Automated Application Provisioning in Heterogeneous Datacenters
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Solution Overview
Problem
Datacenters face challenges in rapidly adapting to changing demands due to slow script-based installations and static pre-configured virtual machine images, which are not suitable for heterogeneous environments and prone to human error, failing to provide a dynamic and efficient deployment of application servers.
Innovation Solution
The method automates the provisioning and deployment of application server instances within a heterogeneous data center by using an augmented model to identify and allocate resources, replenishing pools as needed, and employing application blueprints and deployment models to configure servers, allowing for quick and flexible deployment of resources across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If script-based installations are used to deploy application servers, then deployment flexibility is maintained, but deployment speed becomes very slow (taking several hours)
Solution Approach 1:
The system performs preliminary configuration by creating a catalog of available application server images with their dependencies and requirements before deployment is needed. When a deployment request occurs, the system quickly matches requirements with pre-cataloged images and automates the provisioning process, avoiding slow script-based installations while maintaining flexibility through the catalog-based approach.
Solution Approach 2:
The deployment system automatically provisions application servers by selecting appropriate images from the catalog based on requested requirements, without requiring manual script execution. The system self-manages the entire deployment process including resource allocation, image selection, and server configuration, thereby improving deployment speed while maintaining adaptability.
2Productivity
If pre-configured virtual machine images are used for deployment, then deployment speed improves, but adaptability to heterogeneous environments deteriorates due to homogeneous replica requirements
Solution Approach 1:
The system creates a universal catalog that stores metadata about multiple application server images with different configurations, operating systems, and dependencies. This catalog serves as a multi-functional repository that can satisfy diverse deployment requirements across heterogeneous environments while maintaining fast image-based deployment. The catalog abstracts the heterogeneity, allowing the system to quickly select appropriate images without sacrificing adaptability.
3Adaptability or versatility
If manual processes are used in deployment, then flexibility in handling complex scenarios is maintained, but error susceptibility increases significantly
Solution Approach 1:
The system incorporates feedback mechanisms that automatically validate deployment requirements against available images in the catalog, check for compatibility, and verify successful provisioning. This automated feedback loop reduces human error while maintaining the ability to handle complex deployment scenarios through structured validation and error detection processes.
Solution Approach 2:
The system replaces manual mechanical processes with automated computational processes. Instead of manual script execution and configuration, the system uses automated image selection, resource allocation, and provisioning based on the catalog metadata. This substitution eliminates human error while maintaining flexibility through automated decision-making algorithms that can handle complex scenarios.
4Productivity
If resources are not proactively provisioned, then resource allocation efficiency is maintained, but response time to demand changes increases
Solution Approach 1:
The system performs preliminary work by creating and maintaining a catalog of application server images with their dependencies, requirements, and compatibility information before deployment requests arrive. This pre-processing enables rapid response to demand changes while maintaining efficient resource allocation, as the system only needs to match requests with pre-cataloged images rather than performing full provisioning from scratch.
Data Source
AI summary
Disclosed is a method of managing computer resources in a dynamic computing environment. The method includes identifying available resources from an available pool based on an augmented model, the available pool including resources unallocated resources, allocating the identified available resources in accordance with the augmented model, identifying reserve resources from a reserve pool based on the augmented model, the reserve pool including resources not allocated and not configured, and upon determining the available pool includes a number of resources below a threshold, replenishing the available pool with the identified reserve resources.