Cloud Networking for Big Data
This book introduces two basic big data processing paradigms for batch data and streaming data. Representative programming frameworks are also presented, as well as software defined networking (SDN) and network function virtualization (NFV) technolo
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Deze Zeng Lin Gu Song Guo
Cloud Networking for Big Data
Wireless Networks Series Editor Xuemin Sherman Shen University of Waterloo Waterloo, Ontario, Canada
More information about this series at http://www.springer.com/series/14180
Deze Zeng • Lin Gu • Song Guo
Cloud Networking for Big Data
123
Deze Zeng China University of Geosciences Wuhan, Hubei, China
Lin Gu Huazhong University of Science and Tech Wuhan, Hubei, China
Song Guo School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu City, Japan
ISSN 2366-1186 ISSN 2366-1445 (electronic) Wireless Networks ISBN 978-3-319-24718-2 ISBN 978-3-319-24720-5 (eBook) DOI 10.1007/978-3-319-24720-5 Library of Congress Control Number: 2015952315 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2015 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, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)
Preface
The explosive growth of big data imposes a heavy burden on computation, storage, and communication resources in today’s infrastructure. To efficiently exploit the bulk cloud resources for big data processing, many different parallel cloud computing programming frameworks, such as Apache Hadoop, Spark, and Twitter Storm, have been proposed and widely applied. However, all these programming paradigms mainly focus on data storage and computation, while still treating the communication issue as blackbox. How data are transmitted in the network is transparent to the application developers. Although such paradigm makes application development easy, an increasing concern to manipulate the data transmission in the network according to the application requirements emerges and asks for flexible, customizable, secure, and efficient networking control. The gap between the computation programming and communication programming shall be filled up. Fortunately, the recent deve
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