On optimizing token bucket parameters at the network edge under generalized processor sharing (GPS) scheduling
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Bibliographic record
Abstract
In this paper, we consider the case where non-linear traffic bounds are provided for traffic sources which share a link operating under a generalized processor sharing discipline. We consider the problem of searching for parameters for token bucket traffic shapers which provide linear bounds for the non-linear traffic bounding function in order to make use of results for traffic delay bounds which require a linear traffic bounding function, expressed in the form of token bucket shaper parameters. We formulate an optimization problem to obtain the parameters (i.e., bucket size and token generation rate) with the objective of minimizing a delay bound for a particular traffic source. This method can be used iteratively to obtain good delay bounds for a number of sources. Some typical numerical results obtained from the optimization model are presented. We also propose an alternate method, which we refer to as the composite delay envelope method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it