Job completion prediction using case‐based reasoning for Grid computing environments
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
Abstract One of the main focuses of Grid computing is solving resource‐sharing problems in multi‐institutional virtual organizations. In such heterogeneous and distributed environments, selecting the best resource to run a job is a complex task. The solutions currently employed still present numerous challenges and one of them is how to let users know when a job will finish. Consequently, reserve in advance remains unavailable. This article presents a new approach, which makes predictions for job execution time in Grid by applying the case‐based reasoning paradigm. The work includes the development of a new case retrieval algorithm involving relevance sequence and similarity degree calculations. The prediction model is part of a multi‐agent system that selects the best resource of a computational Grid to run a job. Agents representing candidate resources for job execution make predictions in a distributed and parallel manner. The technique presented here can be used in Grid environments at operation time to assist users with batch job submissions. Experimental results validate the prediction accuracy of the proposed mechanisms, and the performance of our case retrieval algorithm. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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