HyperOXN: Topology and Scheduling for Efficient Large-Scale Networks
Bibliographic record
Abstract
One assists nowadays to the rapid technological evolution of Data Centres (DC). With the advent of new applications such as Cloud Computing, Big Data, and Machine Learning, modern data center network (DCN) architecture has been evolving to meet numerous challenging requirements such as scalability, agility, energy efficiency and high performance. The above new applications are expediting the convergence of High-Performance Computing and Data Centers. Inspired by hypermeshes, this paper presents HyperOXN, a novel cost-efficient topology for exascale DCNs. HyperOXN takes advantage of high-radix switch components leveraging state-of-the-art colorless dense wavelength division multiplexing (DWDM) technologies, effectively supports *-cast traffic and at the same time meets the demands on high throughput and low latency. We also introduce a new Stochastic Differential Equation (SDE) model for traffic control based on traffic classification by taking into account the number and size of packets splitting the DC traffic into two major traffic classes (mice and elephants). Extensive simulations validate the effectiveness and dependability of HyperOXN.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".