Abacus: Accurate behavioral classification of P2P-TV traffic

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Estimating the Transmission Probability in Wireless Networks with Configuration Models

We propose a new methodology to estimate the probability of successful transmissions for random access scheduling in wireless networks, in particular those using Carrier Sense Multiple Access (CSMA). Instead of focusing on spatial configurations of users, we model the interference between users as a random graph. Using configuration models for random graphs, we show how the properties of the medium access mechanism are captured by some deterministic differential equations when the size of the graph gets large. Performance indicators such as the probability of connection of a given node can then be efficiently computed from these equations. We also perform simulations to illustrate the results on different types of random graphs. Even on spatial structures, these estimates get very accurate as soon as the variance of the interference is not negligible.Fil: Bermolen, P.. Universidad de la Republica. Facultad de Ingeniería; UruguayFil: Jonckheere, Matthieu Thimothy Samson. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Larroca, F.. Universidad de la Republica. Facultad de Ingeniería; UruguayFil: Moyal, P.. Universite de Technologie de Compiegne; Franci

Accurate and Fine-Grained Classification of P2P-TV Applications by Simply Counting Packets

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.

Waterfall: rapid identification of IP flows using cascade classification

n the last years network traffic classification has attracted much research effort, given that it represents the foundation of many Internet functionalities such as Quality of Service (QoS) enforcement, monitoring, and security. Nonetheless, the proposed works are not able to satisfactorily solve the problem, usually being suitable for only addressing a given portion of the whole network traffic and thus none of them can be considered an ultimate solution for network classification. In this paper, we address network traffic classification by proposing a new architecture - named Waterfall architecture - that, by combining several classification algorithms together according to a cascade principle, is able to correctly classify the whole mixture of network traffic. Through extensive experimental tests run over real traffic datasets, we have demonstrated the effectiveness of the proposal

Scaling Limits and Generic Bounds for Exploration Processes

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 blocked. Given an initial number of vertices N growing to infinity, we study statistical properties of the proportion of explored (active or blocked) 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 jamming constant, through a diffusion approximation for the exploration process which can be described as a unidimensional process. We then focus on bounding the trajectories of such exploration processes on random geometric graphs, i.e., random sequential adsorption. As opposed to exploration processes on homogeneous random graphs, these do not allow for such a dimensional reduction. Instead we derive a fundamental relationship between the number of explored nodes and the discovered volume in the spatial process, and we obtain generic bounds for the fluid limit and jamming constant: bounds that are independent of the dimension of space and the detailed shape of the volume associated to the discovered node. Lastly, using coupling techinques, we give trajectorial interpretations of the generic bounds.Fil: Bermolen, Paola. Universidad de la Republica. Facultad de Ingeniería; UruguayFil: Jonckheere, Matthieu Thimothy Samson. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Sanders, Jaron. Eindhoven Technical University; Países Bajo