Virtual Machine Placement via Power Cost Optimization
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Solution Overview
Problem
Existing virtual machine management systems fail to optimize energy usage across hardware platforms efficiently, leading to increased power costs and potential service quality issues, especially during periods of high demand.
Innovation Solution
A virtual machine optimizer determines an optimized hardware configuration by analyzing performance factors and power consumption per performance factor, allowing for the reduction of power usage by shutting off or placing hardware in reduced power mode, thereby managing cooling costs and adhering to power consumption limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If virtual machines are distributed across multiple hardware platforms, then service quality and reliability are improved, but energy consumption and power costs increase
Solution Approach 1:
The patent consolidates virtual machines onto fewer hardware platforms by calculating energy costs for different configurations and selecting optimized placements. This merging approach maintains service quality through proper virtual machine distribution while reducing the number of active hardware platforms, thereby lowering overall energy consumption and power costs.
Solution Approach 2:
The system changes the operational parameters of hardware platforms by adjusting power settings and operational states based on virtual machine placement optimization. By calculating energy costs and selecting optimal configurations, the system modifies power consumption parameters while maintaining acceptable service quality levels.
2Productivity
If hardware platforms are operated at full capacity, then performance and productivity are improved, but power consumption and cooling costs increase
Solution Approach 1:
The patent implements dynamic virtual machine placement that adjusts hardware configuration based on current power consumption limits and performance requirements. The system continuously evaluates energy costs and reconfigures virtual machine assignments to optimize the balance between productivity and power consumption, allowing hardware to operate dynamically rather than at fixed capacity levels.
Solution Approach 2:
The system modifies operational parameters by adjusting the number and configuration of active hardware platforms based on power consumption limits. By changing these parameters dynamically, the system maintains adequate performance while reducing power consumption and associated cooling costs during periods when full capacity is not required.
3Loss of energy
If virtual machines are consolidated onto fewer hardware platforms, then energy costs are reduced, but service quality may deteriorate
Solution Approach 1:
The patent performs preliminary calculations of energy costs for various virtual machine configurations before making placement decisions. By pre-evaluating different consolidation scenarios and their impact on service quality, the system can select optimization paths that reduce energy costs while maintaining acceptable service levels, avoiding premature consolidation that would degrade performance.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor service quality metrics alongside energy consumption data. This feedback loop allows the optimization process to adjust virtual machine placement decisions in real-time, ensuring that consolidation actions do not push service quality below acceptable thresholds while still achieving energy cost reductions.
Data Source
AI summary
An optimized placement of virtual machines may be determined by optimizing an energy cost for a group of virtual machines in various configurations. For various hardware platforms, an energy cost per performance value may be determined. Based on the performance usage of a group of virtual machines, a total power cost may be determined and used for optimization. In some implementations, an optimized placement may include operating a group of virtual machines in a manner that does not exceed a total energy cost for a period of time.