Virtual Machine Performance Analyzer for Region Identification
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Solution Overview
Problem
In virtual machine systems, users face challenges in identifying regions of interest in performance charts that show dramatic changes, requiring manual effort to correlate these changes with related events and alarms, especially when the time interval is long.
Innovation Solution
The VM manager includes a performance analyzer that uses mean and variance calculations to automatically determine regions of interest in performance charts, sorting and merging regions based on variance and mean, and selecting the highest ranking regions with high variance for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of performance charts is used to identify regions of interest, then users can correlate changes with events and alarms, but the process requires significant manual effort and time, especially for long time intervals
Solution Approach 1:
The system performs self-service by automatically analyzing performance charts and identifying regions of interest without requiring manual user intervention. The performance analyzer autonomously calculates statistics, detects dramatic changes, and highlights significant regions, freeing users from time-consuming manual analysis while maintaining accurate identification of performance anomalies
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of users manually examining charts and correlating events, a performance analyzer using statistical algorithms and automated event correlation mechanisms substitutes the human analytical process, dramatically reducing time loss while preserving measurement precision
2Ease of operation
If automated performance analysis is implemented, then manual effort is reduced, but the system complexity increases due to variance calculations and region merging algorithms
Solution Approach 1:
The system uses parameter changes strategically by calculating statistical parameters (mean, variance) and using thresholds for dramatic changes to control the complexity. By adjusting these parameters and thresholds, the system balances automation benefits against computational complexity, making the analyzer adaptable to different performance monitoring needs without requiring excessive system complexity
Solution Approach 2:
The performance analyzer extracts only the essential and most relevant features from performance data - specifically focusing on mean, variance, and dramatic changes - rather than analyzing all possible parameters. This extraction approach simplifies the analysis process by concentrating on key indicators while ignoring less relevant details, thereby improving ease of operation without proportionally increasing system complexity
Data Source
AI summary
A method for a processor to provide a chart of a performance metric in a collection interval includes creating regions by dividing the collection interval into regions of increasingly smaller time intervals and determining a mean and a variance for each region based on data points in that region, sorting the regions by their variances and means, and processing the sorted regions. Processing the sorted regions includes removing any child region when its parent region has a variance that substantially represents the child region, and replacing any two neighboring or intersecting regions with a merged region comprising the two neighboring or intersecting regions when the merged region has a variance that substantially represents the two neighboring or intersecting regions. The method further includes generating the chart by visually indicating highest ranking regions by variance in the chart and displaying the chart or transmitting the chart over a computer network.