Parallel job scheduling on multicluster computing system
Why this work is in the frame
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Bibliographic record
Abstract
Cluster computing has come to prominence as a cost-effective parallel processing tool for solving many complex computational problems. The key to making cluster computing work well is the middleware technologies that can manage the policies, protocols, networks, and job scheduling across the interconnected set of computing resources. The research question addressed in this paper is the on-line job scheduling problem for multi-cluster systems. To this end, we propose an on-line dynamic scheduling policy that manages multiple job streams across both single and multiple cluster computing systems with the objectives of improving the mean response time and system utilization. The performance of the proposed scheduling policy is compared against a space-sharing policy and a time-sharing policy. The results of the experiments show that the proposed policy produces significantly better response times than the other two policies.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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