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Main Authors: Dai, Jun, Wang, Xiaorun, Li, Xingde, Yang, Zheng, Fang, Kexiong, Gu, Zhiqun, Wang, Hongxiang, Ji, Yuefeng, Zhang, Jiawei
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.23932
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author Dai, Jun
Wang, Xiaorun
Li, Xingde
Yang, Zheng
Fang, Kexiong
Gu, Zhiqun
Wang, Hongxiang
Ji, Yuefeng
Zhang, Jiawei
author_facet Dai, Jun
Wang, Xiaorun
Li, Xingde
Yang, Zheng
Fang, Kexiong
Gu, Zhiqun
Wang, Hongxiang
Ji, Yuefeng
Zhang, Jiawei
contents We propose MatchRDMA, a proactive, segmented, and rate-matched long-haul RDMA scheme for geo-distributed LLM training over OTN. By coordinating source and destination OTN rates, it improves inter-DC throughput by up to 20x compared with conventional RDMA, and reduces destination-OTN buffer occupancy by up to 62.7%.
format Preprint
id arxiv_https___arxiv_org_abs_2604_23932
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle MatchRDMA: A Segmented and Rate-Matched Long-Haul RDMA Scheme for Geo-distributed LLM Training over OTN
Dai, Jun
Wang, Xiaorun
Li, Xingde
Yang, Zheng
Fang, Kexiong
Gu, Zhiqun
Wang, Hongxiang
Ji, Yuefeng
Zhang, Jiawei
Networking and Internet Architecture
We propose MatchRDMA, a proactive, segmented, and rate-matched long-haul RDMA scheme for geo-distributed LLM training over OTN. By coordinating source and destination OTN rates, it improves inter-DC throughput by up to 20x compared with conventional RDMA, and reduces destination-OTN buffer occupancy by up to 62.7%.
title MatchRDMA: A Segmented and Rate-Matched Long-Haul RDMA Scheme for Geo-distributed LLM Training over OTN
topic Networking and Internet Architecture
url https://arxiv.org/abs/2604.23932