000 03478nam a22005175i 4500
001 978-3-642-18084-2
003 DE-He213
005 20140220083752.0
007 cr nn 008mamaa
008 110205s2011 gw | s |||| 0|eng d
020 _a9783642180842
_9978-3-642-18084-2
024 7 _a10.1007/978-3-642-18084-2
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aPouzols, Federico Montesino.
_eauthor.
245 1 0 _aMining and Control of Network Traffic by Computational Intelligence
_h[electronic resource] /
_cby Federico Montesino Pouzols, Diego R. Lopez, Angel Barriga Barros.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXVI, 312p. 148 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v342
505 0 _aInternet Science -- Modeling time series by means of fuzzy inference systems -- Predictive models of network traffic load -- Summarization and analysis of network traffic flow records -- Inference Systems for Network Traffic Control -- Open FPGA-Based Development Platform for Fuzzy Inference Systems.
520 _aAs other complex systems in social and natural sciences as well as in engineering, the Internet is hard to understand from a technical point of view. Packet switched networks defy analytical modeling. The Internet is an outstanding and challenging case because of its fast development, unparalleled heterogeneity and the inherent lack of measurement and monitoring mechanisms in its core conception. This monograph deals with applications of computational intelligence methods, with an emphasis on fuzzy techniques, to a number of current issues in measurement, analysis and control of traffic in the Internet. First, the core building blocks of Internet Science and other related networking aspects are introduced. Then, data mining and control problems are addressed. In the first class two issues are considered: predictive modeling of traffic load as well as summarization of traffic flow measurements. The second class, control, includes active queue management schemes for Internet routers as well as window based end-to-end rate and congestion control. The practical hardware implementation of some of the fuzzy inference systems proposed here is also addressed. While some theoretical developments are described, we favor extensive evaluation of models using real-world data by simulation and experiments.
650 0 _aEngineering.
650 0 _aInformation systems.
650 0 _aArtificial intelligence.
650 0 _aTelecommunication.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aInformation Systems Applications (incl.Internet).
700 1 _aLopez, Diego R.
_eauthor.
700 1 _aBarros, Angel Barriga.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642180835
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v342
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-18084-2
912 _aZDB-2-ENG
999 _c107368
_d107368