Ants vs. faults: A swarm intelligence approach for diagnosing distributed computing networks
Why this work is in the frame
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Bibliographic record
Abstract
Although much is known about the nature of testing structures for t-diagnosable systems, the problem of efficiently identifying the set of faulty units of a system in which the fault situation is known to be diagnosable remains an outstanding research issue. In this paper, we propose and evaluate an approach, based on swarm intelligence, to identify the set of faulty units in diagnosable systems. We consider t-diagnosable systems under the PMC model, where each node is capable of testing a particular subset of the other nodes in the system. We show that the ant-colony- based fault diagnosis algorithm is efficient, in that, it is able to diagnose a faulty situation in very short periods of time even if the number of faults is around the bound t, and with very few number of ants. The simulation results show that the new adaptive fault identification approach constitutes an addition to existing diagnosis algorithms.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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