Sliding Window Analysis for Fault Identification in Multi-Host Systems
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Solution Overview
Problem
Diagnosing faults in complex multi-host systems is challenging due to asynchronous component operations, transient conditions, and complex interdependencies, making it difficult to determine the root cause of failures and leading to potential false positives and inefficient resource allocation.
Innovation Solution
A diagnostic system utilizing a probabilistic cause/effect model and Bayesian Network to analyze system measurements over historical time windows, distinguishing between transient and actual faults, and performing root cause analysis to identify the source of failures, which includes Evidence Collectors, a Sliding Window Analyzer, and a Root Cause Analyzer to generate fault cause paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fault diagnosis methods are used in multi-host systems, then the system can operate, but false positives increase and resource allocation becomes inefficient
Solution Approach 1:
The patent segments the multi-host system into individual host components and further into specific system elements (CPU, memory, storage, etc.). Each segment is monitored independently with its own probability of failure calculation, allowing precise identification of actual fault sources rather than treating the entire system as a single unit. This segmentation reduces false positives by isolating specific problematic components.
Solution Approach 2:
The patent changes the parameter of fault detection from binary (fault/not fault) to probabilistic (probability of failure). By calculating and comparing probability values against thresholds, the system can dynamically adjust its sensitivity and specificity, improving diagnosis accuracy while reducing false alarms that would otherwise trigger unnecessary resource allocation.
2Difficulty of detecting and measuring
If comprehensive system monitoring is implemented, then fault detection capability improves, but system complexity increases
Solution Approach 1:
The patent introduces a Bayesian Network as an intermediary layer between raw system measurements and fault diagnosis. This intermediary model incorporates domain knowledge and relationships between system elements, transforming complex monitoring data into meaningful probability assessments. The Bayesian Network acts as a mediator that simplifies the complexity by providing a structured framework for analyzing multiple variables simultaneously.
Solution Approach 2:
The patent implements feedback mechanisms where probability of failure calculations are continuously updated based on new measurements and compared against thresholds. This feedback loop allows the system to adapt to changing conditions and learn from historical data, improving fault detection capability while maintaining manageable system complexity through automated decision-making rules.
3Measurement precision
If historical data analysis over long time windows is performed, then transient conditions are better filtered, but response time to actual faults increases
Solution Approach 1:
The patent applies dynamic thresholding where the threshold for fault identification is not fixed but adapts based on historical baseline measurements. The threshold is calculated as a function of historical probability values, allowing the system to automatically adjust its sensitivity. This dynamic approach enables the system to filter transient conditions effectively while maintaining rapid response to actual faults by adapting to changing operational patterns.
Solution Approach 2:
The patent performs preliminary action by establishing baseline probability of failure values during normal operation before faults occur. These historical baselines are used to set dynamic thresholds that account for normal variations in system behavior. By preparing these reference values in advance, the system can quickly distinguish between transient fluctuations and actual faults without requiring long analysis windows during incident response.
Data Source
AI summary
A method and apparatus is provided for determining the most probable cause of a problem observed in a complex multi-host system. The approach relies on a probabilistic model to represent causes and effects in a complex computing system. However, complex systems include a multitude of independently operating components that can cause temporary anomalous states. To reduce the resources required to perform root cause analysis on each transient failure, as well as to raise the confidence in the most probable cause of a failure that is identified by the model, inputs to the probabilistic model are aggregated over a sliding window of values from the recent past.