Software Power Measurement and Hardware Lifespan Prediction
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Solution Overview
Problem
Current energy consumption measurement platforms cannot distinguish the energy consumed by processors executing software from that consumed by idle hardware components, and they lack the ability to accurately measure the energy requirements of different software programs.
Innovation Solution
A system and method for measuring energy usage of software programs, involving baseline and loaded power measurements, with processors executing software on hardware components to determine additional power usage, predicting hardware component failures, and generating power usage plans to extend maintenance schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional energy measurement platforms are used, then general power consumption can be measured, but the energy consumed by processors executing software cannot be distinguished from idle hardware components
Solution Approach 1:
The system segments total power consumption into distinct components: baseline power (idle hardware) and software-induced power (processors executing software). This segmentation enables precise measurement of software energy consumption by subtracting baseline measurements from loaded measurements, resolving the inability to distinguish software-specific energy usage from general hardware consumption.
2Productivity
If hardware components operate continuously to execute software programs, then productivity is improved, but hardware component lifespan decreases due to wear and thermal stress
Solution Approach 1:
The system dynamically adjusts hardware utilization by scheduling software execution based on real-time hardware health status and power consumption patterns. When hardware components show signs of wear or thermal stress, the system can redistribute workloads to healthier components or schedule executions during optimal conditions, thereby maintaining productivity while extending hardware lifespan through adaptive resource management.
Solution Approach 2:
The system implements feedback loops that continuously monitor hardware power consumption, temperature, and performance metrics. This feedback enables the system to optimize software scheduling decisions, adjusting execution timing and resource allocation to minimize thermal stress and wear on hardware components while maintaining high productivity. The feedback mechanism allows dynamic balancing between utilization and preservation goals.
Data Source
AI summary
In some embodiments, the system is directed to servers, systems, and methods configured to accurately measure, archive, and predict energy consumption associated with the execution of software programs. In some embodiments, the system provides a solution to the problem of not being able to accurately measure the power used by a particular program or a group of programs. In some embodiments, system steps include receiving baseline and loaded power measurements of hardware running software, determining the amount of additional power used during one or more programs, and/or storing the additional power usage in association with the one or more programs as software power usage. In some embodiments, the system is configured to predict hardware failure times based on power usage. In some embodiments, the system is configured to generate power usage plans to extend hardware life. In some embodiments, the system is configured to determine customer software power usage for billing purposes.