Edge Device Power Minimization via Process Priority Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Edge devices face challenges in managing power consumption due to increased processing loads and limited battery life, with users lacking visibility into which applications or tasks consume the most power, leading to inefficient energy usage and environmental impact.
Innovation Solution
A system combining pluggable operating system/orchestrator schedulers, telemetry for real-time power utilization data, and AI/ML feedback loops to create optimized power consumption minimization solutions, allowing users to prioritize tasks and manage power resources effectively through a scheduler and user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If edge computing processes are deployed on mobile devices, then processing capabilities and service functionality are improved, but battery life deteriorates due to increased power consumption
Solution Approach 1:
The system dynamically adjusts the priority of running processes based on real-time power consumption telemetry data. The scheduler continuously monitors power usage metrics and reprioritizes processes to balance computational productivity with battery conservation, allowing edge computing tasks to execute when power is available while preserving battery life during low-power states
Solution Approach 2:
The system implements a feedback loop where power consumption telemetry data is collected from running processes, analyzed by AI/ML models, and used to adjust process priorities in real-time. This closed-loop control enables the system to respond to actual power consumption patterns and optimize the balance between processing capabilities and battery life based on measured performance
2Loss of energy
If users uninstall applications to preserve battery life, then power consumption is reduced, but user control and visibility into power usage deteriorate
Solution Approach 1:
The system introduces an intermediary layer between the user and applications through a scheduler and user interface that provides visibility into power consumption by individual processes. This intermediary enables users to make informed decisions about power usage without directly uninstalling applications, as the scheduler automatically manages process priorities based on power consumption data and user preferences
3Productivity
If multiple applications run simultaneously on edge devices, then service functionality and user productivity are improved, but aggregate power consumption and environmental impact worsen
Solution Approach 1:
The system implements partial action by selectively executing only the most important processes based on priority levels derived from power consumption analysis. When power resources are constrained, the scheduler allows only high-priority processes to run at full capacity, while lower-priority processes are throttled or suspended, thereby maintaining essential service functionality while reducing aggregate power consumption
Data Source
AI summary
One example method includes performing, in an edge device that includes a power source, operations including monitoring a running process and obtaining, based on the monitoring, power consumption information associated with the running process, adjusting, based on the power consumption information, a priority of the running process, and providing, to an entity, the power consumption information and/or information concerning the priority of the running process.