Autonomic Share Allocation and Bounded Prediction of Response Times in Parallel Job Scheduling for Grids
Bibliographic record
Abstract
Grid schedulers which need to decide on which sites the jobs are best allocated require controlled and predictable service. Fair-share scheduling has become widely used but lacks a formal model and depends on the current machine load. Existing approaches for response-time prediction still show significant prediction errors, mostly due to problems in dynamic arrival of jobs with potentially higher priority and hard-to-anticipate packing and backfilling effects. Thus, we propose a different job scheduler (Scojo-PECT) which provides a more suitable framework for predictability and service guarantees by employing preemption with coarse-grain time sharing. We formalize the approach via a queuing model to determine the resource shares necessary to meet target service levels. As further extension, Scojo-PECT can adapt resource shares within certain limits to variations in machine load, while maintaining predictability and service guarantees. We demonstrate the feasibility of service control, the tightness of the 95% prediction intervals (0-30% from average), and the high predictability obtained.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".