Performance of fiber delay‐line buffers in asynchronous packet‐based optical switching networks with wavelength conversion
Bibliographic record
Abstract
Summary A major challenge in asynchronous packet‐based optical networks is packet contention, which occurs when two or more packets head to the same output at the same time. To resolve contention in the optical domain, two primary approaches are wavelength conversion andfiber delay line(FDL) buffering. In wavelength conversion, a contending packet can be converted from one wavelength to another in order to avoid conflict. In FDL buffering, contending packets can be delayed for a fixed amount of time. While the performance of wavelength conversion and FDL buffering has been evaluated extensively in synchronous networks with fixed‐sized packets, in this paper, we study the performance of FDL buffers in asynchronous packet‐based optical networks with wavelength conversion. An analytical model is proposed to evaluate the performance in terms of packet loss probability and average delay. Extensive simulation and analytical results show that, with appropriate settings, FDL buffers can perform much better in switches with wavelength conversion than in switches with no conversion. Copyright © 2014 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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".