Operating Metric Comparison for Early Failure Detection
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Solution Overview
Problem
Existing monitoring systems for computer networks and software processes are inadequate in detecting potential system failures in a timely and cost-effective manner, often only identifying issues after a complete or partial failure has occurred.
Innovation Solution
A method and system that compare operating metrics from a target computer system over different time frames to identify differences that may indicate an impending issue, generating warnings and providing instructions for resolution based on the severity of the issue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive monitoring of computer infrastructure is implemented to detect potential failures early, then system reliability is improved, but monitoring cost and complexity increase prohibitively
Solution Approach 1:
The patent extracts and analyzes only the most critical metrics (error rates, response times, resource utilization) rather than monitoring all possible system parameters. By selectively extracting key indicators of system health, the solution achieves reliable failure detection without the prohibitive complexity of comprehensive monitoring.
Solution Approach 2:
The system performs preliminary analysis of metrics trends and patterns to identify potential failures before they occur. By continuously analyzing historical data and detecting anomalies in metric behavior, the system can predict failures and alert administrators proactively, improving reliability without requiring complex real-time intervention systems.
2Measurement precision
If extensive data collection and analysis is performed to detect potential failures, then detection accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent applies partial action by analyzing only the necessary subset of metrics required for failure detection, rather than processing all available data. The system focuses on critical metrics such as error rates, response times, and resource utilization, achieving accurate detection without the time and resource consumption of comprehensive data processing.
Solution Approach 2:
The system performs periodic analysis of metric trends rather than continuous intensive processing. By sampling and analyzing data at strategically determined intervals, the system maintains detection accuracy while significantly reducing processing time and resource consumption compared to continuous comprehensive monitoring.
3Device complexity
If monitoring systems only detect failures after occurrence, then system simplicity is maintained, but loss of time in detecting issues increases
Solution Approach 1:
The system performs preliminary analysis of metric trends and patterns to identify potential failures before they occur. By continuously analyzing historical data and detecting anomalies in metric behavior, the system can predict failures and alert administrators proactively, reducing the time loss associated with reactive failure detection.
Data Source
AI summary
A method for detecting computer issues includes identifying a target computer system. A first set of data for a first time period relating an operating metric from the target computer system are received. The operating metric is stored. A second set of data for a second time period relating to the operating metric is received. The first and second sets of data are compared. A difference between the two sets of data is identified. If the difference between the two sets of data is within a range a warning notification is displayed in a graphical user interface. An input is received in the graphical user interface in response to the warning notification being displayed.