Network monitoring has always played a key role in understanding telecommunication networks since the pioneering time of the Internet. Today, monitoring traffic has become a key element to characterize network usage and users' activities, to understand how complex applications work, to identify anomalous or malicious behaviors. In this paper, we present our experience in engineering and deploying Tstat, a free open source passive monitoring tool that has been developed in the past ten years. Started as a scalable tool to continuously monitor packets that flow on a link, Tstat has evolved into a complex application that gives to network researchers and operators the possibility to derive extended and complex measurements via advanced traffic classifiers. After discussing Tstat capabilities and internal design, we present some examples of measurements collected deploying Tstat at the edge of several ISP networks in the past years. We then discuss the scalability issues that software based tools have to cope with when deployed in real networks, showing the importance of properly identifying bottlenecks.

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This paper proposes KISS, a novel Internet classification engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming applications, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square ( 2 )-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Support Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asymmetry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server protocols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are almost perfect when dealing with new P2P streaming applications.

In this paper we evaluate the energy saving that can be achieved with the energy-aware cooperative management of the cellular access networks of two operators offering service over the same area. We evaluate the amount of energy that can be saved by using both networks in high traffic conditions, but switching off one of the two during the periods when traffic is so low that the desired quality of service can be obtained with just one network. When one of the two networks is off, its customers are allowed to roam over the one that is on. Several alternatives are studied, as regards the switch-off pattern: the one that balances the switch-off frequencies, the one that balances roaming costs, the one that balances energy savings, and the one that maximizes the amount of saved energy. Our results indicate that a huge amount of energy can be saved, and suggest that, to reduce energy consumption, new cooperative attitudes of the operators should be encouraged with appropriate incentives, or even enforced by regulation authorities.

In this paper we study cellular access networks which solely rely on renewable energy. We consider a cellular network in which a mesh of base stations (BSs) that are powered with renewable sources, and interconnected with wireless backhaul links, cover the service area, and provide connection to few, typically remote, wired network accesses to the national and international backbone. In particular, we study how to dimension BS power generators and energy storage. We start by discussing the BS energy need, that depends on both the BS consumption model and the BS traffic profiles. Focusing then on some specific locations, we consider the use of photovoltaic (PV) panels, and dimension them based on the daily energy need of the BS and on typical radiative power of sun in the considered locations. Once the PV system has been dimensioned, we also evaluate the energy storage capacity that is needed to absorb energy production variability due to both daily and seasonal radiative power variations. Finally, we investigate the effectiveness of integrating the PV system with wind turbines, as well as the benefit induced on the system by base station sleep modes.