Resource Criticality Analysis of Static Resource Allocations and Its Applications in WDM Network Planning
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Bibliographic record
Abstract
Various static resource allocation algorithms have been used in WDM networks to allocate resources such as wavelength channels, transmitters, receivers, and wavelength converters to a given set of static lightpath demands. However, although optimized resource allocations can be obtained, it remains an open issue how to determine which resources are the bottlenecks in achieving better performance. Existing static resource allocation algorithms do not explicitly measure the impact of changes of network resources or lightpath demands on the design objective. We propose such a measurement based on the Lagrangian relaxation framework. We use the optimized values of Lagrange multipliers as a direct measurement of the criticality of resources. Such a quantitative measurement can be naturally acquired along with the optimization process to obtain the optimal solution (or a near-optimal solution) to the static routing and wavelength assignment problem. We investigate three practical applications of the resource criticality (RC) analysis in WDM network planning. In the first application, we use our proposed measurement to identify critical resources and thus to decide the best way to add or reallocate resources. In the second application, we estimate the impact of the addition or removal of lightpath demands on the design objective. This kind of estimation helps to set a proper price for lightpath demands. In the third application, the results of the RC analysis are used to speed up the convergence of the optimization process for different network 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 | 0.001 | 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 | 0.001 |
| 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