Power Utilization Estimation via Trained Performance Models
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Solution Overview
Problem
Current methods for estimating power utilization in computer systems, especially those with dynamic power management, often overestimate power needs, leading to increased costs and inefficiencies, as they struggle to accurately account for varying power consumption levels due to changing frequencies and voltages.
Innovation Solution
A system that monitors performance parameters and uses a power-utilization model trained with pattern-recognition and nonlinear regression techniques to estimate power usage, accounting for dynamic power management features by measuring power consumption at different frequencies and loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware power meters are installed to directly measure power usage, then measurement precision is improved, but device complexity and cost increase, and computer systems may need to be powered down for installation
Solution Approach 1:
The patent uses software-based performance parameters (clock rate, temperature, voltage) as intermediaries to indirectly measure power consumption. Instead of directly installing hardware power meters on the computer system, the system monitors these software-accessible parameters and uses them to calculate power usage through a trained model, avoiding the need for physical hardware installation on the computer components
Solution Approach 2:
The patent replaces the mechanical/hardware-based power measurement approach with a software-based computational approach. Instead of using physical power meters that require electrical connections and hardware installation, the system uses software monitoring of performance parameters and computational modeling to determine power consumption, substituting a mechanical measurement system with an information-processing system
2Measurement precision
If power lookup tables are generated for all operating states to accurately estimate power usage, then measurement precision is improved, but device complexity and computational burden increase due to dynamic power management capabilities
Solution Approach 1:
The patent uses a trained power consumption model that dynamically adjusts power estimation based on changing performance parameters (clock rate, temperature, voltage) rather than relying on static lookup tables. The model captures the relationships between these parameters and power consumption through training data, allowing it to adapt to dynamic power management operations without requiring exhaustive tables of all possible operating states
Solution Approach 2:
The patent creates a virtual copy of the power consumption characteristics through a trained model that replicates power usage patterns. Instead of maintaining actual lookup tables for all operating states, the system uses a learned representation (model) that can infer power consumption for any given set of performance parameters, effectively copying the essential power-characteristic relationships without the computational burden of complete state tables
3Loss of energy
If dynamic power management features are implemented to reduce power consumption, then energy efficiency is improved, but measurement precision deteriorates because power consumption varies continuously rather than in discrete states
Solution Approach 1:
The patent employs a dynamic power consumption model that continuously adapts to changing operating conditions. Rather than using static lookup tables designed for discrete operating states, the model incorporates continuous performance parameters (clock rate, temperature, voltage) that dynamically reflect the current state of the computer system, enabling accurate power measurement even during dynamic power management transitions
Solution Approach 2:
The system uses feedback from monitored performance parameters to continuously refine power consumption estimation. By monitoring actual performance parameters during dynamic power management operations and comparing them against the trained model, the system can accurately track real-time power consumption patterns that arise from frequency and voltage changes, maintaining measurement precision despite the dynamic nature of power usage
Data Source
AI summary
One embodiment of the present invention provides a system that estimates a power utilization of a computer system. During operation, a set of performance parameters of the computer system is monitored, wherein the computer system includes a processor. Then the power utilization of the computer system is estimated based on the set of performance parameters and a power-utilization model, wherein the power-utilization model was trained by measuring a power utilization of the computer system while the processor is operating at a first frequency and measuring a power utilization of the computer system while the processor is operating at a second frequency.