Cebinae: scalable in-network fairness augmentation

Cebinae: scalable in-network fairness augmentation
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Cebinae:可扩展的网络内公平性增强

DOI:
10.1145/3544216.3544240
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发表时间:
2022
期刊:
Proceedings of the 2022 ACM SIGCOMM 2022 Conference
影响因子:
--
通讯作者:
Liu, Vincent
Liu, Vincent
中科院分区:
--
文献类型:
--
作者:
Yu, Liangcheng;Sonchack, John;Liu, Vincent

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对于像Internet和许多云的公共网络,终端主机应用程序可以使用他们想要的任何拥塞控制协议。这种协议多样性和应用自主性只会随着时间的推移而增加。虽然网络内对公平的支持是治理不平等的一个有吸引力的解决方案,但现有的解决方案仍然难以扩展到使用当今设备的今天的网络。在本文中,我们提出了Cebinae机制,用于通过对超过其最大-最小公平份额的流量进行惩罚来增强现有的遗留主机网络。Cebinae与当今互联网中的所有拥塞控制协议兼容,可部署在商用可编程交换机上,其规模超过现有替代方案的数量级。
For public networks like the Internet and those of many clouds, end-host applications can use any congestion control protocol they wish. This protocol diversity and application autonomy are only increasing over time. While in-network support for fairness is an attractive solution for reigning in the inequity, existing solutions still have difficulty scaling to today's networks using today's devices. In this paper, we present Cebinae, a mechanism for augmenting existing networks of legacy hosts with penalties for flows that exceed their max-min fair share. Cebinae is compatible with all of the congestion control protocols in today's Internet, is deployable on commodity programmable switches, and scales orders of magnitude beyond existing alternatives.
公平性:拥塞控制评估中的时间处理
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