In view of the requirements of high real-time and calculation accuracy

poor platform scalability

and low resource utilization of power system simulation calculation

an architecture of cloud computing platform for power system simulation based on the OpenStack (an open source infrastructure platform) and Hadoop (a parallel processing framework) is presented for achieving dynamic expansion

efficient computing and massive storage functions at a low cost. Then

by referring to the characteristics of power system simulation task

a virtual machine migration strategy based on multi-objective particle swarm optimization (PSO) algorithm is given for achieving resource scheduling of the cloud computing platform. During the virtual machine migration process

the predictive model based on exponential smoothing is used to determine the hotspots

and the virtual machines are selected with the migration speed and effect taken into account. Meanwhile the multi-objective PSO algorithm is used to search for the target node to ensure the quality of service of power system simulation while attending to the advantage of high resource utilization and low operating costs. Finally

simulation experiments are conducted on the CloudSim to compare the algorithm proposed with the greedy algorithm with migration and the non-migratory algorithm with sequence placement. The results show that the algorithm proposed is batter than the other two in terms of the rate of service level agreement (SLA) violation

the rate of surplus resources

energy consumption and the times of virtual machine migration. The advantages and feasibility of multi-objective PSO algorithm based on virtual machine migration in the resource scheduling of cloud computing platform for power system simulation are verified by experiments. This work is supported by National Natural Science Foundation of China (No. 61074078) and Fundamental Research Funds for the Central Universities (No. 12MS113).