Dynamic Virtual Machine Resource Allocation via Hypervisor Monitoring
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Solution Overview
Problem
Traditional enterprise networks lack an efficient method to dynamically configure virtual machines based on real-time resource utilization, leading to suboptimal allocation of resources and potential performance issues.
Innovation Solution
A system and method that utilize APIs from both physical and virtual machines to monitor resource usage, determine priority levels for processes, and adjust resource allocation dynamically through a hypervisor, enabling optimal configuration by correlating metadata from VIX and WMI APIs to manage CPU, memory, and network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional enterprise networks use static resource allocation for virtual machines, then system simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring virtual machine performance metrics (CPU usage, memory consumption, disk I/O) and automatically adjusting resource allocation based on actual workload demands. This transforms the static configuration into a dynamic system that adapts to changing conditions, resolving the contradiction between simplicity and efficiency by automating the optimization process.
Solution Approach 2:
The system establishes a feedback loop where performance data from virtual machines is collected, analyzed, and used to trigger automatic configuration adjustments. The monitoring component continuously gathers metrics, the analysis component evaluates whether thresholds are exceeded, and the configuration component implements changes accordingly. This closed-loop feedback mechanism enables efficient resource utilization without requiring manual intervention, balancing simplicity and productivity.
2Adaptability or versatility
If manual configuration of virtual machines is used, then system complexity is reduced, but adaptability to changing workload demands deteriorates
Solution Approach 1:
The patent enables virtual machines to self-adjust their resource allocation by implementing automated monitoring and configuration capabilities within the virtualization platform. The system autonomously detects performance issues, analyzes workload patterns, and reconfigures virtual machine settings without human intervention. This self-service approach maximizes adaptability to workload changes while minimizing the operational burden, effectively managing the trade-off between automation and complexity.
3Productivity
If resource allocation is not dynamically adjusted, then system stability is maintained, but performance optimization deteriorates
Solution Approach 1:
The patent implements conservative dynamic adjustment by applying resource allocation changes in incremental steps rather than making drastic modifications. When performance thresholds are exceeded, the system makes partial adjustments to resource allocation, gradually optimizing performance while monitoring system stability. This approach allows performance optimization to proceed without compromising system reliability, as the changes are controlled and reversible if needed.
Data Source
AI summary
A method and apparatus are disclosed to identify the operations/processes performed by one or more virtual machines. In one example method of operation, the system may perform identifying processes currently operating in an operating system and recording process information corresponding to each of the processes in a memory. The method may also include determining a priority for each of the processes currently operating in the operating system and incrementing a current priority of at least one of the processes.