Data Center Control System Optimizing Power and Cooling
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Solution Overview
Problem
Data centers face increasing challenges due to the growing demand for power and cooling resources, leading to elevated operational costs and inefficiencies as the density of computing equipment strains power and cooling systems.
Innovation Solution
Implementing a system and method to dynamically manage and allocate resources within data centers by evaluating and optimizing power consumption and cooling capacity across geographically disparate data center components, allowing for the reallocation of applications and resources to minimize total power consumption and enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing equipment density is increased to meet growing demands, then processing capacity is improved, but power consumption and cooling requirements increase
Solution Approach 1:
The system dynamically changes operational parameters including power allocation, cooling capacity, and application distribution across data center components. By continuously monitoring and adjusting these parameters based on real-time conditions, the system optimizes the balance between processing capacity and power consumption, allowing high-density computing while managing energy usage through adaptive parameter modification.
2Productivity
If computing equipment density is increased, then processing capacity is improved, but cooling system strain increases
Solution Approach 1:
The system applies local quality by distributing applications and workloads to specific data center components based on their individual cooling capacities and thermal characteristics. Rather than uniform distribution, each rack or server is assigned tasks appropriate to its local cooling capabilities, preventing any single cooling system from being overwhelmed while maintaining overall high processing capacity.
Solution Approach 2:
The system dynamically reallocates applications and adjusts cooling resource distribution in response to changing thermal conditions and workload demands. This dynamic approach allows the cooling system to adapt to varying heat generation patterns, maintaining effective cooling without requiring excessive cooling capacity for peak scenarios.
3Productivity
If data center expansion continues to meet demand, then processing capacity is improved, but operational costs increase
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor power consumption, cooling efficiency, and application performance across the data center. This feedback enables real-time optimization of resource allocation, identifying and eliminating energy waste while maintaining processing capacity. The system learns from operational data to improve efficiency over time, reducing operational costs associated with energy consumption and cooling requirements.
Data Source
AI summary
Systems and methods for data center control are provided. The data center includes a plurality of racks and at least one cooling unit. The racks including at least one server. Information regarding the at least one cooling unit, the at least one server, the plurality of racks, and at least one application is received. A rack based cooling metric threshold is also received. The systems and methods can determine a layout of the data center that minimizes total power consumption of the plurality of racks and the cooling unit while maintaining cooling performance at an acceptable level based on the threshold.