Socially-Driven Learning-Based Prefetching in Mobile Online Social Networks

Abstract

Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide the quality of experience support for mobile OSN services, in this paper, we propose a socially-driven learning-based framework, namely Spice, for the media content prefetching to reduce the access delay and enhance mobile user's satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17 000 users during a period of five months, we reveal that the social friendship has a great impact on user's media content click behavior. To capture this effect, we conduct the social friendship clustering over the set of user's friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 80.6% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve up to a factor of 1000 speed-up over the local data training execution on smartphones.