Application Configuration Recommendation via Performance Analytics
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Solution Overview
Problem
Businesses face challenges in determining an optimal hardware configuration for web applications to handle increased loads while meeting performance targets at an acceptable cost, as existing methods lack accuracy and efficiency in recommending hardware configurations for expanding or implementing new applications.
Innovation Solution
A computer-implemented method that collects request-processing performance data from a source hardware system, uses an analytic engine to determine performance measurements, and provides a configuration recommendation for a target hardware system based on specified objectives, bridging the gap between application development and service management phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to determine hardware configuration, then implementation simplicity is maintained, but accuracy and efficiency of configuration recommendations deteriorate
Solution Approach 1:
The system performs self-service by automatically collecting performance data, analyzing it through analytic engines, and generating configuration recommendations without requiring manual expert intervention. The application monitor continuously gathers data and the analytic engine processes it to produce optimized hardware configuration recommendations autonomously.
Solution Approach 2:
The patent replaces manual mechanical processes with automated electronic systems. Instead of manual assessment and configuration determination, the system uses application monitors, analytic engines, and automated data processing to substitute human expertise with computational analysis, thereby improving accuracy while managing complexity through automation.
2Productivity
If existing methods are used for determining hardware configuration, then process simplicity is maintained, but efficiency in recommending configurations deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing performance data before actual configuration decisions are needed. The application monitor gathers data in advance, and the analytic engine prepares configuration recommendations proactively, so when expansion or implementation is needed, optimized configurations are already identified and ready for deployment.
Solution Approach 2:
The system implements feedback mechanisms where performance data from running applications is continuously collected and fed back into the analytic engine. This feedback loop enables the system to learn from actual performance metrics and continuously improve configuration recommendations, increasing efficiency through data-driven iterative optimization.
3Reliability
If performance targets are strictly enforced, then service quality is improved, but hardware cost increases
Solution Approach 1:
The system applies parameter changes by analyzing multiple performance parameters and configuration variables to find optimal settings that meet performance targets. The analytic engine adjusts hardware configuration parameters based on collected performance data, identifying the minimum necessary resources to achieve required performance levels rather than over-provisioning.
Solution Approach 2:
The system applies partial action by determining the minimum necessary hardware configuration to meet performance targets rather than providing excessive capacity. The analytic engine calculates precise configuration recommendations that satisfy performance requirements with optimal resource utilization, avoiding unnecessary hardware overhead while maintaining service quality.
Data Source
AI summary
Various embodiments of a computer-implemented method, computer system and computer program product provide a configuration recommendation. Request-processing performance data of an application is received. The request-processing performance data is collected by an application monitor during an execution of the application on a source hardware system. One or more request-processing performance measurements are determined based on the request-processing performance data. One or more target objectives of the application are received. An analytic engine is invoked to provide a configuration recommendation of a target hardware system on which to execute the application based on one or more request-processing performance measurements and one or more target objectives.