Dynamic CPU Resource Management for Virtual Machines
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Solution Overview
Problem
Current systems management tools lack dynamic capabilities to efficiently manage CPU resources in virtual environments, leading to under- or over-provisioning issues as demands fluctuate, resulting in suboptimal resource utilization.
Innovation Solution
Implementing a method and apparatus for dynamic CPU resource management that collects CPU usage information and adjusts CPU shares of virtual machines based on real-time usage and user-defined criteria, using dynamic programming techniques to balance resource allocation and meet changing demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static or manual resource allocation is used, then system configuration is simple and easy to manage, but resource utilization efficiency deteriorates due to under-provisioning or over-provisioning
Solution Approach 1:
The patent implements dynamic CPU resource allocation that automatically adjusts CPU shares for virtual machines based on real-time monitoring of CPU usage metrics. The system continuously evaluates performance data and modifies resource allocation without manual intervention, transforming the static allocation model into a dynamic one that adapts to changing workload conditions, thereby resolving the contradiction between simple configuration and efficient resource utilization
Solution Approach 2:
The system employs feedback mechanisms by monitoring CPU usage metrics of virtual machines and using this information to automatically adjust CPU share allocations. The closed-loop control continuously gathers performance data, analyzes it against allocation policies, and implements resource reallocation decisions, enabling the system to optimize resource utilization while maintaining automated management without requiring complex manual configuration
2Reliability
If over-provisioning is used to meet worst case scenarios, then system reliability is improved, but resource utilization efficiency deteriorates due to under-utilization
Solution Approach 1:
The dynamic allocation system adjusts CPU shares in real-time based on actual workload demands rather than static worst-case assumptions. By continuously monitoring CPU usage and adapting allocations, the system maintains reliability for critical workloads while preventing resource hoarding during low-demand periods, thus resolving the contradiction between reliability and utilization efficiency
Solution Approach 2:
The system changes the allocation parameters (CPU shares) dynamically based on monitored performance metrics and workload conditions. Rather than fixing parameters to worst-case values, the system adjusts them continuously according to actual system state, enabling reliable operation when needed while optimizing utilization when demand is lower, thereby resolving the reliability-utilization contradiction
3Productivity
If under-provisioning is used to maximize resource availability, then resource utilization efficiency is improved, but system reliability deteriorates due to insufficient resources during peak demand
Solution Approach 1:
The dynamic allocation mechanism enables the system to operate efficiently during low-demand periods by reducing allocations, then automatically increases CPU shares for virtual machines experiencing high workload demands. This real-time adaptability allows the system to maintain high utilization efficiency while ensuring reliability is preserved during peak demand periods through automated resource redistribution
Solution Approach 2:
The system uses feedback from CPU usage monitoring to detect when virtual machines are experiencing resource constraints and automatically reallocates CPU shares to maintain performance levels. This closed-loop control ensures that reliability is maintained during peak demand while maximizing overall resource utilization, resolving the contradiction between efficiency and reliability
Data Source
AI summary
Methods and apparatuses for dynamic CPU resource management are provided. CPU related information is collected for one or more virtual machines. CPU shares and affinity of a virtual machine are dynamically changed, as needed, based on the CPU usage information for the virtual machine and based on a specified priority of the virtual machine.