System Metric Ranking via Correlation Analysis
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Solution Overview
Problem
Current systems face challenges in efficiently monitoring and analyzing vast volumes of system metrics in real-time, particularly in IT, healthcare, and IoT domains, as they rely heavily on domain experts and are resource-intensive, leading to excessive monitoring of redundant metrics.
Innovation Solution
The method ranks system metrics by grouping them into correlation groups based on historic time series data, determining sensitivity and importance, and using machine learning for anomaly detection, combined with domain expert input, to select the most critical metrics for monitoring, thereby reducing resource usage and focusing on essential data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all system metrics are monitored in real-time, then complete system visibility is achieved, but resource consumption becomes excessive
Solution Approach 1:
The patent extracts only the most critical metrics from the complete set of system metrics by calculating importance scores based on correlation analysis and domain expertise. This selective extraction allows monitoring of essential metrics while discarding redundant ones, thereby reducing resource consumption while maintaining reliable system visibility.
Solution Approach 2:
The patent applies local quality by treating different metrics differently based on their individual importance scores. High-importance metrics receive full monitoring attention while low-importance metrics are reduced or eliminated from monitoring, optimizing resource allocation according to local needs rather than uniform monitoring of all metrics.
2Measurement precision
If domain experts manually select metrics for monitoring, then monitoring accuracy is improved, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The patent performs preliminary automated analysis of metric correlations and importance scores before final metric selection. This preliminary action prepares the data and insights needed for accurate metric selection, reducing the time and effort required from domain experts while maintaining high selection accuracy.
Solution Approach 2:
The patent introduces an automated intermediary system that processes metric data, calculates correlations, and generates importance scores. This intermediary handles the time-consuming computational tasks, allowing domain experts to focus on final selection decisions with pre-processed information, thereby reducing overall time loss while maintaining accuracy.
3Loss of information
If thousands of metrics are collected and monitored, then comprehensive system analysis is achieved, but redundant monitoring increases resource usage
Solution Approach 1:
The patent merges correlated metrics into representative groups by identifying metrics with high correlation coefficients. Instead of monitoring multiple highly correlated metrics separately, the system combines them into single representative metrics, reducing redundant monitoring while preserving complete system information through the representative samples.
Solution Approach 2:
The patent changes the monitoring parameter from uniform monitoring of all metrics to differential monitoring based on calculated importance scores. By transforming the monitoring approach from comprehensive to selective based on parameter analysis, the system reduces energy waste on redundant metrics while maintaining information completeness through intelligent parameter-based filtering.
Data Source
AI summary
Ranking system metrics for monitoring by sorting members of a set of system metrics into correlation groups according to correlations among historic time series data, determining a sensitivity of the members of the set of system metrics, determining an importance of the members of the set of system metrics according to the correlation groups and sensitivity, and ranking the members of the set of system metrics according to the importance.