Data mining for modeling chiller systems in data centers
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
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Compute clusters are consuming more power at higher densities than ever before. This results in increased thermal dissipation, the need for powerful cooling systems, and ultimately a reduction in system reliability as temperatures increase. Over the past several years, the research community has reacted to this problem by producing software tools such as HotSpot and Mercury to estimate system thermal characteristics and validate thermal-management techniques. While these tools are flexible and useful, they suffer several limitations. For the average user such simulation tools can be cumbersome to use. These tools may take significant time and expertise to port to different systems. Lastly, such tools produce significant detail and accuracy at the expense of execution time enough to prohibit iterative testing. We propose a fast, easy to use, accurate, portable software tool called Tempest (for temperature estimator) that leverages emergent thermal sensors to enable user profiling, evaluating, and reducing the thermal characteristics of systems and applications. In this paper, we illustrate the use of Tempest to analyze the thermal effects of various parallel benchmarks in clusters.