Quantized Signal Correlation for Computer System Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for analyzing performance metrics in computer systems are inadequate due to their complexity and the sheer number of metrics involved, often requiring manual specification and are time-consuming, error-prone, and fail to capture all dependencies between metrics, especially with changing system topologies.
Innovation Solution
The approach involves converting performance metric signals into quantized signals with a reduced set of allowable values, focusing on the timing of anomalies to identify inter-relationships between metrics, allowing for efficient detection of correlations and dependencies by simplifying the analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual specification of performance metric relationships is used, then user control over analysis is improved, but time consumption and error rate increase
Solution Approach 1:
The system performs automatic analysis of performance metric relationships without requiring manual user specification. The automated anomaly detection and correlation analysis algorithms independently identify metric dependencies, eliminating the need for users to manually define relationships while reducing time consumption and errors.
Solution Approach 2:
The system continuously monitors performance metrics and provides feedback about identified relationships and anomalies. This automated feedback mechanism allows the system to adapt to changing system topologies dynamically, maintaining accurate relationship identification without manual intervention.
2Extent of automation
If Granger causality algorithm is used to detect causal influence, then automated analysis is improved, but complete dependency capture is not achieved
Solution Approach 1:
The patent combines multiple analysis approaches: automated anomaly detection, temporal correlation analysis, and Granger causality testing. This merged approach complements each other's strengths, achieving more complete dependency capture by detecting both temporal patterns and causal relationships simultaneously.
Solution Approach 2:
The system dynamically adapts its analysis based on detected patterns and system state. It adjusts detection sensitivity and correlation thresholds in real-time, allowing it to capture evolving dependencies as system topologies change, thereby improving reliability while maintaining automation.
3Measurement precision
If large numbers of performance metrics are monitored, then system health measurement accuracy is improved, but monitoring complexity and cost increase
Solution Approach 1:
The system extracts and focuses analysis on anomaly timing patterns rather than continuously processing all metric values. By extracting temporal anomaly signals and correlating these simplified representations, the system maintains measurement accuracy while reducing monitoring complexity through selective focus on critical temporal patterns.
Solution Approach 2:
The patent segments the monitoring process into distinct phases: data collection, anomaly detection, correlation analysis, and relationship identification. This segmentation allows systematic processing of large metric sets while managing complexity through structured analysis stages, improving accuracy without proportionally increasing operational complexity.
Data Source
AI summary
Identifying an inter-relationship between performance metrics of a computer system. It is proposed to convert performance metric signals, which represent variations of performance metrics over time, into quantized signals having a set of allowable discrete values. The quantized signals are compared to detect a correlation based on the timing of variations quantized signals. An inter-relationship between the performance metrics may then be identified based on a detected correlation between the quantized signals.