Finite State Machine Invariant Violation Detection for Silent Failures
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Solution Overview
Problem
Current failure diagnosis methods in computing systems are inadequate for detecting silent failures, which can lead to data loss and corruption, as they rely on complex system models or statistical learning that only identify symptoms rather than root causes, failing to localize issues effectively.
Innovation Solution
A method using finite state machine (FSM) based analysis and invariant violation detection, where an FSM model is generated from input data, state-specific invariants are extracted, and localized checks are performed to log silent failures independently of traditional error handling measures, allowing for the identification of previously undetected errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error handling measures and statistical learning based approaches are used, then failure symptoms can be identified, but silent failures cannot be detected and localized
Solution Approach 1:
The patent segments the application execution into discrete states and transitions, creating a Finite State Machine model. This segmentation allows the system to analyze execution flow at a granular level, detecting silent failures by examining state transitions rather than relying on complex system-wide models or statistical analysis of symptoms.
Solution Approach 2:
The patent introduces invariants as intermediary constraints that mediate between the FSM model and failure detection. These invariants serve as simplified rules that can be checked at each state to detect violations indicating silent failures, avoiding the need for complex system models while maintaining detection accuracy.
2Measurement precision
If complex system models are built for failure diagnosis, then root cause identification may be achieved, but the complexity of building and maintaining models increases
Solution Approach 1:
The patent changes the parameter of analysis from complex system-wide state tracking to simple invariant violation checking at FSM states. By transforming the diagnosis approach from model-based to invariant-based, the system achieves precise failure detection without requiring complex system models, maintaining accuracy while reducing complexity.
3Productivity
If statistical learning approaches are used to analyze monitoring data, then failure symptoms can be prioritized, but silent failures remain undetected
Solution Approach 1:
The patent performs preliminary action by establishing the FSM model and invariants before execution analysis. This pre-established framework enables real-time detection of silent failures during normal operation without requiring post-hoc statistical analysis, achieving both speed and reliability in failure detection.
Data Source
AI summary
A method system and computer program product are disclosed for identifying silent failures in an application, comprising of generating a finite state machine (FSM) model of the application based on an input data, extracting state specific invariants relevant to the generated FSM, performing a localized invariant violation check at each state of the generated FSM and upon detection of an invariant violation at any state of the FSM, logging the violation as a silent failure.