Cloud Infrastructure Creation with ML Resource Identity Cards
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Solution Overview
Problem
Designing and managing cloud architectures that utilize resources from multiple third-party providers is time-consuming and requires manual handling of cloud inheritance and dependencies.
Innovation Solution
A system utilizing machine learning models to analyze provider schema and documentation, identify resources, generate identity cards with parameters, and facilitate drag-and-drop resource selection and placement within a design architecture, automatically populating parameters and generating code based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration of cloud resources from multiple providers is used, then developers can create cloud architectures, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service automation by automatically analyzing provider documentation and schemas, identifying resources, generating identity cards with parameters, and configuring cloud infrastructure without manual intervention. The machine learning model autonomously performs tasks that previously required developers to manually configure each resource from multiple providers.
Solution Approach 2:
The patent replaces the mechanical manual process of configuring cloud resources with an automated system using machine learning models. The ML model analyzes documentation, identifies resources, and generates configurations, substituting the manual mechanical process with intelligent automated processing that significantly reduces time and effort.
2Reliability
If manual handling of cloud inheritance and dependencies is performed, then developers can manage resource relationships, but the complexity and error-proneness increase
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a mediator between provider documentation and resource configuration. This intermediary automatically analyzes schemas, identifies relationships and dependencies between resources, and generates correct configurations, eliminating the need for developers to manually manage complex inheritance and dependency relationships.
Solution Approach 2:
The system creates identity cards that are copies or representations of actual cloud resources, complete with their parameters and relationships. These identity cards allow developers to work with simplified representations while the system automatically manages the underlying complexity of resource dependencies and inheritance from multiple providers.
3Measurement precision
If comprehensive provider documentation and schemas are analyzed, then accurate resource identification is achieved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-analyzing and storing provider documentation and schemas in structured formats before they are needed for resource creation. The machine learning model processes and indexes provider information in advance, so when developers need to create resources, the system can quickly retrieve and match appropriate resources without re-analyzing entire documentation sets.
Data Source
AI summary
Systems, tools and methods to allow for the creation and management of any cloud infrastructure through an integrated development environment. The system generates resources and identity cards based on resources available for a service provider. The system provides a graphical development environment to select and place in a user interface the resources to create a system architecture. A user inputs information to a graphical representation of an identity card. Code is generated by the system relating to identity cards and resources selected for the system architecture.