Parallel Mapping with Time Optimization for SLA-Aware Compositional Services in the Business Grid
Why this work is in the frame
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Bibliographic record
Abstract
Service Level Agreements (SLAs) are currently one of the major research topics in Grid computing. Among many system components for supporting of SLA-aware Grid jobs, the SLA mapping module holds an important position and the capability of the mapping module depends on the runtime of the mapping algorithm. With the previously proposed algorithm to optimize the execution time of the workflow, the mapping module may develop into the bottleneck of the system if many requests come in during a short period of time. This paper presents a parallel mapping algorithm to optimize the execution time of the workflow, which can reduce the runtime of the mapping algorithm without reducing the quality of the mapping solutions. Performance measurements thereby deliver evaluation results showing the quality of the method. The speedup of the algorithms and the quality of the solutions are significantly improved when using eight CPUs comparing to using one CPU.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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