Wavelength scheduling algorithms with delayed acceptance for optical burst switching networks
Bibliographic record
Abstract
In typical optical burst switching (OBS) networks a reservation request is either accepted or rejected as soon as it is presented to the employed wavelength scheduling algorithm. This paper proposes a class of wavelength scheduling algorithms that process a batch of data bursts together instead of processing them one by one. When a control burst with a reservation request arrives to a free batch scheduler, the scheduler waits for a small amount of time, called the acceptance delay, before deciding to accept or reject the reservation request. After the acceptance delay has passed, the scheduler processes all the reservation requests that have arrived during the acceptance delay period, then it accepts the requests that maximizes the utilization of the wavelength channels. We describe an optimal batch scheduler that serves as an upper bound on the performance of batch scheduling algorithms. Furthermore, we introduce two novel heuristic batch scheduling algorithms. Simulation results suggest that batch schedulers could decrease the blocking probability by 25% compared to the best previously known wavelength scheduling algorithm
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".