A pseudo-random round-robin packet spraying scheme with congestion-aware path penalties improves flow completion time in simulated AI/ML data center networks.
SeqBalance: Congestion-Aware Load Balancing with no Reordering for RoCE
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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.
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PRIME: Pseudo-Random Integrated Multi-Part Entropy for Adaptive Packet Spraying in AI/ML Data centers
A pseudo-random round-robin packet spraying scheme with congestion-aware path penalties improves flow completion time in simulated AI/ML data center networks.