Management Computer Anomaly Rate Evaluation for Cloud Resource Monitoring
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Solution Overview
Problem
Conventional monitoring methods fail to differentiate between performance issues caused by system resource problems and changes in usage characteristics in cloud environments, leading to increased administrator workload and delayed corrective actions.
Innovation Solution
A management computer system that calculates anomaly rates for both operation performance and usage characteristics, allowing for proper evaluation and notification of operational status, thereby distinguishing between resource issues and usage changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring methods are used to detect performance abnormalities, then abnormalities can be detected when performance indices fall outside reference ranges, but the administrator's workload increases and prompt measures cannot be taken because the cause cannot be differentiated
Solution Approach 1:
The patent segments the abnormality detection process into two independent evaluation dimensions: operation performance anomaly rate and usage characteristic anomaly rate. This segmentation allows the system to separately analyze whether abnormalities stem from system resource issues or usage pattern changes, thereby reducing administrator workload while maintaining detection precision.
Solution Approach 2:
The patent introduces usage characteristic anomaly rate as an intermediary metric that mediates between raw performance data and administrator judgment. By comparing operation performance anomalies against usage characteristic anomalies, the system automatically determines the root cause category, eliminating the need for administrators to manually investigate and differentiate causes.
2Reliability
If monitoring only operation performance indices is performed, then system resource problems can be detected, but changes in usage characteristic cannot be distinguished leading to false abnormality detection
Solution Approach 1:
The patent merges operation performance monitoring with usage characteristic monitoring into a unified evaluation framework. By combining these two monitoring dimensions and comparing their anomaly rates, the system achieves reliable performance monitoring while preserving usage characteristic information, thereby eliminating false positives caused by legitimate usage changes.
Solution Approach 2:
The patent changes the monitoring parameter from solely operation performance indices to a dual-parameter system including both operation performance indices and usage characteristic indices. This parameter expansion enables the system to distinguish between genuine performance degradation and acceptable usage variations, improving monitoring reliability without losing usage information.
3Measurement precision
If manual analysis is performed to determine whether performance issues are caused by system problems or usage changes, then accurate cause identification can be achieved, but the time required for corrective action increases
Solution Approach 1:
The patent implements self-service automation where the monitoring system automatically determines the cause category by comparing anomaly rates without requiring administrator intervention. The system serves itself by autonomously evaluating whether abnormalities stem from system issues or usage changes, thereby maintaining high cause identification accuracy while eliminating the time loss associated with manual analysis.
Solution Approach 2:
The patent establishes a feedback mechanism where the comparison results between operation performance anomaly rate and usage characteristic anomaly rate automatically feed into the cause determination logic. This closed-loop feedback system enables real-time automated cause identification, achieving both high accuracy and rapid response without administrator involvement.
Data Source
AI summary
In a system where resources are used in a varying manner, when performances are monitored based on detected past behavior when the system was operating normally, if performance behavior is detected that is different, it is difficult to determine whether the detected performance behavior results from resources being used differently than when the system was operating normally. According to the present invention, monitoring accuracy is improved by determining whether performance behavior results from change in characteristics relating to the usage of the system by using means for measuring performances when the system is in operation and detecting a performance different from when the system was operating normally, means for measuring characteristics relating to the usage of the system and detecting whether the characteristics are different from when the system was operating normally, and means for comparing performance information with characteristic information relating to the usage of the system.