Probabilistic Fault Diagnosis 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 that uses a probabilistic cause/effect model and a historical time window to gather and analyze system measurements, employing Evidence Collectors, Bayesian Networks, and Root Cause Analyzers to identify real faults and determine their root causes, thereby distinguishing between transient and actual system failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional fault diagnosis methods are used in multi-host systems, then the system complexity is high with many interdependent components, but the ability to accurately determine root causes deteriorates due to asynchronous operations and transient conditions
Solution Approach 1:
The system segments the complex multi-host environment into multiple independent Evidence Collectors, each monitoring specific components. Each collector operates independently and submits evidence to a central Bayesian Network, which processes the information to determine fault causes. This segmentation allows the system to handle complexity by distributing monitoring functions while maintaining accurate root cause analysis through probabilistic inference.
2Reliability
If continuous monitoring of all system components is implemented, then real-time fault detection capability is improved, but the amount of data to process and the computational resources required increase significantly
Solution Approach 1:
The system extracts only the necessary evidence from continuous monitoring by using Evidence Collectors that selectively gather data based on predefined criteria and thresholds. Rather than processing all system data, each collector extracts only relevant information about specific components and submits it to the Bayesian Network, significantly reducing the data volume while maintaining effective fault detection capability.
Solution Approach 2:
The Bayesian Network acts as an intermediary between the Evidence Collectors and the root cause analysis. It processes the evidence submitted by collectors, performing probabilistic inference to determine fault causes. This intermediary layer filters and processes data efficiently, reducing the computational burden on the entire system while maintaining high reliability in fault detection.
3Measurement precision
If probabilistic inference with historical time windows is used, then the ability to distinguish transient conditions from real faults is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary action by maintaining a historical time window of evidence data that is continuously updated. When a new piece of evidence is collected, it is immediately processed against the historical context, allowing the system to quickly distinguish between transient conditions and real faults without requiring extensive retrospective analysis. This preliminary preparation of historical data enables rapid accurate classification.
Data Source
AI summary
A method and apparatus are provided for determining that problems have occurred within a complex multi-host system and for identifying for each problem, sequences of causes and effects called a fault cause path, starting with a root cause. A probabilistic model representing the cause/effect relationships among potential system problems identifies the probability that a problem occurred in the system. Such failure probabilities may be determined based on aggregating, over a recent time interval, probability of failure values determined by the probabilistic model. Each fault cause path may have an associated probability of accuracy value reflecting the expected accuracy of the fault cause path relative to other fault cause paths. When more than one fault cause path is identified, the number and order of the fault cause paths may be ranked and displayed based on their probability of accuracy value.