# Waiting for QUIC: Passive Measurements to Understand QUIC Deployments

arXiv: https://arxiv.org/abs/2209.00965 · HTML: https://mlsanity.com/paper/2209.00965/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2022.

## Built on by (most cited first, 2 of 2)

- [Contrastive Learning and Correlation Clustering for Sequences of Network Telescope Data](https://mlsanity.com/paper/2606.04733/) (2026)
- [A First Glance at Jev for Network Traffic Classification: Accuracy, Processing Time, and Cost](https://mlsanity.com/paper/2610.00376/) (2026)

## Abstract (arXiv)

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.