Digital Twin Energy Management for Data Centers
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Solution Overview
Problem
Data centers and cloud computing systems face significant environmental and economic challenges due to high energy consumption, leading to substantial greenhouse gas emissions and increased operational costs.
Innovation Solution
A method and device for energy management in computing systems, which involves generating a digital twin using energy information from hardware components, virtual machines, and software entities. This digital twin simulates different candidate parameters to determine energy consumption metrics, and a recommendation engine provides suggestions for optimizing energy usage based on these metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data centers increase in size and complexity to meet growing data storage and processing demands, then productivity and service capability improve, but energy consumption increases significantly
Solution Approach 1:
The system changes operational parameters of computing components (CPU frequency, voltage, clock speed) to optimize energy consumption while maintaining productivity. The decision engine adjusts these parameters dynamically based on workload requirements and energy constraints, allowing data centers to process more data with optimized energy usage rather than simply scaling hardware.
Solution Approach 2:
The patent implements dynamic energy management where the system continuously monitors and adjusts energy consumption in real-time based on changing workload demands. The decision engine adapts cooling strategies, power allocation, and resource distribution dynamically, allowing the data center to respond flexibly to varying productivity requirements without linearly increasing energy consumption.
2Measurement precision
If traditional energy management approaches are used without digital twins, then system complexity remains lower, but measurement precision and ability to optimize energy consumption deteriorates
Solution Approach 1:
The patent creates digital twins - virtual copies of physical data center components and systems - to enable precise measurement and simulation of energy consumption without adding physical complexity. These digital models replicate the behavior and energy characteristics of actual hardware, allowing accurate measurement and optimization analysis in a virtual environment before applying changes to the physical system.
Solution Approach 2:
The digital twin acts as an intermediary between the physical data center and the energy management system. It mediates the collection, analysis, and interpretation of energy data, translating complex physical measurements into actionable insights. This intermediary layer enables precise energy measurement and optimization without requiring direct complex instrumentation of every physical component.
3Loss of energy
If comprehensive energy monitoring and simulation systems are implemented, then energy consumption optimization improves, but device complexity and implementation cost increase
Solution Approach 1:
The system implements closed-loop feedback where the digital twin continuously monitors energy consumption, simulates optimization scenarios, and automatically adjusts operational parameters through the decision engine. This feedback mechanism reduces energy waste systematically without requiring complex manual intervention, as the system self-regulates based on real-time performance data and simulation results.
Solution Approach 2:
The patent uses the digital twin to perform preliminary simulation and analysis of energy optimization strategies before implementing them in the physical data center. By pre-evaluating different cooling strategies, power allocation schemes, and resource configurations in the virtual model, the system identifies optimal solutions that reduce energy waste without requiring complex trial-and-error implementation in the physical system.
Data Source
AI summary
In some implementations, a device may receive information identifying a computing system for energy management, the computing system having a set of hardware components, a set of virtual machines, and a set of software entities. The device may generate a digital twin of the computing system for simulation of the set of hardware components, the set of virtual machines, and the set of software entities. The device may determine, using the digital twin of the computing system, a set of energy consumption metrics, for the computing system, associated with a set of candidate parameters. The device may generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters. The device may transmit information associated with identifying the one or more recommendations.