Erschienen: 11.09.2002 Abbildung von Calzarossa / Tucci | Performance Evaluation of Complex Systems: Techniques and Tools | 2002 | Performance 2002. Tutorial Lec... | 2459

Calzarossa / Tucci

Performance Evaluation of Complex Systems: Techniques and Tools

Performance 2002. Tutorial Lectures

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106,99 €

inkl. Mwst.

2002. Buch. viii, 499 S. Bibliographien. Softcover

Springer. ISBN 978-3-540-44252-3

Format (B x L): 15,5 x 23,5 cm

Gewicht: 1580 g

In englischer Sprache

Das Werk ist Teil der Reihe: Lecture Notes in Computer Science; 2459

Produktbeschreibung

Modelingandveri?cationformalisms,solutionmethods, workloadcharacterization,andbenchmarkingareaddressedfromametho- logicalpointofview. Applicationsofperformanceandreliabilitytechniquesto variousdomains,suchas,hardwareandsoftwarearchitectures,wiredand- relessnetworks,Gridenvironments,Webservices,real–timevoiceandvideo applications,arealsoexamined. Thisbookisintendedtoserveasareferenceforstudents,scientists,and- gineersworkingintheareasofperformanceandreliabilityevaluation,hardware andsoftwaredesign,andcapacityplanning. VI Preface Finally,aseditorsofthebook,wewouldliketothankallauthorsfortheir valuablecontributionsandtheire?ortandcooperationinthepreparationof theirmanuscripts. July2002 MariaCarlaCalzarossa SalvatoreTucci TableofContents G-Networks:MultipleClassesofPositiveCustomers,Signals,and ProductFormResults. 1 ErolGelenbe SpectralExpansionSolutionsforMarkov-ModulatedQueues. 17 IsiMitrani M/G/1-TypeMarkovProcesses:ATutorial. 36 AlmaRiska,EvgeniaSmirni AnAlgorithmicApproachtoStochasticBounds. 64 J. M. Fourneau,N. Pekergin DynamicSchedulingviaPolymatroidOptimization. 89 DavidD. Yao WorkloadModelingforPerformanceEvaluation. 114 DrorG. Feitelson CapacityPlanningforWebServices(TechniquesandMethodology). 142 VirgilioA. F. Almeida End-to-EndPerformanceofWebServices. 158 PaoloCremonesi,GiuseppeSerazzi Benchmarking. 179 ReinholdWeicker BenchmarkingModelsandToolsforDistributedWeb-ServerSystems. 208 MauroAndreolini,ValeriaCardellini,MicheleColajanni StochasticProcessAlgebra:FromanAlgebraicFormalismtoan ArchitecturalDescriptionLanguage. 236 MarcoBernardo,LorenzoDonatiello,PaoloCiancarini AutomatedPerformanceandDependabilityEvaluationUsingModel Checking. 261 ChristelBaier,BoudewijnHaverkort,HolgerHermanns, Joost-PieterKatoen Measurement-BasedAnalysisofSystemDependabilityUsingFault InjectionandFieldFailureData. 290 RavishankarK. Iyer,ZbigniewKalbarczyk VIII TableofContents SoftwareReliabilityandRejuvenation:ModelingandAnalysis. 318 KishorS. Trivedi,KalyanaramanVaidyanathan PerformanceValidationofMobileSoftwareArchitectures. 346 VincenzoGrassi,VittorioCortellessa,Ra?aelaMirandola PerformanceIssuesofMultimediaApplications. 374 EdmundodeSouzaeSilva,RosaM. M. Le˜ao,BerthierRibeiro-Neto, S´ergioCampos MarkovianModelingofRealDataTra?c:HeuristicPhaseTypeand MAPFittingofHeavyTailedandFractalLikeSamples. 405 Andr´asHorv´ath,Mikl´osTelek OptimizationofBandwidthandEnergyConsumptioninWireless LocalAreaNetworks. 435 MarcoConti,EnricoGregori ServiceCentricComputing–NextGenerationInternetComputing. 463 JerryRolia,RichFriedrich,ChandrakantPatel EuropeanDataGridProject:ExperiencesofDeployingaLargeScale TestbedforE-scienceApplications. 480 FabrizioGagliardi,BobJones,MarioReale,StephenBurke AuthorIndex. 501 G-Networks: Multiple Classes of Positive Customers, Signals, and Product Form Results Erol Gelenbe SchoolofElectricalEngineeringandComputerScience UniversityofCentralFlorida Orlando,FL32816 erol@cs. ucf. edu Abstract. ThepurposeofthistutorialpresentationistointroduceG- Networks,orGelenbeNetworks,whichareproductformqueueingn- works which include normal or positive customers, as well as negative customers which destroy other customers, and triggers which displace othercustomersfromonequeuetoanother. Wederivethebalanceeq- tionsforthesemodelsinthecontextofmultiplecustomerclasses,show the product form results, and exhibit the tra?c equations which – in thiscase,contrarytoBCMPandJacksonnetworks-arenon-linear. This leadstointerestingissuesofexistenceanduniquenessofthesteady-state solution. GelenbeNetworkcanbeusedtomodellargescalecomputers- temsandnetworksinwhichsignalingfunctionsrepresentedbynegative customersandtriggersareusedtoachieve?owandcongestioncontrol. 1 Introduction In this survey and tutorial, we discuss a class of queueing networks, originally inspired by our work on neural networks, in which customers are either “signals” or positive customers.

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