Workflow Task Redistribution Using Data-Flow Simulation
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Solution Overview
Problem
Existing workflow management systems face challenges in efficiently estimating and controlling resource utilization, improperly utilizing resources, failing to consider shared tasks across different workflows, and optimizing CPU or I/O intensive activities to avoid bottlenecks.
Innovation Solution
A workflow management system that selects a target task set based on data flow, re-distributes tasks to optimize data paths, and simulates data flow to determine an optimized task set, utilizing similarity analysis and resource management to avoid bottlenecks and improve resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workflow tasks are executed sequentially without re-distribution, then the workflow structure is simple, but resource utilization is inefficient and bottlenecks occur
Solution Approach 1:
The patent segments the workflow into multiple task sets that can be independently analyzed and re-distributed. By dividing the workflow into discrete task sets, the system can optimize each segment's resource allocation while maintaining overall workflow functionality, thereby improving resource utilization without creating unmanageable complexity
Solution Approach 2:
The patent introduces dynamic re-distribution of task sets based on data flow analysis and simulation results. The workflow structure transitions from a static sequential execution model to a dynamic model where task sets can be reallocated to different execution paths, enabling the system to adapt to varying workload conditions and optimize resource utilization in real-time
2Productivity
If tasks are re-distributed to optimize data paths, then workflow efficiency improves, but the complexity of managing and simulating multiple task sets increases
Solution Approach 1:
The patent performs preliminary simulation of data flow before actual workflow execution. By pre-simulating multiple potential task set distributions and evaluating their performance characteristics, the system can select the optimal execution path in advance, reducing the complexity of real-time decision-making and enabling efficient workflow execution
Solution Approach 2:
The patent incorporates feedback mechanisms where simulation results are used to guide task set re-distribution decisions. The system continuously monitors workflow execution and uses this feedback to refine future re-distribution strategies, creating a closed-loop optimization system that manages complexity through learned patterns and adaptive algorithms
3Productivity
If similar tasks across different workflows are not shared, then workflow independence is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements a universal task set library that stores optimized task sets which can be shared across multiple workflows. By creating a common repository of reusable task sets, the system enables different workflows to share common processing logic and resources, thereby improving overall resource utilization efficiency while maintaining workflow independence through selective reuse
Data Source
AI summary
Workflow optimization and re-distribution involves a target task set that is determined from a plurality of tasks in a workflow and based on a data flow of the workflow. The target task set is re-distributed to obtain at least one re-distributed task set. Each re-distributed task set indicates a respective data path different from a data path of the target task set and has a same function as the target task set. A simulated data flow for the at least one re-distributed task set is obtained by simulating data flowing in at least one data path indicated by the at least one re-distributed task set. An optimized task set for the target task set is determined based on the simulated data flow for the at least one re-distributed task set.