Data transmission and retrieval in a cloud computing environment are usually handled by storage device providers or physical storage units leased by third parties. Improving network performance considering power connectivity and resource stability while ensuring workload balance is a hot topic in cloud computing. In this research, we have addressed the data duplication problem by providing two dynamic models with two variant architectures to investigate the strengths and shortcomings of architectures in Big Data Cloud Computing Networks. The problems of the data duplication process will be discussed accurately in each model. Attempts have been made to improve the performance of the cloud network by taking into account and correcting the flaws of the previously proposed algorithms. The accuracy of the proposed models have been investigated by simulation. Achieved results indicate an increase in the workload balance of the network and a decrease in response time to user requests in the model with a grouped architecture for all the architectures. Also, the proposed duplicate data model with peer-to-peer network architecture has been able to increase the cloud network optimality compared to the models presented with the same architecture.

云计算环境中的数据传输和检索通常由存储设备提供商或第三方租赁的物理存储单元处理。在确保工作负载平衡的同时，考虑电源连接和资源稳定性来提高网络性能是云计算中的一个热门话题。在这项研究中，我们通过提供具有两种不同架构的两个动态模型来解决数据重复问题，以研究大数据云计算网络中架构的优缺点。数据复制过程的问题将在每个模型中准确讨论。已经尝试通过考虑和纠正先前提出的算法的缺陷来提高云网络的性能。通过仿真研究了所提出模型的准确性。获得的结果表明网络的工作负载平衡增加，对所有架构的分组架构模型中用户请求的响应时间减少。此外，与具有相同架构的模型相比，所提出的具有对等网络架构的重复数据模型能够提高云网络的最优性。