Serendipity of Sharing: Large-Scale Measurement and Analytics for Device-to-Device (D2D) Content Sharing in Mobile Social Networks

The heavy multimedia traffic produced by mobile users poses great challenges for the mobile network operators, especially in the areas with large user densities but limited cellular network capacities (e.g. India). Recently, many studies demonstrate that exploiting the device-to- device(D2D) content sharing in offline Mobile Social Networks is a promising solution to cellular data offloading. However, such approaches are based on either unrealistic assumptions, or limited data analytics caused by small data size (e.g. hundreds of MSN users) or single-dimensional feature (e.g. human mobility only), which severely restricts their applications in practice. To address this issue, this paper performs the first large-scale data measurement and multi-feature analytics of D2D content sharing. Specifically, by using Apache Spark over a 20-server cluster, we analyze the behaviors of 30 million users (with 40 billion D2D transmissions and 16 million content files) of Xender, a leading global D2D sharing platform. Several important features are studied, including performance basics, content properties, location relations, meeting dynamics, and social characteristics. Furthermore, as a proof-of-concept study of our analytics, we also develop a multi-feature learning based framework, which demonstrates the large potentials of predicting and recommending D2D sharing activities using machine learning methods.