Workflow Engine Recommendations for Cross-Environment Deployment
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Solution Overview
Problem
Existing workflow engines require developers to rewrite workflows in different computing languages when switching environments, leading to inefficiencies, downtime, and waste of computational resources due to the lack of engine-agnostic solutions and limited knowledge of available engines.
Innovation Solution
A system that determines operational dependencies of a workflow and generates a feature vector to recommend the most suitable workflow engine using a machine learning model trained on historical workflows, ensuring compatibility and access within an entity's existing infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If developers rewrite workflows in different computing languages when switching workflow engines, then workflows can be executed on different engines, but development time and computational resources are wasted
Solution Approach 1:
The patent creates a copy representation of the workflow in a standardized format that can be executed across different workflow engines without rewriting. The workflow is transformed into a portable representation that preserves its logic and operations while being compatible with multiple engine types, eliminating the need to recreate workflows from scratch when switching engines.
Solution Approach 2:
The patent implements a universal workflow representation that can function across multiple workflow engine types. By standardizing the workflow format and creating engine-agnostic representations, the same workflow can be executed on different engines without modification, achieving multi-functionality and broad compatibility.
2Adaptability or versatility
If developers rewrite workflows in different computing languages when switching workflow engines, then workflows can be executed on different engines, but computational resources are wasted
Solution Approach 1:
The patent performs preliminary transformation of workflows into a standardized, portable representation before deployment. This upfront action converts workflows into an engine-agnostic format that can be reused across different engines, preventing the waste of computational resources that would occur if workflows were rewritten each time an engine change is needed.
Solution Approach 2:
The patent discards the engine-specific implementation details and recovers only the essential workflow logic in a standardized format. By separating the core workflow operations from engine-specific syntax, the system eliminates redundant computational work and allows reuse of the same workflow definition across multiple engines.
3Reliability
If developers are limited to features present within a given workflow engine, then current engine can be used, but adaptability to new operations is reduced
Solution Approach 1:
The patent creates a universal workflow representation that can accommodate features and operations from multiple workflow engines. The standardized format includes capabilities for expressing operations that may not be natively supported by a single engine, allowing workflows to leverage features from different engines while maintaining a consistent representation.
Solution Approach 2:
The patent introduces a standardized workflow representation as an intermediary layer between the workflow logic and the specific engine implementation. This intermediary format acts as a mediator that translates between different engine features and the core workflow operations, enabling access to features from multiple engines without requiring engine-specific code.
4Ease of operation
If developers select workflow engines based on current needs, then immediate requirements are met, but future scalability is limited
Solution Approach 1:
The patent implements a dynamic workflow representation that can adapt to different engine environments. The standardized format allows workflows to be deployed on current engines that meet immediate needs while maintaining the capability to migrate to future engines with enhanced features. The engine-agnostic representation ensures that workflows remain scalable and adaptable as engine technology evolves.
Data Source
AI summary
In some embodiments, reducing usage of computational resources associated with scaling computational-workflows to disparate execution engines via engine-agnostic computational-workflow engine recommendations may be facilitated. In some embodiments, the system receives a computational-workflow configured to execute within a first computational-workflow environment. The system then determines a set of operational dependencies for the computational workflow. The system then generates a feature vector comprising the set of operational dependencies to be inputted into a machine learning model configured to generate a first recommendation indicating a second computational-workflow environment to execute the first computational-workflow. The system may receive the first recommendation from the machine learning model, and deploy the first computational-workflow within the second computational-workflow environment.