计算机科学
网络拥塞
排队
排队论
延迟(音频)
流量控制(数据)
趋同(经济学)
远程直接内存访问
计算机网络
低延迟(资本市场)
实时计算
分布式计算
电信
网络数据包
经济增长
经济
作者
Yukun Zhou,Dezun Dong,Zhengbin Pang,Junhong Ye,Feng Jin
标识
DOI:10.1109/ccgrid54584.2022.00090
摘要
The widespread deployment of Remote Direct Memory Access (RDMA) in datacenter networks increases the stringency for convergence speed when congestion occurs. Fast convergence significantly reduces buffer occupancy, which in turn lessens the probability of triggering Priority-based Flow Control (PFC). Besides, the propagation delay becomes shorter with rapidly growing link speed, which correspondingly makes the queueing delay a major part of end-to-end latency in datacenter networks. Fast convergence and low buffer occupancy become more essential for lowering queue delay and flow complete time. In this paper, we present ERA, an ecn-ratio-based congestion control scheme, which contributes to fast convergence for datacenter networks. ERA consists of two fundamental components: (i) an ECN-marking-ratio-based queue buffer occupancy estimating (QBOE) solution and (ii) a queue-building-rate driven rate adjustment (QDRA) mechanism to achieve fast convergence in several control periods. We conduct extensive experiments to evaluate the performance of ERA, and the results show that ERA greatly accelerates the convergence process compared to other solutions. ERA achieves low tail latency and low buffer occupancy simultaneously.
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