Sequential Cooling Resource Placement in Data Centers
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Solution Overview
Problem
Efficient placement of cooling resources in data centers is challenging due to complex airflow patterns and the vast number of possible layouts, often resulting in over-abundance of cooling resources and wasteful power consumption.
Innovation Solution
A computer-implemented method and system for sequential placement of cooling resources in data centers using a weighted, higher-order cooling metric to optimize the placement of cooling resources, ensuring efficient cooling coverage without waste, by defining a cooling index and iteratively placing cooling resources until a desired redundancy level is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cooling resources are placed to ensure adequate cooling coverage, then cooling performance is improved, but the number of cooling resources increases leading to higher power consumption and resource wastage
Solution Approach 1:
The system performs preliminary simulation of cooling airflow patterns before finalizing the placement of cooling resources. By pre-calculating the cooling coverage and identifying optimal locations through virtual modeling, the system determines the minimum number of cooling resources needed to achieve adequate coverage, preventing both insufficient cooling and excessive resource allocation that would waste energy.
Solution Approach 2:
The system dynamically adjusts placement parameters of cooling resources based on simulated cooling performance metrics. By optimizing parameters such as position, orientation, and operational settings of cooling units, the system achieves adequate cooling coverage with the minimum necessary resources, thereby reducing power consumption and eliminating resource wastage while maintaining reliable cooling performance.
2Adaptability or versatility
If manual methods are used to place cooling resources, then flexibility in design is maintained, but the complexity of optimizing airflow patterns and layout increases
Solution Approach 1:
The system replaces manual optimization processes with automated computer-based simulation and analysis tools. By using computational models to simulate airflow patterns, heat transfer, and cooling coverage, the system automatically identifies optimal placements for cooling resources, eliminating the complex manual calculations and iterative design processes that would otherwise be required while maintaining design flexibility.
Solution Approach 2:
The system introduces a computational simulation environment as an intermediary between design requirements and cooling resource placement decisions. This virtual model serves as a mediator that processes complex airflow dynamics, thermal characteristics, and layout constraints, providing optimized placement recommendations that balance cooling performance with design flexibility without requiring manual optimization of complex interactions.
3Ease of manufacture
If conventional design tools are used, then standardized methodologies are provided, but the ability to optimize for specific airflow patterns and cooling requirements is limited
Solution Approach 1:
The system transitions from static, standardized design methodologies to dynamic optimization processes that adapt to specific airflow patterns and cooling requirements. By incorporating configurable simulation parameters, the system allows users to define custom cooling performance criteria and airflow characteristics, enabling precise optimization of cooling resource placement for each unique data center configuration while maintaining ease of use through automated calculation.
Data Source
AI summary
A computer-implemented method for sequential placement of cooling resources in a data center comprises: defining a weighted, higher-order cooling metric, , representing an overall performance of the cooling resources in the data center; enumerating all possible locations in the data center for placing an additional c cooling resources; and placing the c cooling resources in locations in the data center for which is closest to an optimum value. For a performance metric Mi, the weighted, higher-order cooling performance metric can be defined asM^=∑k=0C-1∑i=1Ra(i,k)Mi(k),where R represents a number of racks in the data center, C represents a total number of cooling resources placed in the data center, i represents one of the R racks in the data center, Mi(k) represents a value of metric Mi when k cooling resources are shut down, and a(i,k) is a weighting function for rack i with the k cooling resources shut down. A system performing the method and computer-readable media having stored thereon instructions causing a processor to perform the method are also provided.