Calculation of loss probability in a partitioned buffer with self-similar input traffic
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Bibliographic record
Abstract
In the differentiated services model, provisioning quantitative assured services is a challenging topic, as it requires loss probability calculation for a partitioned buffer. In this paper, we study such a loss analysis problem with self-similar input traffic, which has never been studied in the open literature. The input is modeled as a fractional Brownian motion process including J classes of traffic. Each class has its unique requirement on packet loss probability. A first-in-first-out buffer partitioned with J-1 thresholds is used to provide J loss priorities. Heuristic expressions of the loss probabilities for all the J classes are derived, and simulation results demonstrate that the heuristic expressions provide an accurate estimate for all the loss probabilities over the entire buffer range.
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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 | 0.001 |
| 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 it