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Network-Assisted Congestion Feedback
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AI
DOI：10.1109/TNSM.2025.3648180.png)
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摘要
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We present Network Congestion Feedback (NCF), a novel congestion control framework that leverages programmable data planes for generating a rich congestion signal for use in the public Internet. NCF makes several contributions, including isolating ‘mice’ and ‘elephant’ flows using separate queues, detecting congestion in the elephants’ queue and generating a rich sub-RTT signal for the concerned senders, and designing a congestion-control algorithm (CCA) that matches a flow’s demands with supply (i.e., available bandwidth) for maximizing utilization and fairness. It extends two key ingredients from prior work on datacenter CCAs—a short control-loop delay and a precise congestion signal—that are crucial for designing an efficient, fair CCA, by adapting them for the more challenging Internet context. NCF isolates mice and elephant flows so that the former cannot unfairly degrade the throughput of the latter, and it guarantees that mice flows experience minimal round-trip times (RTTs) even when contending with elephant flows. NCF virtually eliminates slow-start spikes and achieves high fairness in both shallow and deep-buffer configurations, and even when the flows experience drastically different RTTs. Lastly, NCF offers low flow completion times (FCTs) to short flows even in challenging multiple-bottleneck scenarios.
Keyword:
Congestion control
flow control
data plane programmability
P4
期刊
IF:
5.4
论文数:
618
被引数:
9.2K
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