Anomaly Detection in Complex System Configurations
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Solution Overview
Problem
In complex systems, detecting faulty configurations or components is challenging due to the vast number of components and parameters, making it difficult for human operators to manually analyze and identify defects, and existing automated methods require manual entry of predefined rules that may not cover all aspects.
Innovation Solution
A method that compares configuration data of a target system with data from a selected population to identify anomalies, using a missing mass estimation scheme to calculate thresholds and dynamically update the population membership, thereby reducing the need for manual rule definition and efficiently detecting potential problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of every component and configuration is performed, then detection accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent segments the configuration analysis task by dividing components into groups based on their configuration parameters. Instead of analyzing every component individually, the system identifies representative components from each group and analyzes those, significantly reducing the analysis scope while maintaining detection accuracy through statistical representation of configuration patterns.
Solution Approach 2:
The system performs self-service by automatically learning configuration patterns and anomaly detection rules from historical data without requiring manual rule definition. The anomaly detection mechanism autonomously adapts to new configuration patterns and updates its detection criteria, eliminating the need for continuous manual intervention and rule maintenance.
2Productivity
If predefined rules are used for automated detection, then productivity is improved, but coverage and reliability deteriorate due to incomplete rule sets
Solution Approach 1:
The patent implements dynamic anomaly detection thresholds that automatically adapt based on learned configuration patterns from the population of systems. Instead of using static predefined rules, the system continuously updates its detection criteria to reflect actual configuration variations, ensuring both high productivity through automation and reliable coverage by adapting to new patterns.
Solution Approach 2:
The system changes the parameters used for anomaly detection by learning optimal threshold values and detection criteria from historical configuration data. This allows the detection mechanism to adjust its sensitivity and coverage dynamically, maintaining high productivity while improving reliability through data-driven parameter optimization rather than fixed rules.
3Reliability
If comprehensive rule sets are manually created, then detection coverage is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system eliminates the need for manual rule creation and management by implementing self-learning anomaly detection. The mechanism automatically discovers configuration patterns and generates detection rules from population data, maintaining comprehensive coverage while reducing device complexity by removing the manual rule management interface entirely.
Solution Approach 2:
The patent creates a universal anomaly detection mechanism that can handle diverse configuration types and systems through a single learned model. Instead of requiring separate rules for different component types, the system learns generalizable patterns that apply across multiple system configurations, reducing complexity while maintaining broad detection coverage.
4Measurement precision
If all configuration parameters are analyzed, then detection accuracy is improved, but computational resources and time consumption increase
Solution Approach 1:
The patent segments the configuration parameter space by identifying and analyzing only the most significant parameters that contribute to anomalies. Through statistical analysis of population data, the system determines which parameters have the highest impact on system behavior and focuses computational resources on those, maintaining detection accuracy while reducing overall resource consumption.
Solution Approach 2:
The system performs partial analysis by focusing on the most critical configuration parameters rather than analyzing all parameters exhaustively. By identifying the subset of parameters that contribute most significantly to anomaly detection, the system achieves sufficient detection accuracy with reduced computational effort and energy consumption.
Data Source
AI summary
In accordance with one embodiment, a method for detecting potential problems in the configuration or components of a complex system comprises comparing first configuration data for a first system with second configuration data compiled from analyzing a plurality of systems in a selected population; and reporting anomalies associated with the first system, in response to determining that the first configuration data deviates from components determined to be common to the selected population.