Extremal traffic and worst-case performance for queues with shaped arrivals
Why this work is in the frame
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Bibliographic record
Abstract
. This paper presents some new results and an overview of the authors' recent work in the area of worst-case performance analysis in communication networks with stationary-ergodic traffic and regulated sample paths. Starting with a single-class network node, the problem of maximizing the buffer overflow probability is considered. Maximization is over a suitable class of stochastic processes. The problem is explicitly solved and the extremal process is identified. Moving on to a two-class queue with jointly stationary arrival processes, the analogous problem is considered. Under some rather natural assumptions the problem is again solved explicitly. The final part of the paper consists of a multiclass queue with the additional constraint that the arrival processes are independent. Bounds on performance measures, such as the tail of the stationary delay, are derived. The problem and results of this paper can be seen as being at the intersection between the effective bandwidths approach a...
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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 | 0.000 |
| 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) | 0.005 | 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