Abstract: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 this http URL 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 this http URL 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.
| Subjects: | Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2510.21141 [cs.NI] |
| (or arXiv:2510.21141v2 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2510.21141 arXiv-issued DOI via DataCite |
From: Haarika Manda [view email]
[v1]
Fri, 24 Oct 2025 04:25:16 UTC (188 KB)
[v2]
Fri, 1 May 2026 17:20:17 UTC (240 KB)
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