Predicting Computer Failures Using Abnormal Pattern Files
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Solution Overview
Problem
Computers face failures that can lead to performance issues, data loss, and reduced functionality due to software and hardware component vulnerabilities, necessitating a system to predict and mitigate these failures.
Innovation Solution
A monitoring system that generates prediction data using historical logs, identifies abnormal data-point patterns, and applies override processes to correct predictions, thereby adjusting computer parameters to prevent or minimize failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prediction data is generated using historical logs to forecast future computer performance, then the ability to predict failures is improved, but the complexity of the monitoring system increases
Solution Approach 1:
The system performs preliminary actions by generating prediction data from historical logs before actual failures occur. The monitoring system analyzes past performance patterns and creates forecasts of future system behavior, enabling proactive identification of potential failures before they impact computer operations.
Solution Approach 2:
The patent introduces an intermediary layer consisting of prediction data and abnormal pattern files that mediate between historical logs and failure detection. This intermediary layer processes raw historical data into meaningful predictions and uses pattern-matching files as intermediaries to compare against predicted values, simplifying the overall system architecture.
2Speed
If the system automatically identifies abnormal data-point patterns and applies override processes, then the speed of failure mitigation is improved, but the complexity of data processing increases
Solution Approach 1:
The monitoring system performs self-service by automatically identifying abnormal patterns and applying override processes without human intervention. The system uses customizable program-code files to define abnormal patterns and automatically compares prediction data against these patterns, self-correcting by applying predefined override processes when anomalies are detected.
Solution Approach 2:
The system changes parameters by replacing abnormal data-point values in prediction data with corrected values from override processes. When an abnormal pattern is identified, the system modifies the prediction data parameters to reflect expected normal behavior, enabling rapid correction of forecasted failures through parameter adjustment rather than complex reprocessing.
3Adaptability or versatility
If customizable program-code files are used to define abnormal patterns, then the adaptability of the system is improved, but the difficulty of system configuration increases
Solution Approach 1:
The system segments the pattern recognition functionality into separate customizable program-code files, each defining specific abnormal patterns. This segmentation allows different types of abnormalities to be defined independently in separate files, making the system adaptable to various failure modes while organizing configuration complexity into manageable, modular units.
Solution Approach 2:
The patent uses copying by utilizing pre-defined abnormal pattern templates in program-code files that can be replicated and adapted for different scenarios. Instead of creating complex detection logic from scratch for each failure type, the system copies and adapts standardized pattern definitions, reducing configuration difficulty while maintaining adaptability.
Data Source
AI summary
In some examples, a processing device can receive prediction data representing a prediction. The processing device can also receive files defining abnormal data-point patterns to be identified in the prediction data. The processing device can identify at least one abnormal data-point pattern in the prediction data by executing customizable program-code in the files. The processing device can determine an override process that corresponds to the at least one abnormal data-point pattern in response to identifying the at least one abnormal data-point pattern in the prediction data. The processing device can execute the override process to generate a corrected version of the prediction data. The processing device can then adjust one or more computer parameters based on the corrected version of the prediction data.