Log Data Mining for Cloud Issue Auto-Resolution
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Solution Overview
Problem
In complex cloud computing environments, identifying and diagnosing operational issues across multiple components and services is challenging due to the growing complexity, as log data is often unstructured and buried within large quantities of information, making it difficult for developers and IT teams to quickly identify and resolve problems.
Innovation Solution
A virtual environment management server is configured to automatically diagnose and resolve issues by using data-mining and machine-learning techniques to process log files, pre-process operational information, generate token-weightage matrices, and cluster relevant data, allowing for real-time identification and prediction of anomalies, and automatic resolution of operational issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual log analysis is used to identify operational issues, then developers can understand the problems, but the time required to diagnose and resolve issues increases significantly
Solution Approach 1:
The system performs self-service by automatically analyzing log files, identifying anomalies, and resolving operational issues without requiring manual developer intervention. The patent implements automated log parsing, pattern recognition, and issue resolution mechanisms that operate independently, reducing both the time and human resources needed for problem diagnosis while maintaining high accuracy through systematic analysis of log data.
2Productivity
If automated data-mining techniques are used to process log files, then anomaly detection speed increases, but the complexity of the system increases
Solution Approach 1:
The patent replaces manual mechanical analysis of log files with automated computational systems. Data-mining algorithms, machine learning models, and automated parsing mechanisms substitute for human developers manually examining logs, thereby dramatically increasing processing speed. The system handles large volumes of log data through automated pattern recognition and anomaly detection, eliminating the need for manual intervention while managing complexity through standardized processing pipelines.
3Reliability
If comprehensive log data is collected from multiple components, then the completeness of issue diagnosis improves, but the volume of data to be processed increases
Solution Approach 1:
The system extracts only the relevant and meaningful information from comprehensive log data collected across multiple cloud components. Through automated log parsing and filtering mechanisms, the patent identifies and extracts key operational parameters, error patterns, and anomaly indicators while discarding redundant information. This extraction process maintains complete and reliable diagnosis capability by focusing on critical data points that directly contribute to issue identification and resolution.
Data Source
AI summary
A method may include generating, by a diagnosis manager, a plurality of pre-processed files based on a plurality of log files containing operational information related to one or more of the plurality of modules operating in the cloud environment. The method may include generating a set of weightage matrices based on a plurality of tokens extracted from the plurality of pre-processed files, and identifying a plurality of clusters based on the set of weightage matrices. The method may further include determining, by a resolution manager coupled with the diagnosis manager, an operational issue for a specific module selected from the plurality of modules and associated with a specific cluster selected from the plurality of clusters, based on the subset of tokens associated with the specific cluster; and performing a predefined action on the specific module based on the operational issue.