Departure process characterization of the leaky bucket with modified geometric mode
Bibliographic record
Abstract
An ATM network is expected to support a large number of bursty traffic sources, and therefore, it is critical to control the network traffic in order to provide a desirable level of performance. The "leaky bucket" scheme is a typical policing or usage parameter control (UPC) mechanism in ATM networks. We build a modified geometric model (MGeo) for the interdeparture time distribution of the leaky bucket. The control effects of leaky bucket are extensively examined, from the viewpoint of smoothing out the burstiness of the input traffic, with numerical examples. The smoothing effect is reflected by the squared coefficient of variation (SCV) of the interdeparture time of the departure process from the leaky bucket. We offer a procedure to fit the interdeparture time distribution of the leaky bucket to the MGeo model. We also provide simulation results to verify the model. The trade-off between the burstiness of the departure process and the cell delay is examined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".