Cloud Template Incomplete Reference Resolution
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Solution Overview
Problem
Cloud infrastructure template creation is time-consuming and prone to errors due to unresolved resource references and missing property definitions, leading to failed dependency checks and aborted stack creation in cloud management services.
Innovation Solution
An add-on component for cloud management services processes incomplete templates by applying production rules, statistical models, and decision trees to resolve references and define missing properties, using machine learning methods to determine optimal candidate definitions and quality metrics, and providing user verification for template validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud infrastructure templates require complete definitions of all resources and properties before deployment, then template reliability is improved, but template creation time and complexity increase significantly
Solution Approach 1:
The system performs preliminary analysis of the template to identify unresolved references and missing properties before deployment. By pre-processing the template and preparing candidate definitions in advance, the system eliminates the need for manual completion during deployment, thus improving reliability without significantly increasing creation time.
Solution Approach 2:
The template processing system automatically resolves incomplete definitions by generating candidate resource and property definitions without requiring manual intervention. The system serves itself by identifying gaps and proposing completions, thereby maintaining reliability while reducing the time burden on users.
2Manufacturing precision
If cloud infrastructure templates require complete definitions of all resources and properties before deployment, then manufacturing precision is improved, but ease of manufacture deteriorates
Solution Approach 1:
The system automatically analyzes incomplete templates and generates candidate definitions for missing resources and properties. This self-service capability maintains high definition precision while making template creation easier, as users no longer need to manually complete every detail.
Solution Approach 2:
The system introduces an intermediary processing layer that sits between template creation and deployment. This intermediary automatically resolves incomplete definitions by proposing candidate completions, thereby bridging the gap between imprecise user input and the precision required for successful deployment.
3Reliability
If cloud infrastructure templates require complete definitions of all resources and properties before deployment, then device complexity increases, but reliability improves
Solution Approach 1:
The template processing system is segmented into distinct functional modules: analysis module for identifying unresolved references, candidate generation module for proposing completions, and selection module for choosing the best definition. This segmentation manages complexity by breaking down the overall process into manageable, specialized components while ensuring reliable deployment.
4Ease of operation
If cloud infrastructure templates allow incomplete definitions, then ease of operation is improved, but reliability deteriorates
Solution Approach 1:
The system introduces an intermediary processing layer that automatically resolves incomplete template definitions by generating and selecting appropriate candidate completions. This intermediary ensures that even when users submit incomplete templates for ease of operation, the final deployed infrastructure maintains high reliability through automated completion.
Solution Approach 2:
The template processing system performs self-service by automatically analyzing incomplete definitions, generating candidate completions, and selecting the most appropriate definitions without requiring manual intervention. This maintains ease of operation while ensuring deployment reliability through automated quality control.
Data Source
AI summary
A method of enhancing an incomplete cloud infrastructure template may include receiving a template defining a cloud infrastructure stack associated with a customer account, wherein the template comprises at least one resource definition specifying a first cloud infrastructure resource. The method may further include performing a dependency check of the template. The method may further include identifying, in the resource definition, an unresolved reference to a second cloud infrastructure resource. The method may further include resolving the reference by producing, based on a plurality of templates associated with the customer account, a definition of the second cloud infrastructure resource. The method may further include modifying the template to include the definition of the second cloud infrastructure resource and causing the cloud infrastructure stack to be provisioned based on the template.