Error Causal Analysis for Predicting Application Availability Failures
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Solution Overview
Problem
Conventional error analysis systems fail to provide insights into upcoming errors that affect application availability, leading to poor performance and user experiences, as they only report detected errors without predicting future issues.
Innovation Solution
Implementing multidimensional error causal analysis using machine learning models and algorithms to identify direct and indirect error causes, generate causal statements, and provide predictive error alerts based on error intercorrelations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional error analysis systems merely collect and report detected errors, then error reporting is simple and fast, but insight into upcoming errors and their causes is lost
Solution Approach 1:
The system performs preliminary analysis of error patterns and intercorrelations before actual application failures occur. By continuously monitoring error data and identifying predictive patterns, the system prepares insights about upcoming errors in advance, enabling proactive rather than reactive error handling.
Solution Approach 2:
The patent introduces an intermediary error analysis system that sits between raw error detection and final error reporting. This intermediary layer processes error data through multiple analysis dimensions (error patterns, intercorrelations, predictive modeling) to extract meaningful insights before presenting them to users.
2Measurement precision
If error analysis systems provide detailed predictive insights, then error prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The error analysis process is divided into multiple independent dimensions or segments: error pattern recognition, intercorrelation analysis, predictive modeling, and confidence scoring. Each dimension processes specific aspects of error data independently, allowing parallel computation and reducing overall processing time while maintaining comprehensive analysis.
Solution Approach 2:
The system performs analysis at multiple levels of depth depending on needs. For routine monitoring, partial analysis provides quick insights. When confidence thresholds are met or critical errors occur, more extensive analysis is performed to achieve higher precision, balancing processing time with accuracy requirements.
Data Source
AI summary
Accuracy, reliability, and response speed improvements for software applications executed by a computing system or platform are provided herein. There are provided systems and methods for multidimensional error causal analysis for error intercorrelations that impact application availability. A service provider may utilize different computing services for data processing to provide different computing services to users, such as via websites and/or applications of the service provider. Due to errors, users may be unable to utilize applications or may face decreased performance and application availability. To improve application performance, error causal analysis may be performed that identifies error intercorrelations that impact application availability and other performance by identifying error effects on each other. Causal statements may be intelligently generated to then identify error intercorrelations. Once generated, these statements may be tested and verified to allow debugging teams and others to fix errors that reduce application performance and availability.