Cited By
View all- Ogura MWan JKasahara S(2018)Model Predictive Control for Energy-Efficient Operations of Data Centers with Cold Aisle ContainmentsIFAC-PapersOnLine10.1016/j.ifacol.2018.11.01551:20(209-214)Online publication date: 2018
Conserving cooling and computing power by distributing workloads in data centers
Authors: 
Ruihong Lin, Yuhui Deng, Liyao YangAuthors Info & Claims
Pages 232 - 239
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 linear 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 that of EGA.
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Index Terms
- Conserving cooling and computing power by distributing workloads in data centers
Recommendations
Allocating workload to minimize the power consumption of data centers
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 ...
Joint optimization of idle and cooling power in data centers while maintaining response time
ASPLOS '10Server power and cooling power amount to a significant fraction of modern data centers' recurring costs. While data centers provision enough servers to guarantee response times under the maximum loading, data centers operate under much less loading most ...
Joint optimization of idle and cooling power in data centers while maintaining response time
ASPLOS '10Server power and cooling power amount to a significant fraction of modern data centers' recurring costs. While data centers provision enough servers to guarantee response times under the maximum loading, data centers operate under much less loading most ...
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May 2016
487 pages
ISBN:9781450341288
DOI:10.1145/2903150
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- John Feo,
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Published: 16 May 2016
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View all- Ogura MWan JKasahara S(2018)Model Predictive Control for Energy-Efficient Operations of Data Centers with Cold Aisle ContainmentsIFAC-PapersOnLine10.1016/j.ifacol.2018.11.01551:20(209-214)Online publication date: 2018
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