Fast and Light Bandwidth Testing for Internet Users

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.

[1]  Peter Steenkiste,et al.  Evaluation and characterization of available bandwidth probing techniques , 2003, IEEE J. Sel. Areas Commun..

[2]  Yun Feng,et al.  Challenges in inferring internet congestion using throughput measurements , 2017, Internet Measurement Conference.

[3]  Steven Bauer,et al.  Improving the Measurement and Analysis of Gigabit Broadband Networks , 2016 .

[4]  Renata Teixeira,et al.  Speed Measurements of Residential Internet Access , 2012, PAM.

[5]  Mo Dong,et al.  PCC Vivace: Online-Learning Congestion Control , 2018, NSDI.

[6]  Marcel Dischinger,et al.  Characterizing residential broadband networks , 2007, IMC '07.

[7]  Kozo Satoda,et al.  Experimental comparison of machine learning-based available bandwidth estimation methods over operational LTE networks , 2017, 2017 IEEE Symposium on Computers and Communications (ISCC).

[8]  Ming Zhang,et al.  Congestion Control for Large-Scale RDMA Deployments , 2015, Comput. Commun. Rev..

[9]  P. Lawson,et al.  Federal Communications Commission , 2004, Bell Labs Technical Journal.

[10]  Cheng Jin,et al.  FAST TCP: Motivation, Architecture, Algorithms, Performance , 2006, IEEE/ACM Transactions on Networking.

[11]  Feng Qian,et al.  An in-depth study of LTE: effect of network protocol and application behavior on performance , 2013, SIGCOMM.

[12]  Injong Rhee,et al.  Binary increase congestion control (BIC) for fast long-distance networks , 2004, IEEE INFOCOM 2004.

[13]  J. W. Humberston Classical mechanics , 1980, Nature.

[14]  John P. Rula,et al.  Crowdsourcing ISP characterization to the network edge , 2011, W-MUST '11.

[15]  Jia Wang,et al.  A measurement study of Internet bottlenecks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[16]  Athina Markopoulou,et al.  A System for Crowdsourcing Passive Mobile Network Measurements , 2017 .

[17]  Alexey Ivanov Evaluating BBRv2 on the Dropbox Edge Network , 2020, ArXiv.

[18]  Minlan Yu,et al.  HPCC: high precision congestion control , 2019, SIGCOMM.

[19]  Vyas Sekar,et al.  A First Look at Performance in Mobile Virtual Network Operators , 2014, Internet Measurement Conference.

[20]  Feng Qian,et al.  A First Measurement Study of Commercial mmWave 5G Performance on Smartphones , 2019, ArXiv.

[21]  Haipeng Dai,et al.  Finding Persistent Items in Data Streams , 2016, Proc. VLDB Endow..

[22]  Mats Björkman,et al.  Regression-Based Available Bandwidth Measurements , 2002 .

[23]  Injong Rhee,et al.  CUBIC: a new TCP-friendly high-speed TCP variant , 2008, OPSR.

[24]  Y. Charlie Hu,et al.  Mobility Support in Cellular Networks: A Measurement Study on Its Configurations and Implications , 2018, Internet Measurement Conference.

[25]  Paul Barford,et al.  Revisiting broadband performance , 2012, Internet Measurement Conference.

[26]  Jiri Navratil,et al.  ABwE :A Practical Approach to Available Bandwidth Estimation , 2002 .

[27]  Songwu Lu,et al.  iCellular: Device-Customized Cellular Network Access on Commodity Smartphones , 2016, NSDI.

[28]  Mo Dong,et al.  PCC: Re-architecting Congestion Control for Consistent High Performance , 2014, NSDI.

[29]  Vaibhav Bajpai,et al.  A Survey on Internet Performance Measurement Platforms and Related Standardization Efforts , 2015, IEEE Communications Surveys & Tutorials.

[30]  Åke Arvidsson,et al.  On the Use of TCP BBR in Cellular Networks , 2018, IEEE Communications Magazine.

[31]  Alan Mislove,et al.  A large-scale analysis of deployed traffic differentiation practices , 2019, SIGCOMM.

[32]  Yufei Chen,et al.  RT-WABest: A novel end-to-end bandwidth estimation tool in IEEE 802.11 wireless network , 2017, Int. J. Distributed Sens. Networks.

[33]  L. Peterson,et al.  TCP Vegas: new techniques for congestion detection and avoidance , 1994, SIGCOMM.

[34]  Hari Balakrishnan,et al.  Copa: Practical Delay-Based Congestion Control for the Internet , 2018, ANRW.

[35]  Aaron Striegel,et al.  Leveraging Frame Aggregation for Estimating WiFi Available Bandwidth , 2017, 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[36]  Ann Lehman JMP for basic univariate and multivariate statistics : a step-by-step guide , 2005 .

[37]  Mats Björkman,et al.  A new end-to-end probing and analysis method for estimating bandwidth bottlenecks , 2000, Globecom '00 - IEEE. Global Telecommunications Conference. Conference Record (Cat. No.00CH37137).

[38]  Feng Liu,et al.  AuTO: scaling deep reinforcement learning for datacenter-scale automatic traffic optimization , 2018, SIGCOMM.

[39]  Zhao Wen-tao,et al.  Efficient available bandwidth estimation for network paths , 2008 .

[40]  Takashi Oshiba,et al.  Accurate Available Bandwidth Estimation Robust Against Traffic Differentiation in Operational MVNO Networks , 2018, 2018 IEEE Symposium on Computers and Communications (ISCC).

[41]  Amin Vahdat,et al.  TIMELY: RTT-based Congestion Control for the Datacenter , 2015, Comput. Commun. Rev..

[42]  M. Levandowsky,et al.  Distance between Sets , 1971, Nature.

[43]  Paul Barford,et al.  Cell vs. WiFi: on the performance of metro area mobile connections , 2012, Internet Measurement Conference.

[44]  Elizabeth M. Belding-Royer,et al.  A Study of MVNO Data Paths and Performance , 2016, PAM.

[45]  Richard G. Baraniuk,et al.  pathChirp: Efficient available bandwidth estimation for network paths , 2003 .

[46]  David Clark,et al.  Understanding Broadband Speed Measurements , 2010 .

[47]  Jia Wang,et al.  Locating internet bottlenecks: algorithms, measurements, and implications , 2004, SIGCOMM '04.