Server Task Allocation Based on CRAC 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 solutions, such as CRACs, consume substantial power and are not optimally configured to handle the concentrated heat generated by densely packed server computers.
Innovation Solution
A method and apparatus that allocate processing tasks to servers based on the location and efficiency characteristics of air conditioning units, ensuring that heat generated by task execution is distributed efficiently among air conditioning units, optimizing their operating levels and reducing 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 becomes significant
Solution Approach 1:
The patent applies local quality by allocating processing tasks to servers based on their proximity to different CRAC units and the efficiency characteristics of those CRACs. This creates localized cooling zones where heat generation is optimized to match the most efficient available cooling capacity in each area, rather than using a uniform cooling approach throughout the data center.
Solution Approach 2:
The system dynamically allocates processing tasks based on real-time or near-real-time efficiency characteristics of CRAC units. As CRAC efficiency varies with operating conditions (temperature, humidity, load), the system adapts task allocation dynamically to maintain optimal cooling efficiency, rather than using static task assignment.
2Reliability
If CRACs are deployed to handle concentrated heat from densely packed servers, then adequate cooling is achieved, but the complexity of the cooling system increases
Solution Approach 1:
The patent makes the CRAC system multi-functional by integrating both cooling functionality and efficiency monitoring/communication capabilities. The CRAC units not only provide cooling but also report their efficiency characteristics to the task allocation system, allowing the same infrastructure to serve multiple purposes: thermal management and intelligent load distribution.
Solution Approach 2:
The system introduces an intermediary task allocation mechanism that mediates between processing workloads and cooling capacity. This intermediary layer translates CRAC efficiency characteristics into task allocation decisions, simplifying the overall system by providing a clear mapping between cooling performance and computational workload distribution.
3Productivity
If processing tasks are allocated without considering CRAC efficiency, then task distribution is simple, but heat generated by task execution is not optimally distributed among air conditioning units
Solution Approach 1:
The system performs preliminary action by pre-assessing and storing efficiency characteristics of CRAC units before task allocation decisions are made. This advance preparation of cooling capacity information allows the task allocation system to make informed decisions without complex real-time calculations, improving both cooling efficiency and system responsiveness.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method includes a step of obtaining efficiency characteristic information for each of a plurality of air conditioning units in a location containing a plurality of server computers. The method also includes employing one or more processing units to allocate one or more processing tasks to one of the plurality of server computers based on the efficiency characteristic information.