Application of infrared thermal imaging in blade system temperature monitoring

All objects emit infrared radiation at differing levels, depending on their temperature. Infrared thermal imaging is the technique of producing an image from infrared radiation. This thermal image represents two-dimensional distribution of the infrared radiation emitted by object displaying the object's temperatures. High performance computing data centres deploy high-density blade servers that have high power and cooling requirements. Thermal management techniques are needed in different levels to ensure data centre's reliability and economical operating cost. Appropriately designed and accurate temperature monitoring is essential for every thermal management techniques. Infrared thermal imaging can provide a fine-resolution thermal image of blade systems deployed in data canter. This paper outlines an application of infrared thermal imaging in blade based data centres. For this purpose thermal monitoring of computer cluster at the blade-level is conducted using infrared thermal imaging monitoring system at the blade enclosure-level.

[1]  Vanish Talwar,et al.  Monalytics: online monitoring and analytics for managing large scale data centers , 2010, ICAC '10.

[2]  Manish Marwah,et al.  Data Mining for Modeling Chiller Systems in Data Centers , 2010, IDA.

[3]  Qinghui Tang,et al.  Sensor-Based Fast Thermal Evaluation Model For Energy Efficient High-Performance Datacenters , 2006, 2006 Fourth International Conference on Intelligent Sensing and Information Processing.

[4]  Karolj Skala,et al.  Specific thermographic changes during Walker 256 carcinoma development: differential infrared imaging of tumour, inflammation and haematoma. , 2009, Cancer detection and prevention.

[5]  Cullen E. Bash,et al.  Smart cooling of data centers , 2003 .

[6]  Manish Marwah,et al.  Sustainable operation and management of data center chillers using temporal data mining , 2009, KDD.

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

[8]  Michael Vollmer,et al.  Infrared Thermal Imaging: Fundamentals, Research and Applications , 2010 .

[9]  Jinkyun Cho,et al.  Measurements and predictions of the air distribution systems in high compute density (Internet) data centers , 2009 .

[10]  Tibor Skala,et al.  Extremities Perfusion Stimulation and Dynamic Evaluation by Thermography Analyses , 2010 .

[11]  Rajkumar Buyya,et al.  Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[12]  Eric Stockton Applications for Infrared Thermography at Computer Centers , 2005 .

[13]  Ricardo Bianchini,et al.  Mercury and freon: temperature emulation and management for server systems , 2006, ASPLOS XII.

[14]  Joonwon Lee,et al.  Modeling and Managing Thermal Profiles of Rack-mounted Servers with ThermoStat , 2007, 2007 IEEE 13th International Symposium on High Performance Computer Architecture.

[15]  Jeffrey S. Chase,et al.  ConSil: Low-Cost Thermal Mapping of Data Centers , 2006 .

[16]  Chu Kiong Loo,et al.  An Effective Surveillance System Using Thermal Camera , 2009, 2009 International Conference on Signal Acquisition and Processing.

[17]  Babak Kateb,et al.  Infrared thermal imaging: A review of the literature and case report , 2009, NeuroImage.

[18]  Manish Marwah,et al.  Data analysis, visualization and knowledge discovery in sustainable data centers , 2009, COMPUTE '09.

[19]  Bahram Moshfegh,et al.  Investigation of indoor climate and power usage in a data center , 2005 .

[20]  K. Skala,et al.  Remote control and measurement of temperature over the web , 2005, 47th International Symposium ELMAR, 2005..

[21]  Xiaorui Wang,et al.  Cluster-level feedback power control for performance optimization , 2008, 2008 IEEE 14th International Symposium on High Performance Computer Architecture.