Design and analysis of a credit-based controller for congestion control in B-ISDN/ATM networks
Bibliographic record
Abstract
A credit-based controller (CBC), which has a provision for cell tagging, is proposed for usage parameter control (UPC). Similar in concept to the leaky bucket, the CBC has a credit counter and a data buffer. Credits are accumulated at a rate initiated from the network (equivalent to the sustainable cell rate), and depleted at a rate pertaining to the network traffic load. A source has the prerogative whether or not to send its cells as tagged cells when the credit level is nonpositive. The queueing behavior of the CBC fed by a Marcov modulated source, is analyzed using a stochastic fluid flow model. It is shown that the performance of the CBC can be altered by tuning the weighting parameter within the CBC. Also, adjustments can be made to balance the ratio of tagged and untagged cells sent into the network.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".