A study of routing and wavelength assignment problems in wavelength routed optical networks
Bibliographic record
Abstract
Devising a good routing and wavelength assignment (RWA) scheme is a very important and challenging problem to be addressed in the design of a wavelength routed optical network (WRON).RWA deals with finding a route from a network of physical links for each given pair of source and destination nodes, and appropriately selects and reserves a specific unused wavelength on each of the links along the route.In the context of WRON networks, RWA related problems could be classified into four categories in accordance with the type of traffic pattern and the stage of the design and planning process; they are, namely, static lightpath establishment (SLE) problem, dynamic lightpath establishment (DLE) problem, virtual topology design (VTD) problem, and dynamic label switchedpath provisioning (DLP) problem.In this dissertation, we describe and summarize various representative solution techniques for the above-mentioned problems.For some specific problems, we manage to find better solution techniques that outperform the best known solutions in one or more ways.For the SLE problem, solutions proposed in past studies are considered computationally expensive, especially when large networks are concerned.We experiment with soft-computing techniques and have some success in using tabu search (TS) to solve the SLE problem.The simulation results obtained confirm that our proposed TS algorithm has excellent performance for both small and large networks.In addition, we propose a new way to formulate the SLE problem so that it can be used to optimize the revenue for existing network resource (maximum revenue 3 ATTENTION: The Singapore
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".