Workflow Execution Control for Geospatial Modeling
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Solution Overview
Problem
Geospatial-temporal modeling workflows face challenges due to the complexity of processing expensive datasets, contextual information, and ensemble workflows, often resulting in inefficient resource utilization and unsatisfactory outputs.
Innovation Solution
A computer-implemented method generates workflow execution control rules based on target output metrics for geospatial-temporal modeling workflows. This method monitors workflow execution at runtime, analyzing intermediate and predicted output metrics to determine intervention actions for an automated workflow orchestrator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex geospatial-temporal modeling workflows are executed with expensive datasets and ensemble workflows, then modeling accuracy and comprehensiveness are improved, but resource consumption and execution time increase significantly
Solution Approach 1:
The system performs preliminary analysis of workflow intermediate outputs to predict final output metrics before the workflow completes execution. This early prediction mechanism allows the system to identify workflows that are unlikely to meet target metrics, enabling premature termination or reconfiguration to avoid wasting computational resources on expensive datasets and ensemble workflows that would produce unsatisfactory results
Solution Approach 2:
The system implements a feedback loop that continuously monitors workflow execution, compares intermediate outputs against target metrics, and uses machine learning models to predict final outcomes. This feedback mechanism enables dynamic adjustment of workflow parameters and resource allocation, optimizing the balance between modeling accuracy and resource consumption by scaling ensemble sizes or adjusting processing parameters based on real-time performance assessment
2Productivity
If comprehensive monitoring and prediction mechanisms are implemented for workflow execution, then output quality and resource efficiency are improved, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including a dedicated monitoring module that tracks workflow execution, a prediction module that uses machine learning to forecast output metrics, and a control module that implements decisions based on predictions. These intermediary layers manage the complexity by modularizing the monitoring and control functions, allowing the core workflow execution to remain relatively simple while adding intelligence through specialized sub-components that communicate through standardized interfaces
Data Source
AI summary
Intelligent workflow execution management includes generating, based on target output metrics for a geospatial-temporal modeling workflow, a collection of workflow execution control rules, monitoring execution of the workflow at runtime, the monitoring including monitoring intermediate output of the workflow execution and predicted output metrics, the predicted output metrics being metrics predicted to be obtained from completing the workflow, and determining one or more workflow execution intervention actions for an automated workflow orchestrator to take based on the defined target output metrics and the monitoring of the workflow execution.