Dynamic Resource Allocation for Hard Provisioned Virtual Machines
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Solution Overview
Problem
Existing systems struggle to efficiently manage resource allocation to virtual machines, leading to over or under provisioning, which can impact the quality of computer-implemented services.
Innovation Solution
A method and system for managing resource allocation to virtual machines by obtaining resource consumption estimates from both a hypervisor and an agent hosted by the virtual machine, identifying resource inefficiencies, and remediating them through dynamic resource reallocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are statically allocated to hard provisioned virtual machines, then reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring resource consumption estimates from multiple sources (hypervisor, agents) and automatically adjusting the resource allocation for hard provisioned virtual machines. This allows the system to transition from static to dynamic provisioning, optimizing resource utilization while maintaining service quality through real-time adjustments.
Solution Approach 2:
The system employs feedback mechanisms by obtaining resource consumption estimates from multiple sources including hypervisors and virtual machine agents, comparing these estimates against allocated resources, and using this information to identify and remediate allocation inefficiencies. This closed-loop feedback enables continuous optimization of resource allocation.
2Reliability
If resources are over-provisioned to virtual machines, then reliability is improved, but resource waste increases
Solution Approach 1:
The patent enables virtual machines to self-report their resource consumption through hosted agents that provide accurate consumption estimates. This self-service approach allows the management system to make informed decisions about resource allocation based on actual needs rather than conservative over-provisioning, reducing waste while maintaining reliability.
Solution Approach 2:
The system dynamically changes resource allocation parameters based on monitored consumption patterns. By adjusting the allocated resources to match actual consumption levels identified through multiple estimation sources, the system eliminates both over-provisioning and under-provisioning, optimizing the balance between reliability and resource efficiency.
3Loss of energy
If resources are under-provisioned to virtual machines, then resource efficiency is improved, but service quality deteriorates
Solution Approach 1:
The patent implements preliminary resource allocation adjustments by proactively identifying under-provisioned virtual machines through multi-source consumption estimates and remediating allocation inefficiencies before service quality deteriorates. This preventive approach ensures resources are allocated efficiently without compromising service levels.
Solution Approach 2:
Through continuous feedback from hypervisors and virtual machine agents, the system monitors resource consumption patterns and automatically adjusts allocations to prevent under-provisioning. This real-time feedback mechanism ensures service quality is maintained while optimizing resource efficiency.
Data Source
AI summary
Methods and systems for managing provisioning of virtual machines. Virtual machines may host applications that may provide computer implemented services. Various hardware resources may be allocated to the virtual machines via a hypervisor. As the workloads of the applications change, the virtual machines may become over or under provisioned. To manage provisioning of virtual machines, various types of resource consumption estimates may be obtained. The resource consumption estimates may be used to ascertain how to provision various virtual machines to reduce or eliminate inefficient allocations of hardware resources for use by the virtual machines.