Virtual Hypervisor Abstraction Layer for Cloud Resource Allocation
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Solution Overview
Problem
Cloud computing environments face challenges in optimizing resource allocation due to the separation of application and physical resource management, where solution-specific intelligence is difficult to incorporate into cloud management layers, leading to suboptimal resource utilization and responsiveness.
Innovation Solution
A virtual hypervisor abstraction layer is introduced to provide improved control over resource allocation decisions, allowing solution managers to manage virtual machines efficiently while maintaining the cloud manager's role as the ultimate physical resource manager, with methods for determining resource usage limits, migration, and share adjustments to ensure fair and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud computing separates application management from physical resource management, then cloud providers can aggregate large numbers of applications and achieve higher server utilization efficiency, but solution-specific intelligence becomes difficult to incorporate into cloud management layers
Solution Approach 1:
The system segments cloud management into two distinct layers: a virtualization layer handled by the cloud provider for physical resource aggregation and efficiency, and a solution management layer that retains solution-specific intelligence. This segmentation allows each layer to operate independently with its own optimization goals without interfering with the other.
Solution Approach 2:
The patent introduces an intermediary interface between the cloud provider's virtualization layer and solution managers. This intermediary enables solution-specific intelligence to be incorporated at the application layer while the cloud provider maintains control over physical resource allocation, bridging the gap between centralized efficiency and decentralized adaptability.
2Ease of operation
If cloud providers assume all responsibility for resource allocation and physical hardware management, then enterprises can deploy applications without considering hardware details, but optimization decisions cannot leverage application-specific intelligence
Solution Approach 1:
The system divides management responsibilities into two segments: cloud providers manage physical hardware and virtualization infrastructure, while solution managers handle application-specific optimization decisions. This segmentation enables enterprises to deploy applications simply without hardware concerns while still allowing intelligent optimization at the solution layer.
Solution Approach 2:
Solution managers are empowered to make autonomous optimization decisions for their applications using solution-specific intelligence. The system enables self-service optimization where each solution manager can independently optimize resource allocation for their applications without requiring cloud provider intervention, thereby maintaining both simplicity and efficiency.
3Device complexity
If solution managers give up problem-space specific workload management and rely on cloud-based physical resource management, then cloud providers can centrally manage resources, but solution optimization and service responsiveness deteriorate
Solution Approach 1:
The architecture segments management functions so that cloud providers handle centralized physical resource management while solution managers retain control over problem-space specific workload optimization. This segmentation ensures that service responsiveness is maintained through local optimization decisions while benefiting from centralized resource coordination.
Solution Approach 2:
The system enables local quality optimization where solution managers can apply problem-space specific intelligence to optimize their applications' performance and responsiveness. Each solution manager can tailor resource management to the specific requirements of their application while the cloud provider maintains overall infrastructure efficiency.
Data Source
AI summary
A system and method for allocating resources in a cloud environment includes determining permitted usage of virtual machines and partitioning resources between network servers in accordance with a virtual hypervisor generated in accordance with an abstraction layer configured as an interface between a solution manager and an interface to a cloud network. Resource usage limits are determined for each virtual machine associated with the virtual hypervisor, and the servers are analyzed through the virtual hypervisors to determine if the virtual machines need to be migrated. If reallocation is needed, virtual machine migration requests are issued to migrate virtual machines into a new configuration at the virtual hypervisor abstraction level. The servers are reanalyzed to determine if migration of the new configuration is needed. Shares are computed to enforce balance requirements, and virtual machine shares and limits are adjusted for resources according to the computed shares.