Performance Inconsistency in Large Scale Data Processing Clusters
Why this work is in the frame
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Bibliographic record
Abstract
A large shared computing platform is usually divided into several virtual clusters of fixed sizes, and each virtual cluster is used by a team. A cluster scheduler dynamically allocates physical servers to the virtual clusters depending on their sizes and current job demands. In this paper, we show that current cluster schedulers, which optimize for instantaneous fairness, cause performance inconsistency among the virtual clusters: Virtual clusters with similar loads see very different performance characteristics. We identify this problem by studying a production trace obtained from a large cluster and performing a simulation study. Our results demonstrate that when using an instantaneous-fairness scheduler, a large VC that contributes more resources during underload periods can not be properly rewarded during its overload periods. These results suggest that not using resource sharing history is the root cause for the performance inconsistency.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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