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 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.