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

  • Authors:

  • Hang Liu;Eun Kyung Lee;Dario Pompili;Xiangwei Kong

  • Affiliations:

  • College of Electronic Science and Technology, Dalian University of Technology, Dalian, China 116023;NSF Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, USA 08854;NSF Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, USA 08854;School of Information and Communication Engineering, Dalian University of Technology, Dalian, China 116023

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

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.