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Allocating workload to minimize the power consumption of data centers

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Abstract

Reducing the power consumption has become one of the most important challenges in designing modern data centers due to the explosive growth of data. The traditional approaches employed to decrease the power consumption normally do not consider the power of IT devices and the power of cooling system simultaneously. In contrast to existing works, this paper proposes a power model which can minimize the overall power consumption of data centers by balancing the computing power and cooling power. Furthermore, an enhanced genetic algorithm (EGA) is designed to explore the solution space of the power model since the model is a liner programming problem. However, EGA is computing intensive and the performance gradually decreases with the growth of the problem size. Therefore, heuristic greedy sequence (HGS) is proposed to simplify the calculation by leveraging the nature of greed. In contrast to EGA, HGS can determine the workload allocation of a specific data center layout with only one calculation. Experimental results demonstrate that both the EGA and HGS can significantly reduce the power consumption of data centers in contrast to the random algorithm. Additionally, HGS significantly outperforms EGA in terms of the continuity of workload allocation and execution performance.

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Acknowledgements

We would like to thank the anonymous reviewers for their valuable time and constructive comments. This work was supported by the National Natural Science Foundation (NSF) of China (Grant Nos. 61572232 and 61272073), the NSF of Guangdong Province (S2013020012865), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Department of Computer Science, Jinan University, Guangzhou, 510632, China

    Ruihong Lin & Yuhui Deng

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  1. Ruihong Lin
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  2. Yuhui Deng
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Correspondence to Yuhui Deng.

Additional information

Ruihong Lin is a research student at the Computer Science Department of Jinan University, China. Her current research interests cover energy-efficient computing, data center architecture, and data replication.

Yuhui Deng is a professor at the Computer Science Department of Jinan University, China. Before joining Jinan University, he worked at EMC Corporation as a senior research scientist from 2008 to 2009. He worked as a research officer at Cranfield University, UK from 2005 to 2008. He received his PhD degree in computer science from Huazhong University of Science and Technology, China in 2004. His research interests cover green computing, cloud computing, information storage, computer architecture, performance evaluation, etc.

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Lin, R., Deng, Y. Allocating workload to minimize the power consumption of data centers. Front. Comput. Sci. 11, 105–118 (2017). https://doi.org/10.1007/s11704-016-6035-z

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  • DOI: https://doi.org/10.1007/s11704-016-6035-z

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