TURBOTEST: Learning When Less is Enough through Early Termination of Internet Speed Tests
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910183771340800 |
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| author | Manda, Haarika Sagar, Manshi Yogesh Singh, Kartikay Zhao, Cindy Mangla, Tarun Gill, Phillipa Belding, Elizabeth Gupta, Arpit |
| author_facet | Manda, Haarika Sagar, Manshi Yogesh Singh, Kartikay Zhao, Cindy Mangla, Tarun Gill, Phillipa Belding, Elizabeth Gupta, Arpit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TURBOTEST: Learning When Less is Enough through Early Termination of Internet Speed Tests Manda, Haarika Sagar, Manshi Yogesh Singh, Kartikay Zhao, Cindy Mangla, Tarun Gill, Phillipa Belding, Elizabeth Gupta, Arpit Networking and Internet Architecture Machine Learning 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. |
| title | TURBOTEST: Learning When Less is Enough through Early Termination of Internet Speed Tests |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2510.21141 |