Distributed Workflow Execution via Task Segmentation
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Solution Overview
Problem
As workflows grow in size and complexity, their execution times increase, making modifications and addressing defects increasingly costly and time-consuming, especially when executed on a single computational entity.
Innovation Solution
The workflow execution is optimized by scheduling tasks across multiple autonomous computational entities, balancing computational and communication costs, and utilizing explicit dependencies to fine-grain task grouping and segmentation, allowing for concurrent execution and efficient failure recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workflow is executed on a single computational entity, then task execution is simple to manage, but workflow execution time increases and productivity decreases
Solution Approach 1:
The workflow is divided into multiple independent tasks that can be executed in parallel across different computational entities. Each task represents a discrete unit of work that can be scheduled and executed independently, enabling concurrent processing and improving overall workflow execution speed while maintaining manageable complexity through modular task organization.
2Quantity of substance
If workflow size and complexity increase, then more tasks can be processed, but execution time expands and modification costs increase
Solution Approach 1:
The workflow is segmented into discrete, independently executable tasks that can be processed in parallel. This segmentation allows a large number of tasks to be distributed across multiple computational entities, reducing overall execution time while maintaining the ability to handle complex workflows through systematic task decomposition.
Solution Approach 2:
The workflow execution transitions from a single-dimensional sequential process to a multi-dimensional parallel process by distributing tasks across multiple computational entities. This dimensional expansion enables simultaneous execution of multiple tasks, dramatically reducing execution time for large-scale workflows while maintaining manageable complexity through structured task organization.
3Productivity
If workflow tasks are grouped coarsely, then scheduling is simpler, but fine-grained optimization and efficient resource utilization are lost
Solution Approach 1:
Tasks are segmented into fine-grained units that can be independently scheduled and executed. This fine-grained segmentation enables more precise resource allocation and better utilization of computational entities, as the scheduler can assign specific tasks to appropriate resources based on current system state and task requirements, improving overall productivity.
Solution Approach 2:
The task grouping and scheduling approach is dynamic rather than static. Tasks can be regrouped, rescheduled, and redistributed based on changing system conditions, resource availability, and workflow priorities. This dynamic scheduling enables efficient resource utilization while adapting to varying workflow demands without requiring complex static scheduling structures.
4Ease of repair
If workflow dependencies are not explicitly specified, then workflow definition is simpler, but fine-grained task grouping and failure recovery are constrained
Solution Approach 1:
Workflow dependencies are explicitly specified in advance during workflow definition, before execution begins. This preliminary specification of dependencies enables the scheduling system to perform fine-grained task grouping and optimization based on known dependency relationships. It also facilitates efficient failure recovery by allowing the system to quickly identify which tasks are affected by failures and need to be re-executed, without requiring complex runtime dependency analysis.
Data Source
AI summary
A workflow is designated for execution across a plurality of autonomous computational entities automatically. Among other things, the cost of computation is balanced with the cost of communication among computational entities to reduce total execution time of a workflow. In other words, a balance is struck between grouping tasks for execution on a single computational entity and segmenting tasks for execution across multiple computational entities.