Forecasting Processor Power Demands to Reduce Energy Consumption
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Solution Overview
Problem
Computing devices face inefficiencies in power management, as traditional techniques rely on static user adjustments and do not effectively minimize processor power consumption, which is disproportionately high, especially in devices with multiple processors or cores.
Innovation Solution
Implement predictive models to forecast near-term power demands and user latency tolerances, allowing for proactive reduction of CPU power states, deferring low-priority tasks, and bundling them to maximize idle time, thereby reducing power consumption while maintaining acceptable user-perceived latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional static power management policies are used, then ease of operation is maintained, but power consumption is not minimized
Solution Approach 1:
The system performs self-service by automatically monitoring workload patterns, predicting future power needs, and adjusting processor power states without user intervention. The predictive model and automated policy enforcement enable the system to manage its own power consumption optimally
Solution Approach 2:
The system takes preliminary action by predicting future workload patterns and proactively adjusting power states before actual workload changes occur. This anticipatory approach allows the system to prepare optimal power configurations in advance, minimizing power consumption while maintaining performance readiness
2Use of energy by moving object
If processor power state is reduced to save power, then power consumption decreases, but latency increases
Solution Approach 1:
The system dynamically adjusts processor power states based on real-time workload conditions and predictions. Rather than using static power states, the system continuously adapts power configuration to match actual system needs, optimizing the balance between power savings and latency performance
Solution Approach 2:
The system implements feedback mechanisms by monitoring actual workload patterns, measuring latency performance, and using this information to refine predictive models and adjust power management decisions. This closed-loop approach ensures latency remains within acceptable thresholds while maximizing power savings
3Productivity
If multiple processors are used to handle workload, then productivity increases, but power consumption increases disproportionately
Solution Approach 1:
The system extracts and isolates idle or low-utilization processors from active processing, placing them in reduced power states while maintaining necessary computational capacity through the remaining active processors. This selective power management reduces total power consumption while preserving productivity
Solution Approach 2:
The system applies partial action by activating only the necessary number of processors required for current workload demands, rather than keeping all processors continuously active. This approach maintains sufficient productivity while avoiding excessive power consumption from underutilized processing resources
Data Source
AI summary
Techniques and systems are provided that work to minimize the energy usage of computing devices by building and using models that predict the future work required of one or more components of a computing system, based on observations, and using such forecasts in a decision analysis that weighs the costs and benefits of transitioning components to a lower power and performance state. Predictive models can be generated by machine learning methods from libraries of data collected about the future performance requirements on components, given current and recent observations. The models may be used to predict in an ongoing manner the future performance requirements of a computing device from cues. In various aspects, models that predict performance requirements that take into consideration the latency preferences and tolerances of users are used in cost-benefit analyses that guide powering decisions.