Automated Defect Discovery via System Clustering
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Solution Overview
Problem
Identifying and resolving system defects and optimizations in complex computer systems is tedious, time-consuming, and costly due to the vast amount of data in log files, requiring manual review of gigabytes of information to find hidden issues, which is exacerbated by the increasing complexity of these systems.
Innovation Solution
A system that uses a central repository with machine learning algorithms to analyze system data from multiple computer systems, identifying unhealthy or optimally performing systems, and distributing solutions or optimizations automatically, using clustering techniques like the k-means algorithm to group systems based on performance and configuration attributes, enabling preemptive defect resolution and optimization distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of log files is used to identify system defects, then detailed inspection of individual lines is possible, but the process becomes extremely time-consuming and labor-intensive when dealing with gigabytes of data
Solution Approach 1:
The patent replaces manual mechanical review of log files with an automated machine learning-based system. The system uses clustering algorithms to automatically group and analyze log data, substituting human inspection with computational processing that can handle vast amounts of data quickly and accurately.
Solution Approach 2:
The system enables self-service defect detection by automatically analyzing log files and generating reports without requiring manual intervention. The machine learning models autonomously identify patterns, cluster data, and highlight potential defects, allowing the system to inspect itself and report issues independently.
2Reliability
If comprehensive monitoring of all system components is implemented, then complete defect detection is achieved, but system complexity and resource requirements increase significantly
Solution Approach 1:
The patent segments the monitoring system into modular components: data collection modules, clustering algorithm modules, and defect detection modules. Each module handles specific aspects of monitoring, making the overall system more manageable and easier to implement while maintaining comprehensive coverage through distributed analysis.
Solution Approach 2:
Instead of requiring full manual inspection of every log entry, the system uses machine learning to identify and prioritize the most relevant data points. The clustering algorithms group similar log entries together, allowing the system to focus computational resources on analyzing only the most suspicious or pattern-matching segments rather than everything uniformly.
3Productivity
If automated machine learning analysis is used to process log files, then processing speed and coverage increase, but the complexity of the analysis system increases
Solution Approach 1:
The machine learning system is designed with multi-functionality, where a single clustering algorithm framework can handle various types of log data, defect patterns, and system configurations. This universal approach allows the system to process diverse log formats and identify different types of defects using the same core analytical engine, reducing the need for multiple specialized systems.
Data Source
AI summary
Performance information and configuration information is received for the plurality of computer systems. The computer systems are grouped into a plurality of clusters based at least in part on the performance information, where the plurality of clusters includes a first cluster and a second cluster. A system configuration associated with the first cluster is automatically identified from the configuration information and is automatically sent to the second cluster.