Cloud computing environment is getting more interesting as a new trend of data management. Data replication has been widely applied to improve data access in distributed systems such as Grid and Cloud. However, due to the finite storage capacity of each site, copies that are useful for future jobs can be wastefully deleted and replaced with less valuable ones. Therefore, it is considerable to have appropriate replication strategy that can dynamically store the replicas while satisfying quality of service (QoS) requirements and storage capacity constraints. In this paper, we present a dynamic replication algorithm, named hierarchical data replication strategy (HDRS). HDRS consists of the replica creation that can adaptively increase replicas based on exponential growth or decay rate, the replica placement according to the access load and labeling technique, and finally the replica replacement based on the value of file in the future. We evaluate different dynamic data replication methods using CloudSim simulation. Experiments demonstrate that HDRS can reduce response time and bandwidth usage compared with other algorithms. It means that the HDRS can determine a popular file and replicates it to the best site. This method avoids useless replications and decreases access latency by balancing the load of sites.

随着数据管理的新趋势，云计算环境变得越来越有趣。数据复制已被广泛应用于改善网格和云等分布式系统中的数据访问。但是，由于每个站点的存储容量有限，可以浪费地删除对将来的工作有用的副本，并用价值较小的副本代替。因此，拥有适当的复制策略以在满足服务质量（QoS）要求和存储容量约束的同时动态地存储副本非常重要。在本文中，我们提出了一种动态复制算法，称为分层数据复制策略（HDRS）。HDRS由创建副本组成，该副本可以根据指数增长或衰减速率自适应地增加副本，根据访问负载和标记技术放置副本，最后根据将来的文件价值替换副本。我们使用CloudSim仿真评估不同的动态数据复制方法。实验表明，与其他算法相比，HDRS可以减少响应时间和带宽使用。这意味着HDRS可以确定受欢迎的文件，并将其复制到最佳站点。此方法避免了无用的复制，并通过平衡站点的负载来减少访问延迟。这意味着HDRS可以确定受欢迎的文件，并将其复制到最佳站点。此方法避免了无用的复制，并通过平衡站点的负载来减少访问延迟。这意味着HDRS可以确定受欢迎的文件，并将其复制到最佳站点。此方法避免了无用的复制，并通过平衡站点的负载来减少访问延迟。