TY - GEN
T1 - Performance Characterization of Videoconferencing in the Wild
AU - Varvello, Matteo
AU - Chang, Hyunseok
AU - Zaki, Yasir
N1 - Publisher Copyright:
© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2022/10/25
Y1 - 2022/10/25
N2 - Due to the recent “work from home” trend, recent years have seen a growing research interest in understanding existing commercial videoconferencing systems in terms of their performance and architecture. One important question left unanswered that we tackle in this paper is: what is the performance of videoconferencing in the wild? Answering this generic question is challenging because it requires, ideally, a world-wide testbed composed of diverse devices (mobile, desktop), operating systems (Windows, MacOS, Linux) and network accesses (mobile and WiFi). In this paper, we present such a testbed that we develop to evaluate videoconferencing performance in the wild via automation for Android and Chromium-based browsers. We deploy our testbed via 85 distinct devices worldwide and collect performance metrics from 58 hours’ worth of more than 2,000 videoconferencing sessions from 37 unique countries in the world. This, to the best of our knowledge, is the largest collection of videoconferencing performance data in the wild.
AB - Due to the recent “work from home” trend, recent years have seen a growing research interest in understanding existing commercial videoconferencing systems in terms of their performance and architecture. One important question left unanswered that we tackle in this paper is: what is the performance of videoconferencing in the wild? Answering this generic question is challenging because it requires, ideally, a world-wide testbed composed of diverse devices (mobile, desktop), operating systems (Windows, MacOS, Linux) and network accesses (mobile and WiFi). In this paper, we present such a testbed that we develop to evaluate videoconferencing performance in the wild via automation for Android and Chromium-based browsers. We deploy our testbed via 85 distinct devices worldwide and collect performance metrics from 58 hours’ worth of more than 2,000 videoconferencing sessions from 37 unique countries in the world. This, to the best of our knowledge, is the largest collection of videoconferencing performance data in the wild.
UR - http://www.scopus.com/inward/record.url?scp=85141420487&partnerID=8YFLogxK
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U2 - 10.1145/3517745.3561442
DO - 10.1145/3517745.3561442
M3 - Conference contribution
AN - SCOPUS:85141420487
T3 - Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
SP - 261
EP - 273
BT - IMC 2022 - Proceedings of the 2022 ACM Internet Measurement Conference
PB - Association for Computing Machinery
T2 - 22nd ACM Internet Measurement Conference, IMC 2022
Y2 - 25 October 2022 through 27 October 2022
ER -