Application Blueprint Generator for Cloud Deployment Automation
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Solution Overview
Problem
Current deployment tools for cloud computing platforms are process-driven, rely heavily on custom scripts and property files, and often cause resource contention, making it difficult to automate deployments and manage multi-tier applications effectively, leading to duplication of effort, cost, and configuration errors.
Innovation Solution
The system generates a customized application blueprint by scanning deployed applications using configuration discovery scripts to identify properties, which are then used to create a managed application deployment, automating the conversion of unmanaged applications to managed states and enabling deployment across different cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current deployment tools are used with heavy reliance on custom scripts and property files, then deployment flexibility can be achieved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent creates a blueprint template that captures the essential structure and configuration of an application deployment. This template serves as a reusable copy that can be instantiated multiple times with different parameters, eliminating the need for custom scripts for each deployment while maintaining flexibility through parameterization.
Solution Approach 2:
The deployment tool uses parameterized blueprints where configuration details are stored as variables and properties. Users can modify deployment parameters (such as resource allocation, location, timing) without changing the underlying tool structure or deployment logic, achieving flexibility through parameter changes rather than script customization.
2Reliability
If continuous polling for readiness of execution is used, then deployment reliability can be improved, but loss of energy and use of energy worsen
Solution Approach 1:
Instead of continuous polling, the system uses event-driven architecture where the cloud computing platform notifies the deployment tool of readiness events. The deployment tool transitions from active polling to passive event listening, reducing energy consumption while maintaining reliable deployment through event-triggered execution.
Solution Approach 2:
The system implements a feedback mechanism where the cloud computing platform provides status information about resource readiness through events and notifications. This feedback loop allows the deployment tool to respond appropriately without continuous polling, improving energy efficiency while maintaining deployment reliability through timely status awareness.
3Ease of operation
If a centralized mechanism is used for deployment coordination, then ease of operation can be improved, but resource contention and device complexity worsen
Solution Approach 1:
The patent divides the deployment coordination function into distributed agents running on each compute resource. These agents independently manage local deployment tasks without requiring constant centralized coordination. The centralized tool provides high-level orchestration and conflict resolution, while local agents handle execution details, reducing resource contention through distribution.
Solution Approach 2:
The system dynamically adapts its coordination mechanism based on the deployment context. For simple deployments, centralized coordination suffices; for complex multi-resource deployments, the system automatically engages distributed coordination modes. This dynamic behavior reduces resource contention by using lightweight local coordination where appropriate while maintaining centralized oversight for complex scenarios.
4Ease of manufacture
If traditional deployment tools are used without automation configuration, then ease of manufacture can be maintained, but productivity and loss of time worsen
Solution Approach 1:
The system performs preliminary actions by pre-defining deployment blueprints with standardized templates and configurations. Common deployment scenarios are pre-configured with appropriate resource allocation, timing, and dependency settings. When a deployment is initiated, the system retrieves the appropriate pre-configured blueprint and executes it with minimal customization, dramatically improving deployment speed while maintaining ease of use through template-based approaches.
Data Source
AI summary
Methods and apparatus to generate a customized application blueprint are disclosed. An example method includes determining a first computing unit within an application definition, identifying a property for the first computing unit, and generating an application blueprint based on the identified property of the computing unit.