AI Diagram-to-Deployment Conversion for Microservice Handoffs
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Solution Overview
Problem
The deployment of microservice systems is cumbersome, expensive, and prone to bugs due to unclear deployment processes and lack of documented updates, making it difficult to transition between development teams.
Innovation Solution
A diagram-to-deployment converter using a machine learning model that takes a microservice architecture diagram and design parameters to automatically generate a deployment configuration, and updates both the architecture diagram and deployment configuration in response to changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual conversion of architecture diagrams to deployment configurations is performed, then deployment configurations can be generated, but the process becomes cumbersome, expensive, and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of converting architecture diagrams to deployment configurations with an automated machine learning-based system. The machine learning model automatically extracts information from architecture diagrams and generates deployment configurations, eliminating the need for manual intervention and reducing errors associated with manual processes.
Solution Approach 2:
The system creates automated copies of the conversion process through machine learning models that have been trained on examples of architecture diagrams and their corresponding deployment configurations. Once trained, the model can rapidly generate multiple deployment configurations from different architecture diagrams without additional manual effort, effectively copying the expertise of manual converters.
2Loss of information
If manual conversion process is used, then deployment configurations can be created, but updates and documentation become difficult to track
Solution Approach 1:
The automated machine learning-based conversion system establishes a feedback loop that automatically documents and tracks changes between architecture diagrams and deployment configurations. The system can detect when updates are made to either the diagram or configuration and automatically update the other, maintaining synchronization and creating an audit trail of all changes for better information tracking.
3Productivity
If automated machine learning conversion is implemented, then deployment process is streamlined and errors reduced, but initial setup and model training are required
Solution Approach 1:
The patent applies preliminary action by training the machine learning model in advance on a dataset of example architecture diagrams and their corresponding deployment configurations. This pre-training phase prepares the model to automatically perform conversions without requiring complex real-time processing or extensive manual configuration during actual deployment, thereby simplifying the implementation process.
Data Source
AI summary
A method includes receiving architecture instructions for a plurality of microservices, the architecture instructions comprising one or more design parameters for one or more of the plurality of microservices. The method also includes generating, by a processing device, in view of a machine learning model, a deployment configuration for the architecture instructions, wherein the deployment configuration comprises one or more deployment parameters in view of the one or more design parameters. The method also includes receiving an update to the deployment configuration. The method also includes response to receiving the update, modifying the architecture instructions to reflect the update to the deployment configuration.