Routing and wavelength assignment with multigranularity traffic in optical networks
Why this work is in the frame
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Bibliographic record
Abstract
We propose a novel switching architecture of multigranularity optical cross-connects (MG-OXCs) for dealing with multigranularity traffic in the optical domain. MG-OXCs can cooperate with the generalized multiprotocol label switching (MPLS) control plane, which provides the advantages of cost reduction, better scalability in physical size, and unified traffic management. Detailed discussions are provided on the characteristics and implementation issues for the switching architecture. Based on the proposed MG-OXCs, two routing and wavelength assignment (RWA) with tunnel allocation algorithms are presented: dynamic tunnel allocation (DTA) and capacity-balanced static tunnel allocation (CB-STA). In the former, we use fixed alternate routing with k-shortest paths to inspect network resources along each alternate path for dynamically setting up lightpaths. For the latter, fiber and waveband tunnels are allocated into networks at the planning stage (or off-line) according to weighted network link-state (W-NLS). We will show that with the proposed algorithms, the RWA problem with tunnel allocation in the optical networks containing MG-OXCs can be solved effectively. Simulation is conducted on networks with different percentages of switching capacity and traffic load. The simulation results show that DTA is outperformed by CB-STA in the same network environment due to a well-disciplined approach for allocating tunnels with CB-STA.. We also find that the mix of the two approaches yields the best performance given the same network environment apparatus.
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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 | 0.001 |
| 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