Fair-efficient call admission control policies for broadband networks
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Bibliographic record
Abstract
An application of cooperative game theory to the synthesis of fair call admission controls for multi-service loss networks is presented. Three arbitration schemes are studied: Nash, Raiffa-Kalai-Smorodinsky and modified Thompson. The proposed model for evaluation of these schemes is based on the value iteration algorithm from Markov decision theory. The arbitration schemes are compared with two traditional call admission objectives, traffic maximization and blocking equalization. The comparison demonstrates that the arbitration solutions provide some attractive fairness features not possessed by traditional objectives, especially in overload conditions. In particular, traffic maximization can result in a total rejection of some services under heavy overload. A dynamic arbitration scheme is proposed, where the solution depends on some agreement point related to nominal conditions. In this approach, the increase of the throughput caused by the overload can be fairly distributed among all network users.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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) | 0.000 | 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