A Tool for Automatic Dependability Test in Eucalyptus Cloud Computing Infrastructures
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
Cloud Computing is a paradigm that dynamically provides resources as services through the Internet. The constant concern about the trust placed in cloud computing systems inspires dependability studies. A possible way of performing dependability studies, especially regarding reliability and availability, is through fault injection tools, which enable to observe the system’s behavior during the occurrence of fault events. This paper presents a fault injection tool, called EucaBomber, for reliability and availability studies in the Eucalyptus cloud computing platform. The tool supports fault injections in Eucalyptus hardware and software components at runtime, and also upholds reparation of both types of injected faults. The efficiency of EucaBomber is tested through a case study involving two different scenarios where faults and repairs of hardware and software are injected in the Eucalyptus platform simulating the system's events. Such a tool assists the system administrator and planners to evaluate the system’s availability and maintenance policies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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