StarQUIC: Tuning Congestion Control Algorithms for QUIC over LEO Satellite Networks
Bibliographic record
Abstract
With the deployment of mega constellations of Low-Earth-Orbit (LEO) satellites, low latency and high throughput Internet coverage is extended globally. Latency-sensitive applications can benefit from the inherent lower transmission delay of LEO satellite networks compared to traditional Geostationary-Earth-Orbit (GEO) satellite networks. Starlink employs a globally time-synchronized controller to manage the association of satellite-to-ground communication links with an interval of 15 seconds, at fixed 12-27-42-57 seconds of every minute. Latency spikes and packet losses can occur during the handover period which can degrade the performance of transport layer protocols including TCP and QUIC, which rely on similar congestion control algorithms for fair data transmission. In this paper, we investigate the impact of the frequent Starlink handover events on QUIC performance. By leveraging the predictable handover patterns to avoid unnecessary congestion window reduction, we improved the performance of QUIC by up to 35% in terms of completion time in both network emulation and real-world experiments over Starlink networks. Our approach is independent of specific loss-sensitive congestion control algorithms and can be easily generalized.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".