Dispatching Engine for Dynamic Task Pipeline Orchestration
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Solution Overview
Problem
Current server execution infrastructures fail to orchestrate tasks that require coordination of multiple types, handle retries for failed tasks seamlessly, and resolve race conditions, and lack robust backup functionality for system failures during back-end services.
Innovation Solution
A system and method for managing task execution in a pipeline where tasks are queued for parallel execution, with a dispatching engine monitoring and ensuring that subsequent tasks are executed only after previous tasks have completed, and incorporating error handling to requeue tasks and maintain processing states for resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are executed in parallel without coordination, then productivity increases, but reliability deteriorates due to race conditions and unhandled failures
Solution Approach 1:
The patent introduces a dispatching engine as an intermediary component that coordinates task execution. This engine monitors task states, manages dependencies, and ensures proper sequencing while allowing parallel execution where safe. The dispatching engine acts as a mediator between the parallel task execution system and the pipeline orchestration requirements, resolving race conditions through centralized coordination without completely serializing the execution flow.
Solution Approach 2:
The system dynamically adjusts task execution based on real-time conditions. Tasks can transition between different states (queued, executing, completed, failed) and the system adapts the execution strategy accordingly. The dispatching engine dynamically determines which tasks can proceed in parallel versus which require sequential execution based on dependency relationships and current system state, optimizing both productivity and reliability adaptively.
2Reliability
If complex orchestration is implemented to coordinate multiple task types, then reliability improves, but device complexity increases
Solution Approach 1:
The dispatching engine is designed as a universal coordinator that handles multiple task types (data retrieval, processing, transformation, loading) through a single unified mechanism. Rather than implementing separate orchestration logic for each task type, the system uses a general-purpose dispatching engine that can manage any task within the pipeline, reducing overall system complexity while maintaining reliable coordination.
Solution Approach 2:
The system manages complexity by parameterizing task definitions and dependencies rather than hardcoding orchestration logic. Tasks are defined with parameters specifying their type, dependencies, and execution requirements, allowing the same dispatching engine to handle diverse task scenarios through configuration rather than structural complexity.
3Reliability
If sequential execution is used to ensure proper ordering, then reliability improves, but productivity decreases
Solution Approach 1:
The patent segments the task execution system into independent, manageable units with explicit dependency relationships. Rather than executing all tasks sequentially or all in parallel, the system divides tasks into groups that can be executed independently when their dependencies are satisfied. This segmentation allows the dispatching engine to identify parallel execution opportunities while maintaining proper ordering constraints, achieving both reliability and productivity.
Data Source
AI summary
A system and method are disclosed for managing the execution of tasks. Each task in a first set of tasks included in a pipeline is queued for parallel execution. The execution of the tasks is monitored by a dispatching engine. When a particular task that specifies a next set of tasks in the pipeline to be executed has completed, the dispatching engine determines whether the next set of tasks can be executed before the remaining tasks in the first set of tasks have completed. When the next set of tasks can be executed before the remaining tasks have completed, the next set of tasks is queued for parallel execution. When the next set of tasks cannot be executed before the remaining tasks have completed, the next set of tasks is queued for parallel execution only after the remaining tasks have completed.