Metascheduling Multiple Resource Types using the MMKP
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
Grid computing involves the transparent sharing of computational resources of many types by users across large geographic distances. The altruistic nature of many current grid resource contributions does not encourage efficient usage of resources. As grid projects mature, increased resource demands coupled with increased economic interests will introduce a requirement for a metascheduler that improves resource utilization, allows administrators to define allocation policies, and provides an overall quality of service to the grid users. In this work we present one such metascheduling framework, based on the multichoice multidimensional knapsack problem (MMKP). This strategy maximizes overall grid utility by selecting desirable options of each task subject to constraints of multiple resource types. We present the framework for the MMKP metascheduler and discuss a selection of allocation policies and their associated utility functions. The MMKP metascheduler and allocation policies are demonstrated using a grid of processor, storage, and network resources. In particular, a data transfer time metric is incorporated into the utility function in order to prefer task options with the lowest data transfer times. The resulting schedules are shown to be consistent with the defined policies
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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 | 0.000 |
| 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