Active congestion control using ABCD (available bandwidth-based congestion detection).
Bibliographic record
Abstract
With the growth of the Internet, the problem of congestion has attained the distinction of being a perennial problem. The Internet community has been trying several approaches for improved congestion control techniques. The end-to-end approach is considered to be the most robust one and it has served quite well until recently, when researchers started to explore the information available at the intermediate node level. This approach triggered a new field called Active Networks where intermediate nodes have a much larger role to play than that of the naive nodes. This thesis proposes an active congestion control (ACC) scheme based on Available Bandwidth-based Congestion Detection (ABCD), which regulates the traffic according to network conditions. Dynamic changes in the available bandwidth can trigger re-negotiation of flow rate. We have introduced packet size adjustment at the intermediate router in addition to rate control at sender node, scaled according to the available bandwidth, which is estimated using three packet probes. To verify the improved scheme, we have extended Ted Faber's ACC work in NS-2 simulator. With this simulator we verify ACC-ABCD's gains such as a marginal improvement in average TCP throughput at each endpoint, fewer packet drops and improved fairness index. Our tests on NS-2 prove that the ACC-ABCD technique yields better results as compared to TCP congestion control with or without the cross traffic. Source: Masters Abstracts International, Volume: 43-03, page: 0870. Adviser: A. K. Aggarwal. Thesis (M.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".