Leveraging A Multi-Objective Approach to Data Replication in Cloud Computing Environment to Support Big Data Applications

Abstract

Increased data availability and high data access performance are of utmost importance in a large-scale distributed system such as data cloud. To address these issues data can be replicated in various locations in the system where applications are executed. Replication not only improves data availability and access latency but also improves system load balancing. While data replication in distributed cloud storage is addressed in the literature, majority of the current techniques do not consider different costs and benefits of replication from a comprehensive perspective. In this paper, we investigate replica management problem (which is formulated using dynamic programming) in cloud computing environments to support big data applications. To this end, we propose a new highly distributed replica placement algorithm that provides cost-effective replication of huge amount of geographically distributed data into the cloud to meet the quality of service (QoS) requirements of data-intensive (big data) applications while ensuring that the workload among the replica data centers is balanced. In addition, the algorithm takes into account the consistency among replicas due to update propagation. Thus, we build up a multi-objective optimization approach for replica management in cloud that seeks near optimal solution by balancing the trade-offs among the stated issues. For verifying the effectiveness of the algorithm, we evaluated the performance of the algorithm and compared it with two baseline approaches from the literature. The evaluation results demonstrate the usefulness and superiority of the presented algorithm for conditions of interest.