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Authors : Okan Yilmaz , Ing -Ray Chen Presentator : Mehmet Saglam

Utilizing Call Admission Control for Pricing Optimization of Multiple Service Classes in Wireless Cellular Networks. Department of Computer Science Virginia Polytechnic Institute and State University Northern Virginia Center , USA. Authors : Okan Yilmaz , Ing -Ray Chen

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Authors : Okan Yilmaz , Ing -Ray Chen Presentator : Mehmet Saglam

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  1. UtilizingCallAdmissionControlforPricingOptimizationof Multiple Service Classesin WirelessCellular Networks Department of Computer Science Virginia PolytechnicInstitute and State University Northern Virginia Center, USA Authors : Okan Yilmaz, Ing-Ray Chen Presentator : Mehmet Saglam

  2. Outline • Introduction • System Model • Methodology • AdmissionControlAlgorithms • NumericalAnalysis • Summary

  3. Introduction • DiverseMulti-MediaServices • Non-Real Time Services • Real Time Services

  4. Introduction • REVENUEOPTIMIZATION • QoSrequirements • Total number of channels • Chargeclientsbytheamount of time • Changethepriceperiodically

  5. Introduction • RelatedWork • Calladmissioncontrolforsingle-class network traffic • Calladmissioncontrolformultipleclasses • Concept of maximizingthepayoff of thesystemthroughadmissioncontrol • Admissioncontrolalgorithmsintegrated w/ QoSguarantees • Partitioning-based • Threshold-based • Hybrid  AimstosatisfyQoSrequirements FIXEDPRICE • Thispaperaddresstheissue of determiningOPTIMALPRICING

  6. Introduction • TheGoalof thispaper; • UtilizeadmissioncontrolalgorithmsforrevenueoptimizationwithQoSguaranteestoderive optimal pricing • Show that a hybridadmissioncontrolalgorithmcombiningthebenefits of partitioningandthreshold-basedcalladmissioncontrolwouldperformthebest in terms of pricingoptimization

  7. System Model • Cellular Network • Consist of a number of cells, each of which has a basestation at thecenter • Fixednumber of channels, • Service Classes • Characterizedby service types(Real Time, Non-Real Time) • CallTypes • Handoffcalls • New calls

  8. System Model • Quality of Service Requirements • Each service typerequires a number of BWchannel • Arrival/DepartureRates • Eachcellmakesadmissioncontroldecissionsfornewandhandoffcallrequeststomaximizerevenue • Optimal pricingrelatedtopricingalgorithm • charge-by-time • charge-rate is per time unit

  9. Methodology • Pricing-DemandFunction • Constants • Elasticity: Effect of pricingchanges on service demand • Elastic: Increase in demandfasterthandecrease in pricing • Inelastic: Increase in demand is slowerthandecrease in pricing • Determinedbyanalyzingstatistical data • Proportionalityconstant • Calculatedfrompricing-demandfunction

  10. Methodology • Total RevenueFunction • Obtainmaxrevenuebyusing • Theapproach is toexhaustivelysearchallpossiblecombinations of forall service classesandlookforthebestcombination of service classpricesthatwouldmaximizethesystemrevenue.

  11. Methodology • PricingRange : • Divideintoparts • Total number of possiblepricecombinationforall service classes:

  12. Methodology • Predictthearrivalrates of service classesfor a givenpricecombinations • Determinetherevenuegeneratedunder a calladmissioncontrolalgorithmandstorealltherevenuevalues in ann-dimensionaltable, byeverycellindependently • Collectthetablesandmergethemtodetermine global optimal pricing 12/27

  13. AdmissionControlAlgorithms • Overview of partitioning, threshold-basedandhybridalgorithms • Integratedwithpricingforrevenueoptimization • Quality of Service guarantees • Assumethatthereare 2 service types • Class 1 / highpriority / real time • Class 2 / lowpriority / non-real time • Trafficinputparameters

  14. AdmissionControlAlgorithms PartitioningAdmissionControl 1/2 • Divides total number of channelintofixedpartitionsforreserving a particular service classandcalltype • Identifythebestpartitionthatwouldmaximizethecell’srevenuewhilesatisfyingtheimposedQoSconstraintsdefinedby

  15. AdmissionControlAlgorithms PartitioningAdmissionControl 2/2 • Thesystembehaves as M/M/n/nqueue • Calldroppingandblockingprobabilities can be determinedeasilybycalculatingtheprobability of thepartitionallocatedtoservethespecificcallsbeingfull • Computetherevenueperunit time tothecellby • where • Optimal partitionthatmaxtherevenue can be findbyexhaustivelysearchingallpossibilities

  16. AdmissionControlAlgorithms Threshold-BasedAdmissionControl 1/2 • Whenthenumber of channelsused in thecellexceedsthreshold, thenneworhandoffcallsfrom service class 2(low-priority) will not be admitted • Aimstofind an optimal set of satisfyingtheaboveconditionsthatwouldyeldthehighestrevenuewithQoSguarantees • Thisalgorithm can be analyzedbyusing a SPN model tocompute

  17. AdmissionControlAlgorithms Threshold-BasedAdmissionControl 2/2 • Therevenuegeneratedperunit time couldcalculatedby • The optimal hreshold set can be computedbysearchingthroughallthecombinations • There is no close-form solution • ItrequiresevaluatingtheSPNperformance model togeneratetheblockingprobabilitiesandtherevenueobtainablebythesystem

  18. AdmissionControlAlgorithms HybridPartitioning-ThresholdAdmissionControl 1/2 • Takestheadvantege of bothpartitioningandthreshold-based • Divideschannelsintofixedpartitions • Shares a partitiontoallowcalls of all service classes/typestocompeteforitsusage • Let be thenumbers of callsby service andclasstypesandthenumber of channelsallocatedtothesharedpartition

  19. AdmissionControlAlgorithms HybridPartitioning-ThresholdAdmissionControl 2/2 • Theperformance model forthehybridalgorithm is composed of 2 sub-models • Partitioningalgorithmwith 4 fixedpartitions(M/M/n/n) • Threshold-basedalgorithm • Computetherevenueperunit time bysum of revenueearnedfromfixedpartitionsplusfromsharedpartition • Thistakesminutestosearchforthebestsolutionfor C=80 channels • There is no close-form solution

  20. NumericalAnalysis • Thepaperusednumerical data forpossiblefuturepricecombinations • Comparedperformancecharacteristics of theseadmissioncontrolalgorithmswithQoSguarantees • Class 1 (real-time) has morestringentcallblockingprobabilitiesthanclass 2 (non-real-time), as well as higherpricing • Thecallarrivalprocess is poissonthus, inter-arrival time of calls is exponential (SPN model usedforperformanceevaluation)

  21. NumericalAnalysis PartitioningAdmissionControl • Maxrevenue=664 • v1=80, v2=10 • Therevenueobtainableincreases as theanticipatedarrival rate increases as a result of loweringtheprices

  22. NumericalAnalysis Threshold-basedAdmissionControl • Maxrevenue=722 • v1=80, v2=6 • Bysharingresourcesamong service classesandcontrollingtheeffect of higherclass 2 arrival rate, thresholdalgorithmperformedbetterthanpartitioningalgorithm

  23. NumericalAnalysis HybridAdmissionControl • Maxrevenue=736 • v1=60, v2=8 • Itreserves • Appliesa lowerthresholdtoclass 2 calls in thecommonpartition

  24. NumericalAnalysis • Themultiplexingpower of thesharedpartition is clearlydemonstrated • Theperformance of thresholdalgorithm is comparabletohybridalgorithm • Superiority of hybridalgorithm is theabilitytooptimallyreserveresourcesthroughfixedpartitioningandtooptimallyallocateresourcestothesharedpartition in accordancewiththreshold-basedadmissioncontrolalgorithm

  25. NumericalAnalysis • Eachcellwouldcollectstatistical data periodicallytoestimate a set of referencearrival/departurerates of new/handoffcalls of various service classesbased on statisticalanalysis • Theneachcelldeterminesnew/handoffcallarrivalratesfor a range of “future” potentialpricingforeach service class • The optimal settingsforallfuturepricecombinationsarethensummarized in a revenuetableandreportedto a centralentitywhichcollectsandanalyzesrevenuetables

  26. Summary • A methodologyproposed&analyzedtodetermine optimal pricingforrevenueoptimizationwithQoSguarantees in wireless mobile networks • Theadmissioncontrolalgorithmsareutilized (integratedwithpricing) • Partitioningadmissioncontrol • Threshold-basedadmissioncontrol • Hybridadmissioncontrol • Withinthe 3 algorithmsthehybridschemeperformedthebestcombiningthebenefits of theothers

  27. Questions & Answers ThankYou! MehmetSaglam msaglam@vt.edu

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