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Thermal aware workload placement with task-temperature profiles in a data center

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Abstract

Data centers now play an important role in modern IT infrastructures. Related research shows that the energy consumption for data center cooling systems has recently increased significantly. There is also strong evidence to show that high temperatures in a data center will lead to higher hardware failure rates, and thus an increase in maintenance costs. This paper devotes itself in the field of thermal aware workload placement for data centers. In this paper, we propose an analytical model, which describes data center resources with heat transfer properties and workloads with thermal features. Then two thermal aware task scheduling algorithms, TASA and TASA-B, are presented which aim to reduce temperatures and cooling system power consumption in a data center. A simulation study is carried out to evaluate the performance of the proposed algorithms. Simulation results show that our algorithms can significantly reduce temperatures in data centers by introducing endurable decline in system performance.

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Authors and Affiliations

  1. Pervasive Technology Institute, Indiana University, 2719 E. 10th St., Bloomington, IN, 47408, USA

    Lizhe Wang

  2. Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND, 58108-6050, USA

    Samee U. Khan

  3. School of Computer Science, School of Computing, George Institute of Technology, 266 First Drive, Atlanta, GA, 30332-0765, USA

    Jai Dayal

Authors
  1. Lizhe Wang
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  2. Samee U. Khan
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  3. Jai Dayal
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Correspondence to Lizhe Wang.

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Wang, L., Khan, S.U. & Dayal, J. Thermal aware workload placement with task-temperature profiles in a data center. J Supercomput 61, 780–803 (2012). https://doi.org/10.1007/s11227-011-0635-z

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