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Dynamic resource management has become an active area of research in theCloud Computing paradigm. Cost of resources varies significantly depending onconfiguration for using them. Hence efficient management of resources is ofprime interest to both Cloud Providers and Cloud Users. In this report wesuggest a probabilistic resource provisioning approach that can be exploited asthe input of a dynamic resource management scheme. Using a Video on Demand usecase to justify our claims, we propose an analytical model inspired fromstandard models developed for epidemiology spreading, to represent sudden andintense workload variations. As an essential step we also derive a heuristicidentification procedure to calibrate all the model parameters and evaluate theperformance of our estimator on synthetic time series. We show how good can ourmodel fit to real workload traces with respect to the stationary case in termsof steady-state probability and autocorrelation structure. We find that theresulting model verifies a Large Deviation Principle that statisticallycharacterizes extreme rare events, such as the ones produced by "buzz effects"that may cause workload overflow in the VoD context. This analysis providesvaluable insight on expectable abnormal behaviors of systems. We exploit theinformation obtained using the Large Deviation Principle for the proposed Videoon Demand use-case for defining policies (Service Level Agreements). We believethese policies for elastic resource provisioning and usage may be of someinterest to all stakeholders in the emerging context of cloud networking.

Un mod\`ele de trafic adapt\'e \`a la volatilit\'e de charge d'un  service de vid\'eo \`a la demande: Identification, validation et application  \`a la gestion dynamique de ressources