TY - GEN
T1 - Enabling Passive Measurement of Zoom Performance in Production Networks
AU - Michel, Oliver
AU - Sengupta, Satadal
AU - Kim, Hyojoon
AU - Netravali, Ravi
AU - Rexford, Jennifer
N1 - Publisher Copyright:
© 2022 Copyright held by the owner/author(s).
PY - 2022/10/25
Y1 - 2022/10/25
N2 - Video-conferencing applications impose high loads and stringent performance requirements on the network. To better understand and manage these applications, we need effective ways to measure performance in the wild. For example, these measurements would help network operators in capacity planning, troubleshooting, and setting QoS policies. Unfortunately, large-scale measurements of production networks cannot rely on end-host cooperation, and an in-depth analysis of packet traces requires knowledge of the header formats. Zoom is one of the most sophisticated and popular applications, but it uses a proprietary network protocol. In this paper, we demystify how Zoom works at the packet level, and design techniques for analyzing Zoom performance from packet traces. We conduct systematic controlled experiments to discover the relevant unencrypted fields in Zoom packets, as well as how to group streams into meetings and how to identify peer-to-peer meetings. We show how to use the header fields to compute metrics like media bit rates, frame sizes and rates, and latency and jitter, and demonstrate the value of these fine-grained metrics on a 12-hour trace of Zoom traffic on our campus network.
AB - Video-conferencing applications impose high loads and stringent performance requirements on the network. To better understand and manage these applications, we need effective ways to measure performance in the wild. For example, these measurements would help network operators in capacity planning, troubleshooting, and setting QoS policies. Unfortunately, large-scale measurements of production networks cannot rely on end-host cooperation, and an in-depth analysis of packet traces requires knowledge of the header formats. Zoom is one of the most sophisticated and popular applications, but it uses a proprietary network protocol. In this paper, we demystify how Zoom works at the packet level, and design techniques for analyzing Zoom performance from packet traces. We conduct systematic controlled experiments to discover the relevant unencrypted fields in Zoom packets, as well as how to group streams into meetings and how to identify peer-to-peer meetings. We show how to use the header fields to compute metrics like media bit rates, frame sizes and rates, and latency and jitter, and demonstrate the value of these fine-grained metrics on a 12-hour trace of Zoom traffic on our campus network.
KW - Measurement
KW - Network Performance
KW - Protocol Analysis
KW - Reverse Engineering
KW - Video Conferencing
KW - Zoom
UR - http://www.scopus.com/inward/record.url?scp=85141365185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141365185&partnerID=8YFLogxK
U2 - 10.1145/3517745.3561414
DO - 10.1145/3517745.3561414
M3 - Conference contribution
AN - SCOPUS:85141365185
T3 - Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
SP - 244
EP - 260
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 -