Cloud Application Deployment Task Insertion
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Solution Overview
Problem
The challenge in deploying virtualized applications to cloud infrastructure lies in configuring and managing customized settings across different cloud providers, such as VMware vCloud, Amazon EC2, and Microsoft Azure, where application designers lack initial knowledge of target infrastructure details like IP addresses and task dependencies, leading to potential deployment failures.
Innovation Solution
A method and system that utilize an application management server to generate application blueprints and deployment plans, including phase-based task execution and infrastructure-specific tasks, to ensure correct configuration and dependency management, especially for dynamically allocated IP addresses and task dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If application designers manually configure deployment processes for each cloud platform, then deployment can be performed, but the complexity of managing multiple cloud providers increases significantly
Solution Approach 1:
The patent implements a universal deployment framework that works across multiple cloud providers (VMware vCloud, Amazon EC2, Microsoft Azure) through a common architecture. The system uses standardized interfaces and abstraction layers that allow the same deployment process to be applied to different cloud platforms without requiring separate configuration for each provider, thereby reducing configuration complexity while maintaining broad compatibility.
Solution Approach 2:
The patent introduces an intermediary deployment management system that acts as a mediator between the application and various cloud providers. This intermediary handles the complexity of cloud-specific configurations by translating universal deployment requests into provider-specific commands, shielding application designers from the intricacies of each cloud platform while maintaining adaptability across multiple providers.
2Adaptability or versatility
If IP addresses are allocated dynamically to virtual machines, then cloud infrastructure flexibility improves, but the ability to configure applications at deployment time deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-configuring application deployment processes to query and retrieve dynamically allocated IP addresses before application installation. The system establishes a workflow where IP addresses are obtained through automated discovery mechanisms prior to configuration, allowing the application to be configured with the correct dynamic IP addresses without manual intervention, thus maintaining both flexibility and configuration ease.
Solution Approach 2:
The patent employs feedback mechanisms where the deployment system continuously monitors and retrieves dynamically allocated IP addresses from the cloud infrastructure. The system establishes feedback loops that query the cloud provider's IP allocation status and automatically update the application configuration with the current IP addresses, ensuring that applications are always configured with the correct dynamic addresses without requiring manual reconfiguration.
3Productivity
If deployment tasks are transmitted without considering task dependencies, then deployment speed increases, but deployment reliability decreases due to premature task execution
Solution Approach 1:
The patent applies segmentation by dividing the deployment process into distinct phases or stages, where each phase contains tasks that can be executed independently. The system identifies and separates tasks with dependencies from those without, allowing independent tasks to be executed in parallel for speed while ensuring dependent tasks are executed in the correct sequence, thus maintaining both deployment speed and reliability.
Solution Approach 2:
The patent implements dynamic task scheduling that adapts the deployment process based on real-time task completion status and dependency relationships. The system dynamically adjusts the execution order of tasks, enabling parallel execution of independent tasks to maximize speed while automatically enforcing dependency constraints to ensure reliability. This dynamic approach allows the deployment process to optimize for speed when possible while maintaining correctness when dependencies require sequential execution.
Data Source
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AI summary
A cloud computing environment consists of a cloud deployment platform with an application management server executing thereon, and a cloud management server deployed in a cloud infrastructure. When a cloud-based application is deployed to the cloud infrastructure, a deployment plan for the cloud-based application is read, where the deployment plan comprises a first plurality of tasks to be executed in the cloud infrastructure. A determination is made that one or more custom tasks are required to be executed in the cloud infrastructure. After the determination, the one or more custom tasks are inserted into the first plurality of tasks to generate a second plurality of tasks. The second plurality of tasks is then transmitted to the cloud management server for execution in the cloud infrastructure.