Memory Dump Analysis Server with Cloud Knowledge Base
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Solution Overview
Problem
Existing techniques for error diagnosis in computer systems using memory dump files are inefficient, as large dump files are often lost before analysis due to short retention periods and the time-consuming process for support engineers to diagnose and resolve issues, especially in data centers with multiple devices.
Innovation Solution
An automated system where an analysis server collects and analyzes memory dump files, identifies configuration and call stack data, and searches a cloud-based knowledge base to identify known issues and provide solutions, or reports the absence of matching entries, facilitating timely and responsive error diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If memory dump files are maintained for short periods, then storage space is saved and newer dumps can be stored, but the record of error conditions is lost before analysis
Solution Approach 1:
The patent extracts critical diagnostic information from memory dump files by analyzing call stack data and configuration information, then stores only these extracted insights in the knowledge base rather than retaining the entire dump file. This allows permanent preservation of error condition records while freeing up storage space for new dumps.
Solution Approach 2:
The system performs preliminary analysis of memory dump files automatically when generated, extracting and indexing call stack data and configuration information before the original dump files are deleted. This preliminary action ensures that error condition records are preserved and made searchable in the knowledge base before storage space constraints would cause their loss.
2Measurement precision
If support engineers manually analyze memory dumps, then diagnostic accuracy is maintained, but response time increases and productivity decreases
Solution Approach 1:
The system enables self-service error diagnosis by automatically analyzing memory dump files, extracting diagnostic information, searching the knowledge base for known issues, and generating resolution recommendations without requiring manual intervention from support engineers. This maintains diagnostic accuracy through automated analysis while dramatically improving productivity.
Solution Approach 2:
The system implements feedback mechanisms by automatically searching the knowledge base using extracted call stack and configuration data, comparing findings against known error patterns, and providing immediate diagnostic recommendations. This feedback loop enables rapid error identification and resolution while maintaining high diagnostic accuracy through systematic comparison with stored knowledge.
3Adaptability or versatility
If a large number of computerized devices are monitored, then system coverage is improved, but the ability to respond to all error conditions adequately deteriorates
Solution Approach 1:
The patent creates a universal error diagnosis system where a single knowledge base serves multiple computerized devices across the data center. The knowledge base stores diagnostic information from various device types, and the automated analysis server can process error conditions from any device using the same unified approach, enabling adequate response to all devices regardless of quantity or type.
Solution Approach 2:
The system introduces an intermediary automated analysis server that acts as a mediator between error condition generation and human response. This intermediary automatically processes error conditions from multiple devices, searches the knowledge base, and generates initial responses, thereby maintaining adequate response capability even as the number of monitored devices increases.
4Measurement precision
If memory dump files are analyzed manually, then diagnostic thoroughness is maintained, but time consumption increases
Solution Approach 1:
The patent replaces the mechanical manual analysis process with an automated computer-based system that extracts, analyzes, and indexes memory dump information automatically. The system uses automated algorithms to process call stack data and configuration information, search the knowledge base, and generate diagnostic reports, thereby maintaining diagnostic thoroughness while eliminating time-consuming manual intervention.
Data Source
AI summary
An automatic technique for a timely diagnostic uses a cloud service and includes (1) after a memory dump file is generated, uploading the dump file and system configurations to an analysis server, (2) extracting key call stacks from the dump file in the server, (3) searching a cloud-based knowledge base for entries corresponding to contents of the call stack and the system configurations (like model, SW version, etc.), these entries representing known issues having similar call stack contents and/or system configurations. If relevant knowledge base entries are found, then they are used to identify a root cause and suggest solutions. If no relevant knowledge base entries are found, this result is reported along with any other potentially useful data from the dump file analysis, such as an identification of a product area for a program identified by the dump file name.