A hybrid data replication strategy with fuzzy-based deletion for heterogeneous cloud data centers

At present, huge cloud-based applications have put forward higher requests for data center storage. In a large-scale Cloud environment, data replication provides an appropriate solution for managing data files, which improves data reliability and availability. In this paper, we propose a data replication algorithm called hybrid replication strategy (HRS) that is applied into replica placement, selection, and replacement steps. HRS has three main phases and is suitable for replicating data files in cloud. In the first phase, it selects the best site (i.e., that is the most central site with high number of access) for storing new replica to reduce access time. In the second phase, HRS considers the best replica node for users based on different parameters such as CPU process capability, network transmission capability, I/O capability of disks, load, and network latency. In the third phase, the replacement decision is made in order to provide better response time. HRS can ascertain the importance of valuable replicas on the basis of a fuzzy inference system with three input parameters (i.e., number of accesses, cost, and the last time the replica was accessed). The new replication policy is simulated using the CloudSim toolkit package. Our proposed mechanism replicates the data over the cloud nodes reasonably well and is easily implementable in a real environment. Experiment results prove that HRS can significantly enhance availability, performance and load balance for data-intensive applications. In addition, it stands good without increasing additional overheads.