Heuristics for Planning GMPLS Networks with Conversion and Regeneration Capabilities
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. This paper defines a novel problem of planning realistic GMPLS-based transport networks by (1) considering the whole traffic hierarchy defined in GMPLS; (2) allowing optical signal conversion at all granularity levels; (3) imposing optical reach constraint on the length of all- optical paths. We will call such a problem the routing and multi- granular paths assignment (RMGPA). The objective of the problem is to minimize the weighted port count in the transport network. 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 work, 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