Cloud-Agnostic Virtual Machine Image Migration
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Solution Overview
Problem
The existing technologies face challenges in efficiently migrating and deploying virtual machine images across different cloud platforms due to proprietary image formats, incompatible cloud-specific layers, and the need for multiple conversion tools, leading to resource-intensive and error-prone processes that result in 'cloud lock-in', where users are locked to a specific cloud provider.
Innovation Solution
The system facilitates cross-cloud deployment and migration by obtaining a cloud-agnostic image representation from a first Virtual Machine Image (VMI) by removing cloud-specific layers, allowing the creation of a functionally equivalent VMI on a second cloud using a cloud standardization layer and application deployment layer, enabling seamless migration and deployment across various cloud providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If proprietary cloud-specific image formats and cloud-specific layers are used in Virtual Machine Images, then the image is optimized for a specific cloud platform, but the image cannot be migrated to other cloud platforms without manual conversion and customization
Solution Approach 1:
The patent segments the Virtual Machine Image into distinct layers: a cloud-agnostic base layer containing the operating system and applications, and cloud-specific overlay layers containing platform-specific configurations. This segmentation allows the base layer to be portable across clouds while the overlay layers can be selectively applied or removed based on the target cloud platform.
Solution Approach 2:
The patent introduces a cloud-agnostic base image as an intermediary representation that mediates between different cloud-specific image formats. This base image serves as a universal foundation that can be transformed into cloud-specific images by adding the appropriate overlay layers, eliminating the need for complete manual recreation of images across different clouds.
2Reliability
If manual image conversion and customization processes are used for cross-cloud migration, then cloud-specific optimizations are maintained, but the migration process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent performs preliminary actions by pre-configuring the cloud-agnostic base image with all necessary operating system components, applications, and generic configurations before migration. This preliminary setup eliminates the need for time-consuming post-migration customization and ensures functional equivalence is maintained across different cloud platforms.
Solution Approach 2:
The patent uses the cloud-agnostic base image as a reusable template that can be copied and deployed across multiple cloud platforms. Instead of manually recreating images for each cloud, the same base image can be copied and simply have different cloud-specific overlay layers applied, dramatically reducing migration time and ensuring consistency.
3Productivity
If cloud-specific configuration settings and bootstrapping mechanisms are embedded in images, then the image works optimally on its native cloud platform, but users become locked into that specific cloud provider
Solution Approach 1:
The patent makes the image configuration dynamic by allowing cloud-specific overlay layers to be selectively applied or removed based on the target cloud platform. The base image remains constant and portable, while the overlay layers dynamically adapt to the specific cloud environment, enabling both optimization for each platform and portability across platforms.
Solution Approach 2:
The patent applies local quality by keeping the base image uniform and cloud-agnostic while allowing only the specific overlay layers to contain cloud-platform-specific configurations. This ensures that each cloud platform receives the appropriate local adaptations while the core image remains portable and reusable across different clouds.
Data Source
AI summary
Embodiments disclosed facilitate obtaining a cloud agnostic representation of a first Virtual Machine Image (VMI) on a first cloud; and obtaining a second VMI for a second cloud different from the first cloud, wherein the second VMI is obtained based, at least in part, on the cloud agnostic representation of the first VMI.