A fast scheduling algorithm for all-optical shared-buffer packet switches
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Bibliographic record
Abstract
All-optical shared-buffer packet switches have been studied intensively in literature and many scheduling algorithms have been proposed. However, these algorithms either suffer from not being able to make resource reservation, or require high time-complexity to compute scheduling assignment for packets. In this paper, we propose a fast scheduling algorithm for all-optical shared-buffer packet switches. In our algorithm, packet scheduling is first formulated as a tree-searching problem. By breaking down the search tree into multiple smaller subsets and assigning each subset to a secondary processor, solutions can be obtained in a much shorter duration since the secondary processors are working in parallel. For instance, a scheduling assignment can be calculated for a packet in 55 ns with 8 processors, and in 25 ns with 64 processors, assuming a processor clock rate of 200 MHz. We show that our algorithm can achieve a loss rate of ~10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-7</sup> even at load 0.9 for a 32times32 switch.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it