Consolidated optical flow switching in cloud data centers
Bibliographic record
Abstract
We propose a flexible, software-defined optical switching fabric for cloud data centers, enabling multi-petabit per second network capacities. Our design is based on the cyclic interconnection pattern of arrayed waveguide grating (AWG) devices, whose routing functionality is complemented with recirculation fibers. Unlike traditional optical data center network proposals that rely on two independent fabrics for supporting mice and elephants, our design enables the support of flows of various sizes and requirements using a single AWG-based fabric and yields bandwidth flexibility by integrating wavelength and subwavelength switching granularities. There are two sets of connections paths in our design: dedicated paths between each pair of AWG input and output ports, and shared paths that are set up by multiple recirculation fibers. The recirculation fibers enable the statistical multiplexing of mice. As well, they provide for flexible, on-demand circuit provisioning between input and output ports. Applying Birkhoff-von Neumann matrix decomposition on a residual traffic matrix comprising the demands that cannot be supported through the dedicated paths, we come up with a weighted sum of permutation matrices that get mapped onto the set of available recirculation fibers. The calculated coefficients determine the proportion of a timeframe that the permutation matrices are serviced by distinct fibers. The matrix decomposition requires the combined scheduling of wavelength and time domains so that the AWG can operate as an adaptive flow switching device. Enhancing the functions of our wavelength-routing design with space switching using an optical MEMS switch results in extreme network scales, spanning millions of processing cores.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".