Virtualized Application Performance Classification
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Solution Overview
Problem
Identifying and addressing underperformance in software applications executing within virtualized computing infrastructures is challenging due to the complexity of virtualized resources and configurations, making it difficult to optimize performance effectively.
Innovation Solution
A method using a classifier with machine learning algorithms to analyze profiles of hypervisor, network communication, and data storage characteristics, generating classifications to determine underperforming applications and recommend optimization or resource adjustments for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual trial-and-error approaches are used to optimize virtualized computing infrastructure configuration, then potential performance improvements may be achieved, but the process becomes time consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing performance data, identifying underperformance conditions, and determining optimal configuration changes before implementing them. This proactive approach eliminates time-consuming trial-and-error by pre-calculating the correct optimization path based on analyzed performance metrics and configuration parameters.
Solution Approach 2:
The system implements continuous feedback loops where performance metrics are monitored, analyzed, and used to automatically adjust virtualized computing infrastructure configurations. This closed-loop control ensures reliable performance improvements while minimizing optimization time by using real-time feedback to guide configuration changes without manual intervention.
2Measurement precision
If comprehensive performance monitoring and analysis systems are implemented to identify underperforming applications, then accurate identification is achieved, but system complexity increases
Solution Approach 1:
The performance monitoring and analysis system is designed as a universal platform that handles multiple functions including data collection, performance metric calculation, underperformance detection, and configuration optimization recommendations. This multi-functional approach achieves comprehensive measurement precision while managing system complexity through integration rather than separate specialized components for each function.
Data Source
AI summary
A method of improving performance of a software application executing with a virtualized computing infrastructure wherein the application has associated: a hypervisor profile of characteristics of a hypervisor in the infrastructure; a network communication profile of characteristics of network communication for the application; a data storage profile of characteristics of data storage for the infrastructure; and an application profile defined collectively by the other profiles.