A flexible multipoint-to-point traffic control algorithm for ABR services in ATM networks
Bibliographic record
Abstract
Traffic control for point-to-multipoint ABR services in ATM networks has been studied extensively, but this is not the case for multipoint-to-point (mp-p) traffic control. No consensus has been reached concerning the issue of fairness of bandwidth allocation among multipoint connections, or between point-to-point and multipoint connections. Existing mp-p traffic control schemes for ABR services either support only a limited set of fairness definitions, or are not effective. We define a new type of fairness, and propose a flexible multipoint-to-point traffic control scheme for ABR services. The proposed scheme supports cell merging at both virtual path (VP) and virtual connection (VC) levels, and implements all existing types of fairness currently defined in the literature for mp-p communications. This flexibility allows the network to meet various bandwidth requirements of different application streams. It also permits switch vendors to quickly configure their switches to meet clients' requirements, despite the lack of consensus on the issue of fairness definition. While being flexible, the new algorithm is also fair, fast and robust. Simulation results show that the proposed scheme is fair, and performs as well as, or better than existing mp-p traffic control schemes. It is also easy to incorporate the new algorithm into a multipoint-to-multipoint traffic control scheme such as the framework proposed by Ren, Siu and Suzuki (see IEEE International Conference on Communications, Montreal, Canada, June 1997, and Computer Networks and ISDN Systems, vol.30, no.19, p.1793-1810, 1998).
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".