Grid resource discovery using small world overlay graphs
Bibliographic record
Abstract
Computational grids are believed to be an effective and scalable solution to the problem of resource sharing over large, heterogeneous networks of computing devices. Since grids are highly distributed in nature, one of the most challenging problems is the discovery of dynamic resources in a grid. In this paper we use ideas from P2P systems to propose a solution for the problem. Specifically, we classify nodes as consumers and producers, depending on whether they consume or produce more jobs. Our algorithm connects all producer nodes using a overlay network that is a small-world graph (the graph is produced by adding "shortcut" chords to a circle). The consumer nodes hang off the small world graph. The producer nodes are forced to take part in resource cataloging and discovery. This has three distinct advantages - first, it prevents "freeloading" by forcing producers to do useful work; second, it frees the consumers to only do computations; third, the low diameter of the overlay graph ensures that all resources are within a small number of hops. We simulate and evaluate the performance of our algorithm in realistic traffic conditions. We evaluate the performance of our algorithm using metrics like the average time to answer the query, the average number of requests that were dropped and the average number of hops traveled by query packets. Our experiments show that our algorithm performs well with thousands of nodes
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".