Network planning algorithms for the optical Internet based on the generalized MPLS architecture
Bibliographic record
Abstract
GMPLS is one of the most promising frameworks proposed for the next generation optical Internet, which supports multi-granularity of switching types including fiber-, waveband- and lambda-switching in the optical domain. We present some algorithms for solving the routing and wavelength/tunnel assignment (RWTA) problem based on the multi-granularity 4-tier switching architecture. We also propose novel network planning algorithms, the weighted network link state, and an enhancement to the fixed-alternative routing scheme, the heavy fixed alternative routing (H-FAR), to facilitate RWTA. We show that with the weighted network link state and H-FAR, the RWTA problem in the optical Internet with multi-granularity OXCs (MG-OXCs) can be solved efficiently, and the performance in terms of call blocking rate in a network with MG-OXCs is comparable with that in a lambda-switched network, which means the link utilization of tunnels is close to that of wavelength-switched channels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".