Learning Agent Scheduling for Heterogeneous DAG Task Efficiency
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Solution Overview
Problem
Heterogeneous computing systems face challenges in efficiently scheduling tasks due to real-time constraints, dynamic environmental conditions, and varying processor element capabilities, leading to bottlenecks, underutilization of resources, and potential mission failures.
Innovation Solution
A method for learning agent-based application scheduling in heterogeneous systems, which dynamically schedules tasks of directed acyclic graphs (DAGs) based on constraints, prioritization policies, and system configurations, utilizing a machine learning component to optimize task execution and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling methods are used in heterogeneous systems, then system complexity is reduced and ease of operation is maintained, but task scheduling efficiency deteriorates and resource utilization becomes suboptimal
Solution Approach 1:
The patent implements a learning agent that autonomously schedules tasks without requiring complex external scheduling mechanisms. The agent learns from system performance data and automatically makes scheduling decisions, allowing the system to self-optimize while maintaining operational simplicity. This resolves the contradiction by enabling high scheduling efficiency through self-service rather than complex external control.
Solution Approach 2:
The patent replaces traditional mechanical scheduling algorithms with a machine learning-based agent that uses statistical and probabilistic methods to optimize task scheduling. This substitution allows the system to achieve superior scheduling efficiency and adaptability without proportionally increasing system complexity, as the learning agent handles complex decision-making autonomously.
2Loss of time
If dynamic scheduling is implemented to adapt to changing conditions, then task execution timeliness is improved, but computational overhead and system complexity increase
Solution Approach 1:
The patent employs a learning agent that performs preliminary learning during system operation, building knowledge about task characteristics and system performance in advance. This allows the agent to make rapid scheduling decisions without extensive real-time computation, reducing both task wait times and instantaneous computational overhead by leveraging pre-acquired knowledge.
Solution Approach 2:
The system implements a feedback mechanism where the learning agent continuously monitors scheduling outcomes and uses this information to refine future scheduling decisions. This feedback loop enables the system to adapt dynamically to changing conditions while optimizing computational resource usage, as the agent learns from past experiences rather than requiring exhaustive real-time analysis.
3Productivity
If heterogeneous processor elements are utilized to increase processing capacity, then system productivity improves, but resource utilization efficiency deteriorates due to bottlenecks and underutilization
Solution Approach 1:
The patent applies local quality by assigning specific task types to specific processor elements based on their capabilities and the learned preferences of the scheduling agent. This ensures that each processor element is utilized optimally for tasks it handles most efficiently, preventing both bottlenecks and underutilization while maintaining high overall system productivity and energy efficiency.
Data Source
AI summary
Tasks of directed acyclic graphs (DAGs) may be dynamically scheduled based on a plurality of constraints and conditions, task prioritization policies, task execution estimates, and configurations of a heterogenous system. A machine learning component may be initialized to dynamically schedule the tasks of the DAGs.