Minimizing SONET Add‐Drop Multiplexers in optical UPSR networks using the minimum number of wavelengths
Bibliographic record
Abstract
Abstract In SONET/WDM optical networks, a high‐speed wavelength channel is usually shared by multiplexed low‐rate network traffic demands. The multiplexing is known as traffic grooming and carried out by SONET Add‐Drop Multiplexers (SADM). The maximum number of low‐rate traffic demands that can be multiplexed into one wavelength is called the grooming factor. Because SADMs are expensive network devices, a key optimization problem in optical network design is to groom a given set of low‐rate traffic demands such that the number of required SADMs is minimized. This optimization problem is challenging and NP‐hard even for Unidirectional Path‐Switched Ring networks with unitary duplex traffic demands. In this article, we propose two linear‐time approximation algorithms for this NP‐hard problem based on a novel graph partitioning approach. Both algorithms achieve better worst case performance than the previous algorithms. We also show that the upper bounds obtained by our algorithms are very close to the lower bounds for some instances. In addition, both of our algorithms use the minimum number of wavelengths, which are precious resources as well in optical networks. © 2008 Wiley Periodicals, Inc. NETWORKS, 2009
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".