HierCC: Hierarchical RDMA Congestion Control

Jiao Zhang, Yali Zhang, Zixuan Guan, Zirui Wan, Yinben Xia, Tian Pan, Tao Huang, Dezhi Tang, Yun Lin
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Abstract

RDMA has been increasingly deployed in data centers to decrease latency and CPU utilization. However, existing RDMA congestion control schemes fail to address instantaneous large queue build-up or bandwidth under-utilization associated with frequent traffic bursty. In this paper, we argue that traffic uncertainty is the essential reason that constrains data center congestion control from simultaneously achieving high throughput and deterministic latency. Since aggregated flows within the same rack are relatively long-lived, we propose HierCC, which aggregates flows destined to the same IP in a rack and hierarchically controls the rate of flows. The rate of aggregate flows between racks is controlled by a credit-based congestion control mechanism. Then the bandwidth obtained by an aggregate flow in a rack is allocated to the corresponding individual flows from that rack promptly and accurately. We evaluate HierCC using SystemC and large-scale NS3 simulations. Results indicate that HierCC can significantly mitigate buffer usage and reduce the 99th percentile FCT by up to 20% and 40% compared with HPCC and DCQCN under a realistic workload, respectively.
HierCC:分级RDMA拥塞控制
RDMA越来越多地部署在数据中心中,以减少延迟和CPU利用率。然而,现有的RDMA拥塞控制方案无法解决与频繁的流量突发相关的瞬时大队列建立或带宽利用率不足的问题。在本文中,我们认为流量不确定性是限制数据中心拥塞控制无法同时实现高吞吐量和确定性延迟的根本原因。由于同一机架内的聚合流寿命相对较长,我们提出了HierCC,它聚合了机架中指向相同IP的流,并分层控制流的速率。机架之间的聚合流速率由基于信用的拥塞控制机制控制。然后将机架中聚合流获得的带宽及时准确地分配给该机架中相应的单个流。我们使用SystemC和大规模NS3模拟来评估HierCC。结果表明,在实际工作负载下,与HPCC和DCQCN相比,HierCC可以显著减少缓冲区的使用,并将第99百分位FCT分别降低20%和40%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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