Data Cached in Client 1 Data in Broadcast Program Database in Server. Hot. Cold. e broadcast program a a Queries in Client 1: i, a, c, e, d, g, i, j

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1 DynamicDataDeliveryinWireless QinglongHu1,DikLunLee1,andWang-ChienLee2 CommunicationEnvironments 2GTELaboratoriesIncorporated,40SylvanRoad,Waltham,MA02254, 1UniversityofScienceandTechnology,ClearWaterBay,HongKong, Abstract.Informationbroadcasting,cachingoffrequentlyaccesseddata, andpull-baseddatadeliveryarecommonlyusedtechniquestoreduce dataaccesstimeofwirelessinformationservices.mostofthestudiesin theliteraturefocusedeitheronindividualtechniqueoracombination aredisseminatedthroughvariousstoragemediumsaccordingtothedynamicallycollecteddataaccesspatterns.performanceevaluationofthe modelisconductedbysimulationstudies. ofthemwithsomerestrictiveassumptions.inthispaper,weproposea inanintegratedmanner.aparticularfeatureofourmodelisthatdata dynamicdatadeliverymodelwherethesethreetechniquesworktogether 1Introduction isaccesstimewhichistheaveragetimefromamobilecomputerrequestsa dataitemuntilthedataisreceived.toreduceaccesstimeinamobileenvironment,threemajorclassesoftechniques,namely,caching,push-basedinformation Acriterionoftenusedtoevaluatethedataaccesseciencyofamobilesystem quentlyaccesseddatashouldbecachedintheclient,thelessfrequentlyaccessed cientinformationretrievalinawirelesscommunicationenvironment,mostfre- [IVB94,AAFZ95,AFZ97,SRB97]. broadcastandpulled-baseddatadeliveryhavebeeninvestigatedintheliterature datasubsetistemporarilystoredontheair(throughbroadcastchannels),and therestofthedatacanbepulledfromtheserverviaexplicitclientrequests. Toreducethedisadvantagesoftheabovethreetechniquesandtoachieveef- Datacachingandpush-baseddatadisseminationcanalleviatepull-basedrequestconsiderably,asmostfrequentlyaccessdatacanberetrievedeitherfrom broadcastchannelarebothlimitedresourcesinamobileenvironmentandtheir eciencydependsheavilyontheprecisenessofthedataset,datadistribution shouldbemanagedcarefullytoachievehighutilizationoftheresources.the keypointistodynamicallydecidewhichsubsetofdatashouldbecached,which subsetshouldbebroadcastontheair. broadcast,andpull-basedtechniquestoachieveecientinformationdisseminationbycontinuouslyadjustingtheclientcacheandthebroadcastscheduleto matchtheaccesspatterns.inmobilesystems,theclientscaneithermonitorthe theclientcacheorthebroadcastchannel.sincetheclientcacheandwireless Thispaperinvestigatesthemethodstointegratedatacaching,information

2 broadcastchannelforthearrivalofthedesireddataorissueapullrequesttothe serverforthedataandrespectively,theservercaneitherbroadcastdataaccordingtobroadcastscheduleorbroadcastpulleddatainstead.theclientsmanage thecacheviatraditionalpagereplacementpolicy(i.e.,lru).thehotspotof simulationstudyshowsthattheaccesspatternscanbeaccuratelyestimatedand theuncacheddatacanbeobtainedbyclientmonitoringthecachemisses.our purepull-baseddataservice. inwhichtheserverknowstheexactclientaccesspatternsandisbetterthan basedonittheservercandynamicallyconstructbroadcastprogramtooptimize systemperformance.theresultingperformanceisconsistentwiththeidealcase accesspatterns,whichisdierentfromtheworkin[aafz95].in[aafz95], Acharyaetalassumedthatthedataaccesspatternsareknownandremain unchange.therefore,axedbroadcastschedule,whichisorganizedaccording totheuseraccesspatterns,isrepeatedineverybroadcastcycle.thecached Ourworkisbasedontheassumptionthattheserverdoesnotknowthedata datashouldbetailoredaccordingtothebroadcastscheduleinadditiontothe LRUpolicy:datasetwhoselocalprobabilityofaccessissignicantlygreater thantheirbroadcastfrequency.bytakingthosetwofactorsintoconsideration, withotherschemes.[srb97]investigatedthedynamicadjustmentofthehotspotforthebroadcastprogram.however,datacachingandmultipledisksof dierentsizesandspeedsforthebroadcastprogramwerenotconsidered. showedthathixissuperiorforcachinginthebroadcastsystemscompared Achayaetal[AAFZ95]developedapagereplacementpolicycalledHIXand theevaluationofthedatadeliverysystem.section4presentsexperimentsand disseminationmethods.section3describesthesimulationmodeldevelopedfor theresultsderivedfromthismodel.finally,section5concludesthepaper. Therestofthepaperisorganizedasfollows.Section2proposesdynamicdata 2DynamicDataBroadcastScheduling downlinkchannelforalltheclientsandanshareduplinkchannelforrequests) Oursystemmodelisbasedonthesatellitebroadcastscenario(i.e.,oneshared thepullrequeststotheserver.onceaclientisallocatedtheuplinkchannel,itwill andasymmetriccommunication(i.e.,thedownlinkchannelfromservertoclients hasfarmorecapacitythanthereversedirection). occupythechanneluntiltheserviceends.uponreceiptofthepullrequest,the serverbroadcasttheanswertotheclient.pull-baseddatadeliverycanguarantee allrequesteddatatobenallyobtained.however,bandwidthmaybewasted Forpull-baseddatadelivery,clientscompetefortheuplinkchanneltosend clientpopulation,thedownlinkchannelcaneasilygetcongestedwithseparate withclientrequests. pulledpages.inaddition,theservermustbeinterruptedcontinuouslytodeal becausethepulledpagesareusuallyofinteresttoonlyoneclient1.withalarge 1Although,thisproblemismitigatedtosomeextent,bythefactorthatanypage whichispulledbyoneclientcanbeaccessedonthebroadcastchannelbyanyother client.

3 time.therefore,clientaccesspatternsmustbecollecteddynamically.basedon encesarethattheclientaccesspatternsareunknowninitiallyorcanchangewith theaccesspatterns,theserverconstructsthebroadcastprogram. Themodelextendsthebroadcastdiskmodelproposedin[AFZ97].Thedier- handlingclientpullrequestswhichisimitatedbyanitequeueintheserver. ThequeueisservicedinaFIFOfashionandtheservicerateisdeterminedby thepullbandwidthofthesystem.wheneverthequeueisfull,allclientsare blockedandpullrequestswillbeacceptedbytheserveronlywhenthequeue Similarto[AFZ97],weassumethattheserverhaslimitedthroughputin request. hasspaceavailableagain.therequestforapagethatisalreadyinthequeueis droppedbecausetheprocessingoftheearliermessagewillalsosatisfythenew thecycleversion),isbroadcastatthebeginningofeachcycle.eachactiveclient datadelivery,thecompletebroadcastschedule(i.e.,alistofdataidentiersand keepsonmonitoringtheschedulesandretrievesthebroadcastscheduleintoits cache2.basedontheschedules,theclientcanchoosetheoptimaldatadelivery Inordertorealizedynamicalselectionbetweenpush-basedandpull-based method.thatis,ifthedesireddataitemswillappearwithinacertainbroadcast slots,thentheclientkeepsonmonitoringthebroadcastchannel;otherwise,the clientissuesapullrequesttotheserverandmonitorsthechannelforthearrival ofthedataitem.theserverdeterminesthenextdataitemforbroadcastfrom eitherthebroadcastprogramorthepullrequestswaitinginthequeue.weadopt isbroadcast,acoinweightedbythepercentageofpullbandwidthistossedand dependingontheoutcome,eithertherequestedpageattheheadofthequeueis broadcastortheregularbroadcastprogramcontinues.ifthequeueisempty(i.e., arandomslotallocationschemeproposedin[afz97],inwhichbeforeeverypage frombroadcastprogramdespitethatitistheturnforpulledpages.therefore, nopullrequestsfromtheclients),thenextcandidateforbroadcastingisapage thepercentageofbandwidthallocatedforpullrequestsisanupperbound. [EPW93]inthemodel.InLRU-Kscheme,thecachemanagementkeepstrack thecacheddata.toimprovethehitratioofthecache,weadoptlru-kscheme ofthetimesoflastkreferencestopopulardataitemsandselectionisbasedon accordingtodatacachedandpulledrequests,itisthesecondhotspotbesides Toecientlyutilizethebroadcastchannel,thebroadcastscheduleistailored LRU-Kpolicewhendynamicbroadcastprogramsareusedinthesystem. thepastkhitshistory.in[hll98]weshowthathixpolicydoesworsethan result,issuessuchascacheinvalidationwhichiscloselyrelatedtodataupdates, sumingthattherearenoupdateseitherbytheserverorattheclients.asa arenotdiscussedinthepaper.readersinterestedinthissubjectmayreferto [BI94,HL98,WYC96]. Tobeconcentratedontheissuesweliketoaddressinthispaper,weas- 2Noticethatmonitoringtheschedulescanbequitepowerconsumptionandisnot exible,inalaterstudy[hll98],weintroduceindexingmechanismstoavoidclient keepingonmonitoringthechannelfortheschedules.

4 clientswithoutanyperformancedegradation,itiswellsuitedfordisseminating datatomanyclients.thebroadcastchannelisalinearmedium:aclientwaitsfor Sincebroadcastdatacanbeaccessedconcurrentlybyanarbitrarynumberof 2.1BroadcastScheduling ofdatabroadcast.tomaintainshortaccesslatency,theamountofbroadcast notcachedshouldbeprovidedforbroadcasting). dataneedstobesmall(i.e.,onlythemostfrequentlyaccesseddatawhichare thedesireddataonthechannel.hence,theaccesslatencydependsonthevolume gram)needstobeconstructed.suchthattheserverperiodicallybroadcastsdata accordingtothisbroadcastschedule.asimplemethodcalledatbroadcastisto broadcasteachdataonlyonceineachcycle.notethattheclientsaccesspatterns areusuallyskewed(i.e.,somedataareaccessedmorefrequentlywhiletheothersarelessfrequentlyaccessed),anotherschedulingmethodfordatabroadcast calledbroadcastdiskswasproposedin[aafz95].forbroadcastdisks,theserver dividestheleintogroupsofdataitemswithdierentbroadcastfrequenciesto theclients,theexpectedwaittimeforadataitemonthatdiskistheshortest. imitatemultipledisksspinningatdierentspeeds.thefastestdiskisclosestto forclientstoaccessthem.thebroadcastfrequenciesofdisksareinproportion Thedataitemsontheslowestdiskarefarthestsinceitwilltakealongertime Oncethedatasetforbroadcastisdetermined,abroadcastschedule(pro- forasymmetriccommunicationenvironmentswheretheclientaccesspatterns andretrievedesireddataitemsfromthechannel.broadcastdisksareecient programoftheserver.accordingtotheprogram,clientsmonitorthebroadcast tem.toretrievedatafrombroadcastdisks,theclientsmustknowthebroadcast totheprobabilityofaccessinordertogaintheoptimalaccesstimeofthesys- areskewed.dataontheaircanfurtherbeorganizedasbroadcastdiskswith fastdiskscontainingfrequentlyaccesseddataandslowdisksforlessfrequently accesseddata. namicbroadcastprogramchangesonlythecontentoftheprogram,whileall adoptsbdiskorflatapproachisdenotedasbp.weassumethatthedy- disks(bdisk)andatbroadcast(flat),whiledatadeliverysystemwhich ofdisksisone.hereafter,thetermbroadcastprogramreferstobothbroadcast Flatbroadcastcanberegardedasaspecialbroadcastdiskswherethenumber otherparametersremainxed. allbroadcastbandwidthisdedicatedtopulledpages.onacachemiss,theclient patterninformationisrequiredbytheserverandtheserverworksinapassive thearrivalofthedesireddataitem.itiscalledpure-pull.obviouslynoaccess immediatelysendsapullrequestforthepagetotheserverandthenwaitsfor Aspecialscenarioappearswhenthebroadcastprogramisempty.Therefore, way.thisapproachissimplebuttheserverandtheuplinkchannelmaybeeasily 2.2CollectionofClientAccessPatterns congestedbyalotofseparatepullrequests. metricenvironments[jbea97,wyc96].torecordclientaccessinformation, Bitvectorisawidelyusedmethodindeliveringcompactinformationinasym- eachclientmaintainsabitvectorinwhicheachbitrepresentsadataiteminthe

5 Data Cached in Client 1 Data in Broadcast Program Database in Server Hot Cold i c e a b c d e f g a b c d e f g h i j b d a 7-pages, 3-disks c g e broadcast program a a currentbroadcastprogram(figure1).wheneveranewbroadcastprogramis c a f b Queries in Client 1: i, a, c, e, d, g, i, j cycleversionnumberandresetstheirbitvectorsforthecurrentcycle.every retrieved(i.e.,atthebeginningofabroadcastcycle),theclientsupdatethenew Fig.1.ExampleofaDataStorage. Cache Hit: i, c, e Air Hit: a, d, g Missing: j dataitemonthechannelismarkedtoindicatewhetheritisfromthebroadcast a b c d e f g Bit Vector: Pull Request: j programoritisfromthepullrequests.ifthereisaqueryansweredbybroadcast data(referredtoairhit),thenthecorrespondingbitisset.retrievingdatafrom The MFA value items a,d, g, j increases, while that all other remains unchanged. theclientcache(referredascachehit)andpulleddataonthebroadcastchannel foundfromboththecacheandthebroadcastprogram(calledairmiss),anexplicitpullrequestwillbeissuedbytheclientviatheuplinkchannel.thus,the havenoinuenceonthebitvector.however,whenthedesireddatacannotbe ofaclientallcanbeansweredeitherbycacheddataorbybroadcastdata,the clientaccessstatisticsispiggybackedatthesametime.notethatiftherequests itemsinthebroadcastprogramwhichisquitesmall,theoverheadfortheuplink thebitvector.sincethelengthofthebitvectorequalstothenumberofdata clientaccesspatternwillnotbedeliveredtotheserver. bandwidthislow.afterthisbitvectorispiggybackedtotheserver,itscontent isresetanditisreadyforthecollectionoffutureclientaccess. ThepullrequestconsistsofthedesireddataitemID,thecycleversion,and correspondingtowhichbroadcastprogram.inaddition,eachdataitemisassociatedwithamostfrequentlyaccessed(mfa)counterwhichisinitiallyreset. Thecounterofadataitemisincrementedbyonewheneverthedataitemis pulledbyaclientorthebitcorrespondingtothatdataiteminthereceived Usingthereceivedcycleversionnumber,theserverknowsthebitvector bitvectorisset.asaresult,theservercollectsstatisticalinformationabout theclientaccesspatterns.thisstatisticshelpstheserverdynamicallybuildthe broadcastprogramaccordingtothesystemworkload.inthispaper,theserver selectspageswithhighmfavaluesforbroadcast.notethatacachehithas noinuenceonthemfavalue.therefore,onceadataitemiscachedinthe client,anyfurtherrequestsforitfromtheclientwillhavenocontributiontoits MFAvalueintheserver.Hence,cacheddatanormallyhavelowMFAvalues, thoughtheyaremostfrequentlyretrievedbytheclient.astheaccessstatistics isaccumulated,cacheddatawilleventuallynotbeselectedintothebroadcast programoronlybeplacedonaslowerspinningdisk.intuitively,onlythosedata queryforthemwillhavehighmfavalues.inthisway,theservercantailorthe itemswhichareaccessedbyalargenumberoftheclientsthatoccasionallymake broadcastprogramtotheneedofaparticularclient.

6 3SimulationModeling Server Broadcast Messager Broadcast Channel Database Broadcast program b d a c Push g e Pull MUX a a c a f b Message Manager Page Receiver Access Server Pattern Queue No Client 1 Client 2 Client n modeledbyaprocess3.theprocessconsistsoftwosub-processes,namelythe Yes Bit Double? Dropped Query Generator Query Generator Query Generator Vector QueryGeneratorandthePullManager.TheQueryGeneratorrunsacontinuous Inthispaper,asimulationmodelshowninFigure2isused.Eachclientis Fig.2.SimulationModel. No Yes Q Full? Blocked... loopthatrandomlyrequestsapageaccordingtoaspecicdistribution.itrst Thresh. Thresh. Thresh. Cache Filter Cache Filter Cache Filter checkstheclient'scachetoseewhetheravalidcopyofthedesireddataitems Request Request Request existsthere.iftheanswerisyes,animmediatereplyisdeliveredtotheuser. Uplink Channel processingaswellastherelativespeedsofthecpuandthebroadcastmedium. hasaxed-sizecacheandlru-kpagereplacementpolicyisadopted.after eachrequest,thequerygeneratorwaitsforaperiodofthinktimelongand thenmakesthenextrequest.thinktimeisaparameterusedtomodelworkload Otherwiseasub-processPullManagerisactivated.Weassumethattheclient Afteractivated,thePullManagerissuesapullrequesttotheserverforthe neededdataitemifthedataitemcannotbeobtainedfromthebroadcastdisks orthecostofaccessdataitemfromthedisksexceedsacut-ovaluespeciedby thesystem.ineithercase,theclientmonitorsthebroadcastchanneluntilthe desiredpagearrives.asingleprocess,calledpagereceiver,isusedtomodelall therequestedone,itisbroughtintotheclient'scache. clientsinthesystem.thepagereceivercontinuouslymonitorsthebroadcast seewhethertheincomingpageisthedesiredpageofthatclient.ifthepageis channelandbecomesactivewhenapagearrives.itcheckseachclientinturnto (withparameter)isfrequentlyusedtomodelskewedaccesspatternswhere isaparameternamedaccessskewcoecientandcanvaryfromzerotoone. usedin[knu81])andgaussian(usedin[srb97]aswell).thezipfdistribution Forthedataaccesspatterns,weusetwodierentdistributions:Zipf(also 3In[AFZ97],theentireclientpopulationwasmodeledbytwoclientprocesses,areal clientandavirtualclient.sincetheclient-servermodelisnotmemorylessbecauseof contentionontheuplinkchannelamongtheclients. datacaching,theinterleavingofpushandpullslots,andtheboundedserverqueue. Usingasingleclientprocess(i.e.,virtualclient)maynotexactlyreectthetrue

7 Thedistributionbecomesincreasingly"skewed"asincreases.TheGaussian distribution,normal(;),isusedtomodelthedynamicchangesintheaccess patternswiththecenterofhot-spotandthewidthofhot-spot.duringthe experiments,thevalueofcanvarytocreatetheeectofdynamicworkload theparametersusedtomodeltheresourceandthedataaccesspatternofeach client. andthevalueofreectstheskewofclientaccesspatterns.table1summarizes CacheSize PullReqSizeSizeofapullrequestinbytes ThinkTimeMeanthinktime(inseconds)betweenqueriesforeachclient ThreshFactorValueforclienttoselectbetweenbroadcastandon-demand Clientcachesize(indataitems) Table1.ClientParameterSetting. Zipfdistributionparameter Widthofhot-spot Manager.Aboundedqueueisbuiltfortheuplinkrequestmessage.Whenthe Theserverismodeledbytwoprocesses:BroadcastingManagerandMessage Centerofhot-spot arrivalrateofuplinkrequestsexceedtheservicerateoftheserver,(e.g.thequeue inthequeuearedropped.thequeueisservedinafifofashionandtheservice rateisdeterminedbytheparameter,pullbw.pullbwdeterminesthepercentage isfull),allrequestsareblocked.requestsforpagesthatarealreadyrequested cycle.onlythecontentofthebroadcastprogramistailoredtomeettheaccess broadcastprogramisdynamicallydeterminedbythebroadcastingmanagerper ofthebroadcastslotsallocatedforpagesexplicitlypulledbytheclients.the fromthebroadcastprogram.themessagemanagerreceivespullrequestsfrom thequeue.however,whenthequeueisempty,allbroadcastpagesareselected theclients.fromthesepullmessages,clientaccesspatternscanbeobtained broadcastingpageisselectedalternatelyfromeitherthebroadcastprogramor patternscollectedfromtheclientsviatheuplinkchannel.inthismodel,thenext fromtheserver. DataItemSizeSizeofadataiteminbytes NumClient DatabaseSizeSizeofdatabaseindataitems DownlinkBWDownlinkfromtheservertotheclientchannelbandwidth Numberofclientsinacell Table2.SystemParameterSetting. UplinkBW PullPercentagePercentageofbroadcastslotsallocatedforpullrequests QueneSize ProgramSizeNumberofdistinctdataitemsinbroadcastprogram DiskNum Uplinkfromtheclienttotheserverchannelbandwidth DiskSizei DiskFreqi Sizeofdownlinkchannelqueue(indataietms) NumberofdisksforBDISK Sizeofdiski(indataitems) castprogramarelistedintable2.additionally,theparametersthatdescribe Parameterswhichdescribetheserverresourceandthestructureofthebroad- Relativebroadcastfrequencyofdiski thecongurationandthephysicalresourceinacellarealsoincludedinthe

8 table.weassumethatthereisauplinkchannelfromtheclientstotheserver andashareddownlinkchannel(alsocalledbroadcastchannel)forthereverse communication.thedatabaseismodeledasacollectionofdatabasesizedata itemsofdataitemsizebyteseach.therearenumchannelchannelsinacell. Fortheexclusivechannelallocationapproach,allNumChannelchannelswork ineitheron-demandmodeorbroadcastmode.forbpapproach,thechannel allocationmadebetweenbroadcastandon-demandmodesisxedsuchthatthe ratiobetweenthenumberofbroadcastchannelsandthenumberofon-demand channelsispullvspush.thecommunicationisasymmetric,i.e.,thebandwidth ofdownlinkfromtheservertotheclientdownlinkbwisgreaterthanthatof uplinkfromtheclienttotheserveruplinkbw. 4ExperimentsandResults Parameters DataItemSize DatabaseSize3000dataitemsQueueSize Table3.SystemParameterSetting. PullReqSize DownlinkBW 1000bytesNumClient 10bytesPullPecentage ValuesParameters 10;;2000 Values ThinkTime ProgramSize500dataitemsDiskSizei bpsUplinkBW 10secondsThreshFactorDiskSize1=100,DiskSize2=400 1%ofdownlinkbandwidth 100% 1=3 Inthissection,weinvestigatetheperformanceofsimulatedBPtogetherwith DiskNum CacheSize 300dataitems 50dataitems 2DiskFreqi randomlyselectedinthedatabase DiskFreq1=2,DiskFreq2= Pure-Pullapproach.Theprimaryperformancemetricistheaverageclientaccess time.thesimulationisimplementedusingcsim[sch92].table3denesthe systemparametersettingfortheexperiments.theservercontainsacollection ofself-identifyingdataitemsofequalsize.theclientaccesspatternsareskewed, (i.e.,eitherzipforgaussiandistribution)andtherequesteddatasetsamong Theserverdatabaseconsistsof3000dataitems(orpages),eachdataitemis theclientsarelargelyoverlapped.theclientpopulationvariesfrom10to bytesinsize.Thebroadcastrateis100Kbps,whiletheuplinkrateis1Kbps (1%ofthebroadcastrate).Theclientcachesizeis50pages.Theback-channel diskis400pages,withrelativespinspeedsbeing2and1,respectively. forbroadcast-diskwherethesizeofthefasterdiskis100pages,whiletheslower consecutivequeriesis10seconds.thenumberofdistinctpagesinthebroadcast program(forbothbdiskandflat)is500pages.two-diskbroadcastisused queuecanholdupto100distinctrequests.themeanthinktimebetweentwo sisonthedynamicworkloadenvironments.therearetwocircumstanceswhen dynamicworkloadcanoccur.therstcaseiswhentheclientsjointhesystem thesecondcase,anexistingclientchangesitsaccesshot-spotwithtime.obviously,dynamicworkloadonlyaectstheclientcacheforthepurepullapproach. withanemptycacheandtheserverdoesnotknowtheclientaccesspatterns.in WeexaminetheperformanceofthePure-PullapproachandBPwithempha-

9 cachebutthecontentofthebroadcastprogramaswell. theentirebroadcastslotsarededicatedtothepulledpagesinpure-pullapproach.forbothflatandbdiskapproaches,thethresholdissetto100%. ForBP,onethirdofthebroadcastslotsareallocatedforpulledpages,while ForBP(FLATorBDISK),dynamicworkloadhaseectsonnotonlytheclient Thatis,theclientrstchecksthewholebroadcastprogramtoseewhetherthe desiredpageswillappearwithinthereminingpartofthecurrentbroadcastprogram.iftheanswerisno,thentheclientissuesanexplicitpullrequesttothe server.otherwise,theclientkeepsonmonitoringthebroadcastchannelforthe desireddata. populations.thezipfdistributionparameterissetto0.95.wedeneaclientto thezipfdistributionandevaluatetheadaptivenessofbpfordierentclient Intherstsetofexperimentsweassumethattheclientaccesspatternsfollow 4.1PerformanceforChangingUserPopulationSize beatstablestagewhichisachievedbywaiting2000accessesmoreaftertheclient cachelledandasystemisatstablestagewhenallclientsinthesystemareat stablestage.forasystematstablestage,thewarm-upeectsintheclientcache andthebroadcastprogramareeliminated,suchthattheclientcachescontain thehottestaccessspotandtheserverhasinformationontheaccesspatterns. atinitialstage,boththeclientsandtheserverhavenoknowledgeoftheclient Inthecontrast,theinitialstageofthesystemisfromthetimewhenthesystem beginstoworktothetimewhenthesystemisatstablestage.henceasystem accesspatterns approachesatstablestagewiththatatinitialstageinfigure3.obviously, Fig.3.AccessTimeVSClientPopulation. B_DISK (Initial) B_DISK (Steady) 150 (Initial) (Steady) clientsandtheserverhavetheknowledgeofaccessprobabilityinformation, stage.thisindicatesthatafteracertainperiodof"learning"stageboththe ToseetheadaptabilityofBP,wecomparethesystemperformanceforBP which,inturn,guaranteestheclientstocacheimportantpagesandtheserverto tailorthebroadcastprogramaccordingtoclientdemand.bdiskalwaysgives bothflatandbdiskatsteadystatealwaysperformbetterthanatinitial Number of Clients abetterperformancethanflatapproachatbothstableandinitialstage.this isconsistentwiththeresultsobtainedforstaticbroadcastprogram[afz97]. Access Time

10 system(boththeclientsandtheserver)knowstheexactclientaccesspatterns. idealbroadcastdiskswithrespecttoflatandbdisk.fortheidealones,the thatthemostfrequentlyaccesseddatasubsetiscachedintheclient,theless BPdisseminatesinformationaccordingtotheoptimalaccessdistributionsuch Toprovidecomparisonbaselines,weintroducetheidealBP:idealatand frequentlyaccesseddatasubsetisprovidedbybroadcast,andtherestispulled fromtheserverbyexplicitrequests.thebroadcastprogramforbroadcastdisks isalsoconstructedaccordingtotheaccessprobabilityofthedatasubset.the idealbpistheultimateperformancegoalofoursystem B_DISK (Steady) FLAT,andBDISK,areshowninFigure4.Notethattheresultsshownare Theexperimentalresultsforthethreedatadeliverymethods:Pure-Pull, Fig.4.AccessTimeVSClientPopulation. 150 Ideal B_DISK (Steady) 100 Ideal numberofclientsisbeyond600,pure-pullperformsworsethanbdisk,and timeforthismethodincreasesrapidlyastheclientpopulationgrows.whenthe whenthesystembecomesevenmoreheavilyloaded(i.e.,thenumberofclients obtainedwhenthesystemisatstablestage.asexpected,thepure-pullapproach isgoodonlywhenthesystemworkloadislight(leftsideofthegure).theaccess Number Clients isgreaterthan700),pure-pullgivesworseperformancethanflatandbdisk. However,asclientpopulationsbecomelarge,thecurveforPure-Pullbecomes broadcastchannelformsahot-spot.asaresult,onepulleddataitemcansatisfy at.thereasonisasthenumberofclientsincreasesthepulleddatasetinthe alargenumberofclientrequests.sincethebasicpagereplacementalgorithm forclientcachingisthesameforbpandpure-pull,theimprovementofthe performanceforbpoverpure-pullisaresultofthetailoredbroadcastprogram. andbdiskmayneverachievetheidealperformance,thedynamicbroadcast onesverywell.thereisacertaingapbetweentherealapproacheswiththeideal approaches.thedierenceisduetotheimpreciseaccesspatternscollectedby theserverandtheinaccuratelrukpagereplacementstrategy.though,flat ItisobviousthatbothFLATandBDISKcanmanagetofollowtheideal 4.2PerformanceforChangingHot-Spot programisstillaneectiveapproachbecauseofitsadaptivenesstothesystem. ToevaluatetheperformanceofBPindynamicworkloads,weassumethatthe clientaccesspatternsfollowthegaussiandistribution.suchthatvariesto createtheeectofdynamicworkloadandwhichreectstheskewofclient Access Time

11 accesspatternsis100or300.inthisway,wecanmodeltheeliminationof accesshot-spotsandthegenerationofanewoneinanotherpartofthedatabase (randomlyselected).tointerprettheimpactofsuchhot-spotmigrationonthe systemperformance,weassumedthatallclientshavethesamehot-spotsand theychangeaccessdemandatthesametime.clientsstaywitheachhot-spot fordurationperiodsofsimulationtime Fig.5.MigrationofHot-spot. 250 B_DISK (Skewed=100) FLAT (Skewed=100) Pure-Pull (Skewed=100) 200 B_DISK (Skewed=300) FLAT (Skewed=300) (Skewed=300) andflat.sincewithsmallerduration,thebroadcastprogramofbpmaynot 300,showtwodierentdataaccesspatterns.Formoredynamicworkload(left clientpopulationis1000.twosetofcurves,labeledasskewed100andskewed InFigure5,weshowtheresultsobtainedasafunctionofDuration,wherethe theclientschangeaccesshot-spottoanothernewone.asaresult,thebenet reachsteadystateorthesteadybroadcastprogramisobtainedshortlybefore sideofthegureswheredurationisshort),pure-pulloutperformsbothbdisk (x 10,000) performancethanthepure-pullmethod,whichcanbeobservedintherightpart ofthegure.however,asthedurationincreasesto4106,anyfurtherincrease oftheaircachedoesn'thaveenoughtimetobecomeeective.onlywhenthe ofthedurationwon'timprovetheperformanceofbothbpandpure-pull.it systemreachessteadystateandstaytherelongenough,wouldbpgivebetter accesspattern=100thantheaccesspattern=300,becausefor=100 ComparedwithPure-Pull,BPimprovesthesystemperformancemoreforthe isbecausethatthesystemreachesthesteadystateafterthatdurationperiod. therearemoreairhitsthan=300. 5Conclusions Inthispaper,weproposedadynamicdatadeliverysystemforasymmetriccommunicationenvironments,whereinformationcanberetrievedbydierentmeans (i.e.,clientcache,broadcastchannels,ortheserverdatabasebyexplicitpull sothatonlythemostdesireddatasubsetisscheduledtobroadcast.pullrequests termineddynamicallybytheaccesspatternsoftheclients,notbyprecompiled userproles.thebroadcastprogramistailoredaccordingtoaccessfrequencies requests),toachievethemaximumadvantages.thewaydataarestoredisde- areneededonlywhenthedesireddatacannotbefoundintheclientcacheor thebroadcastprogram. Access Time

12 datadeliverysystem.wefoundthatthesystem(clientsandserver)canadaptto thedynamicallychangingworkloadverywell.generally,broadcastdisksandat broadcasthavebetterperformancethanpurepullapproachwhendataaccess patternsareskewed.forskeweddataaccesspatternsandlessdynamicworkloads,broadcastdisksoutperformatbroadcast.however,purepulltechnique ofdierentbroadcastprogramstructure(i.e.,thesizeofbroadcastprogrametc.) ontheperformanceofdatadelivery.moreover,wewillincludethedissemination ofupdatesinthehierarchicaldatacachingsystem. tweenpullandpushbandwidthaccordingtotheserverworkloadandtheimpact Asfuturework,wewillinvestigatewaystodynamicallyadjusttheratiobe- Asimulationmodelhasbeendevelopedtoevaluatetheperformanceofthe isabetterchoiceforrandomdataaccesspatternsorhighlydynamicworkloads. References [AAFZ95]S.Acharya,R.Alonso,M.Franklin,andS.Zdonik.Broadcastdisks:Data [AFZ97]S.Acharya,M.Franklin,andS.Zdonik.Balancingpushandpullfordata managementforasymmetriccommunicationsenvironments.inproceedings SanJose,California,May1995. oftheacmsigmodconferenceonmanagementofdata,pages199{210, [BI94] D.BarbaraandT.Imielinksi.Sleepersandworkaholics:Cachingstrategies ofdata,pages183{194,tuscon,arizona,may1997. broadcast.inproceedingsoftheacmsigmodconferenceonmanagement [EPW93]E.J.O'Neil,P.E.O'Neil,andG.Weikum.Thelru-kpagereplacementalgorithmfordatabasediskbuering.InProceedingsoftheACMSIGMOD formobileenvironments.inproceedingsoftheacmsigmodconference onmanagementofdata,pages1{12,minneapolis,minnesota,may1994. [HL98] [HLL98]Q.L.Hu,D.L.Lee,andW.-C.Lee.Hierarchicaldatacachinginasymmetric ConferenceonManagementofData,pages297{306,Washington,D.C,May Q.L.HuandD.L.Lee.Cachealgorithmsbasedonadaptiveinvalidation [IVB94]T.Imielinski,S.Viswanathan,andB.R.Badrinath.Energyeciencyindexingonair.InProceedingsoftheInternationalConferenceonSIGMOD, reportsformobileenvironments.clustercomputing,1(1):39{48,feb [JBEA97]J.Jing,O.Bukhres,A.K.Elmargarmid,andR.Alonso.Bit-sequences: pages25{36,1994. communicationenvironments.inunderpreparation,1998. Anewcacheinvalidationmethodinmobileenvironments.ACM/Baltzer [Sch92]H.Schwetman.Csimuser'sguide(version17).MCCCorporation,1992. [SRB97]K.Stathatos,N.Roussopoulos,andJ.S.Baras.Adaptivedatabroadcast [Knu81]D.Knuth.TheArtofComputerProgramming,volume3.Addison-Wesley, CA,1981. MobileNetworksandApplications,2(II),1997. [WYC96]K.-L.Wu,P.S.Yu,andM.-S.Chen.Energy-ecientcachingforwireless 326{335,Athens,Greece,August1997. inhybridnetworks.inproceedingsofthe23rdvldbconference,pages mobilecomputing.in12thinternationalconferenceondataengineering, pages336{345,feb.26-march11996.