Cost-effective heuristics for planning GMPLS transport networks
Why this work is in the frame
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Bibliographic record
Abstract
With the explosive traffic growth of WDM-based transport networks, the development of GMPLS (or multi-granularity)-based transport networks becomes essential to cope with the network scalability problems. Much work has been devoted to the development of Multi-Granular Crossconnect (MG-XC) architectures and planning (or dimensioning) methods. Extending these efforts here, we are defining a novel problem of planning GMPLS-based transport networks by (1) considering the whole traffic hierarchy defined in GMPLS; (2) allowing bifurcation of multi-granularity traffic demands among different physical routes. We will call such a problem the Routing and Multi-Granular Paths Assignment (RMGPA). The objective of the problem is to minimize the total weighted node port count. Due to the computational complexity of the problem, only very-small-sized problems can be solved exactly through Mixed Integer Linear Programming (MILP) optimization. In this paper, we propose novel heuristics that are capable of solving large-sized problems in a reasonable amount of time.
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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