Heavy and light traffic regimes for M|G|infinity traffic models

dc.contributor.advisorMakowski, Armand M.en_US
dc.contributor.authorTsoukatos, Konstantinos P.en_US
dc.contributor.departmentISRen_US
dc.contributor.departmentCSHCNen_US
dc.date.accessioned2007-05-23T10:08:56Z
dc.date.available2007-05-23T10:08:56Z
dc.date.issued1999en_US
dc.description.abstractThe $M|G|infty$ busy server process provides a class of structural models for communication network traffic. In this dissertation, we study the asymptotic behavior of a network multiplexer, modeled as a discrete-time queue, driven by an $M|G|infty$ correlated arrival stream. <p>The asymptotic regimes considered here are those of heavy and light traffic. In heavy traffic, we show that the arising limits are described in terms of the classical Brownian motion and the $alpha$--stable L'{e}vy motion, under short- and long-range dependence, respectively. Salient features are then effectively captured by the exponential distribution and the Mittag-Leffler special function. <p>In light traffic, the analysis reveals the effect of two aspects of the $M|G|infty$ process, i.e., the session duration distribution $G$ and the gradual nature of the arrivals, as opposed to the instantaneous inputs of a standard $GI|GI|1$ queue. We exploit these asymptotic results to construct interpolation approximations for system quantities of interest, applicable to all traffic intensities.en_US
dc.format.extent1114218 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/6112
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; PhD 1999-6en_US
dc.relation.ispartofseriesCSHCN; PhD 1999-3en_US
dc.subjecttraffic modelingen_US
dc.subjectshort/long-range dependenceen_US
dc.subjectheavy/light trafficen_US
dc.subjectBrownian motionen_US
dc.subjectLevy motionen_US
dc.subjectMittag-Leffler functionen_US
dc.subjectIntelligent Signal Processing and Communications Systemsen_US
dc.titleHeavy and light traffic regimes for M|G|infinity traffic modelsen_US
dc.typeDissertationen_US

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