Cross-Platform Model Orchestration for Legacy Software
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Solution Overview
Problem
Managing and executing multiple computer models across different platforms in a cohesive and efficient manner is challenging, particularly for legacy models running on specific software platforms, due to manual execution and configuration difficulties.
Innovation Solution
A system is developed to automate and orchestrate models through a configuration manager, execution manager, and results manager, which generate and execute workstreams with metadata, handle model dependencies, and store results, supporting various software languages and platforms, including Excel-based models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual execution and configuration methods are used for legacy models on specific software platforms, then models can be executed on their native platforms, but the complexity of managing and executing multiple models increases significantly
Solution Approach 1:
The patent introduces an intermediary layer (model execution system) that sits between the user and diverse model platforms. This intermediary automatically configures and executes models on their native platforms without requiring manual intervention, thereby maintaining platform adaptability while reducing management complexity through automation.
Solution Approach 2:
The system creates a universal model execution platform that can handle multiple types of models (R, Python, Excel, SAS) through a single interface. This multi-functional approach allows the system to adapt to various platforms while presenting a consistent user experience, reducing the complexity of managing diverse model execution requirements.
2Reliability
If manual configuration methods are used for each model, then models can be executed with their specific requirements, but the time and effort required to manage multiple models increases
Solution Approach 1:
The system performs preliminary actions by automatically generating configuration files and setting up execution environments before model execution. This pre-configuration approach ensures that each model's specific requirements are met (maintaining reliability) while eliminating the time-consuming manual configuration process for each individual model.
Solution Approach 2:
The model execution system implements self-service by automatically detecting model requirements and configuring execution parameters without user intervention. This self-configuration capability maintains execution accuracy while dramatically reducing the time and effort required to set up and manage multiple models.
3Productivity
If automated orchestration is implemented across diverse platforms, then execution efficiency improves, but the system complexity increases
Solution Approach 1:
The system segments the model execution process into distinct, manageable components: model discovery, configuration generation, execution orchestration, and result collection. This segmentation enables automated orchestration across diverse platforms while keeping system complexity manageable by breaking down the complex automation task into modular, independent functions.
Data Source
AI summary
An example system can include: at least one processor; and non-transitory computer-readable storage storing instructions that, when executed by the at least one processor, cause the system to: generate a configuration manager programmed to configure a workstream including a plurality of models, wherein the workstream defines metadata associated with execution of each of the plurality of models; generate an execution manager programmed to execute each of the plurality of models in the workstream according to the metadata; and generate a results manager programmed to access results of the execution of the workstream.