Bibliographic record
Abstract
TCP is widely implemented for congestion control in current IP networks. As the network bandwidth increases, TCP becomes oscillatory and prone to instability, regardless of the queuing scheme. As a rate-based control scheme, XCP was proposed to obtain high link utilization in high bandwidth-delay product networks, while maintaining small queue size in the routers. XCP explicit feedbacks the congestion information in the bottleneck link to the source, and brings a flurry of research interests recently. However, XCP cannot settle to zero steady-state error due to the capacity estimation error, and may lead to arbitrarily low link utilization. Using solid control theoretical analysis and design, API-RCP has solved the potential problems of XCP successfully. Furthermore, API-RCP has simpler structure for real-time implementation. Due to its satisfactory transient network performance and its stability robustness to the dramatic change of the network traffic, API-RCP is a promising algorithm for practical implementation. Why can API-RCP solve the modeling deficiency of XCP? Can API-RCP be TCP friendly with TCP/RED (the current dominant congestion control mechanism in the Internet) in the same router? To answer these two questions, we have made control theoretical comparison of API-RCP with XCP and run OPNET simulation when the APIRCP and TCP/RED co-exist in the same router.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| 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 | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".