Schedule Decision Device for Multi-Core Task Execution
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Solution Overview
Problem
Existing multi-core scheduling techniques fail to optimize parallel task execution when tasks have different execution periods, leading to potential deadline mistakes and inefficient resource utilization, as they do not adequately account for dependencies and effective scheduling across multiple cores.
Innovation Solution
A schedule decision device that acquires dependencies between sub-tasks, generates candidate schedules, computes effective degrees for each schedule, and decides on the optimal schedule based on these computations to minimize waiting and ensure efficient execution across multiple cores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are scheduled using conventional multi-core scheduling techniques, then parallel execution is achieved, but tasks with different execution periods cause waiting and reduce scheduling optimality
Solution Approach 1:
The patent segments tasks into sub-tasks and generates multiple candidate schedules by dividing task execution into different phases. This allows independent optimization of each sub-task schedule, enabling better handling of tasks with different execution periods and reducing waiting time between task completions.
Solution Approach 2:
The patent changes scheduling parameters by computing effective degrees for different schedule candidates based on task execution periods, priorities, and resource availability. This parameter-based optimization allows the system to select schedules that minimize waiting time while maintaining parallel execution efficiency.
2Device complexity
If conventional scheduling methods are used to assign tasks to cores, then scheduling is simplified, but the schedule is not optimal when tasks have different execution periods
Solution Approach 1:
The patent performs preliminary actions by generating multiple candidate schedules before final execution, computing effective degrees for each candidate, and selecting the optimal schedule in advance. This preliminary optimization ensures scheduling optimality without increasing runtime complexity, as the heavy computation is done during schedule generation rather than during task execution.
3Speed
If tasks are executed in parallel using multiple cores, then execution speed is improved, but deadline mistakes may occur due to improper scheduling
Solution Approach 1:
The patent implements feedback mechanisms by computing effective degrees for schedule candidates that incorporate deadline constraints and task priorities. The scheduling system uses this feedback information to select schedules that ensure deadline compliance while maintaining parallel execution speed, preventing deadline mistakes through informed schedule selection.
Data Source
AI summary
A schedule decision method acquires dependencies of execution sequences required for a plurality of sub tasks into which a first task has been divided; generates a plurality of sub task structure candidates that satisfy said dependencies and for which a plurality of processing devices execute said plurality of sub tasks; generates a plurality of schedule candidates by further assigning at least one second task to each of said sub task structure candidates; computes an effective degree that represents effectiveness of executions of said first task and said second task for each of said plurality of schedule candidates; and decides a schedule candidate used for the executions of said first task and said second task from said plurality of schedule candidates based on said effective degrees.