Curve Fitted Load Test Scheduling for Virtual Users

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Solution Overview

Problem

Automated and simulated load testing faces challenges in generating complex load test schedules that accurately simulate real-world conditions, often requiring manual configuration of virtual users over time.

Innovation Solution

An apparatus that accesses a plurality of points representing virtual users to be scheduled at specific times, applies curve fitting techniques like Bézier curve fitting to generate a load test schedule, and uses coroutines to manage computational resources, enabling the simulation of complex real-world conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual configuration of virtual users is used to create load test schedules, then the schedule can be customized to specific needs, but the complexity and time required increases significantly

Engineering Contradiction:

Improvecustomization capabilityVSAvoidconfiguration complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system automatically generates load test schedules by fitting curves to historically specified points, eliminating the need for manual configuration while preserving customization. The curve fitting algorithm self-adjusts to create optimal schedules based on past data patterns.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system copies historical load test data points and uses curve fitting to generate new schedules, rather than requiring manual creation from scratch. This preserves effective past configurations while automating the schedule generation process.

Inventive Principle:

Principle #26Copying

2Adaptability or versatility

If manual configuration of virtual users is used to create load test schedules, then the schedule can be customized to specific needs, but the time required increases significantly

Engineering Contradiction:

Improvecustomization capabilityVSAvoidschedule generation time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

Historical load test data points are stored and reused as the basis for future schedules. The curve fitting process quickly generates new schedules by building on previously specified configurations, dramatically reducing setup time while maintaining customization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system copies and adapts historical data points through curve fitting to generate new schedules rapidly, avoiding repetitive manual configuration work while preserving effective customization from past tests.

Inventive Principle:

Principle #26Copying

3Reliability

If a large number of virtual users are scheduled to simulate real-world conditions, then the accuracy of load testing improves, but the computational resources required increases

Engineering Contradiction:

Improveload testing accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system uses curve fitting to identify and schedule only the critical points needed to accurately represent load patterns, rather than uniformly distributing all possible virtual users. This achieves accurate simulation with optimized resource usage by focusing on key temporal points.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The curve fitting process transforms historical discrete data points into a continuous mathematical representation, allowing the system to efficiently calculate optimal virtual user scheduling parameters that maintain accuracy while reducing overall computational burden.

Inventive Principle:

Principle #35Parameter changes

4Extent of automation

If complex curve fitting techniques are applied to generate load test schedules, then the automation level increases, but the computational complexity of the scheduling system increases

Engineering Contradiction:

Improveschedule generation automationVSAvoidscheduling system complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The curve fitting module serves multiple functions: it processes historical data, generates schedules, and can be applied to different load testing scenarios. This universal approach automates schedule generation across various contexts without requiring separate complex systems for each case.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20220360517A1Scheduling virtual users based on curve fitting

Publication Date: 2022.11.10 MICRO FOCUS LLC

AI summary

According to examples, an apparatus may include a processor that may schedule virtual users in a load test of a system under test. An apparatus may access user-specified or prestored points. Each point may represent a number of virtual users that is to place a load on the system under test at a given time. The apparatus may apply curve fitting, such as Bézier curve fitting, to generate a curve on a graph in which one axis represents time and another axis represents a number of virtual users at a given time. In some examples, the apparatus may use a smoothing factor to adjust the contours of the generated curve. The apparatus may use the curve to determine curve-fitted points to schedule virtual users at times corresponding to the curve-fitted points. To instantiate the virtual users, the system may use coroutines to reduce computational requirements for instantiating the virtual users.