RaceCC: A rapidly converging explicit congestion control for datacenter networks

Abstract

Congestion control (CC) in datacenter networks has three primary goals: high link utilization, low queuing delay, and rapid convergence to fairness. Most of the host-driven CC schemes perform well in achieving the first two goals, while it is difficult to converge to fairness rapidly. Switch-driven CC schemes can be a compelling alternative to achieve fairness since switches explicitly provide feedback to hosts. However, we found that existing switch-driven CC schemes converge slowly or sometimes could not guarantee low queues.Based on these observations, in this paper, we propose RaceCC, a RApidly Converging Explicit CC to achieve the three primary goals simultaneously. As a switch-driven CC scheme, RaceCC enables flows to reach a fair rate after the first RTT and guarantees high link utilization and low queue length. To converge rapidly, RaceCC adjusts flow rates through an intuitive MIMD method with additive-decrease for short queues and precise update for increase. Meanwhile, RaceCC can be implemented with several simple operations. We theoretically analyze the stability of RaceCC and evaluate its performance through micro-benchmark and large-scale simulations. The results show that RaceCC reduces the overall average and tail flow completion time (FCT) by 20% ∼ 57% and 15% ∼ 63%, compared to DCQCN, TIMELY, HPCC, PowerTCP, RCP and RoCC.