A Data-Placement Strategy Based on Genetic Algorithm in Cloud Computing

With the development of Computerized Business Application, the amount of data is increasing ex-ponentially. Cloud computing provides high performance computing resources and mass storage resources for massive data processing. In distributed cloud computing systems, data intensive computing can lead to data scheduling between data centers. Reasonable data placement can re-duce data scheduling between the data centers effectively, and improve the data acquisition effi-ciency of users. In this paper, the mathematical model of data scheduling between data centers is built. By means of the global optimization ability of the genetic algorithm, generational evolution produces better approximate solution, and gets the best approximation of the data placement at last. The experimental results show that genetic algorithm can effectively work out the approx-imate optimal data placement, and minimize data scheduling between data centers