Efficient estimation of the cell loss probability in a two-buffer PGPS scheduler
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Bibliographic record
Abstract
A key feature of integrated services networks is their ability to provide a variety of quality of service (QoS) guarantees to different applications. To this end scheduling systems can be employed. A primary QoS parameter is the cell loss probability (CLP), whose typical values are very small and difficult to estimate by means of standard simulation schemes. We propose an application of the importance sampling (IS) technique to efficiently estimate the CLP of an ideal two-queue generalized processor sharing (GPS) scheduling discipline. We subsequently apply this algorithm to simulate a realistic scheme, namely the packet-by-packet generalized processor sharing (PGPS). We model input traffic as Markov arrival processes (MAPs). The algorithm we present is based on large deviation results, which provide the asymptotic decay rate of per-session queue length tail distributions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 it