Interoperability among explicit rate congestion control algorithms for ABR service in ATM networks
Bibliographic record
Abstract
This paper investigates the important issue of interoperability among different explicit rate congestion control algorithms in multi-vendor (heterogeneous) ATM networks. We assume that each switch in the network implements only one of several known explicit rate congestion control algorithms. We investigate potential unfairness problems resulting from situations whereby certain ABR sources receive network feedback from different switches and other sources responding to feedback coming from a subset of switches. The paper identifies three types of unfairness problems that arise in such networks. One type of unfairness appears while the sources are increasing their rates and the second type appears while they are decreasing their rates. The third type is a new cause of unfairness generated by the presence of highly bursty VBR traffic which can cause unfairness not only in the steady-state periods but even in the transient-state periods on a network link. In addition to identifying the causes of unfairness, our results provide quantitative evaluation of the level of unfairness as a function of various network and source parameters.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".