Structural conditions for business model design in new information and communication services : a case study of multi-play and MVolP in Denmark and Norway

The report analyses the structural conditions for the design of business models regarding new information and communication services. The services examined are mobile VoIP (MVoIP) and multi-play – services that already are on the market, however in their infancy, and which represent different kinds of services in terms of structural conditions market-wise and in regulatory terms. As the two service categories are relatively new on the market, dominating business model designs have not yet settled and the strategic choices of companies are very open. Being on the market, the discussion on the business model design, however, transcends the purely speculative stage. The structural conditions studied are the market conditions including the regulatory conditions. In addition, the different technological solutions are examined, as MVoIP as well as multi-play include different technology solutions for the delivery of services to users. This means that the analysis includes technological as well as market-based and regulatory elements. The aim of the analysis of the structural conditions is two-fold: On the one hand, to deepen the understanding of the structural condition and, on the other hand, to discuss the conditions for different business model design options. The report examines the regulatory policies and market characteristics in MVoIP and multi-play as a basis for a discussion on how these policies and characteristics affect the business model decisions of service providers in the two areas. Using empirical material from Norway and Denmark, the report presents a comparative analysis of the structural conditions and the business model choices made by actors in the market. The basic theoretical framework for the analysis is the Structure-Conduct-Performance (SCP) framework. The strength of this framework is that it stretches all the way from the structural conditions, through the conduct (business models and strategies) of companies seen in connection with these structural conditions, to the actual performance of companies in the market. The focus of the present report is on the structural conditions with a view to the framework that these conditions constitute for the business model design of companies. The empirical basis of the report consists primarily of interviews with representatives from IT and telecom industry organizations, policy makers and regulators in the telecom area in Norway and Denmark

Next generation network (NGN) challenges on access networks

Telecom infrastructures are facing unprecedented challenges, with increasing demands on network capacity. With the increased demand for high-speed data services and the constant evolution of broadband access technologies, operators are faced with a number of issues when choosing the technology and building the network. Today, network operators are facing the challenge of how to expand the existing access network infrastructure into networks capable of satisfying the user’s requirements. Thus, in this context, providers need to identify the technological solution that enables them to profitably serve customers and support future needs. However, the identification of the “best” solution is a difficult task.info:eu-repo/semantics/publishedVersio

Big Data for Traffic Monitoring and Management

The last two decades witnessed tremendous advances in the Information and Com- munications Technologies. Beside improvements in computational power and storage capacity, communication networks carry nowadays an amount of data which was not envisaged only few years ago. Together with their pervasiveness, network complexity increased at the same pace, leaving operators and researchers with few instruments to understand what happens in the networks, and, on the global scale, on the Internet. Fortunately, recent advances in data science and machine learning come to the res- cue of network analysts, and allow analyses with a level of complexity and spatial/tem- poral scope not possible only 10 years ago. In my thesis, I take the perspective of an In- ternet Service Provider (ISP), and illustrate challenges and possibilities of analyzing the traffic coming from modern operational networks. I make use of big data and machine learning algorithms, and apply them to datasets coming from passive measurements of ISP and University Campus networks. The marriage between data science and network measurements is complicated by the complexity of machine learning algorithms, and by the intrinsic multi-dimensionality and variability of this kind of data. As such, my work proposes and evaluates novel techniques, inspired from popular machine learning approaches, but carefully tailored to operate with network traffic. In this thesis, I first provide a thorough characterization of the Internet traffic from 2013 to 2018. I show the most important trends in the composition of traffic and users’ habits across the last 5 years, and describe how the network infrastructure of Internet big players changed in order to support faster and larger traffic. Then, I show the chal- lenges in classifying network traffic, with particular attention to encryption and to the convergence of Internet around few big players. To overcome the limitations of classical approaches, I propose novel algorithms for traffic classification and management lever- aging machine learning techniques, and, in particular, big data approaches. Exploiting temporal correlation among network events, and benefiting from large datasets of op- erational traffic, my algorithms learn common traffic patterns of web services, and use them for (i) traffic classification and (ii) fine-grained traffic management. My proposals are always validated in experimental environments, and, then, deployed in real opera- tional networks, from which I report the most interesting findings I obtain. I also focus on the Quality of Experience (QoE) of web users, as their satisfaction represents the final objective of computer networks. Again, I show that using big data approaches, the network can achieve visibility on the quality of web browsing of users. In general, the algorithms I propose help ISPs have a detailed view of traffic that flows in their network, allowing fine-grained traffic classification and management, and real-time monitoring of users QoE