Container File Generation via ML-Based Non-Functional Parameter Extraction
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Solution Overview
Problem
Current methods for creating container files in cloud computing environments are inefficient due to the inclusion of unnecessary non-functional parameters, which increase the size and management complexity of containers, and slow down the process of generating and updating container images.
Innovation Solution
A computer-implemented method using a learning model to analyze a codebase, identify relevant non-functional parameters, and automatically generate a container file that includes only the necessary functional and non-functional requirements, thereby optimizing container creation and updating processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to create container files, then all non-functional parameters are included to ensure comprehensive configuration, but the container size increases and management complexity increases
Solution Approach 1:
The patent extracts only the necessary non-functional parameters from the codebase using machine learning analysis, rather than including all possible parameters. The system identifies and extracts specifically relevant parameters such as security requirements, performance constraints, and resource needs, eliminating unnecessary parameters and reducing container management complexity while maintaining comprehensive configuration for essential aspects.
2Reliability
If all non-functional parameters are included in container files, then configuration completeness is improved, but the time required for generating and updating container images increases
Solution Approach 1:
The system extracts only the essential non-functional parameters needed for container configuration, eliminating redundant parameters. This extraction approach maintains configuration completeness for critical aspects while significantly reducing the time required to generate and update container images by processing a smaller, more focused set of parameters.
Solution Approach 2:
The machine learning model performs preliminary analysis of the codebase before container generation, identifying which non-functional parameters are actually relevant to the specific application. This preliminary classification enables faster container image generation by pre-determining the necessary parameter set based on code analysis, rather than processing all possible parameters.
3Measurement precision
If manual classification of non-functional parameters is performed, then accuracy can be maintained, but the time and resources required for container management increase
Solution Approach 1:
The patent replaces manual classification of non-functional parameters with an automated machine learning system. The ML model analyzes the codebase and automatically classifies relevant non-functional parameters, maintaining high accuracy through trained algorithms while eliminating the time-consuming manual process. This substitution preserves measurement precision through systematic analysis while dramatically reducing container management time and resource requirements.
Data Source
AI summary
A computer-implemented method classifies and creates a container file based on non-functional parameters. The method includes analyzing, by a learning model, a codebase. The codebase includes code for one or more applications. The method also includes identifying, based on the analyzing, a set of functional requirements for each application and a set of non-functional parameters. The method further includes classifying a first application of the one or more applications with a first non-functional parameter. The method includes generating a first container file for the first application. The first container file includes the functional requirements for the first application and the first non-functional parameter. The method further includes creating a first container from the first container file.