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 proc...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.展开更多
1 Introduction and main contributions In data-driven applications,such as location based services(LBSs),disease surveillance and social networks,etc.,information fusion is necessary for data owners to obtain better se...1 Introduction and main contributions In data-driven applications,such as location based services(LBSs),disease surveillance and social networks,etc.,information fusion is necessary for data owners to obtain better services.But the aggregated data may contain individuaFs sensi-tive information.Therefore,privacy preserving data fusion has become a substantial issue in data aggregating and mining[1,2].展开更多
文摘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.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.42001398)Natural Science Foundation of Chongqing(cstc2020jcyj-msxmX0635)+4 种基金China Postdoctoral Science Foundation funded project(2021M693929)Science and Technology Research Project of CEC(KJQN201900612)Open Fund of LIESMARS(20S02)PhD Starts Fund of CQUPT(A2019-302)SRTP of CQUPT(A2019-175,A2020-106).
文摘1 Introduction and main contributions In data-driven applications,such as location based services(LBSs),disease surveillance and social networks,etc.,information fusion is necessary for data owners to obtain better services.But the aggregated data may contain individuaFs sensi-tive information.Therefore,privacy preserving data fusion has become a substantial issue in data aggregating and mining[1,2].
