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Augmented Queue: A Scalable In-Network Abstraction for Data Center Network Sharing

Xinyu Crystal Wu, Zhuang Wang, Weitao Wang, T. S. Eugene Ng

2023Year
10Citations
1Top-tier citations

Abstract

Traffic aggregates in cloud data center networks are by and large buffered and transmitted by simple physical FIFO queues. Despite the crucial role they play, a well-known problem of physical FIFO queues is that they are unable to provide precise bandwidth guarantees. This leads to a range of negative impacts spanning the application layer, the transport layer, and the data link layer.

In this paper, we address this problem with Augmented Queue (AQ), a scalable in-network abstraction that provides precise bandwidth guarantees for traffic constituents. AQ serves multiple valuable use cases in data center networks. For example, AQ facilitates the isolation of traffic from different applications; ensures that different congestion control algorithms can properly co-exist; and enforces inbound and outbound bandwidth for virtual machines. We demonstrate via testbed and simulation experiments that AQ can provide precise bandwidth guarantees and scale to millions of traffic constituents.

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