Hypervisor CPU Mapping for Collision-Resistant VM Scheduling
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Solution Overview
Problem
Existing virtualization technologies face inefficiencies in CPU resource utilization due to workload scheduling collisions and migrations between guest virtual machines, leading to decreased performance of physical host computers.
Innovation Solution
A hypervisor-directed approach that predicts CPU usage patterns and distributes workload based on logical to physical CPU relationships, providing CPU allocation recommendations to guest virtual machines to avoid collisions and optimize physical CPU utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workload is distributed across multiple physical CPUs in a virtualized environment, then CPU resource utilization efficiency is improved, but workload scheduling collisions and migrations between guest virtual machines occur, leading to decreased performance
Solution Approach 1:
The hypervisor continuously monitors CPU usage patterns of guest virtual machines and uses this feedback to predict future CPU demands. This feedback mechanism enables the system to adjust workload distribution dynamically, preventing scheduling collisions while maintaining high CPU utilization efficiency.
Solution Approach 2:
The system performs preliminary analysis of CPU usage patterns to predict future CPU demands before workload conflicts occur. By anticipating CPU requirements in advance, the hypervisor can proactively distribute workloads to avoid scheduling collisions and migrations, thereby maintaining both high utilization and stable performance.
2Adaptability or versatility
If guest virtual machines independently schedule workload across physical CPUs, then flexibility in resource allocation is improved, but scheduling collisions and performance degradation occur
Solution Approach 1:
The hypervisor acts as an intermediary between guest virtual machines and physical CPUs. It receives CPU usage data from virtual machines, analyzes patterns, and directs workload distribution decisions. This intermediary role maintains the flexibility of independent VM scheduling while preventing performance-degrading collisions through centralized coordination.
Solution Approach 2:
The patent replaces the mechanical approach of direct VM-to-CPU scheduling with an intelligent system that uses CPU usage pattern analysis and prediction. This substitution enables more sophisticated workload management that maintains flexibility while avoiding the performance penalties of naive scheduling approaches.
3Measurement precision
If CPU usage patterns are analyzed and predicted by the hypervisor, then workload distribution accuracy is improved, but system complexity increases
Solution Approach 1:
The hypervisor performs self-service by automatically collecting CPU usage data from guest virtual machines, analyzing patterns, making predictions, and distributing workloads without external intervention. This automation reduces the operational complexity burden on system administrators while maintaining high prediction accuracy through continuous monitoring and analysis.
Data Source
AI summary
Hypervisor-directed usage of central processing unit (CPU) resources is provided. A hypervisor determines a logical to physical CPU relationship mapping between a subset of a plurality of logical central processing units (CPUs) and a subset of a plurality of physical CPUs using a CPU topology of a computer. The hypervisor distributes information regarding additional available processing capacity of at least one physical CPU to a plurality of guest virtual machines on a per-logical CPU basis based on the logical to physical CPU relationship mapping between the subset of the plurality of logical CPUs and the subset of the plurality of physical CPUs enabling the plurality of guest virtual machines to distribute workload based on predicted processing capacities of corresponding logical CPUs.