Intelligent Resource Pool Manager for Heterogeneous Datacenter Provisioning
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Solution Overview
Problem
Existing datacenter solutions are inadequate for rapidly adapting to changing demands due to slow script-based installations, static pre-configured virtual machine images, and susceptibility to human error, particularly in heterogeneous environments where different operating systems and physical/virtual systems need to coexist.
Innovation Solution
Implementing intelligent resource pool management with hierarchical abstraction and decentralized management using intelligent resource pool managers (IRPMs) that can autonomously draw from and replenish resource pools, allowing for dynamic allocation and configuration of heterogeneous resources, such as physical and virtual servers, networks, and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If script-based installations are used for deploying application servers, then deployment flexibility is achieved, but deployment speed becomes slow (taking several hours)
Solution Approach 1:
The system pre-configures virtual machine images with necessary software and configurations before deployment needs arise. These pre-configured images are stored in a repository and can be rapidly deployed when needed, eliminating the time-consuming script-based installation process while maintaining deployment flexibility through the ability to customize images beforehand.
Solution Approach 2:
Instead of performing full script-based installations each time, the system creates copies of pre-configured virtual machine images and deploys them rapidly. The virtualization platform enables efficient copying and instantiation of VM images across multiple hosts, achieving fast deployment while maintaining configuration consistency and flexibility.
2Speed
If pre-configured virtual machine images are used, then deployment speed improves, but adaptability to heterogeneous environments deteriorates due to homogeneous replica requirements
Solution Approach 1:
The system creates a universal virtual machine image that can function across heterogeneous environments by incorporating multiple operating systems, application configurations, and software stacks within a single image. The virtualization platform allows this universal image to be adapted to different host environments (different hardware, operating systems, networks) while maintaining rapid deployment capability.
Solution Approach 2:
The system allows customization of virtual machine images with local quality variations - different operating systems, applications, and configurations can be embedded in different images or activated based on the target environment. This enables the same deployment mechanism to serve heterogeneous requirements by selecting or configuring the appropriate image variant for each deployment context.
3Ease of operation
If centrally managed homogeneous resource pools are used, then resource allocation simplicity is achieved, but responsiveness to unexpected demand changes deteriorates
Solution Approach 1:
The system implements dynamic resource pools where virtual machine images can be created, modified, and deployed on-demand in response to changing requirements. The platform allows administrators to rapidly provision new image types, update existing images, and allocate resources dynamically across the infrastructure, maintaining operational simplicity while achieving high responsiveness to unexpected demand changes.
4Ease of operation
If manual deployment processes are used, then deployment control is achieved, but susceptibility to human error increases
Solution Approach 1:
The system implements self-service deployment capabilities where the virtualization platform automatically performs configuration, provisioning, and deployment tasks based on predefined templates and policies. This automation eliminates manual errors while maintaining control through configurable parameters, approval workflows, and policy enforcement. The system can automatically validate configurations, detect conflicts, and execute deployments consistently without human intervention in the critical paths.
Data Source
AI summary
Disclosed are methods and systems for intelligent resource pool management of heterogeneous datacenter resources. In one embodiment, intelligent resource pool management is utilized to assist in application provisioning performed based upon a blueprint and deployment model defining requirements of the provisioned application. In other embodiments, intelligent resource pool managers are configured to work in concert with other intelligent resource pool managers and/or a centralized provisioning engine. Resource pools may also be configured in a hierarchical manner whereby higher level resource pools may automatically draw resources from lower level resource pools as directed by one or more intelligent resource pool managers.