Cloud Configuration Engine for Multi-Provider Resource Allocation
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Solution Overview
Problem
Managing the deployment of applications across multiple cloud environments is challenging due to the need for manual resource specification, which is time-consuming and error-prone, and often restricts flexibility, as tenants are limited to a single cloud provider, making it difficult to satisfy performance goals and reduce costs.
Innovation Solution
An intent-based cloud infrastructure configuration engine automates deployment, performance monitoring, and scaling by receiving declarative intent information from users, generating configuration options, and selecting resources from multiple cloud providers to optimize resource allocation based on performance metrics and cost models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual resource specification is used for cloud deployment, then deployment control is maintained, but deployment time increases and errors occur
Solution Approach 1:
The system enables self-service deployment by allowing users to define high-level deployment intents in natural language, while the automated configuration engine handles the complex resource specification, selection, and allocation across multiple cloud providers. This eliminates manual resource configuration while maintaining deployment accuracy through intelligent automation.
Solution Approach 2:
The patent replaces manual mechanical resource specification with an automated configuration engine that uses intent-based AI to interpret user requirements and generate optimal resource configurations. The system substitutes human manual configuration with automated intent recognition and resource allocation mechanisms.
2Ease of operation
If single cloud provider is used, then deployment simplicity is maintained, but flexibility and cost optimization are reduced
Solution Approach 1:
The configuration engine provides universal functionality by supporting multiple cloud providers (AWS, Azure, Google Cloud, etc.) within a single unified system. It can interpret deployment intents and automatically select optimal resources across different cloud providers, making the system both simple to use and highly flexible in terms of cloud provider choice.
Solution Approach 2:
The patent introduces an intermediary configuration engine that acts as a mediator between users and multiple cloud providers. This engine translates simple user intents into complex multi-cloud configurations, handling the complexity of interacting with different cloud providers while presenting a simplified interface to users.
3Productivity
If automated configuration is implemented, then deployment speed increases, but system complexity increases
Solution Approach 1:
The patent extracts the complexity of cloud configuration management into a dedicated configuration engine module. By separating this complexity into a standalone system component, the rest of the deployment process remains simple and user-friendly, while the extracted engine handles the complex tasks of resource selection, validation, and allocation automatically.
4Reliability
If performance monitoring and scaling are added, then service quality improves, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where performance monitoring data is continuously collected and fed back to the configuration engine. This enables automatic scaling decisions based on real-time service performance, improving service quality while managing complexity through closed-loop control that adapts to changing conditions automatically.
Data Source
AI summary
In some examples, a system receives input information for an application, the input information comprising a specification of a performance parameter relating to the application, and information of an arrangement of components of the application. The system generates, based on the input specification of the parameter and the information of the arrangement of components of the application, a plurality of configuration options representing respective different sets of resources, where a first set of resources of the different sets of resources includes resources of a plurality of cloud environments from different cloud providers. The system selects, based on a target goal, a configuration option of the plurality of configuration options, and output deployment information to cause deployment of the selected configuration option. The system adjusts an allocation of resources to the application responsive to performance metrics from a performance monitor that monitors performance of the application after the deployment.