Proactive thermal management in green datacenters

The increasing demand for faster computing and high storage capacity has resulted in an increase in energy consumption and heat generation in datacenters. Because of the increase in heat generation, cooling requirements have become a critical concern, both in terms of growing operating costs as well as their environmental and societal impacts. Presently, thermal management techniques make an effort to thermally profile and control datacenters’ cooling equipment to increase their efficiency. In conventional thermal management techniques, cooling systems are triggered by the temperature crossing predefined thresholds. Such reactive approaches result in delayed response as the temperature may already be too high, which can result in performance degradation of hardware.In this work, a proactive control approach is proposed that jointly optimizes the air conditioner compressor duty cycle and fan speed to prevent heat imbalance—the difference between the heat generated and extracted from a machine—thus minimizing the cost of cooling. The proposed proactive optimization framework has two objectives: (i) minimize the energy consumption of the cooling system, and (ii) minimize the risk of equipment damage due to overheating. Through thorough simulations comparing the proposed proactive heat-imbalance estimation-based approach against conventional reactive temperature-based schemes, the superiority of the proposed approach is highlighted in terms of cooling energy, response time, and equipment failure risk.

[1]  Krishna C. Saraswat,et al.  Scaling trends for the on chip power dissipation , 2002, Proceedings of the IEEE 2002 International Interconnect Technology Conference (Cat. No.02EX519).

[2]  Hong Zhu,et al.  A survey of practical algorithms for suffix tree construction in external memory , 2010 .

[3]  Wu-chun Feng,et al.  Towards efficient supercomputing: a quest for the right metric , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[4]  Roger R. Schmidt,et al.  A methodology for the design of perforated tiles in raised floor data centers using computational flow analysis , 2000, ITHERM 2000. The Seventh Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (Cat. No.00CH37069).

[5]  Roger R. Schmidt MEASUREMENTS AND PREDICTIONS OF THE FLOW DISTRIBUTION THROUGH PERFORATED TILES IN RAISED-FLOOR DATA CENTERS , 2001 .

[6]  Chandrakant D. Patel,et al.  Thermo-Fluids Provisioning of a High Performance High Density Data Center , 2007, Distributed and Parallel Databases.

[7]  Roger R. Schmidt,et al.  Raised floor computer data center: effect on rack inlet temperatures of chilled air exiting both the hot and cold aisles , 2002, ITherm 2002. Eighth Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (Cat. No.02CH37258).

[8]  Cullen E. Bash,et al.  Computational Fluid Dynamics Modeling of High Compute Density Data Centers to Assure System Inlet Air Specifications , 2001 .

[9]  Masaki Nakao,et al.  Which cooling air supply system is better for a high heat density room: underfloor or overhead? , 1991, [Proceedings] Thirteenth International Telecommunications Energy Conference - INTELEC 91.

[10]  Madhusudan K. Iyengar,et al.  Challenges of data center thermal management , 2005, IBM J. Res. Dev..

[11]  Sarita V. Adve,et al.  The impact of technology scaling on lifetime reliability , 2004, International Conference on Dependable Systems and Networks, 2004.

[12]  A Design Guidelines Sourcebook,et al.  HIGH PERFORMANCE CLEANROOMS , 2006 .

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

[14]  Masaki Nakao,et al.  Air flow systems for telecommunications equipment rooms , 1989, Conference Proceedings., Eleventh International Telecommunications Energy Conference.

[15]  M. Nakao,et al.  Airflow distribution in telecommunications equipment rooms , 1990, 12th International Conference on Telecommunications Energy.

[16]  Jeffrey Rambo,et al.  Modeling of data center airflow and heat transfer: State of the art and future trends , 2007, Distributed and Parallel Databases.

[17]  Ayan Banerjee,et al.  Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers , 2009, Comput. Networks.

[18]  Steve Greenberg,et al.  Best Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers , 2006 .

[19]  Ishfaq Ahmad,et al.  A Cooperative Game Theoretical Technique for Joint Optimization of Energy Consumption and Response Time in Computational Grids , 2009, IEEE Transactions on Parallel and Distributed Systems.

[20]  I. E. Idelchik,et al.  Flow Resistance : A Design Guide for Engineers , 1989 .

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

[22]  R. Schmidt,et al.  Raised-floor data center: perforated tile flow rates for various tile layouts , 2004, The Ninth Intersociety Conference on Thermal and Thermomechanical Phenomena In Electronic Systems (IEEE Cat. No.04CH37543).

[23]  Albert Y. Zomaya,et al.  Cooperative power-aware scheduling in grid computing environments , 2010, J. Parallel Distributed Comput..

[24]  Rong Ge,et al.  High-performance, power-aware distributed computing for scientific applications , 2005, Computer.

[25]  Cullen E. Bash,et al.  DIMENSIONLESS PARAMETERS FOR EVALUATION OF THERMAL DESIGN AND PERFORMANCE OF LARGE-SCALE DATA CENTERS , 2002 .