Scale Congestion Control to Ultra-High Speed Ethernet

TL;DR

This paper introduces ASM, a congestion control algorithm designed for ultra-high speed Ethernet, addressing challenges like delay impacts and bandwidth variability, and demonstrating superior performance over existing methods like QCN.

Contribution

The paper presents ASM, a novel, stable, and adaptive congestion control algorithm tailored for 40Gbps+ Ethernet, with insights on queue length derivatives and boundary enforcement.

Findings

ASM outperforms QCN in experiments and simulations.

ASM is stable, fast, and adapts to bandwidth changes.

Using queue length derivatives improves rate adjustment.

Abstract

Currently, Ethernet is broadly used in LAN, datacenter and enterprise networks, storage networks, high performance computing networks and so on. Along with the popularity of Ethernet comes the requirement of enhancing Ethernet with congestion control. On the other hand, Ethernet speed extends to 40Gbps and 100Gbps recently, and even 400Gbps in the near future. The ultra-high speed requires congestion control algorithms to adapt to the broad changes of bandwidth, and highlights the impacts of small delay by enlarging the bandwidth delay product. The state-of-art standard QCN is heuristically designed for the 1Gbps and 10Gbps Ethernet, and unaware of the challenges accompanying the ultra-high speed. To scale congestion control to ultra-high speed Ethernet, we propose the Adaptive Sliding Mode (ASM) congestion control algorithm, which is simple, stable, has fast and smooth convergence process, can tolerate the impacts of delay and adapt to the wide changes of bandwidth. Real experiments and simulations confirm these good properties and show that ASM outperforms QCN. Designing ASM, we find that the derivative of queue length is helpful to rate adjustment because it reflects the difference between bandwidth and aggregated sending rate. We also argue for enforcing congestion control system staying at the congestion boundary line, along which it automatically slides to stable point. These insights are also valuable to develop other congestion control algorithms in ultra-high speed networks.