A MapReduce‐supported network structure for data centers
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Bibliographic record
Abstract
SUMMARY Several novel data center network structures have been proposed to improve the topological properties of data centers. A common characteristic of these structures is that they are designed for supporting general applications and services. Consequently, these structures do not match well with the specific requirements of some dedicated applications. In this paper, we propose a hyper‐fat‐tree network (HFN): a novel data center structure for MapReduce, a well‐known distributed data processing application. HFN possesses the advanced characteristics of BCube as well as fat‐tree structures and naturally supports MapReduce. We then address several challenging issues that face HFN in supporting MapReduce. Mathematical analysis and comprehensive evaluation show that HFN possesses excellent properties and is indeed a viable structure for MapReduce in practice. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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