Dynamic Error Analysis Windows for Computing Error Cascades
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Solution Overview
Problem
Computing environments face challenges in identifying the root cause of errors due to the complexity of error cascades and the difficulty in grouping related errors, leading to inefficient resource usage and repetitive analysis.
Innovation Solution
Dynamically updating error analysis windows based on error similarity, causation, timing, and resource utilization to ensure all related errors are analyzed together, reducing redundant analysis and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static manually defined error analysis window duration is used, then the system configuration is simple, but related errors may be missed or grouped incorrectly leading to incomplete error analysis
Solution Approach 1:
The patent implements dynamic error analysis windows that automatically adjust their duration based on real-time error characteristics. The system monitors error rates, error types, and temporal patterns within windows, then extends or contracts window durations adaptively to capture complete error cascades while excluding unrelated errors, resolving the contradiction between analysis accuracy and system complexity
Solution Approach 2:
The system employs feedback mechanisms where error analysis results from previous windows inform the configuration of subsequent windows. By analyzing error patterns, relationships, and cascades detected in earlier windows, the system adjusts future window parameters to improve detection accuracy without requiring manual intervention, thus improving measurement precision while maintaining automated operation
2Reliability
If a longer error analysis window is used to capture all related errors, then completeness of error grouping improves, but resource consumption and analysis time increase
Solution Approach 1:
The patent uses dynamic window adjustment to extend the error analysis window duration only when error patterns indicate ongoing cascades or related errors. The system monitors error rates and relationships within the window, extending the window selectively rather than using a consistently long duration, thus capturing complete error groups while minimizing unnecessary analysis time for stable error conditions
Solution Approach 2:
The system changes the temporal parameter (window duration) based on error characteristics. When error cascades are detected or error rates increase, the window duration is extended to capture related errors. When errors are stable and isolated, the window returns to baseline duration, optimizing the balance between grouping completeness and analysis efficiency
3Adaptability or versatility
If error analysis windows are manually configured, then implementation is simple, but adaptability to different error patterns and computing environments is poor
Solution Approach 1:
The patent implements self-service error analysis where the system automatically configures and adjusts error analysis windows based on observed error patterns without manual intervention. The system monitors error characteristics, identifies cascades and relationships, and autonomously optimizes window parameters to adapt to different error patterns and computing environments, achieving high adaptability through automated self-adjustment
4Measurement precision
If multiple separate error analyses are performed for different error cascades, then each error can be analyzed in detail, but resource usage increases and analysis efficiency decreases
Solution Approach 1:
The patent merges related errors into unified error analysis windows when error patterns indicate cascades or relationships. By detecting temporal proximity, error type similarities, and causal relationships, the system combines multiple related errors into single analysis units, maintaining detailed analysis capability while reducing the total number of separate analyses required, thus improving productivity without sacrificing analysis detail
Data Source
AI summary
Techniques for improved computing error analysis are provided. An error log for a computing environment is accessed, and an error analysis window is opened based on the error log, the error analysis window having an initial duration. A set of additional error logs, for the computing environment, within the error analysis window are accessed. Based at least in part on the set of additional error logs, a window extension is determined. The error analysis window is extended based on the window extension, and an error summary is generated based on one or more error logs received during the extended error analysis window.