Cross-OS Grafting for Rapid OS Configuration Exploration
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Solution Overview
Problem
Monolithic operating systems like Linux face long build times due to extensive dependencies between components, making it inefficient to explore and test configuration options for performance optimization, especially in DevOps cycles for cloud-native infrastructures.
Innovation Solution
The method employs library operating systems and cross-OS grafting to pre-build modules, generate configuration options, fetch and compile specific modules, link them to create images, and test performance metrics, significantly reducing build times and automating configuration testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If monolithic operating systems are used with extensive component dependencies, then system functionality and reliability are improved, but build time increases significantly
Solution Approach 1:
The patent segments the monolithic operating system into independent, modular components that can be built and tested separately. Each component is encapsulated with its dependencies clearly defined, allowing parallel compilation and reducing overall build time while maintaining system reliability through modular architecture.
Solution Approach 2:
The patent implements preliminary action by pre-building and caching common operating system components and libraries before the actual configuration testing begins. This allows the system to reuse pre-compiled modules during configuration exploration, significantly reducing redundant build time while maintaining system integrity.
2Productivity
If comprehensive configuration option exploration is performed in monolithic OS, then performance optimization is improved, but build and test time increases dramatically
Solution Approach 1:
The patent divides the configuration space into independent modular components, allowing the system to explore configuration options for each module separately rather than re-compiling the entire monolithic system for each configuration change. This segmentation enables efficient performance optimization across thousands of configuration options.
Solution Approach 2:
The patent creates lightweight copies or representations of configuration options that can be tested without full system re-compilation. By using configuration descriptors and metadata that represent different OS settings, the system can evaluate performance across extensive configuration spaces rapidly while maintaining accuracy.
3Ease of operation
If manual trial-and-error method is used to tweak kernel parameters, then expert control is improved, but operational efficiency deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically explore, test, and optimize configuration options without requiring manual expert intervention. The automated framework systematically evaluates configuration parameters, builds corresponding system images, and identifies optimal settings, thereby maintaining expert-level control while dramatically improving operational efficiency.
Solution Approach 2:
The patent incorporates feedback mechanisms that automatically evaluate performance metrics from configuration testing and use this information to guide subsequent configuration exploration. This closed-loop system maintains the precision of expert control by using performance feedback to steer the search toward optimal configurations while eliminating manual trial-and-error processes.
Data Source
AI summary
A method searches and tests for performance optima in an operating system (OS) configuration space. The method includes generating a plurality of OS configurations. For at least a first OS configuration, of the generated OS configurations, the method further includes: fetching a plurality of OS modules based on the first OS configuration; building a first OS image from the fetched OS modules; and testing the first OS image to determine a first value of a performance metric.