A new scheme for TCP congestion control: smooth-start and dynamic recovery
Bibliographic record
Abstract
This paper presents a new approach to TCP congestion control. The new scheme includes two parts: (I) the smooth-start algorithm, which replaces the slow-start algorithm at the start of a TCP connection or after a retransmission timeout, and (2) the dynamic recovery algorithm, which replaces the fast recovery algorithm to recover packet losses when a TCP connection is congested. Both algorithms require modifications only to the sender side of the TCP implementation. Simulation is used to evaluate the performance of the algorithms. The simulation experiments are conducted using the ns simulator to facilitate comparisons with Tahoe, Reno, New-Reno, SACK, and FACK TCP. The simulation results show that the new scheme performs at least as well as SACK and FACK TCP, which in turn consistently outperform TCP Tahoe and Reno. Furthermore, the implementation of the new scheme is simpler than that of SACK and FACK.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".