Internet speed tests are indispensable for users, ISPs, and policymakers, but their static flooding-based design imposes growing costs: a single high-speed test can transfer hundreds of MB, and collectively, platforms like Ookla, M-Lab, and Fast.com generate petabytes of traffic each month. Reducing this burden requires deciding when a test can be stopped early without sacrificing accuracy. We frame this as an optimal stopping problem and show that existing heuristics-static thresholds, BBR pipe-full signals, or throughput stability rules from Fast.com and FastBTS-capture only a narrow slice of the achievable accuracy-savings trade-off. This paper introduces TurboTest, a systematic framework for speed test termination that sits atop existing platforms. The key idea is to decouple throughput prediction (Stage 1) from test termination (Stage 2): Stage 1 trains a regressor to estimate final throughput from partial measurements, while Stage 2 trains a classifier to decide when sufficient evidence has accumulated to stop. Leveraging richer transport-level features (RTT, retransmissions, congestion window) alongside throughput, TurboTest exposes a single tunable parameter epsilon for accuracy tolerance and includes a fallback mechanism for high-variability cases. Evaluation on 1 million M-Lab NDT speed tests (2024-2025) shows that TurboTest achieves 1.8-4.4x higher data savings than an approach based on BBR signals while reducing median error. These results demonstrate that adaptive ML-based termination can deliver accurate, efficient, and deployable speed tests at scale.

翻译：互联网速度测试对于用户、互联网服务提供商（ISP）和政策制定者不可或缺，但其基于静态洪泛的设计造成了日益增长的代价：单次高速测试可能传输数百兆字节的数据，而Ookla、M-Lab和Fast.com等平台每月共同产生拍字节级别的流量。减轻这一负担需要在确保不牺牲准确性的前提下，判断何时可以提前停止测试。我们将此问题建模为最优停止问题，并证明现有启发式方法——包括静态阈值、BBR管道充满信号，或来自Fast.com和FastBTS的吞吐量稳定性规则——仅能捕获准确性-节省权衡中极窄的一部分。本文介绍了TurboTest，一个系统化的速度测试终止框架，可与现有平台协同工作。其核心思想是将吞吐量预测（阶段1）与测试终止（阶段2）解耦：阶段1训练一个回归器，基于部分测量结果估计最终吞吐量；阶段2训练一个分类器，判断何时已积累足够证据以停止测试。通过利用除吞吐量外更丰富的传输层特征（RTT、重传、拥塞窗口），TurboTest暴露了一个单一可调参数epsilon用于控制精度容忍度，并包含一个针对高变异情况的后备机制。对2024-2025年间100万次M-Lab NDT速度测试的评估表明，TurboTest在实现更高数据节省（比基于BBR信号的方法高1.8-4.4倍）的同时，降低了中位误差。这些结果表明，基于自适应的机器学习终止方法能够在大规模部署中提供准确、高效且实用的速度测试。