Application Analyzer for Cloud Container Classification
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Solution Overview
Problem
Existing systems require significant expertise and are time-consuming when moving applications to containers, which is not feasible for frontline IT designers, and there is a need for automated classification of applications for containerization.
Innovation Solution
A policy framework with an application analyzer and deployment controller that automatically analyzes applications based on policy attributes to determine suitability for container or non-container deployment, using automated learning to update policies and manage the lifecycle of deployed servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated learning is applied to update policies and classify applications, then productivity and ease of operation are improved, but device complexity increases
Solution Approach 1:
The system employs automated learning mechanisms where the application analyzer automatically updates policies and classifies applications without requiring manual expert intervention. The policy framework self-adapts by learning from deployment outcomes, enabling the system to service itself and improve over time while maintaining high productivity.
Solution Approach 2:
The patent introduces an application analyzer as an intermediary component that sits between the deployment system and applications. This analyzer uses automated learning to classify applications and determine suitable deployment configurations, effectively mediating the complexity by handling the analytical work automatically rather than requiring human experts to manually assess each application.
2Ease of operation
If automated classification is implemented to reduce expertise requirements, then ease of operation improves, but measurement precision may worsen
Solution Approach 1:
The system incorporates feedback mechanisms where deployment outcomes are fed back to the application analyzer, which uses automated learning to refine its classification accuracy over time. This continuous feedback loop ensures that while the system remains easy to operate without expert intervention, the classification precision improves through experience and learning from actual deployment results.
Solution Approach 2:
The patent implements preliminary policy frameworks and classification rules that are established before deployment occurs. These pre-configured policies provide a foundation for accurate classification, and the automated learning system builds upon this preliminary structure rather than starting from scratch, thereby maintaining both ease of operation and measurement precision.
Data Source
AI summary
A system includes a policy manger that includes a policy to describe policy attributes of an application that define whether the application can be deployed as a container server or as a non-container server. An application analyzer analyzes a given application with respect to the policy attributes to classify the given application as a container model or a non-container model. A deployment controller generates a corresponding container server for the given application if the given application is classified as a container model or generates a corresponding non-container server for the given application if the given application is classified as a non-container model.