Anomaly Prediction via Second-Order Traffic Metric Thresholds
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Solution Overview
Problem
Current methods lack the ability to accurately and efficiently predict anomalies and incidents in computer applications, leading to delayed detection and resolution of issues, which affects user experience and system reliability.
Innovation Solution
A method and apparatus that detect traffic metrics and incident tickets during runtime, calculate a threshold based on second-order differences to maximize recall and precision rates, and use these metrics to predict potential anomalies and incidents in future time periods, thereby enabling proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users discover and report problems after incidents occur, then the reporting process is simple, but the detection is delayed and user experience deteriorates
Solution Approach 1:
The system performs preliminary analysis of traffic metrics to detect anomalies before they escalate into incidents. By continuously monitoring metrics such as request rates, response times, and error rates, and comparing them against learned normal patterns, the system can predict potential failures and trigger alerts or automated responses in advance, eliminating the need to wait for user discovery and reporting.
2Measurement precision
If manual monitoring and reporting methods are used, then the system complexity is low, but the precision of anomaly detection is insufficient
Solution Approach 1:
The patent introduces an intermediary prediction system that sits between raw traffic metrics and incident detection. This system uses machine learning models to process and interpret metrics, transforming raw data into meaningful anomaly predictions. The intermediary layer handles the complexity of pattern recognition and anomaly detection, providing precise predictions without requiring complex manual monitoring systems.
3Loss of information
If traditional incident reporting is used, then the implementation is simple, but the association between anomalies and incidents is unclear
Solution Approach 1:
The system implements feedback loops where predicted anomalies are tracked and correlated with actual incidents. When incidents occur, the system retrospectively analyzes whether predicted anomalies preceded them, using this feedback to continuously improve the prediction model. This creates a closed-loop system that progressively enhances the clarity of anomaly-incident associations through iterative learning from real-world outcomes.
Data Source
AI summary
A method for predicting anomalies in a computer application includes during runtime of the computer application, detecting traffic metrics and incident tickets associated with the computer application, the incident ticket indicating an incident might occur in the computer application; calculating a threshold based on absolute values of second order differences associated with the traffic metrics, wherein the threshold is such that when the absolute value of the second order difference associated with the traffic metrics exceeds the threshold, a recall rate Rrecall that the computer application is recalled is maximized; obtaining predicted metrics of the computer application in a next time period based on the traffic metrics; and in response to an absolute value of a second order difference associated with the predicted metrics exceeding the threshold, predicting potential anomalies of the computer application in the next time period.