Neural Network Task Scheduling with Dynamic Resource Reassignment
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Solution Overview
Problem
In computing systems, inefficient job scheduling of resources can lead to delayed task runtimes and deteriorated quality of service.
Innovation Solution
A task execution method that involves receiving an execution request for a task, analyzing and dividing it into sub-tasks, identifying sub-tasks using a neural network, dividing these into layer tasks, calculating deadline times, and scheduling sub-tasks to resource groups based on availability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are scheduled to resource groups without dynamic reassignment, then scheduling simplicity is maintained, but task execution time increases and quality of service deteriorates
Solution Approach 1:
The scheduling system dynamically reassigns tasks from first resource group to second resource group based on real-time execution status and deadline predictions. The processor continuously monitors task progress and adjusts resource allocation, transforming a static scheduling system into a dynamic one that adapts to changing conditions to reduce overall execution time.
Solution Approach 2:
The system implements feedback mechanisms by monitoring task execution progress and comparing actual runtime against predicted deadline times. Based on this feedback, the processor determines whether to reassign tasks to different resource groups, creating a closed-loop control system that continuously optimizes task execution.
2Measurement precision
If tasks are divided into sub-tasks and layer tasks with neural network identification, then task scheduling precision improves, but system complexity increases
Solution Approach 1:
The system segments tasks into sub-tasks and further divides them into layer tasks corresponding to neural network layers. This segmentation enables precise tracking and scheduling of individual task components, allowing the processor to monitor progress at a granular level and make informed reassignment decisions for each sub-task or layer task independently.
Solution Approach 2:
The processor acts as an intermediary that manages the complex relationships between tasks, sub-tasks, and resource groups. It coordinates the division of tasks into neural network layers and handles the reassignment logic, shielding higher-level system components from the complexity of fine-grained task management while enabling precise scheduling control.
3Reliability
If deadline times are calculated for each sub-task, then task completion timeliness improves, but computational overhead increases
Solution Approach 1:
The system performs preliminary actions by calculating deadline times for tasks, sub-tasks, and layer tasks in advance before execution begins. These pre-calculated deadlines serve as reference points for the processor to monitor progress and determine reassignment needs, reducing the need for complex real-time computations during task execution.
Data Source
AI summary
A task execution method using resources includes receiving an execution request for a first task; analyzing the first task and dividing the first task into a plurality of sub-tasks; identifying a sub-task using a first neural network from among the sub-tasks and dividing the identified sub-task into a plurality of layer tasks corresponding to calculations between layers constituting the first neural network; calculating a deadline time of each of the sub-tasks; scheduling a first sub-task to be scheduled to a first resource group from among the resources; and, when a runtime of the first sub-task exceeds a deadline time of the first sub-task, scheduling a sub-task or a layer task subsequent to the first sub-task to a second resource group.