Pith. sign in

REVIEW 1 cited by

SeqBalance: Congestion-Aware Load Balancing with no Reordering for RoCE

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.09808 v1 pith:QX3HBIPM submitted 2024-07-13 cs.NI

Huimin Luo , Jiao Zhang , Mingxuan Yu , Yongchen Pan , Tian Pan , Tao Huang This is my paper · ORCID
classification cs.NI
keywords seqbalancebalancingloadrdmacenterdataexistingnetworks
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Remote Direct Memory Access (RDMA) is widely used in data center networks because of its high performance. However, due to the characteristics of RDMA's retransmission strategy and the traffic mode of AI training, current load balancing schemes for data center networks are unsuitable for RDMA. In this paper, we propose SeqBalance, a load balancing framework designed for RDMA. SeqBalance implements fine-grained load balancing for RDMA through a reasonable design and does not cause reordering problems. SeqBalance's designs are all based on existing commercial RNICs and commercial programmable switches, so they are compatible with existing data center networks. We have implemented SeqBalance in Mellanox CX-6 RNICs and Tofino switches. The results of hardware testbed experiments and large-scale simulations show that compared with existing load balancing schemes, SeqBalance improves 18.7% and 33.2% on average FCT and 99th percentile FCT.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PRIME: Pseudo-Random Integrated Multi-Part Entropy for Adaptive Packet Spraying in AI/ML Data centers

    cs.NI 2025-07 conditional novelty 5.0 of 10

    A pseudo-random round-robin packet spraying scheme with congestion-aware path penalties improves flow completion time in simulated AI/ML data center networks.

Pith tools