Model-Based Insights on the Performance, Fairness, and Stability of BBR
Abstract
Google's BBR is the most prominent result of the recently revived quest for efficient, fair, and flexible congestion-control algorithms (CCAs). While the performance of BBR has been investigated by numerous studies, previous work still leaves gaps in the understanding of BBR performance: Experiment-based studies generally only consider network settings that researchers can set up with manageable effort, and model-based studies neglect important issues like convergence. To complement previous BBR analyses, this paper presents a fluid model of BBRv1 and BBRv2, allowing both efficient simulation under a wide variety of network settings and analytical treatment such as stability analysis. By experimental validation, we show that our fluid model provides highly accurate predictions of BBR behavior. Through extensive simulations and theoretical analysis, we arrive at several insights into both BBR versions, including a previously unknown bufferbloat issue in BBRv2.
Cite
@article{arxiv.2208.10103,
title = {Model-Based Insights on the Performance, Fairness, and Stability of BBR},
author = {Simon Scherrer and Markus Legner and Adrian Perrig and Stefan Schmid},
journal= {arXiv preprint arXiv:2208.10103},
year = {2022}
}
Comments
Accepted at the ACM Internet Measurement Conference 2022 (IMC'22)