Dynamic workload and cooling management in high-efficiency data centers

Energy efficiency research in data centers has traditionally focused on raised-floor air-cooled facilities. As rack power density increases, traditional cooling is being replaced by close-coupled systems that provide enhanced airflow and cooling capacity. This work presents a model for close-coupled data centers with free cooling, and explores the power consumption trade-offs in these facilities as outdoor temperature changes throughout the year. Using this model, we propose a technique that jointly allocates workload and controls cooling in a power-efficient way. Our technique is tested with configuration parameters, power traces, and weather data collected from real-life data centers, and application profiles obtained from enterprise servers. Results show that our joint workload allocation and cooling policy provides 5% reduction in overall data center energy consumption, and up to 24% peak power reduction, leading to a 6% decrease in the electricity costs without affecting performance.

[1]  David Atienza,et al.  Free cooling-aware dynamic power management for green datacenters , 2012, 2012 International Conference on High Performance Computing & Simulation (HPCS).

[2]  Henry Coles Demonstration of Rack-Mounted Computer Equipment Cooling Solutions , 2014 .

[3]  Ayan Banerjee,et al.  Cooling-aware and thermal-aware workload placement for green HPC data centers , 2010, International Conference on Green Computing.

[4]  C. Patel,et al.  Model-Based Approach for Optimizing a Data Center Centralized Cooling System , 2006 .

[5]  Jim Gao,et al.  Machine Learning Applications for Data Center Optimization , 2014 .

[6]  Sherief Reda,et al.  Techniques for energy-efficient power budgeting in data centers , 2013, 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC).

[7]  Ayan Banerjee,et al.  Energy Efficiency of Thermal-Aware Job Scheduling Algorithms under Various Cooling Models , 2009, IC3.

[8]  SPEC CPU 2006 Benchmark Descriptions , 2006 .

[9]  J. Koomey Worldwide electricity used in data centers , 2008 .

[10]  Albert O. Rabassa Economic performance of modularized hot-aisle contained datacenter PODs utilizing horizontal airflow cooling , 2014 .

[11]  Ricardo Bianchini,et al.  C-Oracle: Predictive thermal management for data centers , 2008, 2008 IEEE 14th International Symposium on High Performance Computer Architecture.

[12]  T. J. Breen,et al.  From chip to cooling tower data center modeling: Part I Influence of server inlet temperature and temperature rise across cabinet , 2010, 2010 12th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[13]  Neil Rasmussen,et al.  Choosing Between Room , Row , and Rack-based Cooling for Data Centers , 2012 .

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

[16]  José Manuel Moya,et al.  Leakage-Aware Cooling Management for Improving Server Energy Efficiency , 2015, IEEE Transactions on Parallel and Distributed Systems.

[17]  Andy B. Yoo,et al.  Approved for Public Release; Further Dissemination Unlimited X-ray Pulse Compression Using Strained Crystals X-ray Pulse Compression Using Strained Crystals , 2002 .