Proactive thermal management in green datacenters

  • Authors:

  • Eun Kyung Lee;Indraneel Kulkarni;Dario Pompili;Manish Parashar

  • Affiliations:

  • NSF Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, USA 08854;NSF Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, USA 08854;NSF Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, USA 08854;NSF Center for Autonomic Computing, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, USA 08854

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2012

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Abstract

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.