Comparison of the use of different ECN techniques for IP multicast congestion control
Bibliographic record
Abstract
We have recently proposed the use of backward explicit congestion notification (BECN) with video multicasting over IP networks. This proposal improved bandwidth utilization and reduced time to react to congestion. In this paper, we present a comparison of the use of different ECN techniques, namely ECN, BECN, and MECN, for IP multicast congestion control. We investigate this in the context of MPEG4 multicasting over IP. We use these ECN techniques in an IP network that supports priority dropping of packets during congestion using RED's extension for service differentiation. This extension recognizes the priority of packets when they need to be dropped and drops lower priority packets first. ECN techniques provide early notification to the sender and/or the receivers about congestion while it is developing in the network. Based on which ECN technique is used, the sender and/or the receivers react to this notification to reduce the congestion in the network. We show the advantages and disadvantages of each ECN technique. Also, through the results, we advocate the use of network support in the form of explicit congestion notification for IP multicast congestion control.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".