Internet DRAFT - draft-cheng-rtgwg-ai-network-reliability-problem
draft-cheng-rtgwg-ai-network-reliability-problem
RTGWG Working Group W. Cheng
Internet Draft China Mobile
Intended status: Informational C. Lin
Expires: April 20, 2024 New H3C Technologies
W. Wang
China Mobile
October 20, 2023
Reliability in AI Networks Gap Analysis, Problem
Statement, and Requirements
draft-cheng-rtgwg-ai-network-reliability-problem-00
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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Table of Contents
1. Introduction...................................................3
1.1. Requirements Language.....................................4
1.2. Terminology...............................................4
2. Existing Mechanisms............................................4
2.1. Routing Convergence in AI network.........................4
2.2. Spine-Leaf topology.......................................5
2.3. Dragonfly topology........................................7
3. Gap Analysis...................................................9
3.1. Fault detection Timing....................................9
3.2. Notifications Event Propagation Timing....................9
3.3. Fault switchover Timing...................................9
4. Problem Statement.............................................10
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...................................12
Authors' Addresses...............................................13
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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.
* 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
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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.
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.
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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.
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.
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+---------+ +---------+
| 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
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.
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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.
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.
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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 |
+-+---+----++ ++----+----++ ++---+----+-+
| | | | | | | | |
| | +----+ | +-----+ | |
| +--------------)--------------+ |
+-+------------------+-------------------+-+
| +------------------------------+ |
| x fail | G0 |
| +-+-+ +---+ +-+-+ |
| |R0 +----------+R1 +-----------+ R2| |
| ++-++ ++-++ ++-++ |
| | | | | | | |
+--)-)------------)-)-------------)-)------+
| | | | | |
N1 N N N N N
Figure 5: Intra-Group Link Failure
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
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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.
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
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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.
* 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.
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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
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
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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
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