Task Scheduling via DAG Conversion for Cyclic Dependencies
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Solution Overview
Problem
Conventional computer applications fail to utilize the full potential of high-speed multi-core processors due to their sequential nature and high inter-dependence of tasks, particularly circular dependencies, leading to scalability issues in processing computationally intense simulations like network validation in optical networks.
Innovation Solution
A scalable task scheduling method that segments tasks into a Directed Acyclic Graph (DAG) using semantic analysis, predicting input and output data, and breaking cycles to enable parallel execution, employing algorithms like Kosaraju's and Johnson's to convert cyclic dependencies into a finite directed graph with no cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tasks are executed sequentially to maintain correct dependency ordering, then task execution correctness is ensured, but processor utilization efficiency deteriorates
Solution Approach 1:
The patent segments the task execution process into distinct phases: dependency analysis phase (building the DAG to identify execution order) and parallel execution phase (executing independent tasks concurrently). This segmentation allows the system to maintain correctness through proper dependency ordering while achieving parallel execution for independent tasks, thus resolving the contradiction between reliability and productivity.
2Ease of operation
If circular dependencies are resolved by sequential execution, then execution simplicity is maintained, but scalability deteriorates
Solution Approach 1:
The patent introduces a Directed Acyclic Graph (DAG) as an intermediary data structure that captures task dependencies and execution order. The DAG serves as a mediator between circular dependencies and parallel execution, allowing the system to break cycles and identify valid execution sequences automatically. This intermediary enables scalable parallel execution while maintaining execution simplicity through automated dependency resolution.
3Measurement precision
If task dependencies are strictly enforced to ensure correctness, then execution accuracy is improved, but parallel execution capability deteriorates
Solution Approach 1:
The patent dynamically adjusts task execution based on the DAG structure. Tasks are executed in parallel when the DAG shows no dependency constraints, and sequential execution is automatically applied when dependencies require specific ordering. This dynamic approach maintains execution accuracy by respecting dependency constraints while maximizing parallel execution capability for independent tasks.
Data Source
AI summary
Scalable task scheduling systems and methods for cyclic interdependent tasks using semantic analysis include, for a software application including a plurality of tasks which are cyclic interdependent tasks, segmenting the plurality of tasks into a task graph with vertices including the plurality of tasks and edges including interdependencies between the plurality of tasks; processing the task graph into a dependency graph which is a Directed Acyclic Graph (DAG); and causing execution of the plurality of tasks in a parallel manner based on the dependency graph.