Analysis Of Data Center Cooling Strategies And The Impact Of The Dynamic Thermal Management On The Data Center Efficiency

The power trend for Server systems continues to grow thereby making thermal management of Data centers a very challenging task. Although various configurations exist, the raised floor plenum with Computer Room Air Conditioners (CRACs) providing cold air is a popular operating strategy. The air cooling of data center however, may not address the situation where more energy is expended in cooling infrastructure than the thermal load of data center. Revised power trend projections by ASHRAE TC 9.9 predict heat load as high as 5000W per square feet of compute servers' equipment footprint by year 2010. These trend charts also indicate that heat load per product footprint has doubled for storage servers during 2000-2004. For the same period, heat load per product footprint for compute servers has tripled. Amongst the systems that are currently available and being shipped, many racks exceed 20kW. Such high heat loads have raised concerns over limits of air cooling of data centers similar to air cooling of microprocessors.The concept of "Dynamic Thermal Management" depends on sensing local data and actuates the cooling resources dynamically thereby improving thermodynamic efficiencies. This will result in potential energy savings. This research is aimed at developing the guidelines for a dynamic thermal management that will monitor the Rack Inlet Temperature (RIT) and provide feedback to control the cooling resources. Commercially available CFD tools are used to formulate data center models. The effect of various data center parameters on the temperature distribution and the flow field is studied. The parametric and optimization techniques are used to determine the optimal layouts for various cooling strategies. In the second phase, analytical models were identified which essentially captured the complexities of temperature distribution within data center and the inter-dependence of individual components on one another. Finally, experimental tests were carried out to collect the temperature data, use analytical models to decide the new set points for cooling resources and validate the guidelines of dynamic thermal management by realizing the energy savings.