Workflow Optimization via Simulation-Based State Matching
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Solution Overview
Problem
Modern industrial workflows face challenges in optimizing resource allocation and meeting quality of service requirements in complex, distributed computing environments, particularly in real-time scenarios like Internet-of-Things environments, where scalability and elasticity are necessary to minimize costs while ensuring efficient execution.
Innovation Solution
A simulation-based online optimization method that collects provenance features from workflow executions, identifies control variables, and adjusts configurations to maximize utility scores by matching similar states in a simulation model, allowing for real-time optimization of resource allocation in Infrastructure as a Service (IaaS) environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workflows are executed in a shared infrastructure environment with large volumes of data and high complexity, then productivity and business automation increase, but resource allocation efficiency and quality of service requirements become difficult to meet
Solution Approach 1:
The system performs preliminary simulation of workflow executions under different resource allocation configurations before actual execution. By pre-evaluating multiple scenarios and identifying optimal configurations in advance, the system can ensure quality of service requirements are met while maintaining high productivity in complex distributed environments
2Productivity
If distributed computing resources are scaled up to handle complex workflows with real-time requirements, then productivity increases, but infrastructure costs increase
Solution Approach 1:
The system changes parameters of control variables (such as resource allocation, scheduling policies, and configuration settings) to optimize workflow execution. By dynamically adjusting these parameters based on simulation results and provenance features, the system achieves high productivity in real-time workflows without requiring excessive computing resources
Solution Approach 2:
The system uses simulation-based optimization to automatically determine optimal resource allocation configurations without requiring manual intervention or over-provisioning. The self-service approach allows the workflow management system to efficiently manage its own resource allocation, reducing the quantity of computing resources needed while maintaining productivity
3Reliability
If control variables are adjusted to optimize workflow execution, then quality of service improves, but system complexity increases
Solution Approach 1:
The system introduces simulation models as an intermediary between workflow definitions and actual executions. The simulation layer evaluates different control variable configurations and identifies optimal settings without requiring direct complex interactions with the underlying infrastructure, thereby improving quality of service while managing system complexity
4Productivity
If simulation models are used to evaluate different configurations, then resource allocation efficiency improves, but computational overhead increases
Solution Approach 1:
The system creates simplified simulation models that copy only the essential characteristics and behaviors needed for evaluation. By using abstracted representations rather than full-scale replicas, the system achieves improved resource allocation efficiency while minimizing the computational overhead required to run simulations
Data Source
AI summary
Techniques are provided for simulation-based online workflow optimization. One method comprises obtaining a state of concurrent workflows; obtaining state similarity functions that assign a similarity score between pairs of states; generating a simulation model of the workflow executions representing different configurations of at least one control variable in the workflow executions by mapping states with a highest similarity; obtaining at least one utility function that assigns a utility score to the states in the simulation model; determining a configuration of the control variable that maximizes the utility score for states in the simulation model; and for new concurrent workflows: obtaining a current state of the new concurrent workflows; identifying a most similar state with one of the determined configurations of the control variable in the simulation model with a highest similarity to the current state; and adjusting the configuration of the control variable of the new concurrent workflows to match the corresponding configuration of the control variable of the most similar state.