Cognitive behavior analysis framework for fault prediction in cloud computing
Bibliographic record
Abstract
Complex computing systems, including clusters, grids, clouds and skies, are becoming the fundamental tools of green and sustainable ecosystems of future. However, they can also pose critical bottlenecks and ignite disasters. The complexity and high number of variables could easily go beyond the capacity of any analyst or traditional operational research paradigm. In this work, we introduce a multi-paradigm, multi-layer and multi-level behavior analysis framework which can adapt to the behavior of a target complex system. It not only learns and detects normal and abnormal behaviors, it could also suggest cognitive responses in order to increase the system resilience and its grade. The multi-paradigm nature of the framework provides a robust redundancy in order to cross-cover possible hidden aspects of each paradigm. After providing the high-level design of the framework, three different paradigms are discussed. We consider the following three paradigms: Probabilistic Behavior Analysis, Simulated Probabilistic Behavior Analysis, and Behavior-Time Profile Modeling and Analysis. To be more precise and because of paper limitations, we focus on the fault prediction in the paper as a specific event-based abnormal behavior. We consider both spontaneous and gradual failure events. The promising potential of the framework has been demonstrated using simple examples and topologies. The framework can provide an intelligent approach to balance between green and high probability of completion (or high probability of availability) aspects in computing systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".