Software Performance Prediction via Environment Scaling
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Solution Overview
Problem
Current tools lack the capability to assist in planning for significantly increased software application usage rates, and they struggle with accurately emulating production environments in test settings, leading to inefficiencies in resource management and performance prediction.
Innovation Solution
A system and method that obtain initial resource parameters, determine performance parameters using a test environment calibrated with an environment scaling factor, and generate a resource recommendation display with multiple options based on a data model, enabling informed decision-making for resource allocation and performance enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current tools are used for planning software application usage, then existing resource management is maintained, but the capability to plan for significantly increased usage rates is lacking
Solution Approach 1:
The system creates a virtual copy of the production environment in a test environment that is calibrated using an environment scaling factor. This copied environment allows accurate prediction of performance under increased usage scenarios without affecting the actual production system, enabling reliable planning for future growth.
Solution Approach 2:
The system modifies test environment parameters by applying an environment scaling factor that calibrates the test environment to accurately emulate production conditions. This parameter adjustment enables the test environment to provide reliable predictions for performance under significantly increased usage rates.
2Ease of operation
If test environment is used to predict production performance, then resource management is simplified, but the test environment does not accurately emulate production environment
Solution Approach 1:
The system adjusts test environment parameters by applying a calibrated environment scaling factor that transforms the test environment into an accurate emulation of production conditions. This enables precise measurement of performance parameters while maintaining the simplicity of using a test environment for predictions.
Solution Approach 2:
The environment scaling factor acts as an intermediary that bridges the gap between the simplified test environment and the complex production environment. It allows the test environment to accurately emulate production behavior without requiring the test environment to be an exact replica, maintaining ease of operation while improving measurement precision.
3Device complexity
If resource parameters are determined without calibration, then system complexity is reduced, but performance prediction accuracy deteriorates
Solution Approach 1:
The system introduces a calibration process that modifies test environment parameters using an environment scaling factor. This adds a layer of complexity to the system but enables accurate determination of performance parameters by ensuring the test environment accurately reflects production conditions.
Data Source
AI summary
Computing platforms, methods, and storage media for managing software application performance are disclosed. Exemplary implementations may: obtain, by a processor, an initial set of resource parameters for a software application; determine, by the processor and using a test environment, performance parameters for the software application based on observed performance parameters in a production environment; determine, by the processor and using a data model, a revised set of resource parameters for the software application to process a revised amount of resource requests greater than the initial amount of resource requests; and dynamically generate, by the processor, a resource recommendation display comprising a plurality of resource recommendation options based on the determined revised set of resource parameters.