Reliable workflow execution in distributed systems for cost efficiency
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
Reliability is of great practical importance in distributed computing systems (DCSs) due to its immediate impact on system performance, i.e., quality of service. The issue of reliability becomes more crucial particularly for `cost-conscious' DCSs like grids and clouds. Unreliability brings about additional-often excessive-capital and operating costs. Resource failures are considered as the main source of unreliability in this study. In this study, we investigate the reliability of workflow execution in the context of scheduling and its effect on operating costs in DCSs, and present the reliability for profit assurance (RPA) algorithm as a novel workflow scheduling heuristic. The proposed RPA algorithm incorporates a (operating) cost-aware replication scheme to increase reliability. The incorporation of cost awareness greatly contributes to efficient replication decisions in terms of profitability. To the best of our knowledge, the work in this paper is the first attempt to explicitly take into account (monetary) reliability cost in workflow scheduling.
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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