Multicast flow control in priority-based IP networks
Bibliographic record
Abstract
In this paper, we present simulation results of our research work in the area of multicast congestion control for video applications. This work is based on our proposal A. Matrawy et al. (2003) of the use of a new form of network support to multicast congestion control. Our approach is to bring together simple, loosely-coupled, router mechanisms and adopt them for the multicast case. We use a variant of the explicit congestion notification (ECN) mechanism to notify the sender of a multicast session of potential network congestion. We develop an end-to-end multicast system using this congestion control scheme. This included the development and tuning of an elaborate rate adaptation algorithm that operates at the sender. We build this system on top of a network that applies packet priority-dropping to insure providing minimum video quality during persistent congestion. In particular, the work is targeted at the IETF assured forwarding (AF) services networks. We show that the synergy of these mechanisms deals with the heterogeneity of receivers in a scalable manner and avoids the major problems of earlier approaches. We believe that this work is of great value to multicasting applications in future QoS-aware networks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".