Time-slotted scheduling for agile all-photonics networks : performance and complexity
Bibliographic record
Abstract
Schedulers in optical switches are still electronic, the performance of these units has a significant impact on the performance of the network and could form a bottleneck in high speed networks, such as AAPN. Four time-slotted scheduling algorithms are investigated in this study, PIM, iSlip, PHM and Adapted-SRA. The study addresses the performance of AAPN for each of the algorithms, and evaluates the hardware complexity, estimating the running time of the algorithms. Performance measures were collected from an OPNET model, designed to emulate AAPN. Furthermore, hardware complexity and timing constraints were evaluated through hardware simulations, for iSlip, and through analysis for the rest of the algorithms. iSlip confirmed its feasibility by meeting the 10us timing constraint set by AAPN. The study revealed the superiority of iSlip and PHM over PIM and Adapted-SRA.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".