Root Cause Identification via Inter-Component Graph Traversal
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Solution Overview
Problem
In complex software interdependent service environments, identifying the root cause of application performance issues is time-consuming and labor-intensive due to the overwhelming volume of performance data collected, often requiring manual sifting through numerous layers of components.
Innovation Solution
An application analysis engine generates an inter-component graph to map component connections, and a traversal module identifies possible execution paths using a correlation module to compare metrics time series, presenting a report that visually identifies the root cause of detected issues from the issue's detection level to its root cause.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sifting through performance data is used to identify root cause, then measurement precision may be improved, but loss of time increases significantly
Solution Approach 1:
The patent replaces the manual mechanical process of sifting through performance data with an automated computer-based system that uses algorithms to analyze metrics time series data, inter-component graphs, and execution paths, thereby eliminating time-consuming manual analysis while maintaining or improving root cause identification accuracy
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a mediator between the collected performance data and the final root cause identification, using correlation modules and traversal algorithms to bridge the gap between raw data and actionable insights without requiring manual intervention
2Measurement precision
If comprehensive performance data collection is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the complex performance monitoring system into distinct functional modules including data collection components, inter-component graph generation modules, metrics time series analysis engines, and root cause identification algorithms, allowing each segment to handle specific aspects of the monitoring task independently and reducing overall system complexity
Solution Approach 2:
The patent introduces intermediary processing layers that organize and structure the comprehensive performance data before analysis, using inter-component graphs and execution path representations to mediate between raw data collection and final root cause identification, thereby managing complexity while preserving measurement precision
3Productivity
If automated root cause analysis is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The patent divides the automated root cause analysis system into specialized modules including traversal modules for navigating inter-component graphs, correlation modules for comparing metrics time series, and reporting modules for presenting results, enabling each segment to perform specific functions efficiently while reducing the complexity burden on the overall system
Solution Approach 2:
The patent replaces complex manual diagnostic procedures with automated computer-based algorithms that systematically analyze performance data, generate inter-component graphs, traverse execution paths, and identify root causes through programmed logic, thereby increasing productivity while managing complexity through algorithmic automation
Data Source
AI summary
By monitoring requests to and from components of an application, an application analysis engine generates an inter-component graph for an application that identifies how the various components in the application are connected. When a performance issue is detected in association with the application, a traversal module traverses the inter-component graph to determine the possible execution paths that may have been the cause of the detected issue. The traversal module transmits requests to the correlation module to compare the metrics time series of the different components in the execution path with the detected issue. The correlation module compares metrics time series with the issue metric to identify correlations between execution patterns. The results of the correlation may be presented in a report that visually identify the root cause of the detected issues.