A new service reservation approach for workflow management
Bibliographic record
Abstract
Many of large-scale scientific applications executed on present-day Grids, such as bioinformatics and computational chemistry, are expressed as complex workflows. To enhance Grid computing paradigm, Web Service has emerged as the de-facto communication mechanism in Grid environments. However, in heterogeneous and dynamic Grid environments, Web Services of the same type and similar functionalities are usually provided by different administrative domains and with different capabilities. This problem makes it difficult to combine suitable and effective Web Services in workflow management. In this paper, we discuss issues such as automatically generating job workflow for Grid and scheduling optimization based on scientists' requirements in context of workflow management system. First, we present a comprehensive workflow management framework in order to automatically map scientific applications to workflow processes. Then we propose a Multiple-object Candidate Algorithm based on a tree-scheduling schema to generate best-effort candidate services for each task in a workflow process. The advantage of this approach is to select Web Services which maximize user's satisfaction as well as ensure workflow optimism accordingly. We have deployed and developed this framework in the Computational Chemistry Grid. The experimental results show that our proposal is feasible and effective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".