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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 4·5 min read
Understanding Online Failure Prediction in Linux Through Complementary Multi-View Explainability
TL;DR
An explainable Online Failure Prediction (OFP) pipeline for Linux OSs was developed, achieving 91-94% detection accuracy on unseen workloads while maintaining low false alarm rates. However, diagnosing failure modes proved challenging under workload shifts.
✦ Why It Matters
Implement explainable OFP systems to enhance trust and response strategies in Linux environments today.
Key Takeaways
- Utilize consensus-based feature selection for better detection.
- Monitor workload shifts to improve failure mode diagnosis.
- Leverage temporal onset analysis for early warnings.
- Incorporate multiple diagnostic mechanisms for comprehensive insights.
How It Works
The OFP pipeline combines several techniques: consensus-based feature selection identifies relevant indicators for failure detection, while temporal onset analysis assesses when failures are likely to occur. Subsystem-level causal analysis helps understand the relationships between different system components and their contributions to failures, enhancing the interpretability of predictions.