@inproceedings{4eede162616a45c9b9cfdae73c470643,
title = "Estimation of the End-to-End Delay in 5G Networks Through Gaussian Mixture Models",
abstract = "Network analytics provide a comprehensive picture of the network's Quality of Service (QoS), including the End-to-End (E2E) delay. In this paper, we characterize the E2E delay of heterogeneous networks when a single known probabilistic density function (PDF) is not adequate to model its distribution. To this end, multiple PDFs, denominated as components, are assumed in a Gaussian Mixture Model (GMM) to represent the distribution of the E2E delay. The accuracy and computation time of the GMM is evaluated for a different number of components. The results presented in the paper consider a dataset containing E2E delay traces sampled from a 5G network, showing that the GMM{\textquoteright}s accuracy allows addressing the rich diversity of probabilistic patterns found in 5G networks and its computation time is adequate for real-time applications.",
keywords = "End-to-End delay, Gaussian mixture model, Quality of service",
author = "Diyar Fadhil and Rodolfo Oliveira",
year = "2022",
month = jun,
doi = "10.1007/978-3-031-07520-9_8",
language = "English",
isbn = "978-3-031-07519-3",
series = "IFIP Advances in Information and Communication Technology",
publisher = "Springer",
pages = "83--91",
editor = "Camarinha-Matos, {Lu{\'i}s M.}",
booktitle = "Technological Innovation for Digitalization and Virtualization",
address = "Netherlands",
note = "13th Advanced Doctoral Conference on Computing, Electrical, and Industrial Systems, DoCEIS 2022 ; Conference date: 29-06-2022 Through 01-07-2022",
}