國立高雄大學圖資館 |

Language: English

Back

Traffic measurement for big network data

Chen, Min.

Traffic measurement for big network data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Traffic measurement for big network databy Shigang Chen, Min Chen, Qingjun Xiao.
Author:	Chen, Shigang.
other author:	Chen, Min.
Published:	Cham :Springer International Publishing :2017.
Description:	vii, 104 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computer networksManagement.
Online resource:	http://dx.doi.org/10.1007/978-3-319-47340-6
ISBN:	9783319473406$q(electronic bk.)

Traffic measurement for big network data
Chen, Shigang.

Traffic measurement for big network data[electronic resource] /by Shigang Chen, Min Chen, Qingjun Xiao. - Cham :Springer International Publishing :2017. - vii, 104 p. :ill. (some col.), digital ;24 cm. - Wireless networks,2366-1186. - Wireless networks..

Introduction -- Per-Flow Size Measurement -- Per-Flow Cardinality Measurement -- Persistent Spread Measurement.

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.

ISBN: 9783319473406$q(electronic bk.)

Standard No.: 10.1007/978-3-319-47340-6doiSubjects--Topical Terms:

182253
Computer networks
--Management.

LC Class. No.: TK5105.5

Dewey Class. No.: 004.6

Traffic measurement for big network data
LDR:02851nmm a2200325 a 4500 001 505679
003 DE-He213
005 20170614114819.0
006 m d
007 cr nn 008maaau
008 171030s2017 gw s 0 eng d
020 $a 9783319473406$q(electronic bk.)
020 $a 9783319473390$q(paper)
024 7 $a 10.1007/978-3-319-47340-6 $2 doi
035 $a 978-3-319-47340-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.5
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
082 0 4 $a 004.6 $2 23
090 $a TK5105.5 $b .C518 2017
100 1 $a Chen, Shigang. $3 762772
245 1 0 $a Traffic measurement for big network data $h [electronic resource] / $c by Shigang Chen, Min Chen, Qingjun Xiao.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a vii, 104 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Wireless networks, $x 2366-1186
505 0 $a Introduction -- Per-Flow Size Measurement -- Per-Flow Cardinality Measurement -- Persistent Spread Measurement.
520 $a 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.
650 0 $a Computer networks $x Management. $3 182253
650 0 $a Computer network architectures. $3 197806
650 0 $a Telecommunication $x Traffic $x Management. $3 246155
650 1 4 $a Engineering. $3 210888
650 2 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Computer Communication Networks. $3 218087
650 2 4 $a Information Systems Applications (incl. Internet) $3 530743
700 1 $a Chen, Min. $3 680255
700 1 $a Xiao, Qingjun. $3 771167
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Wireless networks. $3 731203
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-47340-6
950 $a Engineering (Springer-11647)