Multi-level Image Extraction for Client Computing Environments
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Solution Overview
Problem
Current imaging technologies for client computing environments in mainframe systems are inefficient as they require duplicating entire image transfers, which leads to resource wastage and time consumption, as they lack the ability to differentiate and transfer only the changed portions with intact dependencies, especially when dealing with complex software and middleware changes.
Innovation Solution
A method for multi-level imaging and extraction that allows for the creation of sub-images of application, middleware, and system layers, enabling selective deployment and validation of changes by defining resource dependencies and using extraction levels to incorporate only necessary components into an active container on a guest system, thereby reducing unnecessary duplication and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire image transfers are performed to recreate client computing environments, then complete environment reproduction is achieved, but resource wastage and time consumption increase significantly
Solution Approach 1:
The patent segments the client computing environment image into multiple hierarchical layers including system layer, middleware layer, and application layer. This segmentation enables selective extraction and transfer of only the necessary layers for troubleshooting and validation, avoiding the need to transfer the entire image while maintaining environment reproduction completeness.
Solution Approach 2:
The patent extracts specific sub-images from the hierarchical image structure based on dependency analysis. By identifying and extracting only the necessary layers and components required for troubleshooting a specific client computing problem, the system reduces resource consumption while maintaining the ability to reproduce the necessary environment for validation.
2Reliability
If entire image transfers are performed, then complete environment reproduction is achieved, but deployment time increases significantly
Solution Approach 1:
The patent divides the imaging system into hierarchical layers (system, middleware, application) that can be independently extracted and transferred. This segmentation enables selective deployment of only the necessary layers for troubleshooting, significantly reducing deployment time while maintaining environment reproduction completeness.
Solution Approach 2:
The patent performs preliminary dependency analysis and identifies necessary extraction levels before actual image transfer. By pre-determining which layers and components are needed based on the client computing problem being investigated, the system optimizes deployment time while ensuring complete environment reproduction for validation.
3Adaptability or versatility
If multiple teams recreate client environments independently, then problem diagnosis and validation can be performed, but work duplication increases
Solution Approach 1:
The patent creates a universal imaging system that serves multiple functions: problem diagnosis, solution validation, and environment recreation. The hierarchical image structure with dependency tracking enables any team member to extract and transfer the necessary layers for their specific task, eliminating work duplication while maintaining full diagnostic and validation capabilities.
Solution Approach 2:
The patent implements dependency tracking and extraction level identification that provides feedback about what components are necessary for each task. This feedback mechanism guides teams in extracting only the necessary layers for their specific diagnostic or validation needs, preventing redundant work while maintaining versatility in problem solving.
4Loss of energy
If selective extraction of changed portions is implemented, then resource usage and time are reduced, but dependency management complexity increases
Solution Approach 1:
The patent segments the computing environment into hierarchical layers with clear dependency relationships defined between them. This segmentation approach simplifies dependency management by providing a structured framework where each layer's dependencies are explicitly tracked, making it easier to identify and extract only the necessary portions while managing complexity.
Solution Approach 2:
The patent performs preliminary dependency analysis and creates extraction level definitions before actual image extraction. By pre-establishing the dependency map and extraction levels, the system reduces the complexity of dependency management during extraction operations, as the necessary relationships are already documented and ready for reference.
Data Source
AI summary
A computer-implemented method includes saving a copy of a client computing environment to a computer memory on the host system. The processor writes an image of the client computing environment based on the saved copy of the client computing environment. The image includes an application layer, a middleware layer, and a system layer, and is based on the copy of the client computing environment. The image is extractable as a sub-image that includes one or more of the application layer, the middleware layer, and the system layer. The image includes a resource pattern having dependencies that associate two or more of the application layer, the middleware layer, and the system layer, such that the sub-image is combinable with an existing active container operating on a second computing system. The combination results in a functional copy of the client computing system with the changes extracted from the sub-image.