StarQUIC: Tuning Congestion Control Algorithms for QUIC over LEO Satellite Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it