Characterizing the Impact of Active Queue Management on Speed Test Measurements

TL;DR

This study empirically examines how Active Queue Management (AQM) schemes influence speed test measurements, revealing high variability and emphasizing the importance of calibration for accurate network performance assessment.

Contribution

It provides a detailed empirical analysis of AQM's impact on speed test metrics, highlighting variability across schemes and the need for calibration.

Findings

Measurements vary significantly across different AQM schemes.

A high variance exists in throughput and latency under different load conditions.

Calibration of speed test tools is essential for reliable network performance evaluation.

Abstract

Present day speed test tools measure peak throughput, but often fail to capture the user-perceived responsiveness of a network connection under load. Recently, platforms such as NDT, Ookla Speedtest and Cloudflare Speed Test have introduced metrics such as ``latency under load'' or ``working latency'' to fill this gap. Yet, the sensitivity of these metrics to basic network configurations such as Active Queue Management (AQM) remains poorly understood. In this work, we conduct an empirical study of the impact of AQM on speed test measurements in a laboratory setting. Using controlled experiments, we compare the distribution of throughput and latency under different load measurements across different AQM schemes, including CoDel, FQ-CoDel and Stochastic Fair Queuing (SFQ). On comparing with a standard drop-tail baseline, we find that measurements have high variance across AQM schemes and load conditions. These results highlight the critical role of AQM in shaping how emerging latency metrics should be interpreted, and underscore the need for careful calibration of speed test platforms before their results are used to guide policy or regulatory outcomes.