Performance analysis of token-based fast TCP in systems supporting large windows
Bibliographic record
Abstract
Since TCP can only detect congestion after packet losses have already happened, various forms of fast TCP (FTCP) have been proposed to notify congestion early and avoid packet losses in intermediate nodes by effectively controlling backward ACK flows traversing the same nodes as the forward data packets. Among them, token-based FTCP (TB-FTCP) is a promising approach as it does not need to determine the rate of delaying ACK. The effectiveness of TB-FTCP has been proven for networks in which window size is limited by the bandwidth-delay product (see Peng, F. et al., Proc. 9th Int. Conf. on Computer Commun. and Networks, 2000). A mathematical model for the TB-FTCP is now presented and analyzed numerically for networks in which window size can be larger than the bandwidth-delay product. Results indicate that the proposed method performs extremely well compared to traditional TCP implementations. It is important to note that TCP behavior at end-nodes does not have to be modified in any way.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".