A Telecom Analytics Framework for Dynamic Quality of Service Management

Since the beginning of Internet, Internet Service Providers (ISP) have seen the need of giving to users’ traffic different treatments defined by agree-ments between ISP and customers. This procedure, known as Quality of Service Management, has not much changed in the last years (DiffServ and Deep Packet Inspection have been the most chosen mechanisms). However, the incremental growth of Internet users and services jointly with the application of recent Ma-chine Learning techniques, open up the possibility of going one step forward in the smart management of network traffic. In this paper, we first make a survey of current tools and techniques for QoS Management. Then we introduce clustering and classifying Machine Learning techniques for traffic characterization and the concept of Quality of Experience. Finally, with all these components, we present a brand new framework that will manage in a smart way Quality of Service in a telecom Big Data based scenario, both for mobile and fixed communications.