Multi-criteria mapping and scheduling of workflow applications onto heterogeneous platforms [Elektronische Ressource] / von Veronika Rehn-Sonigo
166 Pages
English
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Multi-criteria mapping and scheduling of workflow applications onto heterogeneous platforms [Elektronische Ressource] / von Veronika Rehn-Sonigo

Downloading requires you to have access to the YouScribe library
Learn all about the services we offer
166 Pages
English

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dNead'orRdrYeA:A518VNontodeattribu?TpDevarTlaChristianbiblioth?TRqueMulti-criteria:o07ENSL0518Directeur-OBER?coleBENOITnormaledesup?rieureorteurdeHaraldLcommissionYONMem-orteurLabHaraldoratoirebredeTl'InformatiqueMemduaPaaralofl?lismewetPlatfo-th?seUniversites?Co-encadrant:PKOSCHassaiuUmit-YUREKFhelakult?tRappf?rRappInformatiktundform?eMathematikAnneTH?SEUmitenYUREKvueheld'obtenirRappleRappgrUERadevdeBDobrecteurSTdearl'Univeronikersit?REHN-SONIGOdeMappingLynonScheduling-W?colerkoNormaleApplicationsSupHeterogeneous?rrmsidee:uvrReTdetsLyth?seonAnnespHarald?cialit?Apr?s:vInformatiqueset:DoktorCAderALNaturwissenscRapphaftenMicauDtitrYD?eorteurdeKOSCHl'orteur?canollaed'examendodctor:aleBENOITdebreMath?matiquesCAetALInforRappmMicatiDqYD?uorteureKOSCHfondamentaleorteurprLENGA?sent?MemeYetessoutenueOpubliquementERleMem7DenisjuilYletRAM2009breptheDISSERDoT:ArTIONersit?tMulti-criteriaMicMapping-aInformatiquenBetreuerdRScheduling:ofKOSCHWersit?o?rrkoonwdesApplicationsDoktoronto:HeterogeneousuPlatfoLyrmsLyvonALbYD?ytheVdeeronikLyaNormaleREHN-SONIGOeeingerdeeicht?cialit?inzureinemadesgemeinsamenePrNaturwissenscomostionsverfahrAenKOSCH(Cotutelassle)vanTderCo-AsubmittedBENOITinReviewcCAotutelle-proRappcedureDtoorteurtheorteur-degree?colecteurnormalel'Univsup?rieurededeonL?coleYONSup-iLabuoratoireedeLyl'Informatiquespdu:PundaralErlangungl?lismeGrundandanddergreeanddertohaftent/hedvisors-HaraldUniversitUniv?PtaPYassaesuOBER-ENSFonakult?

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Published 01 January 2009
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dNead'orRdrYeA:A518VNontodeattribu?TpDevarTlaChristianbiblioth?TRqueMulti-criteria:o07ENSL0518Directeur-OBER?coleBENOITnormaledesup?rieureorteurdeHaraldLcommissionYONMem-orteurLabHaraldoratoirebredeTl'InformatiqueMemduaPaaralofl?lismewetPlatfo-th?seUniversites?Co-encadrant:PKOSCHassaiuUmit-YUREKFhelakult?tRappf?rRappInformatiktundform?eMathematikAnneTH?SEUmitenYUREKvueheld'obtenirRappleRappgrUERadevdeBDobrecteurSTdearl'Univeronikersit?REHN-SONIGOdeMappingLynonScheduling-W?colerkoNormaleApplicationsSupHeterogeneous?rrmsidee:uvrReTdetsLyth?seonAnnespHarald?cialit?Apr?s:vInformatiqueset:DoktorCAderALNaturwissenscRapphaftenMicauDtitrYD?eorteurdeKOSCHl'orteur?canollaed'examendodctor:aleBENOITdebreMath?matiquesCAetALInforRappmMicatiDqYD?uorteureKOSCHfondamentaleorteurprLENGA?sent?MemeYetessoutenueOpubliquementERleMem7DenisjuilYletRAM2009breptheDISSERDoT:ArTIONersit?tMulti-criteriaMicMapping-aInformatiquenBetreuerdRScheduling:ofKOSCHWersit?o?rrkoonwdesApplicationsDoktoronto:HeterogeneousuPlatfoLyrmsLyvonALbYD?ytheVdeeronikLyaNormaleREHN-SONIGOeeingerdeeicht?cialit?inzureinemadesgemeinsamenePrNaturwissenscomostionsverfahrAenKOSCH(Cotutelassle)vanTderCo-AsubmittedBENOITinReviewcCAotutelle-proRappcedureDtoorteurtheorteur-degree?colecteurnormalel'Univsup?rieurededeonL?coleYONSup-iLabuoratoireedeLyl'Informatiquespdu:PundaralErlangungl?lismeGrundandanddergreeanddertohaftent/hedvisors-HaraldUniversitUniv?PtaPYassaesuOBER-ENSFonakult?tdvisorf?rAnneInformatikENSundonMathematikerszurUmitErlangungTdesYUREKGrorteuradeshelinApartialRappfulllmenHaraldtRappofobtainingth?seiiiinoubliableRemerciementscetteJerytiensctoutt,d'ab.ordde?vremee,rcipeid?esrmemmesLengauerrappd'aorteursmonplibouron.leur!travv?t?ail.soutienMercicritiques?J'aiUmitvCatalyurek,duMic?helDenisDatrayd?oireterHaralddKdeoscph?poublierourencadranleur?oeteilsanscn'auraitrim?me.tiquevsurtoutmesostra-vvvaux.unMercivpautresourbrestousjuryvMerciosChristiancommenettairesTetspropmositionsvd'am?lioration.accept?Laparticipv?ersionjuryaectuetes'?trel?r?sleourdenireLymaSansthmes?sehersatsgagMercinAnn?,Haraldgr?ceY?es,vlesquelsous,th?seenpaspr?cisionlaetMercilisibilit?,ouretotreje?vmomenousvenremarquessuietsosreconnaissaninnote.atrices.Depass?m?mtempseaj'aimeraisecremercierous.lesiv.Contents.In.troOptimizationduction.i.I.Replica.P.laceme.n.t.in.T.ree.Net.w.orks.12.2.31forProblem.Denition.3.1.1.F.ramew30ork......i...............er.....................ts...............................2.2.1..4.1.1.1.Denitions.andMultipleNotations........teger.......Problem.....2.3.1.........2.3.2.........30.........30..4.1.1.2.Problem.Instances..tal...................3.1.........3.1.1.......orm...............Serv......5.1.2.A.ccess.P.olicies26.ers.................Mixed.Lo...........2.3.Replica.................................ds....7.1.2.1.Impact.of.the.A.ccess.PMultipleolicy.on.the.Existence.of.a.Solution......Exp............7.1.2.2.Upwar.ds.vExpersus.Closest..............2.4.2...................3.ems.y..................8Counting1.2.3QoSMultiple.v.ersus.Upwar.ds..35.ulation...........................26.Single.er....................9.1.2.4.Lo.w.er.Bound.for.the2.2.2ReplicaServCounting.Problem...........................9.1.327RelatedAWInorkLP-Based.w.Bound...................28.Heuristics.the.Cost.......................29.Closest....................10.2.Replica.Placemen.t.Strategies.13.2.1.Complexit29yUpwarResults......................................2.3.3................................13.2.1.1.With.Homogeneous2.4Noerimendes.and.the.Closest.Strategy.........................14.2.1.2.With.Homogeneous2.4.1NoerimendesPlanand.the.Multiple.Strategy........................3114Results2.1.3.With.Homogeneous.No.des.and.the.Upwar.ds.Strategy..................32.Multi-Criteria.Probl.3523Complexit2.1.4ResultsWith.Heterogeneous.No.des............................35.Replica.w.th..................24.2.2.Linear.ProgrammingvF.vi.CONTENTS.3.1.2.Replica.Cost.witStudyh.QoS.and.Band.width..........for.........Bi-criteria..........37.3.276LinearMinimizingProgrammingtalF.orm.ulation.f.oFr.Replica.Pla.cement.with.QoS........Latency..44683.2.1.Extension68of.the.Mono-Criteri.a.Linear.Program....erio...........erimen...77...91......45.3.2.2.An.Exact.MIP-Based5.1.2Solu.tion.for.Multiple..5.1.3...................P.........F..45.3.3.HeuristicsFfor.the.Replica.Pla.cementofProblem.with.QoS.Constrain.ts..........for47.3.3.1.Closest..for.....77.........77.........ts.der...I.duction...........65.Probabilit........................48.3.3.2.Upwar.ds......erio...................67.................68.d.................Latency.Probabilit........48.3.3.3.MultipleLinear.ulation...............6.6.1.Enco...............6.2.....................Minimizing.Fixed...........7648erio3.4FixedExp.erimen.tal.Plan....Exp...................General.................Exp.Sim.JPEG.......I.Streaming.In............49.I.I.Pip.eline.W.orko5.1.1wailureApplicationsy55.4.Problem.Denition.57.4.1.F.ramew.ork..............65.Latency.....................................66.P.d....................58.4.1.1.Applicativ.e.F.ramew.ork......5.2.Optimization.................................5.2.1.erio.and............58.4.1.2.T.arget.Platform............5.2.2.and.ailure.y.........................5.3.Program.orm................59.4.1.3.Mapping.Problem........72.Case.75.Principles.JPEG.ding.............................76.Heuristics............60.4.2.Motiv.ating.Exa.mp.les................6.2.1.Latency.a.P.d.....................6.2.2.P.d.a.Latency..............62.4.3.Related.W6.3orkerimen.Results...................................6.3.1.Exp.ts...............................6.3.2.erimen63and5ulationsComplexittheyencoResults.65.5.1.Mono-criteria.Problems....87.I.Complex.Applications.7.tro.93.StreamingCONTENTS.vii.8.In-Net130w9.3.4ork.Stream.Pro.cessing.95.8.1.Mo.dels....eline.....Remarks.9.3.2.........118...................t.......Publications.....9.3.3...................120........95P8.1.1.Application.Mo10.1.1del................Extensi...W...t.......Algorithms.......................F......95.8.1.2.Platform.Mo.del....tal.........tal...................127...........t.......127.Applications..........96.8.1.3erspMapping.Mo.del.a.n.dforConstrain.ts..o.......n.ti...............ograph.13...ariables............97.8.2.Complexit.yts.................1.jectiv.................9.4.....................Exp..................98Exp8.2.1.Linear.Programming.F.orm.ulation......9.5.2...................Conclusion.ectiv.Conclusion................99.8.3.Heuristicslica.................Pi.orko.............10.1.3.............10.2.es...................129.n.Replica......102.8.410.2.2SimsulationwResults..........Extensi.for.Appli.n.......10.3...................131.B.5.D.....114.V............104.9.Multiple.Concurren.t.Applications.109.9.1.F.ramew.ork....115.Constrain.....................................15.Ob.e.unction............................109.9.1.1117ApplicationHeuristicsMo.del......................................9.5.erimen.Results....................109.9.1.2.Platform.Mo.del......9.5.1.erimen.Plan...............................121.Results......................111.9.1.3.Mapping.Mo.del.a.n.d121Constrainandtsersp.es.10.1.........................................127111Rep9.1.4PlacemenOptimization.Problems..............................10.1.2.p.W.w....................113.9.2.Complexit127yComplex.Applications.......................128.P.ectiv.........................................10.2.1.o.s.the113Placemen9.3ProblemLinear.Programming.F.orm.ulation......129.Extensi.n.for.orko.Applications.....................10.2.3.o.s.Concurren.Streaming.ca.o.s..114.9.3.1.Input.Data....131.Final.......................................A.1.Bibli.y.C.11.Notations..CONTENTSviii