A measurement study of device‐to‐device sharing in mobile social networks based on Spark

Abstract

SummaryBecause of the exponential growth of mobile users' demand for multimedia services in recent years, the increasing network traffic load gets a close attention of the mobile network operators. For the mobile traffic explosion issue to be solved, there are many efforts trying to offload the mobile traffic from infrastructure cellular links to direct local short‐range communications among groups of users, which is called device‐to‐device sharing (D2D) in mobile social networks. Although there have been a number of studies for improving the exploitation of friends, contents, and sharing performance, there is no any large‐scale measurement‐based study to analyze the realistic D2D sharing service. We focus on the empirical trace from Xender, a popular mobile application for D2D sharing, and implement an effective big data processing platform based on Spark with customized algorithms. Extensive analysis and discussions are carried out from the perspectives of general time series statistics, content properties, and social graph basics. The trace‐driven analysis exploits a number of implications regarding power law distribution for content popularity disparity, clustering effects of user relationships, and so on. We further discuss the potentials of improving Xender's quality of service and optimizing its system resource, and hopefully, our study can offer useful guidelines for not only Xender but also those growing global social D2D sharing services.