Differential Virtual Machine Template Management for Cloud Environments
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Solution Overview
Problem
Virtual machine templates are typically large, consuming significant disk space and bandwidth resources, especially when transferred over networks, due to the need to store and transmit all necessary data for spawning new virtual machines.
Innovation Solution
The method involves creating a template hierarchy using base images and differential templates, where differential templates represent changes relative to base templates, reducing the amount of data needed for transmission by storing only the differences, and utilizing a template library system that allows for efficient generation and spawning of containers based on these templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual machine templates are stored and transferred over the network, then virtual machines can be spawned quickly without manual installation and configuration, but disk space and bandwidth resources are consumed significantly due to the large size of templates
Solution Approach 1:
The patent segments the virtual machine template into a base image and multiple differential templates. Each differential template contains only the changes from its parent template, breaking down the large monolithic template into smaller, manageable segments that can be stored and transferred efficiently.
Solution Approach 2:
The patent extracts only the differential changes from each template and stores them separately from the base image. This extraction principle allows the system to store minimal data (only changes) rather than complete copies of each template, significantly reducing storage and bandwidth requirements.
2Reliability
If complete virtual machine templates are transferred over the network, then all necessary data for spawning virtual machines is available, but bandwidth resources are consumed excessively
Solution Approach 1:
The template data is segmented into a base image and differential layers, where each layer contains only the necessary changes from its parent. This segmentation maintains data completeness while minimizing the amount of data that needs to be transferred over the network.
Solution Approach 2:
The base image is prepared and stored locally in advance, so that when a virtual machine needs to be spawned, only the differential templates (containing the specific changes) need to be transferred over the network. This preliminary preparation reduces network bandwidth consumption.
3Adaptability or versatility
If multiple virtual machine templates are stored, then diverse virtual machine configurations are available, but disk space is consumed significantly
Solution Approach 1:
The patent implements a nested structure where differential templates are nested within a hierarchy based on their parent-child relationships. Each differential template is nested within its parent image or template, forming a tree structure that allows diverse configurations to be stored efficiently by sharing common base images.
Solution Approach 2:
Multiple virtual machine templates that share common configurations are merged by storing only one base image and the differential changes for each variant. This combining principle allows the system to provide diverse configurations while minimizing total storage requirements by eliminating redundant data.
Data Source
AI summary
Techniques are provided for reducing the amount of data that to be transferred in a hybrid cloud system in order to spawn a VM at a private cloud computing system from a template library stored in a public cloud computing system. Instead of storing full virtual machine templates, template libraries at the public cloud computing system are “differential” libraries. Differential templates in these differential libraries only include data that is different as compared with either a base template or another differential template. Assuming a private cloud system already stores a base image, the private cloud system may spawn a VM based on a particular template simply by downloading the appropriate differential templates. This technique reduces the total amount of data that needs to be downloaded from the public cloud system in order to spawn a particular VM.


