A novel flow control scheme for improving TCP fairness and throughput over heterogeneous networks with wired and wireless links
Bibliographic record
Abstract
Most of the recent research on TCP over heterogeneous networks has concentrated on differentiating between packet drops caused by link congestion versus drops caused by link errors. Handling the two types of packet drop differently avoids significant throughput degradations caused by frequent TCP window shut downs due to non-congestive drops. However, TCP also exhibits inherent unfairness for connections with long round-trip times and connections that traverse multiple congested routers. In heterogeneous networks, the difference in bit error rates between wireless and wired links aggravates the situation even more. In this paper, we propose a new TCP bandwidth allocation (NTBA) algorithm to solve these problems. The primary contribution is a wireless access node algorithm with a simple new shadow price scheme provided by the network node. The advantage of our algorithm is that it simplifies the TCP sender-side implementation and keeps the receiver-side protocol stack unchanged. We propose to apply wireless explicit congestion notification (WECN) to decouple congestion control from loss recovery in wireless networks. Simulation results show that not only can the combined NTBA/WECN mechanism improve TCP fairness, but it can also maintain very good throughput performance in the presence of wireless channel losses.
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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 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".