Reducing the power consumption has become one of the most important challenges in designing modem data centers due to the explosive growth of data. The tradi- tional approaches employed to decrease the power consump- ...Reducing the power consumption has become one of the most important challenges in designing modem data centers due to the explosive growth of data. The tradi- tional approaches employed to decrease the power consump- tion 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. Fur- thermore, 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 calcu- lation 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 out- performs EGA in terms of the continuity of workload alloca- tion and execution performance.展开更多
基金We would like to thank the anonymous review- ers 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.
文摘Reducing the power consumption has become one of the most important challenges in designing modem data centers due to the explosive growth of data. The tradi- tional approaches employed to decrease the power consump- tion 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. Fur- thermore, 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 calcu- lation 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 out- performs EGA in terms of the continuity of workload alloca- tion and execution performance.
