Abstract
With the development of Computerized Business Application, the amount of data is increasing exponentially. 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 reduce data scheduling between the data centers effectively, and improve the data acquisition efficiency 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 approximate optimal data placement, and minimize data scheduling between data centers.
Highlights
With the increase of network equipment as well as the development of the Internet, data generation and storage capacity are growing explosively; data centers will face unpredictable visitor volume [1]
Assuming that a cloud computing system is composed by l data centers, and data are divided into n different datasets based on their inherent properties
We find that the data scheduling between data centers of approximate optimal data placement matrices searched by genetic algorithm are always smaller than Monte Carlo algorithm in each case
Summary
With the increase of network equipment as well as the development of the Internet, data generation and storage capacity are growing explosively; data centers will face unpredictable visitor volume [1]. (2015) A Data-Placement Strategy Based on Genetic Algorithm in Cloud Computing. The datasets processed simultaneously by a computation should be placed in the same data center, almost all data processing is completed locally; that is the basic idea of the paper. Replica strategy [13] is an effective measure to reduce the data scheduling and has earned widespread research interests, and it is based on data placement. This paper presents a genetic algorithm-based data placement strategy. A mathematical model of data scheduling between the data centers in cloud computing is built, and the fitness function based on the objective function is defined to evaluate the fitness of each individual in a population. Under the principle of survival of the fittest, the optimal individual can be found during the evolution
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