Traffic Measurement for Big Network Data
This book presents several compact and fast methods for online traffic measurement of big network data. It describes challenges of online traffic measurement, discusses the state of the field, and provides an overview of the potential solutions to major p
- PDF / 7,852,267 Bytes
- 109 Pages / 439.43 x 683.15 pts Page_size
- 50 Downloads / 203 Views
Shigang Chen Min Chen Qingjun Xiao
Traffic Measurement for Big Network 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
Shigang Chen • Min Chen • Qingjun Xiao
Traffic Measurement for Big Network Data
123
Shigang Chen Department of Computer & Information Science University of Florida Gainesville, FL, USA
Min Chen Department of Computer & Information Science University of Florida Gainesville, FL, USA
Qingjun Xiao School of Computer Science and Engineering Southeast University of China Nanjing, Jiangsu, China
This work is supported in part by the National Science Foundation under grants CNS-1409797 and STC-1562485.
ISSN 2366-1186 ISSN 2366-1445 (electronic) Wireless Networks ISBN 978-3-319-47339-0 ISBN 978-3-319-47340-6 (eBook) DOI 10.1007/978-3-319-47340-6 Library of Congress Control Number: 2016954314 © Springer International Publishing AG 2017 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 This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Contents
1
2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Big Network Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Online Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Offline Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Fundamental Primitives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Scalable Counter Architectures for Per-Flow Siz
Data Loading...
