Machine Learning for Networking Second IFIP TC 6 International Confe
This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully review
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Selma Boumerdassi Éric Renault Paul Mühlethaler (Eds.)
Machine Learning for Networking Second IFIP TC 6 International Conference, MLN 2019 Paris, France, December 3–5, 2019 Revised Selected Papers
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this series at http://www.springer.com/series/7409
Selma Boumerdassi Éric Renault Paul Mühlethaler (Eds.) •
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Machine Learning for Networking Second IFIP TC 6 International Conference, MLN 2019 Paris, France, December 3–5, 2019 Revised Selected Papers
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Editors Selma Boumerdassi Conservatoire National des Arts Métiers Paris Cedex 03, France
Éric Renault ESIEE Paris Noisy-le-Grand, France
Paul Mühlethaler Inria Paris, France
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-45777-8 ISBN 978-3-030-45778-5 (eBook) https://doi.org/10.1007/978-3-030-45778-5 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © IFIP International Federation for Information Processing 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
The rapid development of new network infrastructures and services has led to the generation of huge amounts of data, and machine learning now appears to be the best solution to process these data and make the right deci
