OS Component Packaging Using Telemetry-Based Dependency Clusters
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Solution Overview
Problem
Conventional operating systems (OSes) inefficiently hydrate OS components due to unknown dependencies, leading to sequential, piecemeal downloads that waste resources and increase latency, while developer-selected packages often include unused components, wasting storage and processing resources.
Innovation Solution
Machine-learning interdependencies of OS components by analyzing telemetry data from numerous end devices to group interdependent components into clusters, which are then hydrated together as a single package to a client device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If OS components are downloaded sequentially on-demand as applications discover dependencies, then applications can obtain components as needed, but this increases latency and wastes processing and networking resources
Solution Approach 1:
The system pre-computes and stores dependency graphs that map applications to their required OS components before runtime. When an application is launched, the system queries the pre-computed dependency graph to identify all required components upfront, enabling parallel downloading of multiple components simultaneously rather than sequential on-demand retrieval, thus reducing latency while maintaining on-demand availability
Solution Approach 2:
The system dynamically adjusts component download strategies by analyzing real-time application behavior and dependency relationships. It transitions from static sequential downloading to dynamic parallel downloading based on discovered dependencies, optimizing resource utilization and reducing overall download time while adapting to different application scenarios
2Ease of manufacture
If developer-selected packages of OS components are used, then components are pre-packaged for deployment, but these packages frequently include unused components that waste storage and processing resources
Solution Approach 1:
The system extracts only the specific OS components that are actually required by an application from the complete set of available components. By querying the dependency graph with the application's manifest, it identifies and extracts precisely the needed components, excluding all unused components from the package, thus eliminating waste of storage and processing resources while maintaining ease of deployment
Solution Approach 2:
The system tailors component packages to local application requirements rather than using universal pre-packaged sets. Each application receives a customized package containing only its specific dependencies, making the quality and composition of each package locally optimized for that application's needs, thereby reducing overall resource consumption across the system
3Reliability
If OS components are downloaded in batches as dependencies are discovered, then complete functionality is eventually achieved, but valuable networking and storage resources are wasted on multiple download operations
Solution Approach 1:
The system pre-computes complete dependency sets for applications by analyzing application manifests against the dependency graph before any downloading occurs. This preliminary analysis identifies all required components in a single operation, enabling the system to download complete component sets in one batch rather than multiple sequential batches, thus reducing networking and storage resource consumption while ensuring functional completeness
Solution Approach 2:
The system merges multiple individual component download operations into a single consolidated download batch. By combining all required components identified through dependency analysis into one unified package, it eliminates redundant networking operations and storage I/O, reducing energy consumption while maintaining complete functionality
Data Source
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AI summary
The correct clusters of operating system (OS) components to package for hydration to client devices is learned through analyzing telemetry data of numerous end devices. The telemetry data captures snapshots of the OS components that were used or loaded by the end devices during runs of different applications. An OS component service mines this telemetry data and compares the frequency in which pairs of OS components are used by the end devices together during the different applications runs. Using on these frequencies, the OS components are filtered in a relationship graph and an clustering algorithm is applied to determine whether the OS components are interdependent (meaning used together) or independent (meaning not used together). Interdependent OS components are packaged together into a hydration package and may be transmitted to client computing devices requesting any of their constituent OS components.