Automated Cloud Streaming Error Detection via Image Comparison
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Solution Overview
Problem
Current methods for testing cloud streaming servers are labor-intensive, prone to human error, and lack automation, making it difficult to efficiently detect application errors and determine the optimal number of executable applications, which affects service reliability and efficiency.
Innovation Solution
An automated system that uses virtual client modules to capture and compare application execution data with reference images, determining server failures by matching key frames or screens, and adjusts the number of applications being tested to find the optimal load point, thereby reducing test time and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual testing methods are used to test cloud streaming servers, then test flexibility and adaptability are maintained, but labor intensity increases and test accuracy decreases due to human error
Solution Approach 1:
The testing system performs self-testing by automatically executing test applications, capturing screen images, comparing results with reference images, and generating test reports without human intervention. The system autonomously determines server normality based on image comparison algorithms, eliminating manual operation while maintaining high test accuracy.
Solution Approach 2:
The patent replaces manual mechanical testing operations with an automated computer-based system that uses software to execute tests, capture images, and perform comparisons. This substitution of mechanical/manual operations with automated computational processes reduces labor intensity while improving measurement precision through consistent, error-free execution.
2Reliability
If the number of applications being tested is increased to improve server performance measurement, then test comprehensiveness improves, but test time increases and productivity decreases
Solution Approach 1:
The system uses reference images as templates to compare against actual server execution images. By creating reference images from known good states and copying this baseline for comparison, the system can quickly determine server normality without manually analyzing each test result, thus improving test efficiency while maintaining comprehensive testing of multiple applications.
Solution Approach 2:
The patent captures and compares only key screen images or critical regions of interest rather than analyzing entire screen contents or all application behaviors. This partial action approach focuses testing on essential functionality, allowing comprehensive application testing without proportionally increasing test time, thereby maintaining productivity while improving reliability.
3Productivity
If automated testing is implemented to reduce labor and increase productivity, then test speed and productivity improve, but system complexity increases
Solution Approach 1:
The testing system is designed as a multi-functional platform that can test multiple different applications, capture various screen images, perform image comparison, generate reports, and determine server status all through a single unified system. This universal design consolidates multiple functions into one system rather than requiring separate systems for each function, managing complexity while maintaining high productivity.
Solution Approach 2:
The patent introduces a detection server as an intermediary component that coordinates between the cloud streaming server, test applications, and analysis processes. The detection server manages image capture, comparison operations, and result generation, acting as a mediator that simplifies the overall system architecture while enabling automated high-speed testing through centralized control.
4Measurement precision
If comprehensive server monitoring is performed to ensure service reliability, then detection accuracy improves, but processing time increases and productivity decreases
Solution Approach 1:
The system extracts and compares only the essential visual elements or key regions from full screen images rather than analyzing entire images. By taking out only the critical comparison elements needed to determine server normality, the system maintains high detection accuracy while significantly reducing processing time and improving productivity.
Data Source
AI summary
The present invention relates to an application error detection method for a cloud streaming service, and an apparatus and a system therefor. According to the present invention, with respect to an application executed in a streaming server, it is possible to detect an application error by determining whether a reference image coincides with an execution screen of the application.