000 04004nam a22005655i 4500
001 978-3-319-47340-6
003 DE-He213
005 20220801221749.0
007 cr nn 008mamaa
008 161101s2017 sz | s |||| 0|eng d
020 _a9783319473406
_9978-3-319-47340-6
024 7 _a10.1007/978-3-319-47340-6
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
100 1 _aChen, Shigang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957847
245 1 0 _aTraffic Measurement for Big Network Data
_h[electronic resource] /
_cby Shigang Chen, Min Chen, Qingjun Xiao.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aVII, 104 p. 45 illus., 2 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aWireless Networks,
_x2366-1445
505 0 _aIntroduction -- Per-Flow Size Measurement -- Per-Flow Cardinality Measurement -- Persistent Spread Measurement.
520 _aThis 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 _aTelecommunication.
_910437
650 0 _aComputer networks .
_931572
650 0 _aApplication software.
_957848
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aComputer Communication Networks.
_957849
650 2 4 _aComputer and Information Systems Applications.
_957850
700 1 _aChen, Min.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957851
700 1 _aXiao, Qingjun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957852
710 2 _aSpringerLink (Online service)
_957853
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319473390
776 0 8 _iPrinted edition:
_z9783319473413
776 0 8 _iPrinted edition:
_z9783319837161
830 0 _aWireless Networks,
_x2366-1445
_957854
856 4 0 _uhttps://doi.org/10.1007/978-3-319-47340-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80026
_d80026