A New Survey on Improving TCP Performances over Geostationary Satellite Link
Bibliographic record
Abstract
The idea of Internet everywhere makes the assumption that an Internet access should be available even in remote areas without network infrastructure. In this case satellite access represents an attractive solution. Nevertheless, experience shows that over satellite links, TCP is limited in terms of data speeds. Many enhancements and solutions, based for instance on tuning TCP parameters or TCP spoofing, have been proposed to avoid the underutilization of satellite link capacity. These topics have been often addressed, but considering recent high speed TCP variants, the evolution of end users habits, and recently proposed satellite link access scheme, a new study is necessary in order to reconsider some preconceptions and previous recommendations in such a context. This paper proposes an overview of TCP variants and a survey of commonly proposed solutions for TCP over satellite. Then a methodology for TCP performance assessment over satellite links is exposed. The approach is mainly based on a satellite link emulation platform and some tools developed at the ENAC. We assess the gain offered by a split TCP connection with PEPSal comparatively to end to end TCP connections based on NewReno and a recent widely deployed TCP version (TCP Cubic) on an emulated satellite link. Unlike existing studies, we compare PEP advantages with most recent TCP versions and propose as an extension, to assess PEP gain considering the number of simultaneous TCP connections. Finally, the results provided allow us to make some original recommendations toward TCP deployment over satellite links.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".