Cloud Memory Log Analysis for Automated OOM Root Cause Resolution
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Solution Overview
Problem
Diagnosing the root causes of out-of-memory errors in cloud computing environments is challenging due to the complexity and volume of log data, leading to costly and time-consuming manual investigations.
Innovation Solution
An automated memory issue locating process that filters log data based on frequency, timing, and noise patterns to identify memory usage patterns, using a trained generative AI model to determine root causes and perform remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive log analysis is performed to identify root causes of out-of-memory errors, then diagnostic accuracy is improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by proactively monitoring memory usage patterns and generating alerts before out-of-memory errors occur. The log analysis framework is pre-configured with memory-related error patterns, allowing it to quickly identify relevant logs when errors occur without requiring comprehensive analysis of all system logs.
Solution Approach 2:
The system extracts and isolates only the memory-related log entries from the vast volume of system logs using pattern matching and filtering mechanisms. This extraction process separates relevant diagnostic information from irrelevant data, enabling focused analysis that maintains accuracy while reducing time consumption.
2Measurement precision
If comprehensive log analysis is performed to identify root causes of out-of-memory errors, then diagnostic accuracy is improved, but operational complexity increases significantly
Solution Approach 1:
The patent introduces an intermediary automated diagnostic framework that mediates between the complex log data and the technician. This framework includes log collection agents, pattern matching engines, and root cause analysis algorithms that automatically process and interpret logs, reducing the operational complexity for technicians while maintaining diagnostic accuracy.
Solution Approach 2:
The system implements self-service capabilities through automated log analysis and root cause identification. The diagnostic framework automatically collects logs, analyzes patterns, identifies memory issues, and generates recommendations without requiring manual intervention, thereby reducing operational complexity while maintaining high diagnostic accuracy.
3Measurement precision
If manual investigation of log messages is performed to identify root causes, then comprehensive analysis is achieved, but cost and time consumption increase
Solution Approach 1:
The patent replaces the mechanical process of manual log investigation with an automated electronic diagnostic framework. The system uses software agents to collect logs, algorithms to analyze patterns, and automated reasoning to identify root causes, substituting human manual analysis with automated computational processes that achieve comparable or superior comprehensiveness while dramatically improving diagnosis efficiency.
Solution Approach 2:
An automated diagnostic intermediary framework is introduced that performs comprehensive log analysis on behalf of technicians. This intermediary system includes log parsing capabilities, pattern recognition engines, and root cause analysis algorithms that maintain comprehensive analysis quality while eliminating the time-consuming manual investigation process.
4Measurement precision
If the volume of logs generated by cloud computing services is analyzed comprehensively, then diagnostic accuracy is improved, but processing time and resource requirements increase
Solution Approach 1:
The system extracts only memory-related log entries from the vast volume of cloud computing service logs using pattern matching and filtering. This selective extraction isolates relevant diagnostic information from irrelevant data, maintaining diagnostic accuracy while significantly reducing processing time and resource requirements.
Solution Approach 2:
The diagnostic framework performs preliminary filtering and categorization of logs based on memory-related patterns before detailed analysis. This preliminary action pre-processes the log data to identify potentially relevant entries, enabling faster processing while maintaining comprehensive diagnostic accuracy for memory issues.
Data Source
AI summary
A method, computer program product, and computing system for generating a list of out-of-memory errors associated with a cloud computing environment. A log associated with an out-of-memory error is identified. A process from the log associated with the out-of-memory error is identified. A memory usage pattern associated with the process is identified. A root cause for the out-of-memory error is determined in response to identifying the memory usage pattern associated with the process. A remedial action is performed on the cloud computing environment in response to determining the root cause for the out-of-memory error.