Abstract
Bandwidth testing measures the access bandwidth of end hosts, which is crucial to emerging Internet applications for network-aware content delivery. However, today's bandwidth testing services (BTSes) are slow and costly-the tests take a long time to run, consume excessive data usage at the client side, and/or require large-scale test server deployments. The inefficiency and high cost of BTSes root in their methodologies that use excessive temporal and spatial redundancies for combating noises in Internet measurement. This paper presents FastBTS to make BTS fast and cheap while maintaining high accuracy. The key idea of FastBTS is to accommodate and exploit the noise rather than repetitively and exhaustively suppress the impact of noise. This is achieved by a novel statistical sampling framework (termed fuzzy rejection sampling). We build FastBTS as an end-toend BTS that implements fuzzy rejection sampling based on elastic bandwidth probing and denoised sampling from highfidelity windows, together with server selection and multihoming support. Our evaluation shows that with only 30 test servers, FastBTS achieves the same level of accuracy compared to the state-of-the-art BTS (SpeedTest.net) that deploys ∼12,000 servers. Most importantly, FastBTS makes bandwidth tests 5.6× faster and 10.7× more data-efficient.
| Original language | English (US) |
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| Title of host publication | Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021 |
| Publisher | USENIX Association |
| Pages | 1011-1026 |
| Number of pages | 16 |
| ISBN (Electronic) | 9781939133212 |
| State | Published - 2021 |
| Event | 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021 - Virtual, Online Duration: Apr 12 2021 → Apr 14 2021 |
Publication series
| Name | Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021 |
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Conference
| Conference | 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021 |
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| City | Virtual, Online |
| Period | 4/12/21 → 4/14/21 |
Bibliographical note
Funding Information:We sincerely thank the anonymous reviewers for their valuable comments, and our shepherd Prof. Andreas Haeberlen for guiding us through the revision process. Also, we appreciate the generous help from Hongzhe Yang and Jiaxing Qiu in system deployment and text proofreading. This work is supported in part by the National Key R&D Program of China under grant 2018YFB1004700, the National Natural Science Foundation of China (NSFC) under grants 61822205, 61902211, 61632020 and 61632013, and the Beijing National Research Center for Information Science and Technology (BNRist).
Publisher Copyright:
© 2021 by The USENIX Association.