Containerized Workflow Engine Metadata Execution
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Solution Overview
Problem
Existing workflow engines lack portability and scalability, leading to inconsistent results across different environments and insufficient capacity during high-demand situations, as they are often tailored to specific coding languages and execution environments.
Innovation Solution
Implementing containerized workflow engines in a cloud computing environment that deploy workflow engines and application definition metadata into software containers, allowing for user-defined applications to be executed consistently across various platforms, with the ability to scale dynamically to meet demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If workflow engines are tailored to specific coding languages and execution environments, then they can provide specialized functionality, but they lack portability and produce inconsistent results across different environments
Solution Approach 1:
The patent introduces a metadata-based intermediary layer that decouples the workflow engine from specific programming languages and execution environments. The workflow is defined through language-neutral metadata that serves as a mediator between the engine and various execution contexts, ensuring consistent interpretation across different environments while maintaining specialized functionality through metadata configuration.
Solution Approach 2:
The workflow engine is designed to be universal by supporting multiple programming languages and execution environments through a common metadata interface. The same metadata definition can be executed across different platforms and languages, making the engine multi-functional and environment-agnostic while preserving specialized capabilities through metadata parameters.
2Reliability
If traditional workflow engines are deployed in fixed environments, then they maintain stable execution, but they lack scalability during high-demand situations
Solution Approach 1:
The patent implements dynamic scalability by allowing the workflow engine to adapt its execution capacity based on demand. The containerized architecture enables dynamic instantiation of workflow execution instances, and the metadata-driven approach allows flexible resource allocation while maintaining execution stability through consistent metadata interpretation across all instances.
3Adaptability or versatility
If workflow engines are customized for specific organizations, then they address unique needs, but they increase deployment complexity and maintenance costs
Solution Approach 1:
The patent segments the workflow configuration into reusable metadata templates and parameters that can be independently managed. This segmentation allows organizations to customize workflows by configuring metadata parameters rather than modifying the engine itself, reducing deployment complexity while maintaining customization capability. The metadata structure enables modular configuration that can be assembled without complex integration work.
Data Source
AI summary
Containerized workflow engines executing metadata for user-defined applications are described. A system utilizes user selections for configuring a user-defined application to identify application definition metadata. The system stores the application definition metadata for the user-defined application into a persistent storage. A workflow engine executes in a software container in response to receiving user context details and an invocation of the user-defined application. The workflow engine retrieves the application definition metadata from the persistent storage. The workflow engine inputs the user context details and executes the application definition metadata. The workflow engine outputs a result based on executing the application definition metadata.