Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks

Wei Li, Mengzhen Ren, Yazhi Liu (), Chenyu Li, Hui Qian and Zhenyou Zhang
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Wei Li: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Mengzhen Ren: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Yazhi Liu: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Chenyu Li: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Hui Qian: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China
Zhenyou Zhang: College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China

Future Internet, 2024, vol. 16, issue 4, 1-19

Abstract: In order to solve the congestion problem caused by the dramatic growth of traffic in data centers, many end-to-end congestion controls have been proposed to respond to congestion in one round-trip time (RTT). In this paper, we propose a new congestion control mechanism based on backpressure feedback (BFCC), which is designed with the primary goal of switch-to-switch congestion control to resolve congestion in a one-hop RTT. This approach utilizes a programmable data plane to continuously monitor network congestion in real time and identify real-congested flows. In addition, it employs targeted flow control through backpressure feedback. We validate the feasibility of this mechanism on BMV2, a programmable virtual switch based on programming protocol-independent packet processors (P4). Simulation results demonstrate that BFCC greatly enhances flow completion times (FCTs) compared to other end-to-end congestion control mechanisms. It achieves 1.2–2× faster average completion times than other mechanisms.

Keywords: datacenter networks; congestion control; flow control; programmable switches (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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