Scalable architecture and low-latency scheduling schemes for next generation photonic datacenters
Bibliographic record
Abstract
Photonic packet switches potentially provide high switching capacity for next-generation datacenters at low cost, low power, and low footprint. In this paper, we address the scalability and packet scheduling for intra-connectivity of next generation photonic datacenters. We first propose a scalable photonic packet fabric based on a stack of small buffer-less silicon photonic switches which are timeslot synchronized by a central controller. We introduce a photonic fabric interface which offers both data path connectivity to the photonic fabric and control path connectivity to the controller. Then, we present centralized scheduling and control methods for photonic packet switching. Our scheduling methods are low complexity iterative algorithms equipped with starvation avoidance capability to meet the latency requirement for packet transmission through the photonic fabric. Simulation results indicate that our scalable scheduling schemes are capable of controlling average and maximum end-to-end packet delay for intra-connectivity in next generation datacenters which are based on buffer-less photonic switching fabrics.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".