Log Analysis for Anomaly Prediction in Computing Systems
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Solution Overview
Problem
IT infrastructure management faces challenges in efficiently analyzing and responding to anomalies in networked computing environments, as existing methods require manual intervention and are not automated, leading to delayed responses and increased costs.
Innovation Solution
A method and system that analyze aggregated data from networked computing environments to identify message IDs with frequency anomalies, correlate them with potential events, and automate responses using historical data and real-time analysis, integrating parsing, analytical, and cognitive techniques to predict and mitigate events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to analyze system log files and diagnose hardware and software problems, then personnel can identify and resolve issues, but the response time is delayed and operational costs increase
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing log files, identifying anomalies, and detecting hardware/software problems without requiring manual intervention. The computing device autonomously executes the method steps including analyzing log data, determining frequency values of message IDs, correlating anomalies with potential events, and initiating responses, thereby eliminating the time loss associated with human analysis while maintaining reliable issue detection
Solution Approach 2:
The system continuously monitors and analyzes log files in advance to detect anomalies and predict potential events before they manifest as critical failures. By performing preliminary analysis of message ID frequencies and correlating them with known event patterns, the system prepares responses proactively, reducing the response time when actual issues occur
2Reliability
If manual intervention is used to analyze system log files and respond to anomalies, then personnel can diagnose problems, but operational costs increase
Solution Approach 1:
The computing device autonomously performs log analysis, anomaly detection, and problem diagnosis without requiring human personnel intervention. The system automatically executes all method steps including frequency analysis of message IDs, correlation with potential events, and initiation of responses, thereby eliminating the operational costs associated with manual diagnosis while maintaining reliable diagnostic capability through automated algorithms
Solution Approach 2:
The system continuously monitors log files and provides feedback by comparing current message ID frequencies against historical data and known event patterns. This automated feedback loop enables the system to diagnose problems reliably without manual intervention, reducing operational costs by eliminating the need for personnel to review and interpret log data
3Loss of time
If automated analysis of log files is implemented, then response time to anomalies is reduced, but system complexity increases
Solution Approach 1:
The automated analysis system is divided into distinct functional modules: log file parsing, message ID extraction, frequency value calculation, anomaly determination through correlation analysis, and response initiation. Each module performs a specific task independently, which simplifies the overall system architecture despite the automation complexity, and enables efficient processing that reduces response time
Data Source
AI summary
A method for analyzing data of a networked computing environment, the method includes a computer processor analyzing a plurality of data of a networked computing environment aggregated during a first time interval, where the data includes messages that include message IDs. The method further includes identifying a frequency value of occurrences of a message ID within the plurality of data during the first time interval. The method further includes determining whether the frequency value of the occurrences of the message ID during the first time interval correlates to an anomaly that occurs within the networked computing environment. The method further includes responding to determining that the frequency value of the occurrences of message ID within the first time interval correlates to the anomaly by determining a first response to the anomaly. The method further includes initiating the first response to one or more elements of the networked computing environment.