Routing and wavelength assignment with multigranularity traffic in optical networks
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".