An adaptive data replication and placement for efficient data storage of online social networks using two-tier multi-cloud environment

In social media, a huge number of worldwide data objects are posted every day. The contents of these data objects include text, links, images, audio, and videos which could be small, medium, or large and accessed across the world. Moving these data objects into a single cloud service provider (CSP) is risky and results in four-fold obstacles: vendor lock-in, service availability, cost-inefficient use, and increasing latency. Using multiple CSPs to replicate and distribute the data object is a solution to such obstacles. However, replication of data objects among multiple CSPs always introduces a higher cost of creating and maintaining this replication. This study focused on three issues of online social network (OSN) which include: 1) determining the appropriate number of replicas of each data object based on its popularity on the OSN, 2) identifying the suitable datacenters that host the replicas according latency time of different regions, and 3) deciding the suitable storage class for the data object at a specific time of its lifetime. Two algorithms are proposed to adapt the replication and placement of the data object according to its popularity in the OSN. The first algorithm is Dynamic Fixed Time (DFT) which uses fixed time periods for the adaptation of replication and placement. The second algorithm is Dynamic Exponential Time (DET) which determines the data object replication and placement based on exponential time periods. A simulation using synthesized workload generated based on a real Facebook statistic dataset shows that the proposed algorithms produce a cost savings of more than 23%.

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