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Weatherman: Automated, online, and predictive thermal mapping and management for data centers (2006)
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| Venue: | In International Conference on Autonomic Computing |
| Citations: | 47 - 1 self |
BibTeX
@INPROCEEDINGS{Moore06weatherman:automated,,
author = {Justin Moore and Jeffrey S. Chase},
title = {Weatherman: Automated, online, and predictive thermal mapping and management for data centers},
booktitle = {In International Conference on Autonomic Computing},
year = {2006}
}
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Abstract
Abstract — Recent advances have demonstrated the potential benefits of coordinated management of thermal load in data centers, including reduced cooling costs and improved resistance to cooling system failures. A key unresolved obstacle to the practical implementation of thermal load management is the ability to predict the effects of workload distribution and cooling configurations on temperatures within a data center enclosure. The interactions between workload, cooling, and temperature are dependent on complex factors that are unique to each data center, including physical room layout, hardware power consumption, and cooling capacity; this dictates an approach that formulates management policies for each data center based on these properties. We propose and evaluate a simple, flexible method to infer a detailed model of thermal behavior within a data center from a stream of instrumentation data. This data — taken during normal data center operation — includes continuous readings taken from external temperature sensors, server instrumentation, and computer room air conditioning units. Experimental results from a representative data center show that automatic thermal mapping can predict accurately the heat distribution resulting from a given workload distribution and cooling configuration, thereby removing the need for static or manual configuration of thermal load management systems. We also demonstrate how our approach adapts to preserve accuracy across changes to cluster attributes that affect thermal behavior — such as cooling settings, workload distribution, and power consumption. I.
Keyphrases
data center predictive thermal mapping workload distribution thermal behavior approach adapts normal data center operation complex factor representative data center show thermal load management practical implementation manual configuration instrumentation data coordinated management computer room air continuous reading hardware power consumption heat distribution flexible method cooling cost key unresolved obstacle cooling configuration thermal load management system physical room layout external temperature sensor power consumption thermal load detailed model potential benefit abstract recent advance automatic thermal mapping data center enclosure management policy experimental result server instrumentation system failure
