【摘要】 在社区资源调试优化中,针对云计算环境下的智能社区中数据量很大,均衡性地对数据调度,能够让智能社区网络的负载更低,具有重要的意义。但是,随着智能社区数据种类的增加,数据的异构性和动态性特征更加明显,均衡性调度中对最优调度解的选择存在了多个条件最优的问题,导致无法形成最优解约束,均衡性很差。提出一种改进的云计算的智能社区数据的调度方法。建立智能社区数据的调度模型,将智能社区数据的调度问题看作时间、费用、安全性和可靠性这四种因素的组合问题,也就是一个搜索最优解的问题,将所有可能性的调度方法看作搜索空间,利用蚁群算法对智能社区数据的调度方案进行寻优,最终获得最优调度方案。仿真结果表明,改进算法能够有效提高智能社区数据调度的均衡性。更多还原

【Abstract】 Cloud computing environment is a big amount of data in intelligent community,balance data scheduling can make low network load for intelligent community and have the vital significance.However,with the increase of smart community data types,heterogeneity and dynamic characteristics of the data are more apparent,balance scheduling in the selection of the optimal scheduling path for multiple optimal problems may lead to the optimal constraint and very poor balance.An improved data scheduling method of cloud computing intelligence community is presented.Intelligent community data scheduling model is established,the scheduling problem of intelligent community data is considered as a combination problem of four factors(time,cost,safety and reliability),which is a searching optimal solution problem.All possibilities of scheduling algorithm are considered as the search space,the ant colony algorithm is used to serch the optimization for intelligent community data,and the optimal scheduling scheme is obtained.The simulation results show that the proposed algorithm can effectively improve the scheduling equilibrium of the smart community data.更多还原