Platform-Independent Cloud Applications with Flexible Automated Deployment
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Solution Overview
Problem
Cloud application developers face challenges in writing applications that can run on multiple cloud platforms without significant code changes, as different platforms require learning new skills and rewriting code for different computation models, leading to errors and resource inefficiencies.
Innovation Solution
A method for developing platform-independent cloud applications using a Logical Application Model (LAM) that abstracts cloud-specific dependencies, allowing deployment across various platforms without code changes, by dynamically selecting optimal deployment models and automatically generating Infrastructure-as-Code (IaC) scripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud application developers write applications for multiple cloud platforms, then platform compatibility is improved, but code complexity and development time increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (abstraction layer) between the application code and cloud platform-specific services. This intermediary handles platform-specific variations, allowing developers to write platform-agnostic code while the intermediary translates it to platform-specific implementations, thereby reducing code complexity and improving platform compatibility
Solution Approach 2:
The patent creates universal abstractions that can work across multiple cloud platforms. By designing components with universal interfaces and behaviors that can be implemented differently for each platform, the system achieves multi-functionality where the same code base can deploy to various cloud platforms without significant modification
2Adaptability or versatility
If developers learn multiple cloud platform skill sets for deployment, then deployment capability is improved, but learning time and error rate increase
Solution Approach 1:
The patent introduces a deployment intermediary or abstraction layer that handles platform-specific deployment complexities. This intermediary manages the translation between a unified deployment interface and platform-specific deployment requirements, allowing developers to deploy to multiple platforms without learning each platform's specific deployment procedures
Solution Approach 2:
The system enables self-service deployment by automatically handling platform-specific configuration and deployment steps. The abstraction layer and automated tooling perform the complex platform-specific tasks without requiring developer intervention or expertise, thereby reducing learning time and errors
3Adaptability or versatility
If computation model is changed at deployment time, then deployment flexibility is improved, but code rewriting is required
Solution Approach 1:
The patent introduces an intermediary layer that sits between the application logic and the computation model execution. This intermediary handles the translation and adaptation of code to different computation models (FaaS, CaaS, VMaaS) at deployment time, allowing flexible model selection without requiring code rewriting by maintaining a consistent interface layer
Data Source
AI summary
A method for developing a platform-independent cloud application with a flexible deployment model and performing automated deployment on a target cloud platform is fulfilled in the ongoing description by (i) obtaining user-written application source code targeted to a Logical Application Model, (ii) obtaining from the user an Application Manifest that is agnostic to specific deployment models and target clouds, (iii) combining the user-written application source code with system-generated bindings and a main file to obtain a final application source code, (iv) dynamically determining based on heuristics or user-provided preferences, the optimal deployment model for each specific component of the cloud application, (v) transforming the Application Manifest into a deployment configuration file based on the optimal deployment model and the target cloud platform, (vi) automatically generating IaC scripts based on the deployment configuration file, and (vii) automatically deploying executable components of the cloud application on the target cloud platform.