Dynamic scheduling for heterogeneous Desktop Grids
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
Desktop grids have emerged as an important methodology to harness the idle cycles of millions of participant desktop PCs over the Internet. However, to effectively utilize the resources of a desktop grid, it is necessary to use scheduling policies suitable for such systems. A scheduling policy must be applicable to large-scale systems involving large numbers of machines. Also, the policy must be fault-aware in the sense that it copes with resource volatility. Further adding to the complexity of scheduling for desktop grids is the inherent heterogeneity of such systems. Sub-optimal performance would result if the scheduling policy does not take into account information on heterogeneity. In this paper, we suggest and develop several scheduling policies for desktop grid systems involving different levels of heterogeneity. In particular, we propose a policy which utilizes the solution to a linear programming problem which maximizes system capacity. We consider parallel applications that consist of independent tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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