Design of evolutionary algorithm for the optimization of cloud storage deployment

Aiming at the optimization of cloud storage deployment in cloud computing environment, an evolutionary algorithm is proposed. Firstly, a cloud storage model with three-tier structure is designed by using the object storage method. In this model, the cloud storage deployment is defined as a multi-objective optimization problem. This problem can be solved by using the traditional particle swarm optimization (PSO) algorithm or the genetic algorithm (GA). However, the convergence speed and the scheduling efficiency are not satisfactory. To overcome the above limitation, a hybrid evolutionary algorithm is presented by combining PSO and GA. The optimization design process of this evolutionary algorithm is analyzed. Moreover, the proposed algorithm is tested on the simulation platform CloudSim. Meanwhile, the improvement of main performance index such as load balance is evaluated. The experimental results show that the proposed algorithm efficiently improves the performance of cloud storage deployment. Compared with other approaches, this evolutionary algorithm can achieve better configuration of performance index.