How to Enhance the Efficiency of Loss-Less Optical Burst Switching Networks with the Streamline Effect
Bibliographic record
Abstract
With the ongoing steady traffic increase in the Internet, the wavelength usage of the supporting optical networks is a critical network efficiency parameter. Therefore, this paper suggests a way how to efficiently and economically achieve this goal in the context of optical burst switching, a very promising technology that has been proposed to overcome the shortcomings of conventional WDM deployment, such as lack of fine bandwidth granularity in wavelength routing and electronic speed bottlenecks in the presence of bursty traffic. In order to mitigate the burst loss and achieve high network efficiency we adapt the loss-less paradigm defined by Coutelen et al. (2010), i.e., the CAROBS framework. In classical OBS networks, the streamline effect ensures a very low level of contention, i.e., efficient transmission, hence we define a routing guided only by the streamline effect. The resulting routing problem is formulated as an optimization model which is solved using a decomposition technique to increase the scalability of the solution process.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".