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

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.

[1]  John S. Zelek,et al.  Sparse Disparity Map from Uncalibrated Infrared Stereo Images , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[2]  Li-Minn Ang,et al.  Survey of image compression algorithms in wireless sensor networks , 2008, 2008 International Symposium on Information Technology.

[3]  Surya Prakash,et al.  3D Mapping of Surface Temperature Using Thermal Stereo , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[4]  Joonwon Lee,et al.  A CFD-Based Tool for Studying Temperature in Rack-Mounted Servers , 2008, IEEE Transactions on Computers.

[5]  Jeffrey O. Kephart,et al.  Towards data center self-diagnosis using a mobile robot , 2011, ICAC '11.

[6]  Amin Vahdat,et al.  Managing energy and server resources in hosting centers , 2001, SOSP.

[7]  David S. Taubman,et al.  High performance scalable image compression with EBCOT , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[8]  Gaurav S. Sukhatme,et al.  Networked infomechanical systems: a mobile embedded networked sensor platform , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[9]  Hendrik F. Hamann,et al.  Thermal zones for more efficient data center energy management , 2010, 2010 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[10]  John S. Zelek,et al.  Structure from Infrared Stereo Images , 2008, 2008 Canadian Conference on Computer and Robot Vision.

[11]  Jeffrey S. Chase,et al.  Balance of power: dynamic thermal management for Internet data centers , 2005, IEEE Internet Computing.

[12]  Dario Pompili,et al.  Proactive thermal management in green datacenters , 2012, The Journal of Supercomputing.

[13]  Chandrakant D. Patel,et al.  B13-115 A VISION OF ENERGY AWARE COMPUTING FROM CHIPS TO DATA CENTERS , 2003 .

[14]  Sandeep K. S. Gupta,et al.  Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach , 2008, IEEE Transactions on Parallel and Distributed Systems.

[15]  C.D. Patel,et al.  Dynamic thermal management of air cooled data centers , 2006, Thermal and Thermomechanical Proceedings 10th Intersociety Conference on Phenomena in Electronics Systems, 2006. ITHERM 2006..

[16]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.

[17]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[18]  Rajarshi Das,et al.  Autonomic multi-agent management of power and performance in data centers , 2008, AAMAS.

[19]  Sandeep K. S. Gupta,et al.  Software Architecture for Dynamic Thermal Management in Datacenters , 2007, 2007 2nd International Conference on Communication Systems Software and Middleware.

[20]  Manish Parashar,et al.  Enabling autonomic power-aware management of instrumented data centers , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[21]  Jerome M. Shapiro,et al.  Embedded image coding using zerotrees of wavelet coefficients , 1993, IEEE Trans. Signal Process..

[22]  Nasser M. Nasrabadi,et al.  Image coding using vector quantization: a review , 1988, IEEE Trans. Commun..

[23]  Jeffrey S. Chase,et al.  Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers , 2005, USENIX Annual Technical Conference, General Track.

[24]  Chin-Chen Chang,et al.  Intelligent systems for future generation communications , 2010, The Journal of Supercomputing.

[25]  M. Kunt,et al.  Second-generation image-coding techniques , 1985, Proceedings of the IEEE.

[26]  Chang Nian Zhang,et al.  A hybrid approach of wavelet packet and directional decomposition for image compression , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).

[27]  Cullen E. Bash,et al.  Computational Fluid Dynamics Modeling of High Compute Density Data Centers to Assure System Inlet Air Specifications , 2001 .

[28]  Chandrakant D. Patel,et al.  Thermo-Fluids Provisioning of a High Performance High Density Data Center , 2007, Distributed and Parallel Databases.

[29]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[30]  Norman D. Black,et al.  Second-generation image coding: an overview , 1997, CSUR.

[31]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[32]  Stan Sclaroff,et al.  Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements , 2006, Comput. Vis. Image Underst..

[33]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.