Virtual machine placement method based on improved antlion algorithm

Virtual machine placement is a key step in the process of virtual machine consolidation. The quality of the virtual machine placement method usually affects the resource utilization efficiency and performance of the cloud data center. Such problems can be solved by establishing a multi-objective optimization model. Currently, cloud data centers have high energy consumption, low resource utilization, and resource fragmentation. In view of the above situation, a virtual machine placement strategy based on MALO algorithm is proposed. By establishing a multi-objective and multi-constrained virtual machine placement model, the energy consumption, resource utilization, and resource fragmentation are optimized. And on the basis of the Antlion algorithm, by improving the boundary change strategy of the solution space and the location selection strategy of ants random walk, finally the position of the ants is corrected beyond the boundary, so that the diversity of the population can be better guaranteed, which can better Jump out of the local optimal solution. Based on the virtual machine placement platform, the simulation experiments of MALO algorithm and four other virtual machine placement algorithms are carried out. The experimental results show that compared to the Antlion algorithm, BRC algorithm, MBFD algorithm and FFD algorithm, the MALO algorithm has a certain improvement effect in reducing energy consumption, improving resource utilization and reducing resource fragmentation.