Virtual Machine Resource Allocation via Dynamic Module Adjustment
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Solution Overview
Problem
Existing virtual machine management systems face challenges in dynamically optimizing system resource allocation, leading to under- or over-utilization of resources, which can result in inefficiencies and increased costs.
Innovation Solution
A method that involves generating requests for performance characteristics of virtual machines, analyzing responses to determine the need for adding or removing processing modules, and automatically adjusting the number of processing modules within a processing group to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If system resources are over-allocated to virtual machines, then performance and reliability demands are met, but hardware utilization decreases and costs increase
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring virtual machine performance characteristics and automatically adjusting the number of processing modules assigned to each VM. The system transitions from static resource allocation to dynamic adjustment, allowing resources to be reallocated based on actual performance needs, thereby optimizing the balance between reliability and hardware utilization.
Solution Approach 2:
The system establishes a feedback loop where performance characteristics of virtual machines are monitored, analyzed, and used to determine resource allocation adjustments. This feedback mechanism enables the system to respond to changing workloads and automatically optimize resource distribution, ensuring reliable performance while maximizing hardware utilization efficiency.
2Loss of energy
If system resources are under-allocated to virtual machines, then hardware utilization increases, but performance and reliability may be compromised
Solution Approach 1:
The system dynamically adjusts resource allocation based on monitored performance characteristics, ensuring that virtual machines receive sufficient resources to maintain required performance levels while avoiding over-allocation. This dynamic approach allows the system to optimize hardware utilization without compromising reliability, adapting resource distribution to actual workload demands.
Solution Approach 2:
The feedback mechanism continuously monitors virtual machine performance and uses this information to determine appropriate resource allocation levels. By analyzing performance characteristics in real-time, the system can identify when resources are insufficient and automatically adjust allocations to maintain reliability while maximizing hardware utilization efficiency.
3Reliability
If the number of processing modules is increased to meet performance demands, then virtual machine performance improves, but system complexity and management overhead increase
Solution Approach 1:
The system implements self-service automation where the management software automatically monitors performance characteristics, analyzes resource requirements, and adjusts processing module allocations without manual intervention. This automated self-service approach handles the complexity of resource management internally, improving virtual machine performance while keeping the user interface simple and reducing operational complexity.
Solution Approach 2:
The automated feedback loop continuously monitors performance characteristics and automatically adjusts processing module allocations based on analyzed data. This closed-loop automation eliminates manual management complexity by having the system manage itself, ensuring optimal performance while maintaining simple system administration through automated decision-making.
Data Source
AI summary
According to one embodiment, a method for managing one or more virtual machines includes generating a request for at least one performance characteristic for at least one virtual machine, the at least one virtual machine being associated with a processing group, the processing group including one or more processing modules; receiving a response to the generated request for at least one performance characteristic for the at least one virtual machine; automatically determining whether an increase in the number of processing modules included in the processing group is required, by analyzing the received response to the generated request; and, in response to a determination that an increase in the number of processing modules included in the processing group is required, automatically adding at least one processing module to the processing group.