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020 _a9783031792601
_9978-3-031-79260-1
024 7 _a10.1007/978-3-031-79260-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aMazumdar, Ravi R.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981692
245 1 0 _aPerformance Modeling, Stochastic Networks, and Statistical Multiplexing, Second Edition
_h[electronic resource] /
_cby Ravi R. Mazumdar.
250 _a2nd ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXIV, 197 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
505 0 _aIntroduction to Traffic Models and Analysis -- Queues and Performance Analysis -- Loss Models for Networks -- Stochastic Networks and Insensitivity -- Statistical Multiplexing.
520 _aThis monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in computing performance measures. The monograph also covers stochastic network theory including Markovian networks. Recent results on network utility optimization and connections to stochastic insensitivity are discussed. Also presented are ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. In particular, the important concept of effective bandwidths as mappings from queueing level phenomena to loss network models is clearly presented along with a detailed discussion of accurate approximations for large networks.
650 0 _aArtificial intelligence.
_93407
650 0 _aCooperating objects (Computer systems).
_96195
650 0 _aProgramming languages (Electronic computers).
_97503
650 0 _aTelecommunication.
_910437
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aCyber-Physical Systems.
_932475
650 2 4 _aProgramming Language.
_939403
650 2 4 _aCommunications Engineering, Networks.
_931570
710 2 _aSpringerLink (Online service)
_981693
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031792595
776 0 8 _iPrinted edition:
_z9783031792618
830 0 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
_981694
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79260-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c85225
_d85225