Characterizing Flow, Application, and User Behavior in Mobile Networks: A Framework for Mobile Big Data

Abstract

The recent explosion of data traffic calls for specialized systems to monitor the status of networks. Traditionally, Internet service providers collect and analyze IP flow data as they present an aggregated view of traffic. In the era of mobile big data, new approaches are required to address new challenges regarding the flow characterization in the next generation wireless networks. In this article, we propose a framework for mobile big data, referred to as FMBD, which provides massive data traffic collection, storage, processing, analysis, and management functions, to cope with the tremendous amount of data traffic. In particular, by analyzing the specific characteristics of the mobile big data from flow, application, and user behavior, such as high volume, diversity of applications, and spatio-temporal distribution, our proposed FMBD demonstrates its capability to offer real data-based advice to address new challenges for future wireless networks from the viewpoints of both operators and individuals. Tested by real mobile big data, FMBD has been operational for more than five years, and can be generalized to other environments with massive data traffic or big data. Introduction