An Adaptive Generalized Scheduler for Grid Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The compute resources of a grid resource-service provider may be distributed over a wide geographical area. If the resource-service provider is to use his resources effectively, in addition to the characteristics of the compute-nodes and the applications, the characteristics of the communication network must also be known. A generalized scheduler should be able to handle a diverse set of jobs, with arbitrary inter-dependencies among processes and arbitrary communication channel delays. On a grid, the scheduling algorithm should respond quickly to the changing workload and environmental conditions without causing much overhead. Hence it should be able to customize its strategy in accordance with the prevailing conditions. In this paper, the algorithm for an adaptive scheduler, which can be used to map a set of jobs, of wide diversity, to a dynamic set of nodes, with prior reservations, is being presented. The scheduler has been tested extensively.
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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 | 0.000 |
| 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