Two-Phase Computation and Data Scheduling Algorithms for Workflows in the Grid
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
In this paper, a workflow scheduling approach, which consists of two algorithms, is proposed. A submitted workflow is first partitioned into subgraphs on the global Grid level by the graph partitioning algorithm according to features of the workflow itself and the status of selected available resource clusters. Then, at the resource cluster level, metatasks in each subgraph are allocated to computational resources by the metatask mapping algorithm. To reduce the total makespan of a workflow, the schedule of raw input data preloading are considered by the two algorithms. This two-phase approach does not require detailed resource information or control privilege on every Grid resource for Grid schedulers at the global Grid level, so that the dependence on Grid information services is reduced and the higher priority of local resource management policies is respected.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.003 | 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