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Efficient Traffic Management for ML Workloads in Datacenter Networks

The paper discusses the importance of network efficiency for AI/ML workloads in datacenter networks, highlighting challenges such as bursty traffic and flow collisions. It explores the need for high bandwidth and low latency for both AI training and inference tasks, emphasizing the impact of network performance on training step time and throughput. The introduction of CSIG (Congestion Signaling) protocol is proposed as a practical and effective in-band signaling solution for congestion control, traffic management, and network debuggability. CSIG offers fixed-length simple summaries of path bottlenecks and is designed for brownfield deployment with backward compatibility and interoperability.

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Efficient Traffic Management for ML Workloads in Datacenter Networks

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