Cloud Application Deployment Architecture Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deploying applications in a cloud computing environment can be challenging due to the numerous options available, making it time-consuming to determine the best architecture that meets all platform and middleware requirements.
Innovation Solution
A method and system that analyze metadata of uploaded applications and compare it to previously deployed applications, presenting users with proposed architectures for deployment, allowing selection and continuous monitoring for potential improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually evaluate numerous architecture options for cloud deployment, then deployment accuracy and suitability can be improved, but deployment time and complexity increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing application metadata, comparing it with previously deployed applications, and generating recommended architectures without requiring manual user evaluation of multiple options. The system serves itself by building and utilizing a knowledge base of deployment patterns to automatically determine suitable architectures.
Solution Approach 2:
The system performs preliminary action by pre-analyzing and storing metadata from previously deployed applications in a knowledge base before new deployment requests arrive. This pre-processing enables rapid comparison and recommendation generation when new applications need deployment, avoiding time-consuming manual analysis.
2Reliability
If users manually determine optimal architecture for each application, then platform and middleware requirements can be met, but the process becomes complex and time-consuming
Solution Approach 1:
The system applies universality by creating a multi-functional platform that handles metadata extraction, comparison, analysis, and architecture recommendation generation through a single integrated system. This universal approach replaces multiple separate manual processes with one automated system that handles all deployment determination tasks.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the application and deployment environment. This intermediary automatically compares application metadata with the knowledge base and generates architecture recommendations, eliminating the need for users to directly navigate complex platform and middleware configuration options.
3Measurement precision
If comprehensive metadata analysis is performed on uploaded applications, then architecture recommendation quality improves, but processing time increases
Solution Approach 1:
The system applies partial action by focusing metadata analysis on the most critical elements needed for architecture determination rather than performing exhaustive analysis of all application aspects. This selective analysis maintains sufficient accuracy for deployment decisions while reducing processing time compared to comprehensive analysis of every application detail.
Data Source
AI summary
An approach for deploying and managing applications in a networked computing environment (e.g., a cloud computing environment). A user uploads an application for deployment in the networked computing environment. Metadata of the application is analyzed and compared to metadata of previously deployed applications. Using the comparison, a set of architectures used in conjunction with previously deployed application(s) with similar platform and middleware requirements are presented to a user. The user can select an architecture for deploying the application. The application is continuously monitored after deployment, and alternative architectures to improve the application can be presented to the user, if desired.