Packet spraying approaches are increasingly deployed in datacenter networks. However, their combination with existing congestion control algorithms (CCAs) may lead to poor QoS, especially when some of the paths are congested. In this paper, we first model the throughput collapse of a wide array of CCAs when some of the paths are congested. We explain that since CCAs are typically designed for single-path routing, their estimation function focuses on the latest feedback and mishandles feedback that reflects multiple paths. We propose using a median feedback that is more robust to the varying signals that come with multiple paths. We introduce MSwift and MNSCC, which apply this median principle to Google's Swift and Ultra Ethernet's NSCC. We demonstrate that they can improve both CCAs, reaching better QoS both under congested paths and in uncongested networks.

翻译：分组喷洒技术正越来越多地部署于数据中心网络中。然而，当部分路径出现拥塞时，该技术与现有拥塞控制算法（CCAs）的结合可能导致服务质量（QoS）下降。本文首先对多种CCA在多路径拥塞场景下的吞吐量坍塌现象进行建模分析。我们发现，由于CCA通常针对单路径路由设计，其估计函数聚焦于最新反馈信号，难以正确处理反映多路径特征的反馈信息。为此，我们提出采用中位数反馈机制——该机制对多路径带来的变化信号具有更强的鲁棒性。我们进一步将这一中位数原则分别应用于谷歌Swift算法和超以太网NSCC算法，提出了MSwift与MNSCC两种改进方案。实验表明，这两种算法在拥塞路径与非拥塞网络中均能显著提升原有CCA的性能，实现更优的QoS。