Peer-to-Peer streaming (P2P-TV) applications offer the capability to watch real time video over the Internet at low cost. Some applications have started to become popular, raising the concern of Network Operators that fear the large amount of traffic they might generate. Unfortunately, most of P2P-TV applications are based on proprietary and unknown protocols, and this makes the detection of such traffic challenging per se. In this paper, we propose a novel methodology to accurately classify P2P-TV traffic and to identify the specific P2P-TV application which generated it. Our proposal relies only on the count of packets and bytes exchanged among peers during small time-windows: the rationale is that these two counts convey a wealth of useful information, concerning several aspects of the application and its inner workings, such as signaling activities and video chunk size. Our classification framework, which uses Support Vector Machines, accurately identifies P2P-TV traffic as well as traffic that is generated by other kinds of applications, so that the number of false classification events is negligible. By means of a large experimental campaign, which uses both testbed and real network traffic, we show that it is actually possible to reliably discriminate between different P2P-TV applications by simply counting packets.

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By constructing jointly a random graph and an associated exploration process, we define the dynamics of a "parking process" on a class of uniform random graphs as a measure-valued Markov process, representing the empirical degree distribution of non-explored nodes. We then establish a functional law of large numbers for this process as the number of vertices grows to infinity, allowing us to assess the jamming constant of the considered random graphs, i.e. the size of the maximal independent set discovered by the exploration algorithm. This technique, which can be applied to any uniform random graph with a given degree distribution, can be seen as a generalization in the space of measures, of the differential equation method introduced by Wormald.

Abstract. We present a novel methodology to accurately classify the traffic generated by P2P-TV applications, relying only on the count of packets they exchange with other peers during small time-windows. The rationale is that even a raw count of exchanged packets conveys a wealth of useful information concerning several implementation aspects of a P2P-TV application -such as network discovery and signaling activities, video content distribution and chunk size, etc. By validating our framework, which makes use of Support Vector Machines, on a large set of P2P-TV testbed traces, we show that it is actually possible to reliably discriminate among different applications by simply counting packets.

We consider exploration algorithms of the random sequential adsorption type both for homogeneous random graphs and random geometric graphs based on spatial Poisson processes. At each step, a vertex of the graph becomes active and its neighboring nodes become explored. Given an initial number of vertices $N$ growing to infinity, we study statistical properties of the proportion of explored nodes in time using scaling limits. We obtain exact limits for homogeneous graphs and prove an explicit central limit theorem for the final proportion of active nodes, known as the \emph{jamming constant}, through a diffusion approximation for the exploration process. We then focus on bounding the trajectories of such exploration processes on random geometric graphs, i.e. random sequential adsorption. As opposed to homogeneous random graphs, these do not allow for a reduction in dimensionality. Instead we build on a fundamental relationship between the number of explored nodes and the discovered volume in the spatial process, and obtain generic bounds: bounds that are independent of the dimension of space and the detailed shape of the volume associated to the discovered node. Lastly, we give two trajectorial interpretations of our bounds by constructing two coupled processes that have the same fluid limits.Comment: 25 pages, 3 figure