Adaptive Virtual Machine Scheduling Algorithm Based on Improved Particle Swarm Optimization

With the rapid development and popularization of cloud computing, how to reduce the energy consumption of cloud computing data centers and improve the utility of data centers is one of the urgent problems to be solved. In this paper, focusing on four dimensions of CPU, memory, network bandwidth, and disk, we establish a virtual machine(VM) scheduling model based on multi-objective optimization, which can minimize the data center energy consumption and maximize data center utility. And we propose a VM scheduling algorithm based on improved particle swarm optimization(PSO) to solve the model. The improvement includes adaptive parameter adjustment. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments. The experimental results show that the adaptive VM scheduling algorithm based on improved PSO can improve the efficiency of the data center while reducing energy consumption.