Data Center Thermal Ranking for Targeted Device Cooling
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Solution Overview
Problem
Current thermal management in data centers is inefficient due to generic and workload-agnostic cooling policies, leading to increased temperatures, reduced device performance, and higher power consumption, with hot air rising and causing uneven cooling challenges within racks.
Innovation Solution
A method and system for predicting future thermal conditions of computing devices using thermal data analysis, clustering, and confidence analysis to assign thermal status labels, allowing for context-aware cooling adjustments and targeted cooling solutions based on device-specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If generic cooling policies are applied to all computing devices, then cooling coverage is provided, but thermal management efficiency deteriorates and power consumption increases
Solution Approach 1:
The patent segments computing devices into different thermal clusters based on their thermal characteristics, workload patterns, and environmental factors. This segmentation enables differentiated cooling policies for each cluster, replacing the previous generic one-size-fits-all approach and improving thermal management efficiency while reducing energy waste.
Solution Approach 2:
The system dynamically adjusts cooling policies based on real-time thermal data, predicted future thermal states, and changing workload conditions. This dynamic adaptation allows the cooling system to respond to actual thermal needs rather than applying static generic policies, thereby reducing power consumption while maintaining effective thermal management.
2Productivity
If device-specific thermal analysis is implemented, then thermal management efficiency improves, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically collecting thermal data, analyzing thermal patterns, predicting future thermal states, and generating cooling recommendations without requiring manual intervention. This automation handles the complexity internally while providing simple, actionable insights to administrators, thereby improving thermal management efficiency without proportionally increasing operational complexity.
Solution Approach 2:
The system implements continuous feedback loops where thermal data from computing devices is collected, analyzed, and used to generate cooling recommendations that are then applied. The results are monitored and fed back into the system for continuous optimization. This feedback mechanism manages system complexity through structured, automated processes while delivering high thermal management efficiency.
3Temperature
If cooling is increased for all devices, then temperature control is maintained, but power consumption increases
Solution Approach 1:
The patent applies local quality by providing customized cooling solutions tailored to the specific thermal characteristics, workload patterns, and environmental conditions of each computing device or thermal cluster. Instead of uniformly increasing cooling for all devices, the system applies cooling only where and when needed, maintaining temperature control while minimizing unnecessary energy consumption.
Solution Approach 2:
The system changes cooling parameters dynamically based on predicted future thermal states and actual thermal conditions. By adjusting cooling intensity, timing, and target devices based on multiple thermal parameters and predictions, the system maintains effective temperature control while optimizing power consumption by avoiding excessive or unnecessary cooling.
Data Source
AI summary
Techniques described herein relate to methods and systems for thermal management of a thermal environment. The method may include using thermal data items from computing devices and time series analysis to predict future thermal values for the thermal data items; performing a clustering analysis using the predicted future thermal values to assign cluster labels to the computing devices; using the cluster labels and the predicted future thermal values to assign predicted thermal status labels to the computing devices; assigning a confidence value to the predicted thermal status labels and ranking the computing devices based on the confidence values; performing an analysis to determine a thermal data item contributing to the assigned thermal status; and sending the results to a thermal environment administrator.