Precise ECN in WAN
draft-hu-rtgwg-pre-ecn-wan-01
| Document | Type |
This is an older version of an Internet-Draft whose latest revision state is "Active".
Expired & archived
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|---|---|---|---|
| Author | Jiayuan Hu | ||
| Last updated | 2026-09-02 (Latest revision 2026-03-01) | ||
| RFC stream | (None) | ||
| Formats | |||
| Stream | Stream state | (No stream defined) | |
| Consensus boilerplate | Unknown | ||
| RFC Editor Note | (None) | ||
| IESG | IESG state | Expired | |
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | (None) |
This Internet-Draft is no longer active. A copy of the expired Internet-Draft is available in these formats:
Abstract
This draft defines the precise ECN during used in WAN. With the growing demand for AI computing power, the computational capacity of a single Artificial Intelligence Data Center (AIDC) can no longer meet the requirements of large-scale model training. This has led to the emergence of cross-AIDC distributed model training, driving the need for transmitting RoCEv2 packets over WAN networks. AI training is highly sensitive to network packet loss, where even minimal packet loss can significantly degrade training efficiency. Additionally, elephant flows and extreme concurrent traffic impose higher demands on network performance. ECN achieves active feedback of network congestion by setting ECN flag bits in the header of IP packets, which is an effective traffic control method. RFC6040 introduces the application of ECN in WAN. However, due to the much higher end-to-end delay in WAN than in DC, and the frequent occurrence of instantaneous traffic bursts in WAN, it is easy to trigger ECN at the wrong time. This draft focuses on the precise use of ECN in WAN, by introducing different reactions of ECN in different WAN transmission scenarios
Authors
(Note: The e-mail addresses provided for the authors of this Internet-Draft may no longer be valid.)