Model-Based Differencing for Selective Microservice Deployment
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Solution Overview
Problem
Modern software systems with microservices architecture face inefficiencies in deploying updates due to poorly understood component dependencies, leading to unnecessary rebuilding and redeployment of non-relevant system portions, resulting in prolonged deployment times and disruptions to running systems.
Innovation Solution
A system that analyzes current and previous component versions of application services to determine differences, generating instructions for selective updates to a target computing environment, thereby identifying only the components and dependencies that require changes, allowing for efficient and rapid deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional deployment methods are used to update application services, then all components are rebuilt and redeployed, but this results in prolonged deployment times and unnecessary disruptions to running systems
Solution Approach 1:
The patent segments the deployment process by analyzing component dependencies to identify only the specific services and components that require updates. Instead of redeploying the entire system, the deployment generator creates targeted deployment packages for affected components only, thereby reducing deployment time while maintaining system stability for non-affected parts
Solution Approach 2:
The patent performs preliminary analysis of component dependencies before deployment to pre-identify which services are affected by the upcoming changes. This advance preparation allows the system to selectively deploy only necessary components, avoiding unnecessary full-system redeployment and reducing overall deployment time
2Productivity
If selective update approach is implemented, then deployment time is reduced, but this requires automated identification of dependent components which increases system complexity
Solution Approach 1:
The patent implements self-service through automated dependency analysis where the system automatically identifies affected components and generates deployment instructions without manual intervention. The deployment generator component autonomously analyzes service dependencies, determines which components need updates, and creates selective deployment packages, thereby improving productivity while managing complexity through automation rather than manual processes
Data Source
AI summary
A system includes a memory that stores computer-executable components and a processor, operably coupled to the memory, that executes the computer-executable components stored in the memory. The computer-executable components include a deployment generator component that analyzes current component versions of application services and determines differences with previous deployment versions of the application services deployed to a target computing environment. A service deployment output component generates instructions to selectively update the application services to the target computing environment based on the determined differences between the previous deployment versions and the current component versions of the application services.