Arrangement for managing data center operations to increase cooling efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data centers face significant economic losses due to server shutdowns caused by heat overload, and existing cooling systems, such as CRACs, consume substantial power while not always operating at optimal efficiency.
Innovation Solution
A method and system that allocate processing tasks to servers based on the thermal proximity and efficiency characteristics of air conditioning units, ensuring heat is distributed efficiently among them, thereby optimizing cooling power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CRACs are used to provide enhanced cooling power in data centers, then server computers can be cooled reliably to avoid shutdowns and damage, but the power consumption of the cooling system increases significantly
Solution Approach 1:
The patent applies local quality by distributing processing tasks to servers based on their thermal proximity to different CRAC units. Each server is assigned tasks that generate heat patterns matching the cooling capacity and efficiency characteristics of nearby CRACs. This ensures that cooling resources are locally optimized for each server location, improving overall cooling reliability while reducing total power consumption by avoiding over-cooling or inefficient centralized cooling approaches.
2Reliability
If CRACs operate at high capacity to handle concentrated heat from servers, then adequate cooling is provided, but the efficiency of individual CRAC units decreases
Solution Approach 1:
The patent segments the data center cooling system into multiple zones, each served by specific CRAC units. Processing tasks are allocated to servers based on which CRAC zone they fall into, effectively dividing the total cooling load into manageable segments. This segmentation allows each CRAC to operate at optimized capacity levels rather than all CRACs running at high capacity, thereby maintaining cooling adequacy while improving overall energy efficiency and reducing losses.
Solution Approach 2:
The system dynamically changes operational parameters by adjusting processing task allocation based on real-time CRAC efficiency characteristics and thermal conditions. When CRAC efficiency varies due to environmental factors or operational state, the system modifies task distribution parameters to match servers with the most efficient available cooling resources, thereby maintaining adequate cooling while minimizing energy losses.
Data Source
AI summary
A method includes a step of storing in a memory efficiency characteristic information for each of a plurality of air conditioning units in a location containing a plurality of server computers, the efficiency characteristic information including information representative of an efficiency performance curve for a range of variable cooling output. The method also includes identifying a current thermal load on each of the plurality air conditioning units. The method further includes employing the stored efficiency characteristic information and the current thermal load to identify a first air conditioning unit having a least additional power consumption required to increase cooling output. One or more processing units are employed to allocate one or more processing tasks to one of the plurality of server computers based on the identified first air conditioning unit.