Selective Data Replication for Online Social Networks with Distributed Datacenters

Though the new OSN model, which deploys datacenters globally, helps reduce service latency, it causes higher inter-datacenter communication load. In Facebook, each datacenter has a full copy of all data, and the master datacenter updates all other datacenters, generating tremendous load in this new model. Distributed data storage, which only stores a user's data to his/her geographically closest datacenters mitigates the problem. However, frequent interactions between distant users lead to frequent inter-datacenter communication and hence long service latencies. In this paper, we aim to reduce inter-datacenter communications while still achieving low service latency. We first verify the benefits of the new model and present OSN typical properties that underlie the basis of our design. We then propose Selective Data replication mechanism in Distributed Datacenters (SD3). Since replicas need inter-datacenter data updates, datacenters in SD3 jointly consider update rates and visit rates to select user data for replication; furthermore, SD3 atomizes users' different types of data (e.g., status update, friend post, music) for replication, ensuring that a replica always reduces inter-datacenter communication. SD3 also incorporates three strategies to further enhance its performance: locality-aware multicast update tree, replica deactivation, and datacenter congestion control. The results of trace-driven experiments on the real-world PlanetLab testbed demonstrate the higher efficiency and effectiveness of SD3 in comparison to other replication methods and the effectiveness of its three schemes.