<title>Traffic classification and service in all-optical networks</title>
Bibliographic record
Abstract
All-optical networks require end-to-end lightpaths to be established for traffic to flow. Given that there are no wavelength converters present in the network, each lightpath only accommodate a single wavelength. It is shown that as the lightpaths traverse more hops, the blocking probability increases.This is causes the Fairness Problem. We introduce the Traffic Classification and Service Method (ClaServ), which optimizes the Fairness Problem, as well as reduce the traffic blocking probability when the networks require lower blocking probability. The combination of the Waveband Access Range (WAR) and the Waveband Reservation (WRsv) methods changes the traffic's distribution among the wavebands on each link of the path to control the degree of the interference among the classified traffic. Under certain traffic load, by setting the range of accessible wavebands and reserved wavebands for classified traffic, the network can achieve both the fairness and low blocking probability for all type of traffic. The simulation results show that for a 4x4 Mesh-Torus network the ClaServ method can greatly reduce the blocking probability for longer lightpaths by a factor of 100. It is also described how the ClaServ method can easily be implemented into a distributed signaling protocol.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.025 |
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".