Task Scheduling Based on Performance Control Conditions
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Solution Overview
Problem
In multiprocessor systems, existing task scheduling methods fail to efficiently manage workload and performance across heterogeneous processing units, leading to suboptimal system performance and unnecessary energy consumption.
Innovation Solution
A method and apparatus that assign tasks to either high-performance or low-power processing units based on workload thresholds and performance control conditions, allowing for dynamic task migration between processing units to optimize system performance and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are assigned to high-performance processing units regardless of workload, then system performance is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts task assignment based on performance control conditions and workload status. The scheduler unit monitors processing unit states and migrates tasks between high-performance and low-power processing units in real-time, transforming the static task assignment into a dynamic adaptation process that resolves the contradiction between performance and energy consumption.
Solution Approach 2:
The system changes the operational parameters of processing units by switching between different performance modes (high-performance vs. low-power) based on detected performance control conditions. This parameter change allows the system to optimize the balance between productivity and energy usage by selecting appropriate processing units for specific task conditions.
2Use of energy by moving object
If tasks are assigned based on workload distribution, then energy consumption is reduced, but system performance deteriorates when performance control conditions occur
Solution Approach 1:
The scheduler unit implements feedback mechanisms by continuously monitoring performance control conditions and workload status of processing units. When performance control conditions are detected, the system receives feedback and automatically migrates tasks to appropriate high-performance processing units, ensuring performance requirements are met while maintaining energy efficiency during normal operation.
Solution Approach 2:
The task assignment strategy dynamically transitions between energy-efficient mode (based on workload) and performance-optimized mode (when performance control conditions are detected). This dynamic adaptation allows the system to switch between contrasting assignment strategies based on real-time conditions, resolving the contradiction between energy consumption and system performance.
3Productivity
If task migration is performed frequently to optimize performance, then system performance is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary detection of performance control conditions before task migration is needed. By monitoring and detecting performance control conditions in advance, the scheduler unit can proactively migrate tasks to appropriate processing units, reducing the frequency and complexity of reactive migration decisions and simplifying the overall scheduling mechanism.
Data Source
AI summary
Provided is a task scheduling method. The method may include: assigning a task to one of first processing units functionally connected to an electronic device; and migrating, at least partially on the basis of a performance control condition related to the task, the task to one of second processing units for processing.