LAUSR

The explosive growth of Internet of Things is generating massive data which are normally stored in data centers. The power consumption has become a very important challenge of designing modern data centers due to the explosive growth of data. The power consumed by cooling system accounts for about half of the total power consumption. Reducing the peak inlet temperature of racks residing in data centers can effectively decrease the temperature requirement of supplied cold air, thus cutting down the cooling cost. Task distribution in data centers has a significant impact on this inlet temperature. Many investigations have been conducted on achieving an optimal task distribution in terms of the air organization [e.g., genetic algorithm (GA)]. However, the existing methods can be easily trapped into a local optimum. This paper constructs a power model to correlate the task assignment, heat recirculation, inlet temperature, and cooling cost in the homogeneous and heterogeneous data centers with under-floor air supply. Furthermore, genetic simulated annealing algorithm is proposed and designed to enhance the traditional GA and assign tasks in the data centers according to the corresponding air organization by integrating the advantages of simulated annealing, thus minimizing the inlet temperature and reducing the cooling cost. Experimental results indicate that the proposed approach can effectively decrease the temperature requirement of supplied cold air and reduce the power consumption of the cooling system in contrast to the traditional GA and ant colony algorithm, especially when the data centers are with medium utilization.