System Dump Error Analysis via Call Stack Comparison
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computing systems lack an efficient method to automatically identify and address known errors from system dump data, which can lead to delayed resolution of errors across multiple systems.
Innovation Solution
A method and system for analyzing system dump data by extracting and comparing diagnostic data, including call stack data and thread attributes, to identify known errors and provide software updates to affected servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If system dump data is manually analyzed to identify errors, then error identification accuracy can be maintained, but analysis time and productivity are significantly reduced
Solution Approach 1:
The system enables self-service error identification by automatically comparing system dump data against a database of known errors. The computing system autonomously extracts diagnostic data, performs comparisons, and identifies errors without requiring manual analysis, thereby maintaining accuracy while dramatically improving productivity.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational methods. Diagnostic data extraction, comparison operations, and error identification are performed through automated software systems rather than human analysts, substituting mechanical human effort with computational automation.
2Reliability
If comprehensive diagnostic data is extracted and compared across multiple systems, then error identification reliability is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the necessary diagnostic data elements from system dumps that are relevant for error identification. By selectively extracting call stack data, register values, and memory contents rather than processing entire system states, the system maintains high reliability while reducing processing complexity.
Solution Approach 2:
The error identification process is segmented into distinct modular operations: data extraction, data normalization, database querying, and result synthesis. This segmentation allows each component to be optimized independently and simplifies the overall complex process into manageable, reliable steps.
3Measurement precision
If system dump data from multiple servers is collected and analyzed, then error pattern recognition accuracy is improved, but data transmission and storage requirements increase
Solution Approach 1:
The system extracts only essential diagnostic data from system dumps for transmission and storage. By identifying and extracting only the critical error-related information rather than transmitting complete system dump files, the system achieves accurate error pattern recognition while minimizing data transmission volume and storage requirements.
Data Source
AI summary
Described are methods, systems, and apparatus, including computer program products for analyzing system dumps. A method for analyzing a first system dump is performed by a computing system. The first system dump is received by the computing system, the first system dump generated by a first server in response to the first server detecting a first error. First diagnostic data are extracted, by the computing system, from the first system dump, the first diagnostic data comprising at least first call stack data for the first server. Second diagnostic data are identified by the computing system that are indicative of the first error by comparing the first call stack data of the first diagnostic data to second call stack data of the second diagnostic data.