Dynamic Resource Allocation for Virtual Machines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing resource management systems for virtual machines face inefficiencies due to over or under provisioning, leading to suboptimal performance of computer-implemented services, as they struggle to accurately allocate resources based on changing workloads and differentiate between productive and unproductive resource usage.
Innovation Solution
A method and system that obtain resource consumption estimates from both hypervisors and agents hosted by virtual machines, classify workload characteristics, and dynamically adjust resource allocations to identify and remediate inefficiencies, ensuring that virtual machines are provisioned with resources proportional to their needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated to virtual machines based on static provisioning, then resource allocation simplicity is maintained, but resource utilization efficiency deteriorates due to over or under provisioning
Solution Approach 1:
The patent implements dynamic resource allocation for hard provisioned virtual machines by continuously monitoring resource consumption estimates from multiple sources (hypervisor, agents) and adjusting allocations based on changing workload conditions. This transforms static provisioning into a dynamic system that adapts to actual resource needs, resolving the contradiction between allocation simplicity and utilization efficiency.
Solution Approach 2:
The system employs feedback mechanisms by obtaining resource consumption estimates from multiple sources, comparing these estimates against allocated resources, and using this information to identify and remediate provisioning inefficiencies. This closed-loop feedback enables the system to automatically adjust allocations, improving efficiency without requiring complex manual intervention.
2Measurement precision
If resource consumption is monitored from multiple sources, then measurement precision improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary resource manager component that coordinates between multiple monitoring sources (hypervisor, virtual machine agents) and the provisioning system. This intermediary consolidates resource consumption estimates from various sources, reconciles differences, and presents unified measurements, thereby improving measurement precision while managing system complexity through centralized coordination.
3Adaptability or versatility
If dynamic resource adjustment is implemented, then resource allocation adaptability improves, but provisioning stability deteriorates due to frequent changes
Solution Approach 1:
The system performs preliminary actions by obtaining resource consumption estimates from multiple sources before making provisioning decisions. By gathering and analyzing data from hypervisors and agents in advance, the system can make informed, stable adjustments rather than reactive changes, thereby maintaining provisioning stability while achieving adaptability through deliberate, data-driven reallocations.
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.