Virtual Machine Packing Using Resource Scarcity
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Solution Overview
Problem
The complex process of placing virtual machines onto host devices in data centers is inefficient due to the need for optimizing resource allocation during varying demand periods, leading to high electricity consumption and costs.
Innovation Solution
A method that calculates scarcity values for multiple parameters to determine the optimal placement of virtual machines on hosts, prioritizing those that consume scarce resources, using a heuristic to evaluate and select the best combinations across multiple scenarios, thereby reducing energy usage and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If virtual machines are consolidated to certain host machines during low demand times, then electricity costs are reduced, but the complexity of resource allocation increases
Solution Approach 1:
The patent transforms the resource allocation problem into a scoring system by changing parameters: it assigns scarcity values to different resource parameters (CPU, memory, storage, network) and calculates overall host scores by multiplying host capacity by overall scarcity. This parameter transformation converts a complex multi-dimensional allocation problem into a simplified single-dimensional sorting problem, resolving the contradiction between energy efficiency and allocation complexity.
Solution Approach 2:
The patent segments the resource allocation process into distinct steps: calculating scarcity for individual parameters, computing overall scarcity, determining host scores, sorting hosts, and selecting virtual machines. This segmentation breaks down the complex allocation problem into manageable sequential operations, reducing the perceived complexity while achieving optimal energy-saving consolidation.
2Productivity
If hosts are selected based on scarcity values for optimal VM placement, then resource utilization efficiency is improved, but the computational complexity of the packing problem increases
Solution Approach 1:
The patent applies parameter changes by transforming multiple resource constraints into a single scarcity-based scoring metric. Instead of handling multiple constraints simultaneously (which increases complexity), it converts CPU capacity, memory, storage, and network resources into weighted scarcity values, then combines them into an overall host score. This allows efficient VM placement based on a single sorting operation rather than complex multi-constraint optimization.
3Measurement precision
If multiple scenarios are evaluated to determine optimal VM placement, then placement accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating scarcity values for all resource parameters and computing overall host scores before the actual VM placement decision. By preparing the scoring framework in advance and sorting hosts by their scarcity-based scores, the system eliminates the need for time-consuming iterative evaluations during the placement phase, achieving both accuracy and efficiency.
Data Source
AI summary
A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.