Abstract Resource Provisioning Across Heterogeneous Cloud Platforms
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Solution Overview
Problem
Conventional methods for deploying and maintaining cloud resources across heterogeneous cloud environments require significant developer effort due to the need for environment-specific scripts and deep understanding of each cloud environment, leading to performance degradation, security vulnerabilities, and incompatibilities.
Innovation Solution
A system and method that utilizes abstract resource definitions and resource managers to automate the deployment and maintenance of cloud resources across multiple cloud environments, using cloud-agnostic syntax and state system APIs to monitor and reconcile differences, ensuring consistent deployment and updates across diverse cloud platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If environment-specific scripts are used for deployment, then deployment accuracy for each cloud environment is improved, but device complexity and developer overhead increase significantly
Solution Approach 1:
The patent implements a universal provisioning system that can deploy cloud resources across multiple heterogeneous cloud environments (AWS, Azure, GCP, on-premise) using a single abstract resource definition language. The system translates this abstract definition into environment-specific deployment configurations automatically, eliminating the need for developers to write separate scripts for each cloud provider while maintaining deployment accuracy for each environment.
Solution Approach 2:
The system introduces an intermediary layer (provisioning system) between the abstract resource definition and the cloud environment-specific implementation details. This intermediary automatically handles the translation and adaptation of resource definitions to match each cloud environment's specific requirements, resolving the contradiction between universal simplicity and environment-specific precision.
2Reliability
If environment-specific scripts are maintained, then deployment reliability for each cloud environment is improved, but loss of time for updates and maintenance increases
Solution Approach 1:
The system maintains a single universal resource definition that serves all cloud environments. When updates are needed, developers modify the abstract definition once, and the system automatically generates updated environment-specific configurations for all cloud environments simultaneously, dramatically reducing maintenance time while preserving reliability through consistent abstract definitions.
Solution Approach 2:
The system implements automated detection of drift between the abstract resource definition and actual cloud environment states. When discrepancies are detected, the system automatically initiates reconciliation processes to align the cloud environments with the desired abstract definition, ensuring continuous reliability without manual intervention and reducing maintenance time.
3Manufacturing precision
If deep understanding of each cloud environment is required, then deployment precision is improved, but ease of operation deteriorates
Solution Approach 1:
The provisioning system acts as an intermediary that absorbs the complexity of cloud environment specifics. Developers interact only with the simplified abstract resource definition language, while the system handles the complex translation to cloud environment-specific APIs and configurations, maintaining both precision and ease of operation.
Solution Approach 2:
The system creates abstract representations (copies) of cloud resources that capture essential functional characteristics without exposing environment-specific implementation details. These abstract resource definitions can be deployed across different cloud environments without requiring developers to understand the underlying environmental nuances, as the system handles the adaptation automatically.
4Adaptability or versatility
If separate deployment scripts are used for each cloud environment, then adaptability to specific cloud environments is improved, but productivity decreases due to significant developer overhead
Solution Approach 1:
The system enables a single abstract resource definition to adapt to multiple cloud environments automatically. The provisioning system translates the universal definition into environment-specific configurations, achieving both versatility across cloud providers and high productivity by eliminating repetitive script writing and maintenance work.
Solution Approach 2:
The system replaces the mechanical process of manually writing and maintaining separate deployment scripts for each cloud environment with an automated translation mechanism. The abstract resource definition is automatically converted into cloud environment-specific deployment configurations, significantly improving developer productivity while maintaining full adaptability to different cloud platforms.
Data Source
AI summary
A method for deployment of cloud resources in one or more cloud environments includes receiving a request, through a user interface, to deploy a cloud resource, the request comprising an abstract resource definition and one or more target deployment locations; identifying and executing a first resource manager associated with a first target deployment location of the one or more target deployment locations; generating, with the first resource manager based on the abstract resource definition, a first manifest for deployment of the cloud resource at the first target deployment location; deploying, with the first resource manager, an instance of the cloud resource on a first cloud-computing infrastructure defined by the first target deployment location, wherein the instance is based on the first manifest; and returning, through the user interface, information corresponding to the instance of the cloud resource deployed on the first cloud-computing infrastructure.