Lag Correlation Analysis for Service Performance Leading Indicators
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
IT administrators face challenges in identifying leading indicators of service performance degradation due to the large number of metrics collected, which often require manual threshold setting and rule establishment to detect system performance issues effectively.
Innovation Solution
A computer-implemented method that identifies service metrics, determines abnormalities in infrastructure metrics within a time window, calculates the degree of lag correlation, and selects candidate infrastructure metrics with significant correlation to provide early warnings of service performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual threshold setting and rule establishment are used to detect system performance degradation, then detection accuracy may be improved, but operational complexity and time consumption increase significantly
Solution Approach 1:
The system automatically performs threshold setting and rule establishment by analyzing historical metric data and identifying correlations between infrastructure metrics and service metrics. The automated anomaly detection mechanism eliminates the need for manual configuration while maintaining high detection accuracy through self-learning from past performance patterns.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on learned patterns from historical data rather than using fixed manual settings. By changing parameters automatically based on data analysis, the system achieves both high detection accuracy and operational simplicity.
2Reliability
If all collected metrics are monitored for service performance degradation, then detection completeness is improved, but processing complexity and computational resources increase
Solution Approach 1:
The system extracts and focuses only on the most relevant infrastructure metrics that have proven correlations with service performance degradation. By taking out and prioritizing key metrics rather than monitoring all metrics equally, the system maintains detection completeness while reducing processing complexity and computational overhead.
Solution Approach 2:
The system segments metrics into different categories and prioritizes analysis of infrastructure metrics that show strong lag correlation with service metrics. This segmentation allows comprehensive monitoring while managing complexity by focusing computational resources on the most critical metric relationships.
3Extent of automation
If traditional monitoring methods are used without lag correlation analysis, then implementation simplicity is maintained, but ability to provide early warnings is reduced
Solution Approach 1:
The system performs preliminary analysis of lag correlations between infrastructure metrics and service metrics to identify leading indicators. By understanding the temporal relationships and time lags between different metric types, the system can provide early warnings before service degradation actually occurs, maintaining automation while reducing time loss.
Data Source
AI summary
The present description refers to a computer implemented method, computer program product, and computer system for identifying a service metric associated with a service, identifying one or more abnormalities of one or more infrastructure metrics that occur within a time window around an abnormality of the service metric, determining a set of candidate infrastructure metrics for the service metric based on how many times an abnormality of an infrastructure metric occurred within a time window around an abnormality of the service metric, determining a degree of lag correlation for each candidate infrastructure metric with respect to the service metric, selecting one or more candidate infrastructure metrics having a degree of lag correlation that exceeds a threshold to be a leading indicator infrastructure metric for the service metric, and providing a performance degradation warning for the service when an abnormality of one of the leading indicator infrastructure metrics is detected.