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- W3172735361 abstract "Information exchange speed on the Internet is measured with latency: the duration of the elapsed time between the sending of the first bit of a request and the reception of the first bit of the response. In this thesis carried out in collaboration with the company Citrix, we are interested in the analysis and modeling of latency data in a context of Internet traffic optimization.Citrix collects data through two different channels generating latency measurements suspected to share common properties. First, we study a probability distribution matching problem where the outputs are the transported probability distributions of the inputs under an unknown deterministic transport, and where the observables are independent samples drawn according to these probability distributions. We study an estimator of this transport and prove its convergence properties. We show that our estimator can be used to match the distributions of latency measurements from the two channels.Then, we propose a modeling strategy to predict the process obtained by calculating the moving median of latency measurements on regular partitions of the interval [0, T] with mesh D > 0. We show that the conditional mean of this process, which plays a major role in Internet traffic optimization, is correctly described by a decomposition into Fourier series and that its conditional variance forms clusters which are modeled using an ARMA Seasonal-GARCH process, i.e. an ARMA-GARCH process with additional deterministic seasonal terms. The predictive performance of this model is compared to benchmark models used in the industry. A new measure of the amount of residual information not captured by the model based on a certain entropy criterion is introduced.We then address the problem of outage detection in the Internet. We propose a change detection algorithm in the distribution of a latency data stream based on the comparison of two sliding windows using a certain weighted Wasserstein distance.Finally, we describe how to minimize the size of the training data sets used by the predictive algorithms to limit the calculation costs without impacting accuracy." @default.
- W3172735361 created "2021-06-22" @default.
- W3172735361 creator A5059455736 @default.
- W3172735361 date "2020-10-02" @default.
- W3172735361 modified "2023-09-23" @default.
- W3172735361 title "Statistical modeling and analysis of Internet latency traffic data" @default.
- W3172735361 hasPublicationYear "2020" @default.
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