[Idr] Re: New Version Notification for draft-xu-idr-fare-01.txt

[Reshma Das <[e-mail]>]

Hi Xiaohu,

FYI the base draft(draft-ietf-idr-link-bandwidth) is being revived to support both transitive and non-transitive use cases. This was presented in Mondays IDR session: (https://www.youtube.com/watch?v=ePPCAPOSQfM)

Please go through the session and see if the procedures described in the session work for your use case also.

I think, it is worth updating the base draft as a single source of truth to accommodate all use cases. That provides the most interop.

Since this is an effort initiated by IDR chairs, you are more than welcome to contribute to this effort as part the IDR WG.

Thanks & Regards,
Reshma Das






Juniper Business Use Only
From: Tiger Xu <[e-mail]>
Date: Tuesday, July 23, 2024 at 5:21 AM
To: "[e-mail]" <[e-mail]>
Subject: [Idr] 转发: New Version Notification for draft-xu-idr-fare-01.txt
[External Email. Be cautious of content]


Hi all,


[I-D.ietf-idr-link-bandwidth] has specified a way to perform weighted ECMP based on link bandwidths conveyed in the non-transitive link bandwith extended community.  However, it is impractical to enable adaptive routing in a 5-stage CLOS network where eBGP is used as the underlay routing protocol by directly using the non-transitive link bandwidth extended community due to the following constraints as mentioned in [I-D.ietf-idr-link-bandwidth].



   "No more than one link bandwidth extended community SHALL be attached

   to a route.  Additionally, if a route is received with link bandwidth

   extended community and the BGP speaker sets itself as next-hop while

   announcing that route to other peers, the link bandwidth extended

   community should be removed.  The extended community is optional non-

   transitive."



Hence, this document defines a new extended community referred to as Path Bandwidth Extended Community and describes how to use this newly defined path bandwidth extended community to achieve adaptive routing in a 5-stage CLOS network.

Any comments or suggestions are welcome.

Best regards,
Xiaohu

发件人: [e-mail] <[e-mail]>
日期: 星期二, 2024年7月23日 20:10
收件人: Hang Wu <[e-mail]>, Hongyi Huang <[e-mail]>, Junjie Wang <[e-mail]>, Peilong Wang <[e-mail]>, Qingliang Zhang <[e-mail]>, Shraddha Hegde <[e-mail]>, Tiezheng <[e-mail]>, Xiaohu Xu <[e-mail]>, Yadong Liu <[e-mail]>, Yinben Xia <[e-mail]>, Zongying He <[e-mail]>
主题: New Version Notification for draft-xu-idr-fare-01.txt
A new version of Internet-Draft draft-xu-idr-fare-01.txt has been successfully
submitted by Xiaohu Xu and posted to the
IETF repository.

Name:     draft-xu-idr-fare
Revision: 01
Title:    Fully Adaptive Routing Ethernet using BGP
Date:     2024-07-21
Group:    Individual Submission
Pages:    11
URL:      https://www.ietf.org/archive/id/draft-xu-idr-fare-01.txt<https://urldefense.com/v3/__https:/www.ietf.org/archive/id/draft-xu-idr-fare-01.txt__;!!NEt6yMaO-gk!BsDSnsFLb6W4-Lajl-xxaXcmXRW7wXVX0MA0XgKCNzxE7qZuS-ZSs6XdMulI50_F6N2dwNfDjF9oFlBWUnMYvHC2$>
Status:   https://datatracker.ietf.org/doc/draft-xu-idr-fare/<https://urldefense.com/v3/__https:/datatracker.ietf.org/doc/draft-xu-idr-fare/__;!!NEt6yMaO-gk!BsDSnsFLb6W4-Lajl-xxaXcmXRW7wXVX0MA0XgKCNzxE7qZuS-ZSs6XdMulI50_F6N2dwNfDjF9oFlBWUpaxF4l8$>
HTMLized: https://datatracker.ietf.org/doc/html/draft-xu-idr-fare<https://urldefense.com/v3/__https:/datatracker.ietf.org/doc/html/draft-xu-idr-fare__;!!NEt6yMaO-gk!BsDSnsFLb6W4-Lajl-xxaXcmXRW7wXVX0MA0XgKCNzxE7qZuS-ZSs6XdMulI50_F6N2dwNfDjF9oFlBWUrMnB8kg$>
Diff:     https://author-tools.ietf.org/iddiff?url2=draft-xu-idr-fare-01<https://urldefense.com/v3/__https:/author-tools.ietf.org/iddiff?url2=draft-xu-idr-fare-01__;!!NEt6yMaO-gk!BsDSnsFLb6W4-Lajl-xxaXcmXRW7wXVX0MA0XgKCNzxE7qZuS-ZSs6XdMulI50_F6N2dwNfDjF9oFlBWUiaGAFEl$>

Abstract:

   Large language models (LLMs) like ChatGPT have become increasingly
   popular in recent years due to their impressive performance in
   various natural language processing tasks.  These models are built by
   training deep neural networks on massive amounts of text data, often
   consisting of billions or even trillions of parameters.  However, the
   training process for these models can be extremely resource-
   intensive, requiring the deployment of thousands or even tens of
   thousands of GPUs in a single AI training cluster.  Therefore, three-
   stage or even five-stage CLOS networks are commonly adopted for AI
   networks.  The non-blocking nature of the network become increasingly
   critical for large-scale AI models.  Therefore, adaptive routing is
   necessary to dynamically load balance traffic to the same destination
   over multiple ECMP paths, based on network capacity and even
   congestion information along those paths.



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