The impact of runtime estimation inaccuracy on scheduler performance
Bibliographic record
Abstract
It has been shown that runtime estimation errors have a large impact on scheduler performance. In previous research, scheduling algorithms were mainly used in a homogeneous environment. In this paper, we investigate several scheduling heuristics that are commonly used in the grid environment. We systematically study how runtime relative estimation errors affect the scheduler performance in different grid scenarios by conducting experiments using simulation. We choose Dynamic-selection, Min-min, Seg-min-min, Max-min, and Sufferage as our scheduling algorithms for the experiments. Our results show interesting trends: (1) increased estimation error results in degrading performance of all tested scheduling heuristics, making them even worse than the basic Round-Robin approach if errors are large; however, locally, performance is sometimes better and, in some special cases, estimation errors do not affect scheduler performance; (2) unlike in general, increased estimation errors diminish the performance difference among individual heuristics; (3) there is a performance threshold, no matter how large the estimation errors are; (4) increased accuracy of runtime estimation improves performance in general.
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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 | 0.008 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".