Load Test Metric Extraction for Low-Input Performance Modeling
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Solution Overview
Problem
Conventional performance modeling systems are inefficient, inflexible, and cumbersome, requiring excessive user interactions and computational burdens to establish performance modeling parameters, failing to accurately identify relationships between performance metrics and system environments, and providing limited functionality in load testing.
Innovation Solution
A user-friendly graphical user interface that allows for limited user inputs to determine computing-resource metrics and simulate system performance, integrating multiple applications and tools to automate load tests, and dynamically adjust load values, while providing real-time load test results and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems require multiple graphical user interfaces and extensive user interactions to capture digital information, then the system can gather comprehensive performance modeling parameters, but the overall burden on implementing devices increases and interaction times lengthen
Solution Approach 1:
The system performs preliminary actions by automatically extracting performance metrics from multiple data sources before the user needs to provide input. The automated metric extraction component gathers data from application servers, databases, and other systems in advance, so that when the user interacts with the performance modeling system, comprehensive parameters are already available or can be quickly assembled without requiring the user to navigate through multiple interfaces.
Solution Approach 2:
The system introduces an intermediary component - the automated metric extraction system - that acts as a mediator between the user and the performance modeling process. This intermediary automatically collects and processes data from various sources, translating complex data gathering requirements into automated operations, thereby reducing the burden on the user while maintaining comprehensive parameter collection.
2Adaptability or versatility
If conventional systems require a long list of metrics to be provided by the client device, then the system can perform comprehensive performance modeling, but additional steps and user interactions are required
Solution Approach 1:
The system implements self-service by having the automated metric extraction component independently gather and process performance metrics from multiple data sources without requiring user intervention. The system serves itself by automatically extracting, validating, and assembling the necessary metrics, thereby maintaining comprehensive performance modeling capability while significantly reducing the number of metrics the user must manually provide.
Solution Approach 2:
The automated metric extraction component serves multiple functions: it extracts metrics from various data sources, validates metric formats, assemblies metric sets, and prepares data for performance modeling. This multi-functional approach allows the system to maintain versatile performance modeling capabilities while reducing operational complexity for the user.
3Ease of manufacture
If conventional systems use generic methods to gather load test input metrics, then the system can maintain simplicity, but the functionality is limited and unwarranted assumptions are made
Solution Approach 1:
The system transitions from static, generic metric collection methods to dynamic, adaptive extraction processes. The automated metric extraction component dynamically adjusts its data collection and processing strategies based on the specific performance modeling requirements, the available data sources, and the context of each performance test scenario, thereby enhancing functionality while maintaining operational simplicity.
Solution Approach 2:
The system changes parameters by automatically adjusting metric extraction criteria, data formats, and collection methods based on the specific performance modeling needs. Rather than using fixed generic methods, the system adapts its parameter settings to optimize both simplicity and functionality for each specific load test design scenario.
4Reliability
If conventional systems require excessive standardized inputs, then the system can ensure data consistency, but the system becomes cumbersome and confusing
Solution Approach 1:
The system replaces the mechanical process of manual data entry and standardized input validation with automated metric extraction and processing mechanisms. The automated system handles data consistency through programmatic validation and transformation, eliminating the need for users to navigate complex standardized input forms while maintaining reliable data consistency through automated checks and conversions.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating computing-resource metrics based on limited user inputs provided through user interaction with a graphical user interface and then performing a system load test simulating system performance. In particular, in one or more embodiments, the disclosed systems provide a graphical user interface that requires limited user input to generate a load test that can determine computing-resource metrics based on user input of a load test script, test dataset, target throughput metric, and a load metric.