Traffic Measurement for Big Network Data (Record no. 80026)

000 -LEADER
fixed length control field 04004nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-319-47340-6
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801221749.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 161101s2017 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319473406
-- 978-3-319-47340-6
082 04 - CLASSIFICATION NUMBER
Call Number 621.382
100 1# - AUTHOR NAME
Author Chen, Shigang.
245 10 - TITLE STATEMENT
Title Traffic Measurement for Big Network Data
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2017.
300 ## - PHYSICAL DESCRIPTION
Number of Pages VII, 104 p. 45 illus., 2 illus. in color.
490 1# - SERIES STATEMENT
Series statement Wireless Networks,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- Per-Flow Size Measurement -- Per-Flow Cardinality Measurement -- Persistent Spread Measurement.
520 ## - SUMMARY, ETC.
Summary, etc 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 problems. The authors introduce the problem of per-flow size measurement for big network data and present a fast and scalable counter architecture, called Counter Tree, which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. Unlike traditional approaches to cardinality estimation problems that allocate a separated data structure (called estimator) for each flow, this book takes a different design path by viewing all the flows together as a whole: each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. A framework of virtual estimators is designed to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. To conclude, the authors discuss persistent spread estimation in high-speed networks. They offer a compact data structure called multi-virtual bitmap, which can estimate the cardinality of the intersection of an arbitrary number of sets. Using multi-virtual bitmaps, an implementation that can deliver high estimation accuracy under a very tight memory space is presented. The results of these experiments will surprise both professionals in the field and advanced-level students interested in the topic. By providing both an overview and the results of specific experiments, this book is useful for those new to online traffic measurement and experts on the topic.
700 1# - AUTHOR 2
Author 2 Chen, Min.
700 1# - AUTHOR 2
Author 2 Xiao, Qingjun.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-47340-6
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2017.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Telecommunication.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer networks .
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Application software.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Communications Engineering, Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Communication Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer and Information Systems Applications.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2366-1445
912 ## -
-- ZDB-2-ENG
912 ## -
-- ZDB-2-SXE

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