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Engineering with Computers

, Volume 27, Issue 4, pp 381–391 | Cite as

Task scheduling with ANN-based temperature prediction in a data center: a simulation-based study

  • Lizhe Wang
  • Gregor von Laszewski
  • Fang Huang
  • Jai Dayal
  • Tom Frulani
  • Geoffrey Fox
Original Article

Abstract

High temperatures within a data center can cause a number of problems, such as increased cooling costs and increased hardware failure rates. To overcome this problem, researchers have shown that workload management, focused on a data center’s thermal properties, effectively reduces temperatures within a data center. In this paper, we propose a method to predict a workload’s thermal effect on a data center, which will be suitable for real-time scenarios. We use machine learning techniques, such as artificial neural networks (ANN) as our prediction methodology. We use real data taken from a data center’s normal operation to conduct our experiments. To reduce the data’s complexity, we introduce a thermal impact matrix to capture the spacial relationship between the data center’s heat sources, such as the compute nodes. Our results show that machine learning techniques can predict the workload’s thermal effects in a timely manner, thus making them well suited for real-time scenarios. Based on the temperature prediction techniques, we developed a thermal-aware workload scheduling algorithm for data centers, which aims to reduce power consumption and temperatures in a data center. A simulation study is carried out to evaluate the performance of the algorithm. Simulation results show that our algorithm can significantly reduce temperatures in data centers by introducing an endurable decline in performance.

Keywords

Data center Green computing Workload scheduling 

Notes

Acknowledgments

Work conducted by Lizhe Wang and Gregor von Laszewski is supported (in part) by NSF 0963571 and 2010 HP Annual Innovation Research Awards. Work conducted by Fang Huang is supported by the Fundamental Research Funds for the Central Universities (Grant No. ZYGX2009J073), the National Natural Science Foundation of China (Grant No. 41001221).

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Lizhe Wang
    • 1
  • Gregor von Laszewski
    • 1
  • Fang Huang
    • 2
  • Jai Dayal
    • 3
  • Tom Frulani
    • 4
  • Geoffrey Fox
    • 1
  1. 1.Pervasive Technology InstituteIndiana UniversityBloomingtonUSA
  2. 2.Institute of Geo-Spatial Information Technology, College of AutomationUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  3. 3.College of Computing, George Institute of TechnologyAtlantaUSA
  4. 4.Center for Computational ResearchNYS Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, SUNYBuffaloUSA

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