Motion-Aware Scheduler for Mobile OS Task Prioritization
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Solution Overview
Problem
Preemptive operating systems in mobile devices lack awareness of device motion, leading to suboptimal task scheduling and performance degradation in applications that require low latency, such as real-time sensor data analysis, due to prioritization conflicts and limited system bandwidth.
Innovation Solution
A scheduler that utilizes motion information from accelerometers to determine current and future use characteristics of a device, prioritizing tasks based on user activity and resource availability, and throttling or stopping lower priority tasks when conflicts arise to ensure efficient resource allocation and maintain low latency for critical applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preemptive operating systems prioritize tasks based on application priority and time of day, then system scheduling is simplified, but performance degradation occurs in real-time applications due to lack of motion awareness
Solution Approach 1:
The scheduler performs preliminary actions by determining use characteristics before scheduling tasks. Motion information is analyzed in advance to predict device state, and scheduling decisions are made proactively rather than reactively, ensuring real-time applications receive appropriate priority before performance degradation occurs
Solution Approach 2:
The system implements feedback by continuously monitoring motion information from accelerometers and active applications, using this data to dynamically adjust scheduling decisions. The scheduler receives feedback about device motion state and application priorities, then adapts task scheduling accordingly to maintain real-time performance
2Productivity
If the scheduler allocates system bandwidth to multiple applications simultaneously, then multi-tasking capability is improved, but latency increases for low-latency applications requiring dedicated resources
Solution Approach 1:
The scheduling system dynamically adjusts resource allocation based on real-time motion characteristics and application priorities. When motion patterns indicate critical real-time tasks, the scheduler dynamically shifts bandwidth allocation to prioritize those tasks, reducing latency while maintaining multi-tasking capability during normal operation
Solution Approach 2:
The system changes scheduling parameters based on determined use characteristics. Motion information triggers parameter changes in task priority levels and time slice allocations, allowing the system to switch between multi-tasking mode and real-time optimized mode by adjusting scheduling parameters
3Measurement precision
If the operating system processes sensor data in real-time with high priority, then sensor application accuracy is improved, but other lower priority tasks experience performance degradation
Solution Approach 1:
The scheduler implements periodic action by allocating dedicated time slices for sensor data processing at specific intervals determined by motion characteristics. During these periodic high-priority windows, sensor processing occurs with maximum accuracy, while other tasks execute during lower-priority intervals, balancing measurement precision with overall system productivity
Data Source
AI summary
A method and apparatus to provide a scheduler comprising determining a current use characteristic for the device based on motion information and active applications, and scheduling a future task.