On Minimizing TCP Retransmission Delay in Softwarized Networks
Bibliographic record
Abstract
Today’s Internet mainly relies on TCP protocol to ensure reliable communications between two endpoints. Unfortunately, this widely-used protocol may incur significant delay when transmitting lost packets. Indeed, with TCP, the source of the data detects lost packets using a timeout or duplicated acknowledgements before retransmitting them. As a result, the delay needed for a packet to reach the destination may become significant. This delay is estimated to be at least three times the end-to-end delay when the packet is lost once and could be even worse when the same packet is lost several times. As a matter of fact, this high delay cannot be tolerated by critical applications.To address this problem, in this paper, we focus on minimizing TCP retransmission delays and we introduce a novel network function called Transport Assistant that could be deployed within the network in order to cache, detect and retransmit lost packets. Thanks to this function, there is no need to wait for the source to detect and retransmit lost packets as the TA ensures packet retransmission from the network itself and thereby minimize retransmission delays. Through extensive experiments, we show that the TA allows to outperform the standard TCP by minimizing the average packet transmission time, the flow completion time, the packet loss and the number of retransmitted packets from the source.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".