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001 978-3-031-79989-1
003 DE-He213
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007 cr nn 008mamaa
008 220601s2010 sz | s |||| 0|eng d
020 _a9783031799891
_9978-3-031-79989-1
024 7 _a10.1007/978-3-031-79989-1
_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 _aMo, Jeonghoon.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983810
245 1 0 _aPerformance Modeling of Communication Networks with Markov Chains
_h[electronic resource] /
_cby Jeonghoon Mo.
250 _a1st ed. 2010.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2010.
300 _aIX, 80 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 _aPerformance Modeling -- Markov Chain Modeling -- Developing Markov Chain Performance Models -- Advanced Markov Chain Models.
520 _aThis book is an introduction to Markov chain modeling with applications to communication networks. It begins with a general introduction to performance modeling in Chapter 1 where we introduce different performance models. We then introduce basic ideas of Markov chain modeling: Markov property, discrete time Markov chain (DTMC) and continuous time Markov chain (CTMC). We also discuss how to find the steady state distributions from these Markov chains and how they can be used to compute the system performance metric. The solution methodologies include a balance equation technique, limiting probability technique, and the uniformization. We try to minimize the theoretical aspects of the Markov chain so that the book is easily accessible to readers without deep mathematical backgrounds. We then introduce how to develop a Markov chain model with simple applications: a forwarding system, a cellular system blocking, slotted ALOHA, Wi-Fi model, and multichannel based LAN model. The examples cover CTMC, DTMC, birth-death process and non birth-death process. We then introduce more difficult examples in Chapter 4, which are related to wireless LAN networks: the Bianchi model and Multi-Channel MAC model with fixed duration. These models are more advanced than those introduced in Chapter 3 because they require more advanced concepts such as renewal-reward theorem and the queueing network model. We introduce these concepts in the appendix as needed so that readers can follow them without difficulty. We hope that this textbook will be helpful to students, researchers, and network practitioners who want to understand and use mathematical modeling techniques. Table of Contents: Performance Modeling / Markov Chain Modeling / Developing Markov Chain Performance Models / Advanced Markov Chain Models.
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)
_983814
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031799884
776 0 8 _iPrinted edition:
_z9783031799907
830 0 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
_983815
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79989-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c85563
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