Early P2P-TV systems have already attracted millions of users, and many new commercial solutions are entering this market. Little information is however available about how these systems work, due to their closed and proprietary design. In this paper, we present large scale experiments to compare three of the most successful P2P-TV systems, namely PPLive, SopCast and TVAnts.Our goal is to assess what level of "network awareness" has been embedded in the applications. We first define a general framework to quantify which network layer parameters leverage application choices, i.e., what parameters mainly drive the peer selection and data exchange. We then apply the methodology to a large dataset, collected during a number of experiments where we deployed about 40 peers in several European countries.From analysis of the dataset, we observe that TVAnts and PPLive exhibit a mild preference to exchange data among peers in the same autonomous system the peer belongs to, while this clustering effect is less intense in SopCast. However, no preference versus country, subnet or hop count is shown. Therefore, we believe that next-generation P2P live streaming applications definitively need to improve the level of network-awareness, so to better localize the traffic in the network and thus increase their network-friendliness as well.

Early P2P-TV systems have already attracted millions of users, and many new commercial solutions are entering this market. Little information is however available about how these systems work. In this paper we present large scale sets of experiments to compare three of the most successful P2P-TV systems, namely PPLive, SopCast and TVAnts. Our goal is to assess what level of "network awareness" has been embedded in the applications, i.e., what parameters mainly drive the peer selection and data exchange. By using a general framework that can be extended to other systems and metrics, we show that all applications largely base their choices on the peer bandwidth, i.e., they prefer high-bandwidth users, which is rather intuitive. Moreover, TVAnts and PPLive exhibits also a preference to exchange data among peers in the same Autonomous System the peer belongs to. However, no evidence about preference versus peers in the same subnet or that are closer to the considered peer emerges. We believe that next-generation P2P live streaming applications definitively need to improve the level of network-awareness, so to better localize the traffic in the network and thus increase their network-friendliness as well.

Network processors are special-purpose programmable units deployed in many modern high-speed network devices, which combine flexibility and high performance. However, software development for these platforms is traditionally cumbersome due both to the lack of adequate programming abstractions and to the impossibility of reusing the same software on different hardware platforms.In this context, the Network Virtual Machine (NetVM) aims at defining an abstraction layer for the development of portable and efficient data-plane packet processing applications. Portability and efficiency are achieved altogether by virtualizing the hardware and by capturing in the programming model the peculiar characteristics of the application domain.This paper validates the NetVM model, demonstrating that the proposed abstraction coupled with a proper implementation of the NetVM Framework is able to provide generality (i.e., capability to support a wide range of applications), software portability across heterogeneous network processor architectures, and efficiency of the generated code, often exceeding the one obtained using state-of-the-art compilers.

After P2P file-sharing and VoIP telephony applications, VoD and live-streaming P2P applications have finally gained a large Internet audience as well. In this work, we define a framework for the comparison of these applications, based on the measurement and analysis of the traffic they generate. In order for the framework to be descriptive for all P2P applications, we first define a minimum set of observables of interest: such features either pertain to different layers of the protocol stack (from network up to the application), or convey cross-layer information (such as the degree of awareness, at overlay layer, of properties characterizing the underlying physical network). The framework is compact (as it allows to represent all the above information at once), general (as is can be extended to consider features different from the one reported in this work), and flexible in both space and time (as it allows different levels of spatial aggregation, and also to represent the temporal evolution of the quantities of interest). Using the minimum feature set, we analyze some of the most popular P2P application nowadays, highlighting their main similarities and differences. We then apply the framework, using also different features and metrics, to two interesting case study: namely, the detection of malfunctioning or misbehaving peers, and a fine-grained analysis of P2P network-awareness and friendliness.

Internet video and peer-to-peer television (P2P-TV) are attracting more and more users: chances are that P2P-TV is going to be the next Internet killer application. In recent years, valuable effort has been devoted to the problems of chunkscheduling and overlay management in P2P-TV systems. However, many interesting P2P-TV proposals have been evaluated in an idealistic environment: in this work, we instead study them by taking special care in defining realistic conditions for their evaluation. In particular we analyze the impact that signaling errors can have on a push-based P2P-TV overlay by means of simulation. Results are expressed in terms of both user-centric and system-centric indexes: our main finding is that push P2P-TV systems are deeply affected by even very rare signaling errors, which are often overlooked without justification.