Online dynamic replication and placement algorithms for cost optimization of online social networks in two-tier multi-cloud

Abstract

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-ineffective use, and increasing latency. Using multiple CSPs to replicate and distribute the data object solves such obstacles. However, replicating data objects among multiple CSPs increases the cost of creating and maintaining this replication. This study focuses 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 to 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 to adapt 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 a synthesized workload generated based on a real Facebook statistic dataset shows that the proposed algorithms produce a monetary cost savings of more than 23% compared to the Static Replication and Local Placement (SRLP) algorithm.