Integrated traffic grooming in converged data-optical networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Optical dense wavelength division multiplexing (DWDM) has yielded unprecedented levels of bandwidth scalability. In order to exploit these gains, new converged multiservice transport setups have been evolved, most notably under the multiprotocol label switching (MPLS) and generalized MPLS (GMPLS) frameworks. These paradigms offer very efficient data-optical integration and enable a host of new service capabilities. As operators deploy these new technologies, the provisioning of "subwavelength" demands over wavelengths has become a crucial requirement, i.e., traffic engineering/grooming. This work addresses data-optical grooming in converged GMPLS networks. Here, novel integrated constraint-based routing algorithms are developed to provision subwavelength demands at both packet-switching and lightpath routing levels. Simulations indicate notable performance gains and resource efficiencies with the proposed schemes.
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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 | 0.001 |
| 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