Dynamic Rule-Based Crash Dump Analyzer
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Solution Overview
Problem
The increasing size of crash dumps from high-end computer systems poses challenges in uploading and analyzing them, leading to bandwidth limitations, lengthy compression times, and security concerns, especially when symbol files are involved.
Innovation Solution
A dynamic rule-based crash dump analysis system that offloads manual analysis using decision trees to create a small analysis file, allowing for remote analysis without uploading the entire dump, and securely transfers only required symbol data, using a decision tree parser and rule engine to automate the debugging process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If crash dump files are uploaded to remote support sites for analysis, then complete debugging information is available, but bandwidth limitations and file transfer failures occur due to the large size of crash dumps
Solution Approach 1:
The system segments the crash dump analysis process into two parts: (1) local preprocessing that extracts only the necessary debugging information and creates a small analysis file, and (2) remote analysis of this condensed file. This segmentation eliminates the need to transfer the entire large crash dump file while preserving all essential debugging information.
Solution Approach 2:
The system extracts only the essential debugging information from the large crash dump file through automated analysis using decision trees. This extraction process creates a small analysis file containing only the relevant data needed for debugging, leaving behind the unnecessary bulk of the original crash dump data.
2Volume of stationary object
If compression algorithms are applied to reduce crash dump file size, then file size is reduced, but compression and decompression take hours or even days for extremely large files
Solution Approach 1:
The system performs preliminary analysis and information extraction locally before any file transfer or compression operations. By creating the small analysis file on-site through automated decision tree evaluation, the system eliminates the need for time-consuming compression and decompression of large files, as only the condensed analysis file needs to be transferred and processed.
Solution Approach 2:
Instead of compressing the entire crash dump file, the system extracts only the essential debugging information into a small analysis file. This extraction approach reduces file size dramatically without requiring compression algorithms, thereby avoiding the hours or days of processing time that compression would require for extremely large files.
3Ease of operation
If symbol files are transferred to customer sites for crash dump analysis, then complete symbol lookup capability is available, but security risks arise from exposing these important company assets
Solution Approach 1:
The system introduces an intermediary mechanism where the debugger on the customer site communicates with a remote symbol server through controlled API calls. Instead of transferring the entire symbol file, the intermediary server provides on-demand symbol lookup services, allowing complete symbol capability while maintaining security by keeping symbol files centralized and protected.
Solution Approach 2:
Instead of transferring the complete symbol file (excessive action), the system provides only the specific symbol data needed at any given moment through controlled API calls (partial action). This approach maintains complete symbol lookup capability while minimizing security exposure by transferring only necessary data portions on an as-needed basis.
4Adaptability or versatility
If manual analysis processes are used by engineers, then flexible decision-making is possible, but the process is repetitive and time-consuming
Solution Approach 1:
The system implements self-service through automated decision trees that encode expert engineering knowledge. The automated analyzer independently evaluates crash dump data, executes decision logic, and generates analysis results without requiring manual engineer intervention for each analysis case, thereby eliminating repetitive manual work while maintaining the flexibility of expert decision-making paths.
Solution Approach 2:
The system transforms the analysis process from manual parameter evaluation to automated parameter-based decision making. By encoding decision logic into structured decision trees with specific evaluation parameters and thresholds, the system maintains the flexibility of expert decision-making while dramatically increasing analysis speed through automated execution of these parameter-based rules.
Data Source
AI summary
A method and system for dynamic rule-based automatic crash dump analysis are described. In an example, a dynamic rule-based crash dump analysis system retrieves debug symbol data, rules, and commands from a server over a network. The actions are executed based on the retrieved rules in order to automatically analyze a crash dump using a debugger and the debug symbol data. During the process of analyzing the crash dump, the system parses output from the debugger for further rule processing and creates a human-readable analysis file from the parsed output.