A new service reservation approach for workflow management
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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