Automated Configuration Error Correction in Computing Clusters
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Solution Overview
Problem
Tracking and correcting configuration changes in large computing clusters is difficult due to their frequency and number, often resulting in errors that cause performance and execution issues.
Innovation Solution
Systems and methods that periodically analyze configuration logs using clustering techniques and Natural Language Processing to identify and correct configuration errors by sending instructions to the computing cluster based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If configuration changes are made frequently to address performance and security issues, then system adaptability is improved, but tracking and managing these changes becomes difficult
Solution Approach 1:
The system implements automated feedback loops by monitoring configuration logs, detecting errors, and automatically applying corrections. This closed-loop approach tracks all configuration changes and their outcomes, providing visibility into the configuration management process while maintaining adaptability through automated response to detected issues.
Solution Approach 2:
The configuration management system performs self-service by automatically detecting configuration errors through log analysis and applying corrections without human intervention. This automation reduces the burden of tracking manual configuration changes while maintaining system adaptability through autonomous error correction.
2Manufacturing precision
If manual tracking of configuration changes is performed, then configuration accuracy is improved, but time consumption and operational overhead increase
Solution Approach 1:
The system replaces manual mechanical tracking processes with automated computational analysis. Configuration logs are automatically parsed and analyzed using natural language processing and clustering algorithms, eliminating manual tracking while maintaining or improving configuration accuracy through systematic automated detection of errors and patterns.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between configuration changes and their tracking. This intermediary automatically processes configuration logs, identifies errors, and generates correction instructions, thereby maintaining configuration accuracy while eliminating the need for direct manual tracking of each configuration change.
3Stability of the object's composition
If configuration errors are not corrected promptly, then system stability is maintained, but performance and resource efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by proactively analyzing configuration logs to detect errors before they significantly impact system performance. Automated error detection and correction instructions are prepared in advance, allowing prompt remediation that maintains both system stability and performance by addressing issues before they escalate.
Solution Approach 2:
The system implements continuous feedback monitoring of configuration logs to detect errors early. This real-time feedback enables rapid detection and correction of configuration issues, maintaining system stability while preventing performance degradation by addressing errors promptly through automated correction mechanisms.
Data Source
AI summary
A system includes a memory and a processor. The processor is configured to access one or more configuration logs generated by a computing cluster. The processor is further configured to determine, by analyzing the one or more configuration logs, a particular service running on the computing cluster that has generated a plurality of errors within the plurality of log messages. The processor is further configured to determine whether the particular error has previously occurred. The processor is further configured to, in response to determining that the particular error has previously occurred, generate and send one or more commands to the computing cluster. The one or more commands are operable to change a current configuration value for the particular service running on the computing cluster to a new configuration value. The new configuration value is based on a historical value stored in the database of historical configuration errors.