A hybrid load balancing algorithm for coarse-grained applications.
Why this work is in the frame
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Bibliographic record
Abstract
A non-preemptive hybrid load balancing algorithm is proposed for heterogeneous distributed computing environment, since no single load balancing algorithm works well for all kinds of applications and environments. The agents' computing capabilities may also change during runtime because of the background load. This algorithm makes use of the idea of several sub-algorithms. The hybrid model initially classifies the computers and jobs into different groups. Two priority queues are maintained at each worker to record the processes' estimated computing time and the real time. Based on historical experiences, a centralized scheduler can dynamically change the parameters in order to improve the overall performance during runtime. The algorithm balances the work load of coarse-grained applications with interdependent processes such as matrix computation or image processing. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .G35. Source: Masters Abstracts International, Volume: 42-03, page: 0962. Adviser: A. K. Aggarwal. Thesis (M.Sc.)--University of Windsor (Canada), 2003.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 it