Predicting social dynamics based on network traffic analysis for CCN/ICN management

Proliferation of online social networks (OSNs) has resulted in an unprecedented surge in the volume of multimedia content consumed by users on a daily basis. Popular OSNs such as Facebook enable users to view and share embedded videos and images on their feeds, which increases visibility, prompting repeated requests for the same piece of content. Maintaining desirable quality of service for all users becomes challenging in such a scenario, especially when low-bandwidth cellular network is being used for data download. Such problems have prompted the research community to focus heavily on the emerging paradigm of Information-or Content-Centric Networking (ICN/CCN), where in-network content management (e.g., content distribution, caching, etc.) forms the crux of an enhanced user experience. In this abstract, we argue that social dynamics among OSN users can provide concrete hints regarding future popularity of content. We propose a strategy to identify viewing and sharing patterns of Facebook users served by a cellular base station, by analyzing network traffic. We utilize these patterns to infer social dynamics among cellular users (mapped to cellphone numbers). We validate our strategy with proof-of-concept experiments on real data, and extensive simulations on a simulation framework proposed by us.