The performance of TCP congestion control algorithm over high-speed transmission links
Bibliographic record
Abstract
In the Internet, many applications use TCP (Transmission Control Protocol) as the main transport protocol. TCP congestion control algorithm has proved to be inadequate as the speed of the transmission links increases and users demand higher throughput. The main feature of TCP congestion control algorithm is its additive increase/multiplicative decrease (AIMD) property. AIMD requires very low packet loss rates, which is not possible with the present optical transmission links, in order to achieve higher throughputs. The proposed solutions for this problem fail to protect bandwidth share of the low throughput users. We propose that low and high throughput user traffic is stored in separate queues and the two queues are served according to the weighted-round-robin (WRR) service discipline. The simulation results show that this improves the performance of TCP over high-speed links and preserves fairness to the low-throughput users.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".