Scenario-Aware CPU Scheduling for Battery Endurance and Smooth Performance
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Solution Overview
Problem
The increasing power consumption of terminal devices outpaces the growth in battery capacity, leading to inadequate battery endurance and inefficient resource allocation due to conventional resource scheduling schemes that fail to adapt to specific user scenarios, resulting in excessive energy consumption and resource wastage.
Innovation Solution
A resource scheduling method that dynamically adjusts CPU power consumption and process priorities based on user scenarios and system load by analyzing GPU usage, peripheral events, and power mode, utilizing a scenario recognition engine to determine scheduling policies that balance performance and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the CPU power is increased to handle high load values, then the processing performance is improved, but the energy consumption increases excessively
Solution Approach 1:
The patent applies dynamics by making the CPU power adjustment dynamic rather than static. The system continuously monitors task load values and user scenarios, then dynamically adjusts CPU power consumption in real-time. This allows the CPU to operate at high performance only when needed (high load scenarios) and reduce power consumption during low-load scenarios, resolving the contradiction between maintaining high processing performance and reducing energy consumption.
Solution Approach 2:
The patent changes the parameter of CPU power consumption based on different user scenarios and load values. By establishing a correspondence relationship between user scenarios and CPU power consumption parameters, the system adjusts the CPU power level according to the current scenario (e.g., gaming scenario vs. office scenario). This parameter change enables the system to optimize the balance between processing performance and energy consumption for different operational contexts.
2Reliability
If the CPU runs in high-performance state continuously, then the terminal device performance is maintained, but the battery endurance is reduced
Solution Approach 1:
The system dynamically adjusts CPU power consumption based on real-time monitoring of task load values and user scenarios. Instead of maintaining continuous high-performance operation, the CPU power is adjusted up or down according to the current scenario. This dynamic adjustment ensures the terminal device maintains reliable performance when high load scenarios are detected while extending battery endurance during low-load scenarios, effectively resolving the contradiction between performance reliability and battery endurance.
Solution Approach 2:
The patent implements feedback by continuously monitoring task load values and user scenarios, then using this information to adjust CPU power consumption. The system receives feedback about the current operational state (through load value monitoring and scenario identification) and responds by adjusting CPU power accordingly. This feedback mechanism ensures the system maintains performance reliability when needed while optimizing battery endurance based on actual usage patterns.
3Device complexity
If conventional resource scheduling is used, then the implementation is simple, but the adaptability to different user scenarios is poor
Solution Approach 1:
The patent segments the resource scheduling process into multiple independent modules: user scenario identification module, task load value calculation module, and CPU power adjustment module. Each module performs a specific function and can operate independently. This segmentation maintains relative implementation simplicity while enhancing scenario adaptability, as each module can be optimized or configured separately for different scenarios without requiring complete system redesign.
Solution Approach 2:
The patent enhances scenario adaptability by introducing parameter changes based on different user scenarios. The system identifies the current user scenario (e.g., gaming, office, video playback) and adjusts scheduling parameters accordingly. This parameter-based approach allows the same scheduling framework to adapt to multiple scenarios without requiring scenario-specific scheduling algorithms, thus maintaining implementation simplicity while improving versatility.
Data Source
AI summary
Some embodiments of this application provide a resource scheduling method and an electronic device, and relate to the field of power electronic technology. The method includes: —displaying a first window in response to a first operation of a user, wherein the first window corresponds to a first process; adjusting a process priority and an I/O priority of the first process and power consumption of a CPU based on a first scheduling policy or a second scheduling policy.