Cloud Resource Configuration Engine Automating Service Deployment
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Solution Overview
Problem
In cloud-based computing architectures, users spend significant time on infrastructure-level details and development cycles are prolonged due to the need for specific resource configuration, making service deployment inefficient.
Innovation Solution
A cloud computing environment with a configuration engine and resource allocation engine that autonomously manages and allocates resources based on defined service requirements, allowing developers to focus on functionality and operating characteristics without needing to know specific resource details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If users manually configure specific computing resources for service deployment, then service deployment can be achieved, but development time and effort increase significantly
Solution Approach 1:
The system enables self-service through automated resource provisioning. The service deployment automatically triggers resource allocation and configuration without manual intervention. The platform self-manages the entire lifecycle from resource selection to service instantiation, eliminating the need for users to manually configure computing resources.
Solution Approach 2:
The system performs preliminary actions by pre-configuring resource templates and deployment patterns. Common service configurations are prepared in advance as reusable templates, allowing rapid deployment when services are instantiated. The platform pre-establishes resource relationships and configurations that can be automatically applied during service deployment.
2Manufacturing precision
If services are developed with specific resource dependencies, then resource configuration can be optimized, but adaptability to different cloud environments decreases
Solution Approach 1:
The system implements universality through abstract resource models and standardized interfaces. Services are developed using platform-agnostic resource descriptions that can map to multiple underlying computing resources. The same service definition can be deployed across different cloud environments, hardware configurations, and resource types, achieving both precision and portability.
Solution Approach 2:
The system segments the resource configuration into two independent layers: service logic layer and resource implementation layer. The service definition contains only high-level resource requirements without specific implementation details. The platform handles the mapping between service requirements and actual computing resources, allowing services to be decoupled from specific resource configurations.
3Ease of operation
If users manage infrastructure-level details, then resource control is improved, but operational complexity increases
Solution Approach 1:
The system introduces an intermediary layer between users and infrastructure details. The platform abstraction layer mediates between service definitions and actual resource management. Users interact with simplified service templates while the platform automatically handles complex resource provisioning, configuration, and lifecycle management, reducing operational complexity while maintaining control capabilities.
Solution Approach 2:
The system uses copying by creating virtual representations of computing resources. Instead of directly managing physical hardware, the platform works with virtual resource models that replicate essential characteristics. Service deployments copy resource configurations from templates, and the platform manages the mapping between virtual and physical resources, simplifying user interaction while maintaining precise control.
Data Source
AI summary
One embodiment of the present invention sets forth a cloud computing environment that includes a service cloud and one or more services accessing the service cloud. The service cloud includes multiple resources of different types that support the execution of the services accessing the service cloud. Each resource and service in the cloud computing environment is configured via a centralized configuration service. In addition, resource allocation and predictive performance monitoring engines allocate resources and monitor the resources allocated to the services accessing the service cloud.