Server Air Circulation Control Using Predictive Temperature Models
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Solution Overview
Problem
The temperature control systems in computer facilities face challenges in balancing energy consumption and efficiency while maintaining optimal server operation, particularly due to uncertainties in temperature control and variable server loads.
Innovation Solution
A temperature control method and system that utilize learning models to predict temperature changes in servers, allowing for proactive adjustments to the temperature control device to optimize energy efficiency and prevent overheating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the temperature control device is over-engineered to maintain low temperature and dissipate heat immediately, then the server cooling reliability is improved, but the energy consumption increases excessively
Solution Approach 1:
The system performs preliminary temperature prediction using a learning model to forecast future temperature changes. Based on the predicted temperature, the temperature control device is adjusted in advance rather than reacting to current temperature conditions, allowing for more efficient energy usage while maintaining cooling reliability
Solution Approach 2:
The system continuously monitors actual temperature data and compares it with predicted temperature values. This feedback mechanism allows the system to learn from prediction errors and improve future predictions, while also enabling dynamic adjustment of the temperature control device to optimize energy consumption based on actual vs. predicted temperature differences
2Use of energy by moving object
If the temperature control device responds to high intake air temperature with delayed activation, then the energy consumption is reduced, but the temperature control reliability deteriorates due to time delay uncertainty
Solution Approach 1:
The learning model predicts future temperature changes before they occur, allowing the temperature control device to be adjusted proactively rather than reactively. This eliminates time delay uncertainty by acting in advance based on predictions, ensuring temperature control reliability while avoiding unnecessary energy consumption from delayed responses
3Measurement precision
If the temperature control system considers variable gradient changes due to rack height and server load, then the temperature distribution accuracy is improved, but the device complexity increases
Solution Approach 1:
The system divides the rack into multiple segments or zones based on height and server load characteristics. Temperature prediction and control are performed independently for each segment, allowing accurate consideration of variable gradient changes without requiring a single complex system-wide controller, thus managing device complexity through modular segmentation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively reduces computational complexity and improves prediction accuracy by establishing distributed learning models for each server, enabling timely adjustments to maintain optimal temperatures and reduce energy waste.
Implementation Method 1
driving an temperature control device to generate air circulation for a first server and a second server
Data Source
AI summary
An temperature control method including the following steps: driving an temperature control device to generate air circulation for a first server and a second server; monitoring operation state of the temperature control device, the first server and the second server continuously to establish a first learning model; receiving an temperature control state data of the temperature control device, a first state data of the first server, and a second state data of the second server, wherein the first state data includes a first temperature, and the second state data includes a second temperature; inputting the temperature control state data, the first state data, and the second state data into the first learning model to obtain a first temperature prediction value output by the first learning model; and adjusting the temperature control device according to the first temperature prediction value.