Data Center Cooling Control via Multi-Objective Optimization
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Solution Overview
Problem
Existing cooling control methods in data centers fail to optimally balance power consumption and cooling efficiency, leading to high energy expenditure and potential overheating issues.
Innovation Solution
A method that optimizes the power consumption of aisle cooling units and server fans by using multi-objective optimization techniques to determine Pareto optimal solutions for cold aisle temperature and airflow levels, ensuring reduced total power consumption while maintaining sufficient cooling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If cooling power is increased to maintain servers within temperature specifications, then server temperature control is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts cooling parameters (temperature setpoints, airflow rates) based on real-time server temperature measurements and workload conditions. This allows the cooling system to operate at optimal efficiency points rather than maintaining fixed aggressive cooling settings, reducing power consumption while maintaining temperature specifications.
Solution Approach 2:
The system implements closed-loop feedback control by continuously monitoring server temperatures and cooling system performance, then adjusting cooling operations accordingly. This feedback mechanism enables the system to reduce cooling power when temperatures are acceptable and increase power only when necessary, optimizing the trade-off between temperature control and energy consumption.
2Stability of the object's composition
If cooling power is increased to prevent hot spots, then temperature uniformity is improved, but power consumption increases
Solution Approach 1:
The system applies differentiated cooling strategies to different zones within the data center based on local temperature conditions and server workload. Rather than uniformly increasing cooling power throughout the facility, the system targets specific hot spots with additional cooling only where needed, maintaining temperature uniformity while minimizing overall power consumption.
3Loss of energy
If manual temperature adjustment is used to avoid overcooling, then cooling efficiency is improved, but operator intervention time increases
Solution Approach 1:
The system implements self-adjusting cooling control that automatically monitors temperature conditions and adjusts cooling operations without operator intervention. The system uses embedded sensors, optimization algorithms, and automated control to prevent overcooling and maintain efficient operation, eliminating the need for manual monitoring and adjustment while reducing cooling energy waste.
4Temperature
If server fan speed is increased to cool internal heat sources, then server component temperature is improved, but power consumption of fans increases
Solution Approach 1:
The system dynamically adjusts server fan speeds based on real-time thermal conditions and workload demands. Rather than maintaining constant high fan speeds, the system modulates fan operation to match actual cooling needs, reducing fan power consumption during low-demand periods while ensuring adequate cooling when temperatures require it.
Data Source
AI summary
A method of controlling the cooling of servers in a data center including a plurality of server racks each including a plurality of servers, the cooling being provided by aisle cooling units via a cold isle and by server fans, wherein the method includes: obtaining temperature measurements from temperature sensors in the data center, performing an optimization of the total power consumption of the aisle cooling units and of the servers based on the temperature measurements, with constraints on a cold aisle temperature and on an air flow level of the aisle cooling units, and controlling the aisle cooling units based on the optimization.