Wavelength retuning without service interruption in an all‐optical survivable network
Bibliographic record
Abstract
Abstract This paper proposes a new wavelength retuning (WRT) scheme in an all‐optical WDM network. Compared with the existing WRT schemes developed for all‐optical networks, which can alleviate the wavelength‐continuity constraint but cannot avoid service interruption or data loss, the proposed scheme is able to alleviate the wavelength‐continuity constraint and reduce the connection blocking probability with no service interruption to the on‐going traffic. This is achieved by allocating two routes, one for active path and one for backup path, to each incoming connection request and conducting WRT only on the backup path. The backup path provides an alternate path in case of a failure, while the active path carries traffic under normal conditions. Thus, WRT on the backup path will not cause any impact on data transmission. An optimal backup path WRT scheme and a heuristic algorithm are developed and the performance evaluation on the proposed schemes is presented. The simulation results show that the proposed optimal scheme reduces the connection blocking probability by 46.8% on average, while the proposed heuristic scheme reduces the blocking probability by 28.3% on average, all compared with the scheme without WRT. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".