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Thermal-aware Workload Distribution for Data Centers with Demand Variations

Published: 29 May 2024 Publication History

Abstract

Thermal-aware workload distribution is a common approach in the literature for power consumption optimization in data centers. However, data centers also have other operational costs such as the cost of equipment maintenance and replacement. It has been shown that server reliability depends on frequency of their temperature variations, arising from workload transitions due to dynamic demands. In this work, we formulate a nonlinear optimization problem that considers the cost of workload transitions in addition to IT and cooling power consumption. To approximate the solution, we first linearize the problem; the result is a mixed integer linear programming problem. A modified heuristic is then proposed to approximate the solution of the linear problem. Finally, a Model Predictive Control (MPC) approach is integrated with the proposed heuristics for automatic workload reconfiguration when future demand is not known exactly, but predictions are available. Numerical results show that the proposed schemes are attractive in different settings.

References

[1]
A. D. Carnerero, D. R. Ramirez, T. Alamo, and D. Limon. 2022. Probabilistically Certified Management of Data Centers Using Predictive Control. IEEE Transactions on Automation Science and Engineering 19, 4 (oct 2022), 2849–2861.
[2]
N. EI-Sayed, I. A. Stefanovici, G. Amvrosiadis, and A. A. Hwang. 2012. Temperature Management in Data Centers: Why Some (Might) Like It Hot. SIGMETRICS ’12: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems (jun 2012), 163–174.
[3]
R. Gupta, S. Asgari, H. Moazamigoodarzi, D. G. Down, and I. K. Puri. 2021. Energy, Exergy and Computing Efficiency Based Data Center Workload and Cooling Management. Applied Energy 299 (oct 2021), 117050.
[4]
S. MirhoseiniNejad, G. Badawy, and D. G. Down. 2018. EAWA: Energy-aware Workload Assignment in Data Centers. 2018 International Conference on High Performance Computing & Simulation (HPCS) (2018), 36–44.
[5]
S. Mirhoseininejad, G. Badawy, and D. G. Down. 2021. A Data-driven, Multi-set Point Model Predictive Thermal Control System for Data Centers. Journal of Networks and Systems Management 29, 7 (jan 2021).
[6]
S. Rostami, D. G. Down, and G. Karakostas. 2023. Linearized Data Center Workload and Cooling Management. arXiv:2304.04731 [eess.SY] (apr 2023).
[7]
S. Rostami, D. G. Down, and G. Karakostas. 2023. Thermal-aware Workload Distribution for Data Centers with Demand Variations. arXiv:2308.12559 [eess.SY] (aug 2023).
[8]
Z. Xiong, Z. Yuan M. Zhao, J. Xu, and L. Cai. 2023. Energy-saving Optimization of Application Server Clusters Based on Mixed Integer Linear Programming. J. Parallel and Distrib. Comput. 171 (jan 2023), 111–129.

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IGSC '23: Proceedings of the 14th International Green and Sustainable Computing Conference
October 2023
80 pages
ISBN:9798400716690
DOI:10.1145/3634769
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2024

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Author Tags

  1. data center
  2. model predictive control
  3. switching cost
  4. thermal-aware workload distribution

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