Automated Software Image Assembly via Parameter Mapping
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Solution Overview
Problem
Conventional software images are either monolithic and difficult to debug and test due to their large size and complexity, or they are broken down into individual features that require manual combination, leading to human error and logistical challenges in maintaining static images for various releases.
Innovation Solution
A software image production server that automatically generates software images by mapping adjustable parameters to expected levels of capability, allowing users to specify desired capabilities, thereby producing a software image with settings that match the closest expected level of capability, improving stability, performance, debugability, and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software images are made monolithic to include all features, then completeness and versatility are improved, but complexity and difficulty of debugging increase
Solution Approach 1:
The software image is segmented into modular components that can be independently selected and combined. Each module represents a discrete feature or functionality that can be packaged separately, allowing the system to assemble customized software images by selecting only the required modules rather than including all possible features in a single monolithic image.
Solution Approach 2:
The software image configuration transitions from static pre-defined bundles to dynamic assembly based on real-time requirements. The system maintains a repository of modular software components that can be dynamically selected, combined, and assembled into customized software images according to specific user needs and system requirements.
2Adaptability or versatility
If software images are broken down into individual features for customization, then adaptability is improved, but manual combination effort and human error increase
Solution Approach 1:
The system provides self-service automated assembly of software images by maintaining a repository of modular components with defined interfaces and dependencies. When a user specifies requirements, the system automatically selects appropriate modules, resolves dependencies, and assembles the software image without requiring manual intervention, thereby eliminating human error while preserving customization capabilities.
Solution Approach 2:
The system uses parameter-based configuration where software images are defined by sets of parameters specifying required features, performance criteria, and constraints. These parameters automatically drive the selection and assembly process, transforming the customization task from manual feature combination to automated parameter-driven configuration.
3Adaptability or versatility
If multiple static software images are maintained for different releases and feature combinations, then versatility is improved, but manufacturing complexity and maintenance effort increase
Solution Approach 1:
A single universal software image generation system serves multiple purposes by maintaining a repository of modular software components that can be assembled into various configurations. Instead of maintaining separate static images for different releases, the system uses one universal assembly process that can produce any required software image variant by selecting different combinations of modular components based on release requirements.
Solution Approach 2:
The software image delivery model transitions from static pre-built images to dynamic on-demand assembly. The system maintains a persistent repository of modular software components that can be dynamically assembled into the required configuration at the time of deployment, eliminating the need to pre-build and maintain multiple static software image versions for different feature combinations and releases.
4Productivity
If automated software image production is implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary organization of software components into standardized modular units with defined interfaces and metadata before the automated assembly process. This preliminary structuring of the software repository with consistent naming conventions, dependency declarations, and capability metadata simplifies the automated selection and assembly process, making the system more manageable despite the automation complexity.
Data Source
AI summary
In one embodiment, a server generates expected levels of capability associated with possible combinations of settings for first and second adjustable parameters for an aspect of a software image feature. The server receives an indication of a desired level of capability for the aspect of the software image feature and, based on the indication, identifies a particular expected level of capability associated with a particular possible combination of the settings for the first and second adjustable parameters. The particular expected level of capability is closer to a desired level of capability for an aspect of a software image feature than the other expected levels of capability associated with the possible combinations of the settings for the first and second adjustable parameters. The server produces a software image that includes the particular possible combination of the settings for the first and second adjustable parameters.