Data Replication and Placement Strategies in Distributed Systems: A State of the Art Survey

Data replication is an important mechanism for managing data distribution. The general purpose of data replication is to place data in different locations so that it is available to the requester as quickly as possible. In replication, multiple copies of data are stored in multiple locations, which increases availability, fault tolerance, load balancing, and scalability. It also decreases bandwidth consumption and response time. In this paper, data replication scenarios classify to the types of methods, the number of replications, locations, best replication based on energy, types of architecture, replication management, and criteria selection for best replication. The parameters of a good replication strategy are identified and expressed, which simultaneously include reducing access time, reducing bandwidth consumption, increasing the availability of storage resources, and balancing replication. By examining and analyzing of recent papers, solutions classify to the dynamic solution, meta-heuristic solution, multiple criteria decision-making solution, and machine learning solution. Our research show that the best of which is the multiple criteria decision-making solution methods.