Robust resource reservation in virtual wireless networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we study resource reservation in virtual wireless networks with the aim of minimizing the operational cost. With this regard, the main constraint facing the operator is that only limited information about future traffic demand is typically available to the operator. To address this issue, we investigate reservation policies that are robust to the worst-case traffic demand which fits the available information i.e., the policies that minimize the worst-case expected operational cost. The problem is formulated for several resource reservation options that are commonly offered in practice. For each case, convexity of the problem is discussed and the its dual form is presented as a semidefinite program. While, semidefinite programs can be solved in polynomial time, the optimal closed-form reservation policies are obtained for several practical cases. Moreover, the worst-case cost of these policies are analytically compared to the expected cost of the algorithm that has full knowledge of the future demand. The theoretical analysis is supplemented with numerical results to demonstrate the behavior of our algorithms in terms of cost in some example traffic scenarios.
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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