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Thermal camera networks for large datacenters using real-time thermal monitoring mechanism

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Abstract

Thermal cameras provide fine-grained thermal information that enables monitoring and autonomic thermal management in large datacenters. The real-time thermal monitor network employing thermal cameras is proposed to cooperatively localize hotspots and extract their characteristics (i.e., temperature, size, and shape). These characteristics are adopted to classify the causes of hotspots and make energy-efficient thermal management decisions such as job migration. Specifically, a sculpturing algorithm for extracting and reconstructing shape characteristics of hotspots is proposed to minimize the network overhead. Experimental results show the validity of all the algorithms proposed in this paper.

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Acknowledgement

We acknowledge the contribution of Alex Weiner served as a technical reviewer.

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Authors and Affiliations

  1. College of Electronic Science and Technology, Dalian University of Technology, 2 Linggong Road, Dalian, Liaoning, 116023, China

    Hang Liu

  2. NSF Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, 94 Brett Road, Piscataway, NJ, 08854, USA

    Eun Kyung Lee & Dario Pompili

  3. School of Information and Communication Engineering, Dalian University of Technology, 2 Linggong Road, Dalian, Liaoning, 116023, China

    Xiangwei Kong

Authors
  1. Hang Liu
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  2. Eun Kyung Lee
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  3. Dario Pompili
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  4. Xiangwei Kong
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Corresponding author

Correspondence to Hang Liu.

Additional information

This work was performed while H. Liu was visiting the CPS Lab, Rutgers University.

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Liu, H., Lee, E.K., Pompili, D. et al. Thermal camera networks for large datacenters using real-time thermal monitoring mechanism. J Supercomput 64, 383–408 (2013). https://doi.org/10.1007/s11227-012-0781-y

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  • DOI: https://doi.org/10.1007/s11227-012-0781-y

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