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The growth of User-Generated Content (UGC) traffic makes the understanding of its nature a priority for network operators, content providers and equipment suppliers. In this paper, the authors study a four-month dataset that logs all video requests to Daily-Motion made by a fixed subset of users. They were able to infer user sessions from raw data, to propose a Markovian model of these sessions, and to study video popularity and its evolution over time. The presented results are a first step for synthesizing an artificial (but realistic) traffic that could be used in simulations or experimental testbeds.
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