Abstract:

With the development of the Internet,the scale of the data centers continues to expand,with the prominent problem being how to ensure the safe operation of data centers and reduce the operation energy consumption.The current research focuses only on reducing the energy consumption of the data center,but does not consider the ambient temperature of the servers.If the load continues to increase in the high temperature area,it may lead to local hot spot problems and cause the refrigeration equipment to be in the over-cooling state,resulting in the overall increase of the energy consumption.To solve this problem,this paper proposes an energy-aware virtual machine placement strategy that can avoid hot spots while reducing the energy consumption of the data center.The strategy consists of two parts of the algorithm.The first part is the best adaptation algorithm which sorts the physical machine sequence according to the available CPU resource size.For the current virtual machine request,the physical machine with the smallest urgent value is selected as the target location according to the calculation method of temperature urgent value proposed in this paper,and the sequence of the target physical machine is binarized as the initial population of the genetic algorithm.In the second part of the genetic algorithm,the population is cross-mutated,the next-generation population is selected through the fitness value calculated by the fitness function,and the algorithm finally obtains the optimal solution through continuous iterative calculations.To verify the effectiveness of the strategy proposed in this paper,corresponding experiments are carried out on the cloudsim simulation computing platform.The simulation results show that the proposed method reduces not only the operating energy consumption but also the temperature fluctuation value between the servers to avoid the occurrence of hot spots.

Key words: data center, energy consumption, genetic algorithm, virtual machine placement