Performance analysis of a backward reservation protocol in networks with sparse wavelength conversion
Bibliographic record
Abstract
In sparse wavelength conversion networks only a few nodes support wavelength conversion. The optical paths in the network consist of a group of segments where each segment independently must meet the wavelength continuity constraint when setting up lightpaths across them. In this paper, we propose a distributed control algorithm called first-available that can efficiently be used to assign wavelengths in networks with sparse wavelength conversion. The wavelength reservation protocol described is a backward reservation protocol. In previous research it has been found that backward reservation algorithms do not offer much improvement in the case where optical converters are used. First-available was compared to other backward reservation algorithms such as first-fit and random and was shown to outperform those in the case of sparse wavelength conversion. Also, compared to the case of no conversion in the network the use of the first-available algorithm in combination with using converters gives a lower average blocking probability. In previous papers, we have outlined a method called OBGP to support lightpath setup and management. We have used OBGP to implement and simulate the first-available algorithm in OPNET. From our simulation results we also collected nodal statistics, and based on these we studied where should be the optimal placement of the converters using the first-available algorithm.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".