Selective Data replication for Online Social Networks with Distributed Datacenters

Though the new OSN model with many worldwide distributed small datacenters helps reduce service latency, it brings a problem of higher inter-datacenter communication load. In Facebook, each datacenter has a full copy of all data and the master datacenter updates all other datacenters, which obviously generates tremendous load in this new model. Distributed data storage that only stores a user's data to his/her geographically-closest datacenters mitigates the problem. However, frequent interactions between far-away users lead to frequent inter-datacenter communication and hence long service latency. In this paper, we aim to reduce inter-datacenter communications while still achieve low service latency. We first verify the benefits of the new model and present OSN typical properties that lay the basis of our design. We then propose Selective Data replication mechanism in Distributed Datacenters (SD3). In SD3, a datacenter jointly considers update rate and visit rate to select user data for replication, and further atomizes a user's different types of data (e.g., status update, friend post) for replication, making sure that a replica always reduces inter-datacenter communication. 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.