A desired load distribution model for scheduling of unrelated parallel machines
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
Scheduling problems concern the allocation of limited resources over time among both parallel and sequential activities. Load balancing has been adopted as an optimization criterion for several scheduling problems. However, in many practical situations, a load-balanced solution may not be feasible or attainable. To deal with this limitation, this paper presents a generic mathematical model of load distribution for resource allocation, called desired load distribution (DLD). The objective is to develop a DLD model for scheduling of unrelated parallel machines that can be used both in centralized resource management settings and in agent-based distributed scheduling systems. The paper describes the proposed DLD model in details, presents a dynamic programming based optimization algorithm for the proposed model, and then discusses its application to agent-based distributed 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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