Leveraging thermal storage to cut the electricity bill for datacenter cooling

The electricity cost of cooling systems can account for 30% of the total electricity bill of operating a data center. While many prior studies have tried to reduce the cooling energy in data centers, they cannot effectively utilize the time-varying power prices in the power market to cut the electricity bill of data center cooling. Thermal storage techniques have provided opportunities to store cooling energy in ice or water-based tanks or overcool the data center when the power price is relatively low. Consequently, when the power price is high, data centers can choose to use less electricity from power grid for cooling, resulting in a significantly reduced electricity bill. In this paper, we design and evaluate TStore, a cooling strategy that leverages thermal storage to cut the electricity bill for cooling, without causing servers in a data center to overheat. TStore checks the low prices in the hourahead power market and overcools the thermal masses in the datacenter, which can then absorb heat when the power price increases later. On a longer time scale, TStore is integrated with auxiliary thermal storage tanks, which are recently adopted by some data-centers to store energy in the form of ice when the power price is low at night, such that the stored ice can be used to cool the datacenter in daytime. We model the impacts of TStore on server temperatures based on Computational Fluid Dynamics (CFD) to consider the realistic thermal dynamics in a data center with 1,120 servers. We then evaluate TStore using workload traces from real-world data centers and power price traces from a real power market. Our results show that TStore achieves the desired cooling performance with a 16.8% less electricity bill than the current practice.

[1]  Lance Edgar Basgall.  Thermal energy storage design for emergency cooling , 2010 .

[2]  Jeffrey S. Chase,et al.  Weatherman: Automated, Online and Predictive Thermal Mapping and Management for Data Centers , 2006, 2006 IEEE International Conference on Autonomic Computing.

[3]  Manish Marwah,et al.  Optimal Fan Speed Control for Thermal Management of Servers , 2009 .

[4]  J. Contreras,et al.  Forecasting next-day electricity prices by time series models , 2002 .

[5]  Kurt Roth,et al.  Cool Thermal Energy Storage , 2006 .

[6]  Cullen E. Bash,et al.  Thermal considerations in cooling large scale high compute density data centers , 2002, ITherm 2002. Eighth Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (Cat. No.02CH37258).

[7]  Cullen E. Bash,et al.  Viability of Dynamic Cooling Control in a Data Center Environment , 2006 .

[8]  Xiaorui Wang,et al.  SHIP: Scalable Hierarchical Power Control for Large-Scale Data Centers , 2009, 2009 18th International Conference on Parallel Architectures and Compilation Techniques.

[9]  T. N. Vijaykumar,et al.  Joint optimization of idle and cooling power in data centers while maintaining response time , 2010, ASPLOS XV.

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

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

[12]  J. Contreras,et al.  Forecasting Next-Day Electricity Prices by Time Series Models , 2002, IEEE Power Engineering Review.

[13]  Antonio J. Conejo,et al.  Electricity price forecasting through transfer function models , 2006, J. Oper. Res. Soc..