Waiting for QUIC: Passive Measurements to Understand QUIC Deployments

QUIC experiences a rapid adoption since its standardization in 2021, and hypergiants configure their infrastructure to optimize for QUIC performance. In this paper, we introduce a passive measurement method to study both the progressive rollout and individual hypergiant configurations during the last five years. By analyzing backscatter traffic of the UCSD network telescope, we are able to make the following observations. First, Meta, Google, and Cloudflare configure significantly different maximal retransmission numbers and timeouts. Second, we can identify different off-net deployments of hypergiants, using packet features, such as QUIC connection IDs, packet coalescence, and packet lengths. Third, we observe changing hypergiant deployment configurations during our different measurement periods. Fourth, connection IDs can allow further insights into load balancer deployments, such as the number of servers. We bolster our results using two orthogonal measurements: passive recording of QUIC flows and active probing.

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Publication Details

Journal
Proceedings of the ACM on Networking
Published
2025-11-24
DOI
https://doi.org/10.1145/3768988
Primary Topic
Advanced Optical Network Technologies
Type
article
Field-Weighted Citation Impact
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article

Waiting for QUIC: Passive Measurements to Understand QUIC Deployments

Johannes Zirngibl, Thomas C. Schmidt, Raphael Hiesgen, Matthias Wählisch et al.
Proceedings of the ACM on Networking
Advanced Optical Network Technologies
article

Waiting for QUIC: Passive Measurements to Understand QUIC Deployments

Johannes Zirngibl, Thomas C. Schmidt, Raphael Hiesgen, Matthias Wählisch, Jonas Mücke, Patrick Sattler, Jan Luxemburk, Georg Carle, Marcin Nawrocki
article en

Abstract

QUIC experiences a rapid adoption since its standardization in 2021, and hypergiants configure their infrastructure to optimize for QUIC performance. In this paper, we introduce a passive measurement method to study both the progressive rollout and individual hypergiant configurations during the last five years. By analyzing backscatter traffic of the UCSD network telescope, we are able to make the following observations. First, Meta, Google, and Cloudflare configure significantly different maximal retransmission numbers and timeouts. Second, we can identify different off-net deployments of hypergiants, using packet features, such as QUIC connection IDs, packet coalescence, and packet lengths. Third, we observe changing hypergiant deployment configurations during our different measurement periods. Fourth, connection IDs can allow further insights into load balancer deployments, such as the number of servers. We bolster our results using two orthogonal measurements: passive recording of QUIC flows and active probing.

Proceedings of the ACM on NetworkingVol. 3(CoNEXT4)
NetScout (United States) (US), Max Planck Institute for Informatics (DE), Czech Technical University in Prague (CZ), CESNET, zájmové sdružení právnických osob (CZ), Technical University of Munich (DE), HAW Hamburg (DE), Technische Universität Dresden (DE)
Openalex Percentile: Top 64%
Advanced Optical Network Technologies
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