Common Model Descriptor for Cloud Infrastructure Translation
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Solution Overview
Problem
The complexity and time-consuming nature of creating data models for cloud and container technologies, along with the issue of vendor lock-in due to tight coupling with infrastructure providers, hinder service agility and innovation in deploying virtualized applications.
Innovation Solution
A processor-implemented system and method that generates a common model descriptor based on reverse transformation, allowing for the identification of supported platforms and transformation into multiple model descriptors, enabling platform-independent specifications and auto-translation to target provider specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specific data models are created for each cloud or container technology, then the models can be tightly coupled with infrastructure providers, but this results in vendor lock-in and reduced service agility
Solution Approach 1:
The patent implements a universal data model framework that can represent multiple cloud and container technologies (AWS, Azure, GCP, Kubernetes, Docker) through a single common model structure. This universal model serves multiple functions by adapting to different infrastructure providers without requiring separate custom models for each, thereby eliminating vendor lock-in while maintaining reliable representation of infrastructure resources.
Solution Approach 2:
The patent introduces an intermediary layer consisting of transformation scripts and mapping mechanisms that translate between specific infrastructure provider models and a common data model. This intermediary enables compatibility with multiple providers without tight coupling, allowing the system to maintain service agility while ensuring reliable model representation across different cloud and container technologies.
2Ease of operation
If customized configurations are performed for each infrastructure provider, then the virtualized applications can be deployed to specific platforms, but this increases design and implementation complexity
Solution Approach 1:
The patent segments the model creation process into two distinct parts: a common data model that represents the infrastructure-agnostic portion, and transformation scripts that handle provider-specific configurations. This segmentation allows the majority of the model to be created once and reused across multiple providers, reducing design and implementation complexity while maintaining the ability to deploy to specific platforms through the modular transformation layer.
Solution Approach 2:
The patent performs preliminary action by establishing a common data model framework in advance that defines the core structure and relationships. This preliminary model serves as a template that can be quickly adapted to different infrastructure providers through configuration and transformation scripts, eliminating the need to design and implement complete custom models for each provider from scratch, thereby reducing complexity while preserving deployment capability.
3Manufacturing precision
If multiple data models are created for different technologies, then each model can be optimized for its specific platform, but this consumes significant time and resources
Solution Approach 1:
The patent merges the creation of multiple technology-specific models into a single common data model that captures the essential structure and relationships applicable across cloud and container technologies. By combining the common elements into one unified model and using transformation scripts to adapt it to specific platforms, the system maintains model accuracy for each technology while dramatically improving creation efficiency, as the common model serves as a reusable foundation for all providers.
Data Source
AI summary
In traditional systems and methods, to provide infrastructure, a plurality of data models needs to be created individually for each of the respective cloud or container technologies. The creation of data models is complex, time consuming, and has tight coupling with the Infra provider, resulting in vendor lock-in. Embodiments of the present disclosure, implements method of generating a model descriptor corresponding to plurality of specifications by (a) receiving, at a reverse transformation layer, a specific model descriptor as an input for a required target platform; (b) generating, by a common model descriptor generator, a common model descriptor based on a reverse transformation, wherein the reverse transformation comprising step of detecting supported platform by scanning the inputted specific model descriptor and invokes a specific reverse transformer; and (c) transforming, by a forward transformation layer, the common model descriptor to multiple model descriptors by invoking a plurality of transformers