Enhancing network performance with TCP rate control
Bibliographic record
Abstract
In TCP (transmission control protocol), congestion control as well as error recovery are implemented by a sliding window. The dynamics of TCP (specifically, a mismatch between the TCP window and the bandwidth-delay product of the network) can sometimes cause the network switches or routers to accumulate large queues, resulting in buffer overflows, reduced throughput, unfairness and underutilization. It is generally accepted that there is a limit as to how much control can be accomplished from the congestion control mechanisms in the end systems. Some mechanisms are thus needed in the intermediate network elements to complement the endpoint congestion avoidance mechanisms. Network layer enhancements such as scheduling mechanisms and packet drop policies have been proposed which are aimed at improving fairness and throughput of the competing endpoint applications. We describe a new TCP rate control scheme based on a simple recursive algorithm. The idea behind the algorithm is to match the network load to the available resources by modifying at an intermediate network element, the receiver's advertised window in TCP acknowledgments returning to the sources. The scheme can be implemented in a router or switch for bandwidth management and does not require knowledge of network delays or maintenance of the per-flow state.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".