A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers

• The paper presents a dynamic, cost-aware, optimized data replication strategy.
• The concept of knapsack has been used to optimize the cost of replication.
• The strategy involves re-replication also, without compromising data availability.
• Mathematical descriptions and illustrations have been provided for different phases.
• The strategy has been simulated and evaluated to demonstrate its effectiveness.

In cloud computing, it is important to maintain high data availability and the performance of the system. In order to meet these requirements, the concept of replication is used. As the number of replicas of a data file increases, the data availability and the performance also increases, but at the same time, the cost of creating and maintaining new replicas also increases. In order to enjoy the maximum benefits of replication, it is essential to optimize the cost of replication. The cloud systems are heterogeneous in nature as the different data centers have different policies, hardware and software configurations. As a result of this, the replicas of a data file placed at different data centers have different availabilities and replication costs associated with them. In this paper, a dynamic, cost-aware, optimized data replication strategy is proposed that identifies the minimum number of replicas required to ensure the desired availability. The concept of knapsack has been used to optimize the cost of replication and to re-replicate the replicas from higher-cost data centers to lower-cost data centers, without compromising the data availability. Mathematical descriptions and illustrations have been provided for the different phases of the proposed strategy, keeping in mind the heterogeneous nature of the system. The proposed strategy has been simulated using the CloudSim toolkit. The experimental results indicate that the strategy is effective in optimizing the cost of replication and increasing the data availability.