Class-Based Grid Resource Management Strategies for On-Demand Jobs
Why this work is in the frame
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Bibliographic record
Abstract
Grid computing has emerged as a new paradigm for distributed systems, which promotes sharing of distributed resources. To maximize its benefits, it is essential to discover the resources available on the grid, and then effectively map the jobs to the resources for maximizing a given objective function. This paper focuses on the problem of matching of jobs to resources in a computing grid. Jobs are classified based on their service demands. Matching policies that use only the knowledge of job classes are introduced in this paper; simulation experiments demonstrate the effectiveness of these policies. Under a variety of different workload parameters the proposed matching policies demonstrate a performance comparable to, or better than, the well-known Minimum Completion Time matching policy, which is based on detailed a priori knowledge of jobs and resource characteristics.
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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 | 0.000 | 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