | ++-++          ++-++           ++-++     |
            |  | |            | |             | |      |
            +--)-)------------)-)-------------)-)------+
               | |            | |             | |
              N1 N            N N             N N
                  Figure 5: Intra-Group Link Failure




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   If a link failure occurs in Intra-Group link, The failure can be
   detected through BFD quickly by R0. Intra-Group link failure is a
   type of local link failure.

   Once the failure is detected, R0 in the group switches the traffic
   to the backup path R0->R1->R2 for forwarding, Then the traffic is
   forwarded through the Inter-Group Link.

            N2 N N N N N    N N N N N N     N N N N N N
             | | | | | |    | | | | | |     | | | | | |
            ++-+-+-+-+-++  ++-+-+-+-+-++   ++-+-+-+-+-++
            |     G1    |  |     G2    |...|    G8     |
            +-+---+----++  ++----+----++   ++---+----+-+
              |   |    |    |    |    |     |   |    |
         fail x   |    +----+    |    +-----+   |    |
              |   +--------------)--------------+    |
            +-+------------------+-------------------+-+
            |   +------------------------------+       |
            |   x fail                         |   G0  |
            | +-+-+          +---+           +-+-+     |
            | |R0 +----------+R1 +-----------+ R2|     |
            | ++-++          ++-++           ++-++     |
            |  | |            | |             | |      |
            +--)-)------------)-)-------------)-)------+
               | |            | |             | |
              N1 N            N N             N N
                  Figure 6: Inter Link Failure



   If a link failure occurs in Inter-Group link, R0 cannot directly
   detect link failures and needs to be informed by a remote device
   detecting the link failure. R0 responds to the remote link failure
   by selecting a new path for forwarding. Inter-Group link failure is
   a type of remote link failure.

   For Intra-Group Link failures, the main time taken for switching
   lies in the detection of the link failure.

   For Inter-Group Link failures, it is necessary to detect the link
   failure, then transmit it to R0, and finally respond to the remote
   link failure by switching to a new path for forwarding.

3. Gap Analysis

3.1. Fault detection Timing

   Ethernet links may support failure signaling or detection standards
   such as Connectivity Fault Management (CFM) as described in
   [IEEE8021Q]; this may make failure detection more robust.

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   Alternatively, some platforms may support Bidirectional Forwarding
   Detection (BFD) [RFC5880] to allow for sub-second failure detection
   and fault signaling to the BGP process.  However, the use of either
   of these presents additional requirements to vendor software and
   possibly hardware. Since links in modern data centers are
   predominantly point-to-point fiber connections, a physical interface
   failure is often detected in milliseconds and subsequently triggers
   a BGP reconvergence.

   3.2. Notifications Event Propagation Timing

   After detecting a link failure, devices typically notify other
   devices through a link-state protocol or BGP route withdrawal, which
   typically takes milliseconds to complete.

   3.3. Fault switchover Timing

   Local link failure:

   The existing mechanism allows for local detection of link failures,
   which can be directly handled by the hardware to switch between ECMP
   links. In the scenario depicted in Figure 1, when R11 detects a link
   failure to R23, the hardware switches directly to the second ecmp
   link. In this case, the switchover time is mainly determined by the
   link failure detection time.

   Remote link failure:

   Currently, there is no mechanism available to support this method of
   fast switchover for remote link failures. It can only rely on the
   routing protocol to perform a new routing calculation, including IGP
   SPF (Shortest Path First) or BGP route calculation, which typically
   takes seconds or even more.


4. Problem Statement

   The number of parameters required for AI learning and training can
   vary greatly depending on the specific model and task at hand. For
   large AI models, the number of parameters for AI training can reach
   the millions.

   And for large models, the training time for AI can take even several
   months or longer.

   When a link failure occurs, the impact on AI training is as follows:

      Performance impact: This includes issues such as training being
      stopped or RDMA not having a timeout processing mechanism.


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      Breakpoint reboot: The training process is paused and the system
      needs to be rebooted at a breakpoint. This can take anywhere from
      30 minutes to several hours. The training task cannot proceed
      until the fault is resolved.

   During AI training, the switch time for link failures should be as
   short as possible to minimize the impact on the training process.
   Typically, for most enterprises, the switch time for network link
   failures should be controlled within the millisecond or even
   microsecond range in order to minimize disruptions to the stability
   and performance of AI training. Otherwise, if there is a prolonged
   link failure, AI training would need to be restarted.

   However, the current situation is that the failure rate of switches
   and optical modules is high, and the switch time is far from
   reaching the microsecond level, and even fails to achieve the
   millisecond level in most cases.

5. Requirements for AI network Mechanisms

   In summary, For AI training networks, it is required to switch to an
   available link within microseconds after a link failure occurs. new
   requirements for the existing network for AI training include:

   1) a new fault detection mechanism that can quickly detect the
      status of local and remote link failures; It is required to
      achieve link fault detection time in the microsecond range, while
      the current leading BFD (Bidirectional Forwarding Detection) for
      link detection requires at least several tens of milliseconds.

   2) New techniques are needed to proactively eliminate link
      congestion that may be caused by link switchover. In the scenario
      of large workloads in AI training networks, once link congestion
      occurs, it will result in more severe network failures.

   3) a new cross-device fault notification mechanism that enables
      other devices concerned with the fault to receive notifications
      quickly; It is required to achieve link fault detection time in
      the microsecond range, while the current leading BFD
      (Bidirectional Forwarding Detection) for link detection requires
      at least several tens of milliseconds.

   4) a new fast table switching mechanism that can swiftly switch to
      backup links in response to remote link failures; For local link
      failure switchover, the current mechanisms like FRR can achieve
      millisecond-level performance, but further optimization is
      required for AI networks. On the other hand, for remote link
      failure switchover, there is currently no fast switchover
      mechanism available. It relies on re-routing calculation
      convergence through routing protocols. Even with optimizations

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      like BGP PIC, it only reduces the rate of table distribution from
      the control plane to the forwarding plane.

   5) expansion of the control plane to maintain this rapid remote link
      switching mechanism. If a suitable fast switchover solution at
      the forwarding plane is implemented for remote link failure, it
      would still require expanding the control plane protocols to
      maintain fast switchover entries and distribute them to the
      hardware.

6. Security Considerations

   TBD.

7. IANA Considerations

This document does not request any IANA allocations.


8. References

8.1. Normative References

   [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate

             Requirement Levels", BCP 14, RFC 2119, March 1997.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC

              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,

              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

8.2. Informative References

   TBD














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Authors' Addresses

   Weiqiang Cheng
   China Mobile
   China

   Email: chengweiqiang@chinamobile.com


   Changwang Lin
   New H3C Technologies
   China

   Email: linchangwang.04414@h3c.com


   Wenxuan Wang
   China Mobile
   China

   Email: wangwenxuan@chinamobile.com


   Bohua Xu
   China Unicom
   China

   Email: xubh15@chinaunicom.cn






















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