Co-Scheduling Computational and Networking Resources in E-Science Optical Grids
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
With e-science applications becoming more and more data-intensive, data is generally generated and stored at different locations and can be divided into independent subsets to be analyzed distributed at many compute locations across an optical grid. It is required to achieve an optimal utilization of optical grid resources. This is generally achieved by minimizing application completion time, which is calculated as the sum of times spent for data transmission and analysis. We propose a Genetic Algorithm (GA) based approach that co-schedules computing and networking resources to achieve this objective. The proposed approach defines a schedule of when to transfer what data subsets to which sites at what times in order to minimize data processing time as well as defining the routes to be used for transferring data subsets to minimize data transfer times. Simulation results show the advantages of the proposed approach in both minimizing the maximum application completion time and reducing the overall genetic algorithm execution time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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