Adaptive Sabotage-Tolerant Scheduling for Peer-to-Peer Grids
Bibliographic record
Abstract
Computational grids are an infrastructure to aggregate computing power to support and improve performance of parallel applications. Some of them evolved in the sense of forming free-to-join communities over the Internet and became peer-to-peer (P2P) grids. One of the main problems associated with users freely joining and leaving grid communities is that cheating users may corrupt the final computation. Sabotage tolerance techniques, generally based on replication, tackle this problem by estimating the computation correctness. The use of credibility-based techniques in the task scheduling may promote high confidence levels for the computation results, whilst minimizing replication costs, when compared to the traditional voting technique. This work aims at evaluating the usage of scheduling heuristics that adapt themselves to the machines' confidence level in P2P grids. Three scheduling heuristics were evaluated. They present advantages and disadvantages, leading us to the conclusion that the performance of the scheduling heuristics is deeply influenced by the grid environment.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".