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Effect of Rack Server Population on Temperatures in Data Centers

Effect of Rack Server Population on Temperatures in Data Centers. Rajat Ghosh, Vikneshan Sundaralingam, Yogendra Joshi. CEETHERM Data Center Laboratory G.W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta, GA 30332-0405 Yogendra.Joshi@me.gatech.edu

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Effect of Rack Server Population on Temperatures in Data Centers

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  1. Effect of Rack Server Population on Temperatures in Data Centers Rajat Ghosh, Vikneshan Sundaralingam, Yogendra Joshi CEETHERM Data Center Laboratory G.W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta, GA 30332-0405 • Yogendra.Joshi@me.gatech.edu • 404-385-2810 Collaborators: Pramod kumar, Vaibhav Arghode, Steven Isaacs.

  2. Outline • Problem Statement. • Methodology • Experiments. • Computational fluid dynamics (CFD) analysis. • Results. • Conclusions. Ghosh, Joshi: ITherm 2012

  3. Problem Statements • Characterize rack-level air temperatures for a full-capacity server rack. • Estimate effect of rack server population on air temperatures in a data center. • Estimate effect of server location on its CPU temperature and average fan speed. Ghosh, Joshi: ITherm 2012

  4. Experimental Setup • A data center with 10 server racks arranged in a 5x2 architecture. • Raised floor plenum supply and overhead plenum return. • Alternating cold-aisle/ hot-aisle. • CRAC-1 is only active CRAC • Measurement Uncertainty: • Temperature: • Flowrate: • Length: Ghosh, Joshi: ITherm 2012

  5. Test Rack • Server rack (consists of 42 1-U server and headnode) with heterogeneous heat load ranging from 240 W to 0 W. Ghosh, Joshi: ITherm 2012

  6. Containment System Exhaust Plane Hot Exhaust Air Hot Aisle Containment Cold Aisle Containment Test Rack Perforated Tile Cooling Air from Plenum • Isolate airflow into the test rack. Ghosh, Joshi: ITherm 2012

  7. Tube y x Thermocouple Steel Frame z x 600 Thermocouple Grid • Grid: 21 T-type copper-constantan thermocouplesmade from 28 gauge (0.9 mm diameter) wire. • Response time: 28 ms • x-axis: Parallel to rack width. • y-axis: Parallel to tiles. • z-axis: parallel to rack height. Ghosh, Joshi: ITherm 2012

  8. Experimental Procedure • Deploy the rack-level containment. • Vary the server population in the test rack (N=42, 32, 22,12) and measure air temperatures in cold and hot aisles. • Vary the location of a server stack and measure temperatures of CPUs and speeds of fans inside servers. Ghosh, Joshi: ITherm 2012

  9. Server Population as Parameter Ghosh, Joshi: ITherm 2012

  10. Varying Position of a Server Stack Ghosh, Joshi: ITherm 2012

  11. CFD Simulation • x-dimension: 2.46 m • y-dimension: 0.60 m • Z-dimension: 1.95 m • 1-U server: 1m x 0.6 m x0.4 m • Server fan: • Tile: Velocity inlet with 0.8 m/s to match 639 CFM (0.3 m3/s) supply • Exhaust: Pressure outlet • Server inlet and outlet: Porous jump • Grid number: 1.4 millions for grid-independent solution Ghosh, Joshi: ITherm 2012

  12. Transient CRAC Supply Air Temperature • CRAC-1 has return air temperature control. • Variable supply air temperature • Mean=12.2 0C. Std. Dev.=0.9 0C. Ghosh, Joshi: ITherm 2012

  13. Cold Aisle Temperature Variation • N=42 • With height in the cold aisle, average temperature varies irregularly. Ghosh, Joshi: ITherm 2012

  14. Hot Aisle Temperature Variation • N=42 • With height in the hot aisle, average temperature varies irregularly. Ghosh, Joshi: ITherm 2012

  15. CFD-predicted Airflow • N=42 • Recirculation in the airflow explains irregular pattern of air temperatures in the cold and hot aisles. Ghosh, Joshi: ITherm 2012

  16. Temperature Difference Variation • N=42 • Temperature difference increases with height. Ghosh, Joshi: ITherm 2012

  17. Average Temperature in Exhaust Plane • Average temperature in the exhaust plane increases with server population. Ghosh, Joshi: ITherm 2012

  18. Effect of Server Population on Temperature Difference • For all heights, temperature difference increases with server number. Ghosh, Joshi: ITherm 2012

  19. Effect of Containment • The containment system reduces average temperature in the cold aisle • - Blocks hot air recirculating from other parts of the room. Ghosh, Joshi: ITherm 2012

  20. Effect of Server Location • Keeping server stack near the highest possible location is more energy-efficient practice in this case • Lowest CPU temperature. • Lowest average server fan speed. Ghosh, Joshi: ITherm 2012

  21. Conclusion • Rack-level temperature field (average temperatures in cold and hot aisles; and average temperature difference between cold and hot aisles) is characterized for a full capacity server rack (N=42). • Air recirculation thorough the void affects convective temperature field. • Rack server population has a significant impact on air temperatures • Temperature difference across the rack increases with the server population in the test rack. • Recommended best practice for filling out a server stack in an empty stack • Sever stack should be placed at the highest possible location. Ghosh, Joshi: ITherm 2012

  22. Acknowledgement The authors acknowledge support for this work from IBM Corporation, with Dr. Hendrik Hamann as the Technical Monitor. Acknowledgements are also due to the United States Department of Energy as the source of primary funds. Additional support from the National Science Foundation award CRI 0958514 enabled the acquisition of some of the test equipment utilized. Ghosh, Joshi: ITherm 2012

  23. Questions? Ghosh, Joshi: ITherm 2012

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