TY - GEN
T1 - Fast and light bandwidth testing for internet users
AU - Yang, Xinlei
AU - Wang, Xianlong
AU - Li, Zhenhua
AU - Liu, Yunhao
AU - Qian, Feng
AU - Gong, Liangyi
AU - Miao, Rui
AU - Xu, Tianyin
N1 - Publisher Copyright:
© 2021 by The USENIX Association.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85106174946
T3 - Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021
SP - 1011
EP - 1026
BT - Proceedings of the 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021
PB - USENIX Association
T2 - 18th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2021
Y2 - 12 April 2021 through 14 April 2021
ER -