Natural-Language Infrastructure-as-Code Generation for Cloud Platforms
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Solution Overview
Problem
Configuring cloud infrastructure requires technical expertise and is cumbersome and error-prone, leading to incorrect configurations and delays in software development or deployment due to the vast number of cloud platforms and resources available.
Innovation Solution
A system using artificial intelligence, specifically machine learning-based language models, generates infrastructure-as-code (IaC) from natural language requests, iteratively refining configurations until the desired state is achieved, and supports multiple cloud platforms without the need for extensive repositories or indexes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually configure cloud resources using traditional methods, then they can access and control cloud platform resources, but the process becomes cumbersome and error-prone requiring significant technical expertise
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the cloud platform configuration system. The AI assistant receives natural language requests from users, translates them into appropriate configuration actions, and executes them on the cloud platform. This intermediary layer shields users from the complexity of traditional configuration methods while maintaining full control over cloud resources.
Solution Approach 2:
The patent replaces the mechanical interaction required in traditional cloud configuration (manual navigation through complex interfaces, filling out numerous forms, understanding technical parameters) with a natural language-based system. Users can configure cloud resources by simply typing or speaking their requirements in plain language, eliminating the need for technical expertise in cloud platform-specific configuration syntax and procedures.
2Adaptability or versatility
If cloud platforms provide millions of different resource types, then they offer comprehensive functionality, but configuring resources becomes increasingly difficult and time-consuming
Solution Approach 1:
The AI assistant implements a feedback mechanism where it first presents a summary of the user's configuration request to the user before execution. This allows users to verify that the AI correctly interpreted their natural language request, especially important when dealing with complex cloud resources. Users can confirm, correct, or refine their requests, ensuring accurate configuration without manually navigating through numerous resource options.
Solution Approach 2:
The system performs preliminary analysis and interpretation of user requirements by the AI assistant before actual configuration execution. The AI assistant pre-processes natural language requests, identifies the intended cloud resources and configuration parameters, and prepares the configuration action. This preliminary step reduces the time users would otherwise spend manually exploring and selecting from millions of resource types.
3Manufacturing precision
If users have full control over configuration parameters, then they can achieve precise customization, but the configuration process becomes more complex and error-prone
Solution Approach 1:
The AI assistant serves as a intelligent intermediary that manages the complexity of configuration parameters. It understands user intent from natural language requests, automatically maps this to the appropriate configuration parameters for specific cloud resources, and handles parameter validation. This ensures precise customization while reducing errors, as the AI assistant has knowledge of correct parameter values and relationships that would be difficult for users to掌握 manually.
Solution Approach 2:
The AI assistant performs self-validation and self-correction of configuration parameters. It automatically checks for conflicts, validates parameter values against resource requirements, and corrects errors before configuration execution. This self-service capability maintains configuration precision while significantly reducing the error rate, as the system catches and fixes issues without requiring user intervention.
Data Source
AI summary
A system receives a natural language request for configuring a computing infrastructure using a cloud platform. The system executes a machine learning based language model to generate infrastructure-as-code (IaC) to configure a cloud platform to obtain the desired computing infrastructure. The system may display the IaC generated by the machine learning based language model via a user interface as an example for use by the user. The system may send instructions to the cloud platform to provision computing infrastructure in accordance with the IaC obtained from the machine learning based language model. The system may repeatedly determine whether the desired computing infrastructure is deployed on the cloud platform and if the computing infrastructure currently provisioned on the cloud platform fails to match the desired computing infrastructure according to the natural language request, the system reconfigures the computing infrastructure deployed on the cloud platform.