Rate-based congestion control for tree-based reliable multicast
Bibliographic record
Abstract
Although a few rate-based congestion control algorithms have been proposed for multicast, the effectiveness of these protocols on tree-based reliable multicast has not been evaluated. In addition, the tree-based recovery structure of reliable multicast could be exploited to enhance the performance and/or scalability of the congestion control scheme. In this paper, we extend TFMCC, a rate-based congestion control scheme designed for group-based (unreliable) multicast, to support tree-based reliable multicast. We first conducted experiments to investigate how network parameters may affect the sharing of bandwidth between TFMCC and TCP. Observations drawn from these experiments allowed us to avoid the situations in which multicast flows using TFMCC may be disadvantaged by TCP flows. Secondly we extend TFMCC to support tree-based reliable multicast. Specifically, we propose the use of the tree-based structure to calculate RTTs and to consolidate rate values at the designated receivers (DRs) in a scalable manner; and the calculation of retransmission rates of the DRs. Our simulation results show that the proposed protocol is effective, TCP-friendly and responsive to network conditions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".