Fast workflow scheduling for grid computing based on a multi-objective Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Task scheduling and resource allocation are two of the most important issues in grid computing. In a grid computing system, the workflow management system receives inter-dependent tasks from users and allocates each task to an appropriate resource. The assignment is based on user constraints such as budget and deadline. Thus, the workflow management system has a significant effect on system performance and efficient resource use. In general, optimal task scheduling is an NP-complete problem. Hence, heuristic and meta-heuristic methods are employed to obtain a solution which is close to optimal. In this paper, workflow management based on a multi-objective Genetic Algorithm (GA) is proposed to improve grid computing performance. In grid computing, task runtime is an important parameter. Thus the proposed method considers a workflow as a collection of levels to eliminate the need to check workflow dependencies after a solution is obtained for the next population. As a result, both scheduling time and solution quality are improved. Results are presented which show that the proposed method has better performance compared to similar techniques.
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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