icdcsw 2017: program at a glance · joao e. ferreira, university of sao paulo, brazil ... nader...

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ICDCSW 2017

IEEE 37th International Conference on Distributed Computing Systems Workshops

June 5, 2017 Atlanta, USA

Editors

Dr. Aibek Musaev University of Alabama, USA

Dr. Joao E. Ferreira University of Sao Paulo, Brazil

Dr. Teruo Higashino University of Osaka, Japan

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Table of Contents

Workshops Hotel Floor Plan ................................................................................................... 4

Message from the ICDCS 2017 Workshops Chairs ................................................................ 5

Organizing Committee ............................................................................................................ 6

Program at a Glance ............................................................................................................... 7

Schedule: Monday, June 5, 2017 ............................................................................................ 8

ADSN 2017 Workshop Abstracts .......................................................................................... 15

BGP 2017 Workshop Abstracts ............................................................................................ 16

CCN-CPS 2017 Workshop Abstracts .................................................................................... 18

HotPOST 2017 Workshop Abstracts .................................................................................... 21

IoTCA 2017 Workshop Abstracts ......................................................................................... 23

JCC 2017 Workshop Abstracts ............................................................................................. 25

PED 2017 Workshop Abstracts ............................................................................................. 28

PSBD 2017 Workshop Abstracts .......................................................................................... 29

WoSC 2017 Workshop Abstracts .......................................................................................... 30

NSF-JST 2017 Workshop Abstracts ..................................................................................... 31

Local Information ................................................................................................................. 32

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Workshops Hotel Floor Plan

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Message from the ICDCS 2017 Workshops Chairs

Itisgreathonorforustowelcometothe10workshopstobeheldalongwiththe37thIEEEInternationalConferenceonDistributedComputingSystems,ICDCS2017inAtlanta,GA,USAonJune5–8,2017.

TheICDCS2017editionincludesthefollowinginternationalworkshops:the16thInternationalWorkshoponAssuranceinDistributedSystemsandNetworks,ADSN2017,the9thInternationalWorkshoponHotTopicsinPlanet-ScaleMobileComputingandOnlineSocialNetworking,HotPOST’17,the8thInternationalWorkshoponJointCloudComputing,JCC2017,the2ndInternationalWorkshoponCommunication,Computing,andNetworkinginCyberPhysicalSystems,CCN-CPS2017,theInternationalWorkshoponPrivacyandSecurityinBigDataEcoSystem,PSBD2017,the1stInternationalWorkshoponIntegratingProcess-oriented,Event-basedandData-drivenSystems,ICDCS-PED2017,theInternationalWorkshopontheInternetofThingsComputingandApplications,IoTCA2017,the1stInternationalWorkshoponServerlessComputing,WoSC2017,theInternationalWorkshoponBigGraphProcessing,BGP2017andthe1stUS-JapanWorkshoponCollaborativeGlobalResearchonApplyingInformationTechnology.Wewouldliketothankalloftheworkshoporganizerswhoproposedandheldworkshops.Withouttheirefforts,wewouldnothavebeenabletoprovidehigh-qualityworkshops.

WewouldalsoliketothanktotheICDCS2017organizers,especiallyforGeneralCo-ChairsCaltonPu,MasaruKitsuregawa,KarlAbererandtheProgramChairLingLiuwhokindlysupportedoureffortsforsuccessofthetenworkshops.

Finally,wehopethatICDCS2017Workshopsprovideastimulatingforumfordevelopingnewideasinemergingfieldscoveredbythe10workshops.

WorkshopsChairs

JoaoE.Ferreira,UniversityofSaoPaulo,Brazil

TeruoHigashino,UniversityofOsaka,Japan

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Organizing Committee

Workshops Program Chairs Joao E. Ferreira, University of Sao Paulo, Brazil Teruo Higashino, University of Osaka, Japan

Workshops Publication Chair

Aibek Musaev, University of Alabama, USA Finance Manager (non-volunteer, reporting to ICDCS General Chairs)

Carrie Stein, Ohio State University, USA

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Program at a Glance

Monday,June5,2017

Time Track1(SalonII)

Track2(SalonIV)

Track3(SalonVI)

Track4(Atlanta)

Track5(Columbia)

Track6(Savannah)

7:00-8:00 ContinentalBreakfast(Foyer)8:00-9:30 Workshop:

CCN-CPS Workshop:HotPOST

Workshop:PSBD

Workshop:JCC

Workshop:NSF-JSTDay1

9:30-10:00 CoffeeBreak(Foyer)10:00-12:00 Workshop:

CCN-CPSWorkshop:

ADSNWorkshop:HotPOST

Workshop:PSBD

Workshop:JCC


12:00-13:30 Lunch(PhoenixBallroom)13:30-15:30 Workshop:

CCN-CPSWorkshop:

ADSNWorkshop:PED-BGP

Workshop:IoTCA

Workshop:WoSC


15:30-16:00 CoffeeBreak(Foyer)

16:00-17:00Workshop:CCN-CPS

Workshop:ADSN

Workshop:PED-BGP

Workshop:IoTCA

Workshop:WoSC


17:00-18:00 Workshop:

PED-BGP

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Schedule: Monday, June 5, 2017

TenWorkshopsandtwotutorials(seeProgramataglanceandworkshopswebsiteandICDCS2017websitefordetails)

7:00-8:00 Monday, June 5, 2017 ContinentalBreakfastLocation:Foyer

8:00-9:30 Monday, June 5, 2017 Workshop:CCN-CPS,Session1Location:SalonII

SessionChair:NaderMohamed(MiddlewareTechnologiesLab.)

PoliciesGuidingCohesiveInteractionsamongInternetofThingswithCommunicationCloudandSocialNetworks HenryHexmoor(SouthernIllinoisUniversity)

EnhancedSecurityofBuildingAutomationSystemsThroughMicrokernel-BasedControllerPlatforms XiaolongWang(UniversityofSouthFlorida),RichardHabeeb(UniversityofSouthFlorida),XinmingOu(UniversityofSouthFlorida),SiddharthAmaravadi(KansasStateUniversity),JohnHatcliff(KansasStateUniversity),MasaakiMizuno(KansasStateUniversity),MitchellLNeilsen(KansasStateUniversity),RajRajagopalan(Honeywell),SrivatsanVaradarajan(HoneywellAerospaceAdvancedTechnologyLabs)

HighlevelDesignofaHomeAutonomousSystemBasedonCyberPhysicalSystemModeling BasmanAlhafidh(FloridaInstituteofTechnology),WilliamH.Allen(FloridaInstituteofTechnology)

Workshop:HotPOST,Session1Location:SalonVI

KeynoteSpeech:AMarkovGameTheoreticApproachforPowerGridSecurity CharlesA.Kamhoua(AirForceResearchLaboratory)

Router-basedBrokeringforSurrogateDiscoveryinEdgeComputing JulienGedeon(TechnischeUniversitätDarmstadt),ChristianMeurisch(TechnischeUniversitätDarmstadt),DishaBhat(TechnischeUniversitätDarmstadt),MichaelStein(TechnischeUniversitätDarmstadt),LinWang(TechnischeUniversitätDarmstadt),MaxMühlhäuser(TechnischeUniversitätDarmstadt)

ModelingtheSpreadofInfluenceforIndependentCascadeDiffusionProcessinSocialNetworks ZeshengChen(IndianaUniversity-PurdueUniversityFortWayne),KurtisTaylor(IndianaUniversity-PurdueUniversityFortWayne)

Workshop:PSBD,OpeningandInvitedTalksLocation:Atlanta

KeynoteSpeech:BigData-SecurityandPrivacy(andTransparency) ElisaBertino(PurdueUniversity)

InvitedTalk:SupportingTime-varyingPrivacywithSelf-emergingData BalajiPalanisamy(UniversityofPittsburgh)

Workshop:JCC,Session1Location:Columbia

HeterogeneousMalwareSpreadProcessinStarNetwork LiboJiao(TsinghuaUniversity),HaoYin(TsinghuaUniversity),DongchaoGuo(TsinghuaUniversity),YongqiangLyu(TsinghuaUniversity)

CostReductioninHybridCloudsforEnterpriseComputing BiyuZhou(InstituteofComputingTechnology,ChineseAcademyofSciences),FaZhang(InstituteofComputingTechnology,ChineseAcademyofSciences),JieWu(TempleUniversity),ZhiyongLiu(InstituteofComputingTechnology,ChineseAcademyofSciences)

DC-RSF:ADynamicandCustomizedReputationSystemFrameworkforJointCloudComputing

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FanghuaYe(SunYat-senUniversity),ZibinZheng(SunYat-senUniversity),ChuanChen(SunYat-senUniversity),YurenZhou(SunYat-senUniversity)

WebServiceApplianceBasedonUnikernel KaiYu(NationalLabforParallelandDistributedProcessing),ChengfeiZhang(NationalLabforParallelandDistributedProcessing),YunxiangZhao(NationalLabforParallelandDistributedProcessing)

AnalysisandEvaluationoftheGASModelforDistributedGraphComputation WangJinyan(NationalLabforParallelandDistributedProcessing),ZhangChengfei(NationalLabforParallelandDistributedProcessing)

TrafficSignsDetectionBasedonFasterR-CNN ZhongrongZuo(NationalLabforParallelandDistributedProcessing),KaiYu(NationalLabforParallelandDistributedProcessing),QiaoZhou(NationalLabforParallelandDistributedProcessing),XuWang(NationalLabforParallelandDistributedProcessing),TingLi(NationalLabforParallelandDistributedProcessing)

JCLedger:ABlockchainBasedDistributedLedgerforJointCloudComputing XiangFu(NationalUniversityofDefenseTechnology),HuaiminWang(NationalUniversityofDefenseTechnology),PeichangShi(NationalUniversityofDefenseTechnology),YingweiFu(NationalUniversityofDefenseTechnology),YijieWang(NationalUniversityofDefenseTechnology)

CorporationArchitectureforMultipleCloudServiceProvidersinJointCloudComputing PeichangShi(NationalUniversityofDefenseTechnology),HuaiminWang(NationalUniversityofDefenseTechnology),XikunYue(NationalUniversityofDefenseTechnology),ShilanYang(NationalUniversityofDefenseTechnology),ShangzhiYang(NationalUniversityofDefenseTechnology),YuxingPeng(NationalUniversityofDefenseTechnology)

SharingPrivacyDatainSemi-TrustworthyStoragethroughHierarchicalAccessControl YuzhaoWu(TsinghuaUniversity),YongqiangLyu(TsinghuaUniversity),QianFang(TsinghuaUniversity),GengZheng(TsinghuaUniversity),HaoYin(TsinghuaUniversity),YuanchunShi(TsinghuaUniversity)

Workshop:NSF-JSTLocation:Savannah

9:30-10:00 Monday, June 5, 2017 CoffeeBreakLocation:PhoenixBallroom


SessionChair:JameelaAlJaroodi(RobertMorrisUniversity)

ACyberPhysicalBuses-and-DronesMobileEdgeInfrastructureforLargeScaleDisasterEmergencyCommunications MamtaNarang(AucklandUniversityofTechnology),WilliamLiu(AucklandUniversityofTechnology),JairoAGutierrez(AucklandUniversityofTechnology),LucaChiaraviglio(UniversityofRomeTorVergata)

APerformanceComparisonofContainersandVirtualMachinesinWorkloadMigrationContext KumarGaurav(VMwareSoftwareIndiaPvtLtd),PavanKarkun(VMwareSoftwareIndiapvtLTD),Y.C.Tay(NationalUniversityofSingapore)

TowardsService-OrientedMiddlewareforCyberPhysicalSystems NaderMohamed(MiddlewareTechnologiesLab.),SanjaLazarova-Molnar(UniversityofSouthernDenmark)

NetworkingandCommunicationinCyberPhysicalSystems ImadJawhar(UAEUniversity),JameelaAl-Jaroodi(RobertMorrisUniversity)

Workshop:ADSN,Session1:Keynote,Session2:AssuringTemporalFairnessandSecuringCommunicationLocation:SalonIV

KeynoteSpeech:DependabilityChallengesin5GCellularNetworks DouglasM.Blough(GeorgiaInstituteofTechnology)

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UnderstandingandImprovingTemporalFairnessonanElectronicTradingVenue HaydenMelton(DeakinUniversity)

CertificateLessCryptography-basedRuleManagementProtocolforAdvancedMissionDeliveryNetworks JonghoWon(PurdueUniversity),AnkushSingla(PurdueUniversity),ElisaBertino(PurdueUniversity)

Workshop:HotPOST,Session2Location:SalonVI

ThankYouForBeingAFriend:AnAttackerViewonOnline-Social-Network-basedSybilDefenses DavidKoll(UniversityofGoettingen),MartinSchwarzmaier(UniversityofGoettingen),JunLi(UniversityofOregon),Xiang-YangLi(UniversityofScienceandTechnologyofChina),XiaomingFu(UniversityofGoettingen)

EfficientDynamicServiceFunctionChainCombinationofNetworkFunctionVirtualization WenkeYan(BeijingUniversityofPostsandTelecommunications),KonglinZhu(BeijingUniversityofPostsandTelecommunications),LinZhang(BeijingUniversityofPostsandTelecommunications),SixiSu(BeijingUniversityofPostsandTelecommunications)

WhenAugmentedRealitymeetsBigData CarlosBermejo(TheHongKongUniversityofScienceandTechnology),ZhanpengHuang(TheHongKongUniversityofScienceandTechnology),TristanBraud(TheHongKongUniversityofScienceandTechnology),PanHui(TheHongKongUniversityofScienceandTechnology)

SamplingBasedEfficientAlgorithmtoEstimatetheSpectralRadiusofLargeGraphs SamarAbbas(LahoreUniversityofManagementSciences),JuvariaTariq(LahoreUniversityofManagementSciences),ArifZaman(LahoreUniversityofManagementSciences),ImdadullahKhan(LahoreUniversityofManagementSciences)

ExtemporaneousMicro-MobileServiceExecutionWithoutCodeSharing ZhengSong(VirginiaTech),MinhLe(UtahStateUniversity),Young-WooKwon(UtahStateUniversity),EliTilevich(VirginiaTech)

PreventingColludingIdentityCloneAttacksinOnlineSocialNetworks GeorgesA.Kamhoua(FloridaInternationalUniversity),NikiPissinou(FloridaInternationalUniversity),S.S.Iyengar(FloridaInternationalUniversity),JonathanBeltran(FloridaInternationalUniversity),CharlesKamhoua(AirForceResearchLaboratory),BrandonLHernandez(UTRGV),LaurentNjilla(AirForceResearchLaboratory)

Workshop:PSBD,ResearchSessionLocation:Atlanta

Anovelgame-theoreticmodelforcontent-adaptiveimagesteganography QiLi(HunanUniversity),XinLiao(HunanUniversity),GuoyongChen(HunanUniversity),LipingDing(GuangzhouBranchofInstituteofSoftware,ChineseAcademyofScience)

AFine-grainedAccessControlSchemeforBigDataBasedonClassificationAttributes TengfeiYang(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),PeisongShen(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),XueTian(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),ChiChen(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences)

Social-AwareDecentralizationforEfficientandSecureMulti-PartyComputation YuzheTang(SyracuseUniversity),SuchetaSoundarajan(SyracuseUniversity)

StatisticalAnomalyDetectiononMetadataStreamsviaCommoditySoftwaretoProtectCompany ChristineChen(UniversityofPortland),JamesGurganus(MicroSystemsEngineering,Inc.)

Computationalimprovementsinparallelizedk-anonymousmicroaggregationoflargedatabases AhmadMohamadMezher(UniversitatPolitècnicadeCatalunya(UPC)),AlejandroGarcíaÁlvarez(UniversitatPolitècnicadeCatalunya(UPC)),DavidRebollo-Monedero(UniversitatPolitècnicadeCatalunya(UPC)),JordiForné(UniversitatPolitècnicadeCatalunya(UPC))

Workshop:JCC,Session2Location:Columbia

AReliabilityBenchmarkforBigDataSystemsonJointCloudYingyingZheng(InstituteofSoftware,ChineseAcademyofSciences),LijieXu(InstituteofSoftware,ChineseAcademyofSciences),WeiWang(InstituteofSoftware,ChineseAcademyofSciences),WeiZhou(KSYUN),YingDing(ChangchunUniversityofScienceandTechnology)

UCPR:UserClassificationandInfluenceAnalysisinSocialNetwork CongZha(TsinghuaUniversity),YongqiangLv(TsinghuaUniversity)

AdaptiveRoutingAlgorithmforJointCloudVideoDelivery

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ZexunJiang(TsinghuaUniversity),HaoYin(TsinghuaUniversity)

TowardsEfficientResourceManagementinVirtualClouds BoAn(PekingUniversity),JunmingMa(PekingUniversity),DonggangCao(PekingUniversity),GangHuang(PekingUniversity)

MonitoringandBillingofALightweightCloudSystemBasedonLinuxContainer YujianZhu(PekingUniversity),JunmingMa(PekingUniversity),BoAn(PekingUniversity),DonggangCao(PekingUniversity)

Buildingemulationframeworkfornon-volatilememory GuoliangZhu(NationalUniversityofDefenseTechnology),KaiLu(NationalUniversityofDefenseTechnology),XiaopingWang(NationalUniversityofDefenseTechnology)

Seflow:EfficientFlowSchedulingforData-ParallelJobs QiaoZhou(NationalLabforParallelandDistributedProcessing),ZiyangLi(NationalLabforParallelandDistributedProcessing),PingZhong(CentralSouthUniversity),TianTian(NationalLabforParallelandDistributedProcessing),YuxingPeng(NationalLabforParallelandDistributedProcessing)

OnlineEncodingforErasure-CodedDistributedStorageSystems FangliangXu(NationalUniversityofDefenseTechnology),YijieWang(NationalUniversityofDefenseTechnology),XingkongMa(NationalUniversityofDefenseTechnology)

Workshop:NSF-JST,Session1Location:Savannah

AcceleratingBigDataInfrastructureandApplications KevinBrown(TokyoInstituteofTechnology),TianqiXu(TokyoInstituteofTechnology),KeitaIwabuchi(TokyoInstituteofTechnology),KentoSato(LawrenceLivermoreNationalLaboratory),AdamMoody(LawrenceLivermoreNationalLaboratory),KathrynMohror(LawrenceLivermoreNationalLaboratory),NikhilJain(LawrenceLivermoreNationalLaboratory),AbhinavBhatele(LawrenceLivermoreNationalLaboratory),MartinSchulz(LawrenceLivermoreNationalLaboratory),RogerPearce(LawrenceLivermoreNationalLaboratory),MayaGokhale(LawrenceLivermoreNationalLaboratory),SatoshiMatsuoka(TokyoInstituteofTechnology)

DisasterNetworkEvolutionUsingDynamicClusteringofTwitterData KrishnaKant(TempleUniversity),YilangWu(AizuUniversity),ShanshanZhang(TempleUniversity),JunboWang(AizuUniversity),AmitangshuPal(TempleUniversity)

Single-epochsupernovaclassificationwithdeepconvolutionalneuralnetworks AkisatoKimura(NTT),IchiroTakahashi(KavliIPMU,TheUniversityofTokyo),MasaomiTanaka(NationalAstronomicalObservatoryofJapan),NaokiYasuda(KavliIPMU,TheUniversityofTokyo),NaonoriUeda(NTT),NaokiYoshida(KavliIPMU,TheUniversityofTokyo)

EnablingLargeScaleDeliberationusingIdeationandNegotiation-SupportAgents KatsuhideFujita(TokyoUniversityofAgricultureandTechnology),TakayukiIto(NagoyaInstituteofTechnology),MarkKlein(MIT)

12:00-13:30 Monday, June 5, 2017 LunchLocation:Foyer


SessionChair:UttamGhosh(TennesseeStateUniversity)

OptimalDeploymentofChargingStationsforElectricVehicles:AFormalApproach AmarjitDatta(TennesseeTechnologicalUniversity),BrianLedbetter(TennesseeTechnologicalUniversity),MohammadAshiqurRahman(TennesseeTechnologicalUniversity)

FormalVerificationofControlStrategiesforaCyberPhysicalSystemAmjadGawanmeh(KhalifaUniversityofScienceandTechnology),AliAlwadi(AucklandUniversityofTechnology),SaziaParvin(UniversityofNewSouthWales)

LightweightDetectionandIsolationofBlackHoleAttacksinConnectedVehicles SamiAlbouq(OaklandUniversity),ErikFredericks(OaklandUniversity)

AnewthreatassessmentmethodforintegratinganIoTinfrastructureinaninformationsystem

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BrunoDorsemaine(OrangeLabs),Jean-PhilippeGaulier(OrangeLabs),Jean-PhilippeWary(OrangeLabs),NizarKheir(Thales),PascalUrien(TelecomParisTech)

Workshop:ADSN,Session3:NetworkAssuranceLocation:SalonIV

FaultySensorDataDetectioninWirelessSensorNetworksUsingLogisticalRegressionTianyuZhang(UniversityofHyogo),QianZhao(UniversityofHyogo),YukikazuNakamoto(UniversityofHyogo)

AnAdaptability-EnhancedRoutingMethodforMultipleGateway-basedWirelessSensorNetworksUsingSecureDispersedDataTransfer RyumaTani(HiroshimaCityUniversity),KentoAoi(HiroshimaCityUniversity),EitaroKohno(HiroshimaCityUniversity),YoshiakiKakuda(HiroshimaCityUniversity)

ProgressiveDownloadMethodBasedonTimer-DrivenRequestingSchemesUsingMultipleTCPFlowsonMultiplePaths HiroakiHoriba(HiroshimaCityUniversity),TokumasaHiraoka(HiroshimaCityUniversity),JunichiFunasaka(HiroshimaCityUniversity)

Workshop:PED-BGP,Session1Location:SalonVI

Keynotespeech:Application-awaredatadissemination BettinaKemme(McGillUniversity)

WED-SQL:ARelationalFrameworkforDesignandImplementationofProcess-AwareInformationSystems BrunoPadilha(UniversityofSaoPaulo),AndréLuisSchwerz(FederalUniversityofTechnology),RafaelLiberatoRoberto(FederalUniversityofTechnology)

QueryingWorkflowLogsYanTang(UniversityofCaliforniaatSantaBarbara),JianwenSu(UniversityofCaliforniaatSantaBarbara)

Ontheintegrationofevent-basedandtransaction-basedarchitecturesforSupplyChains ZhijieLi(IndianaUniversity–PurdueUniversityIndianapolis),HaoyanWu(IndianaUniversity–PurdueUniversityIndianapolis),BrianKing(IndianaUniversity–PurdueUniversityIndianapolis),ZinaBen-Miled(IndianaUniversity–PurdueUniversityIndianapolis),JohnWassick(TheDowChemicalCompany),JeffreyTazelaar(TheDowChemicalCompany)

Workshop:IoTCA,Session1Location:Atlanta

KeynoteSpeech:TheInternetofThings,People,andSystems:FromtheEdgetotheCloud SchahramDustdar(TUWien)

TowardsPrivacy-AwareSmartBuildings:Capturing,Communicating,andEnforcingPrivacyPoliciesandPreferences PrimalPappachan(UniversityofCaliforniaIrvine),MartinDegelingy(CarnegieMellonUniversity),RobertoYus(UniversityofCaliforniaIrvine),AnupamDasy(CarnegieMellonUniversity),SrutiBhagavatulay(CarnegieMellonUniversity),WilliamMelichery(CarnegieMellonUniversity),PardisEmamiNaeiniy(CarnegieMellonUniversity),ShikunZhangy(CarnegieMellonUniversity),LujoBauery(CarnegieMellonUniversity),AlfredKobsa(UniversityofCaliforniaIrvine),SharadMehrotra(UniversityofCaliforniaIrvine),NormanSadeh(CarnegieMellonUniversity),NaliniVenkatasubramanian(UniversityofCaliforniaIrvine)

DeployingData-DrivenSecuritySolutionsonResource-ConstrainedWearableIoTSystem HangCai(WorcesterPolytechnicInstitute),TianlongYun(WorcesterPolytechnicInstitute),JosiahHester(DartmouthCollege),KrishnaK.Venkatasubramanian(ClemsonUniversity)

AMotifbasedIoTFrameworkforDataEfficiency AkashSahoo(TexasA&MUniversity),RabiMahapatra(TexasA&MUniversity)

Workshop:WoSC,Session1Location:Columbia

KeynoteSpeech:ServerlessComputing:PatternsandRoadAhead RogerBarga(AmazonWebServices)

Ripple:HomeAutomationforResearchDataManagement RyanChard(ArgonneNationalLaboratory),KyleChard(ComputationInstitute,UniversityofChicagoandArgonneNationalLab),JasonAlt(NationalCenterforSupercomputingApplications),DilworthParkinson(LawrenceBerkeleyNationalLaboratory),SteveTuecke(ComputationInstitute,UniversityofChicagoandArgonneNationalLab),IanFoster(ArgonneNationalLaboratory&TheUniversityofChicago)

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Pipsqueak:LeanLambdaswithLargeLibraries EdwardOakes(UniversityofWisconsin-Madison),LeonYang(UniversityofWisconsin-Madison),KevinHouck(UniversityofWisconsin-Madison),TylerHarter(MicrosoftGraySystemsLab),AndreaC.Arpaci-Dusseau(UniversityofWisconsin-Madison),RemziH.Arpaci-Dusseau(UniversityofWisconsin-Madison)

LeveragingtheServerlessArchitectureforSecuringLinuxContainersNiltonBila(IBM),PaoloDettori(IBM),AliKanso(IBM),YujiWatanabe(IBM),AlaaYoussef(IBM)


15:30-16:00 Monday, June 5, 2017 CoffeeBreakLocation:Foyer


SessionChair:BrunoDorsemaine(OrangeLabs)

ASecurityFrameworkforSDN-enabledSmartPowerGrids UttamGhosh(TennesseeStateUniversity),PushpitaChatterjee(SRMRESEARCHINSTITUTE),SachinShetty(OldDominionUniversity)

Real-timeMonitoringSteamGeneratorsusingaHybridImagingSystem MahmoudMeribout(PetroleumInstitute),ImranSaied(PetroleumInstitute),EsraAlHosani(AdcoGroup)

SecuringbigDataEfficientlythroughMicroaggregationTechniqueandHuffmanCompression ShakilaMahjabinTonni(BangladeshArmyInternationalUniversityofScienceandTechnology),MohammadZahidurRahman(JahangirnagarUniversity),SaziaParvin(UniversityofNewSouthWales),AmjadGawanmeh(KhalifaUniversityofScienceandTechnology)

ModelBasedEnergyConsumptionAnalysisofWirelessCyberPhysicalSystems JingLiu(PekingUniversity),PingWang(PekingUniversity),JinlongLin(PekingUniversity),Chao-HsienChu(PennsylvanniaStateUniversity)

Workshop:ADSN,Session4:Panelon“AssuranceinInternetofThings(IoT)”Location:SalonIV

Moderator:EitaroKohno

Workshop:PED-BGP,Session2Location:SalonVI

CacheDOCS:ADynamicKey-ValueObjectCachingService JulienGascon-Samson(UniversityofBritishColumbia),MichaelCoppinger(McGillUniversity),FanJin(McGillUniversity),JörgKienzle(McGillUniversity),BettinaKemme(McGillUniversity)

WolfPath:Acceleratingiterativetraversing-basedgraphprocessingalgorithmsonGPU HuanzhouZhu(UniversityofWarwick),LigangHe(UniversityofWarwick)

ANovelAuction-basedQueryPricingSchema XingwangWang(JilinUniversity),XiaohuiWei(JilinUniversity),ShangGao(JilinUniversity),YuanyuanLiu(JilinUniversity),ZongpengLi(UniversityofCalgary)

BlockGraphChi:EnablingBlockUpdateinOut-of-coreGraphProcessing ZhiyuanShao(HuazhongUniversityofScienceandTechnology),ZhenjieMei(HuazhongUniversityofScienceandTechnology),XiaofengDing(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology)

Workshop:IoTCA,Session2Location:Atlanta

CoTWare:ACloudofThingsMiddleware JameelaAl-Jaroodi(RobertMorrisUniversity),NaderMohamed(MiddlewareTechnologiesLab.),ImadJawhar(MidcompResearchCenter)

SecuringtheInternetofThings:AMeta-StudyofChallenges,Approaches,andOpenProblems

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MahmudHossain(UniversityofAlabamaatBirmingham),RagibHasan(UniversityofAlabamaatBirmingham),AnthonySkjellum(AuburnUniversity)

InternetofThingsFrameworkforSmartLearningAnalyticsAliYavari(SwinburneUniversityofTechnology),RezaSoltanpoor(RMITUniversity)

Workshop:WoSC,Session2Location:Columbia

ServerlessComputing:Design,Implementation,andPerformance GarrettMcGrath(UniversityofNotreDame),PaulR.Brenner(UniversityofNotreDame)

PaneldebateonthenoveltyandchallengesofserverlesscomputingParticipants:TBA


17:00-18:00 Monday, June 5, 2017 Workshop:PED-BGP,Session2Location:SalonVI

IncrementalParallelComputingusingTransactionalModelinLarge-scaleDynamicGraphStructures AnandTripathi(UniversityofMinnesota),RahulR.Sharma(UniversityofMinnesota),ManuKhandelwal(UniversityofMinnesota),TanmayMehta(UniversityofMinnesota),VarunPandey(UniversityofMinnesota)

AgainstSigned-GraphDeanonymizationAttacks:PrivacyProtectionforSocialNetworks JianliangGao(CentralSouthUniversity),YuLiu(CentralSouthUniversity),PingZhong(CentralSouthUniversity),JianxinWang(CentralSouthUniversity)

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ADSN 2017 Workshop Abstracts

UnderstandingandImprovingTemporalFairnessonanElectronicTradingVenueHaydenMelton(DeakinUniversity)

Fairness,ingeneral,isatopicthathasreceivedmuchattentioninresearchondistributedsystems.Intheirapplicationaselectronictradingvenues,however,temporalfairnessremainsatopicthatispoorlyunderstood.Thisisconcerningbecauseoperatorsofthesevenuesgenerallyhaveobligationstoensuretheirfairness.Consequently,thispaper(1)describeswhattemporalfairnessisandisnot,(2)identifiesthingsthatcanmakeitelusive,and(3)describesamechanismforimprovingitthatwasrecentlyretrofittedtoamajorFXtradingvenue:ThomsonReutersMatching.

CertificateLessCryptography-basedRuleManagementProtocolforAdvancedMissionDeliveryNetworksJonghoWon(PurdueUniversity),AnkushSingla(PurdueUniversity),ElisaBertino(PurdueUniversity)

AssuredMissionDeliveryNetwork(AMDN)isacollaborativenetworktosupportdata-intensivescientificcollaborationsinamulti-cloudenvironment.Eachscientificcollaborationgroup,calledamission,specifiesasetofrulestohandlecomputingandnetworkresources.SecurityisanintegralpartoftheAMDNdesignsincetherulesmustbesetbyauthorizedusersandthedatageneratedbyeachmissionmaybeprivacy-sensitive.Inthispaper,weproposeaCertificateLesscryptography-basedRule-managementProtocol(CL-RP)forAMDN,whichsupportsauthenticatedruleregistrationsandupdateswithnon-repudiation.WeevaluateCL-RPthroughtest-bedexperimentsandcompareitwithotherstandardprotocols.

FaultySensorDataDetectioninWirelessSensorNetworksUsingLogisticalRegressionTianyuZhang(UniversityofHyogo),QianZhao(UniversityofHyogo),YukikazuNakamoto(UniversityofHyogo)

Wirelesssensornetworks(WSNs)arecommonlyusedtomonitorchangesinanenvironmentandpreventdisasterssuchasstructuralinstability,forestfires,andtsunamis.WSNsshouldrapidlyrespondtochangesandmustprocessandanalyzesensordatainadistributedwaytominimizebatteryconsumption.Ontheotherhand,machinelearning(ML)algorithmsisapowerfultoolfordataanalyzing.However,MLalgorithmsaresocomplexthatcannotbeexecutedonresourceconstrainedsensornodes.AnotherchallengeofusingMLalgorithmsinWSNsisthatMLalgorithmsaredifficulttobedistributedoneverysensornode.BecauseMLalgorithmsarebasedonstatistics'methodsthatneedcollectingamountofdatatoapproachaccuracy.Inthispaper,weproposeamethodthatdividesalogisticalregressionMLmethodintotwosteps,thendistributesthetwostepsontosinknodeandsensornodestodetectfaultysensordata.

AnAdaptability-EnhancedRoutingMethodforMultipleGateway-basedWirelessSensorNetworksUsingSecureDispersedDataTransferRyumaTani(HiroshimaCityUniversity),KentoAoi(HiroshimaCityUniversity),EitaroKohno(HiroshimaCityUniversity),YoshiakiKakuda(HiroshimaCityUniversity)

Inconventionalwirelesssensornetworks(hereinafterreferredtoasWSNs),thesinglesinknodemodelhasbeenemployedtocollectandstorethemeasureddatatoprovidetotheexternalusersofWSNs.However,thesinglesinkmodelofWSNscanbethesinglepointoffailureforsomeusage.Tocounterthisproblem,wecanemploymultiplegateway-basedWSNs.Inaddition,WSNsaresusceptibletovariouskindsofattackssuchaseavesdropping.Tocountereavesdropping,wealreadyhaveproposedthesecretsharingscheme-basedsecuredisperseddatatransfermethod(hereinafterreferredtoasthesecuredisperseddatatransfermethod).Whilewehadconfirmedthatthesecuredisperseddatatransfermethodiseffectivetocountereavesdroppingthroughtheuseofradioareadisjointmultiplepaths,wealsofoundthatthesecuredisperseddatatransfermethodcannotbeeffectiveinsevereenvironmentssuchasinanetworkwithalowdensityofnodes.

AProgressiveDownloadMethodBasedonTimer-DrivenRequestingSchemesUsingMultipleTCPFlowsonMultiplePathsHiroakiHoriba(HiroshimaCityUniversity),TokumasaHiraoka(HiroshimaCityUniversity),JunichiFunasaka(HiroshimaCityUniversity)

Duetothewidespreaduseofbroadbandcommunicationmedia,theconventionalTCPcannotfullyutilizesuchbroadbandwidth,somanyimprovementsonTCPitselfandalotofacceleratingmethodswhichusemultipleTCPflowshavebeenproposed.Inaddition,videohostingservicesontheInternetasanewmediumhavebecomepopular,andprogressivedownloadingmethods,whichdownloadsegmentedvideodatawhilereplayingthem,areadoptedonvarioussites.TheplaybackqualityofprogressivedownloadmethodshasbeenimprovedbytheexistingmethodwhichestablishesmultipleTCPflowsoneachofmultiplepaths.However,theexistingmethodassumesthatbandwidth,delay,andpacketlossrateofeachpathareknown.Therefore,inthispaper,amethodusingthetimer-drivenrequestingschemewhichistobeeffectiveevenwhenbandwidth,delay,andpacketlossratearenotgiven.Moreover,itfeaturesduplicaterequestingschemetocopewithqualitydeteriorationinvideoplaybackduetoout-of-orderblockarrivalswhenapplyingprogressivedownloadusingmultiplepaths.Thispaperevaluatestheproposedmethodcomparingwiththeexistingmethodbysimulation.Asaresult,itisfoundthattheproposedmethodyieldshighperformanceenoughtokeepthevideoqualityhigherthantheexistingmethodeventhoughthenetworkconditionisnotclarifiedinadvance.Theproposalcanberegardedasanassurancenetworktechnologysinceitcanadapttothecurrentnetworkstatusandkeeptheplaybackratehigh.

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BGP 2017 Workshop Abstracts

WolfPath:Acceleratingiterativetraversing-basedgraphprocessingalgorithmsonGPUHuanzhouZhu(UniversityofWarwick),LigangHe(UniversityofWarwick)

Thereisthesignificantinterestnowadaysindevelopingtheframeworksofparallelizingtheprocessingforthelargegraphssuchassocialnetworks,Webgraphs,etc.Mostparallelgraphprocessingframeworksemployiterativeprocessingmodel.However,bybenchmarkingthestate-of-artGPU-basedgraphprocessingframeworks,weobservedthattheperformanceofiterativetraversing-basedgraphalgorithms(suchasBreadFirstSearch,SingleSourceShortestPathandsoon)onGPUislimitedbythefrequentdataexchangebetweenhostandGPU.Inordertotackletheproblem,wedevelopaGPU-basedgraphframeworkcalledWolfPathtoacceleratetheprocessingofiterativetraversing-basedgraphprocessingalgorithms.InWolfPath,theiterativeprocessisguidedbythegraphdiametertoeliminatethefrequentdataexchangebetweenhostandGPU.Toaccomplishthisgoal,WolfPathproposesadatastructurecalledLayeredEdgelisttorepresentthegraph,fromwhichthegraphdiameterisknownbeforethestartofgraphprocessing.InordertoenhancetheapplicabilityofourWolfPathframework,agraphpreprocessingalgorithmisalsodevelopedinthisworktoconvertanygraphintotheformatoftheLayeredEdgelist.WeconductedextensiveexperimentstoverifytheeffectivenessofWolfPath.TheexperimentalresultsshowthatWolfPathachievessignificantspeedupoverthestate-of-artGPU-basedin-memoryandout-of-memorygraphprocessingframeworks.

ANovelAuction-basedQueryPricingSchemaXingwangWang(JilinUniversity),XiaohuiWei(JilinUniversity),ShangGao(JilinUniversity),YuanyuanLiu(JilinUniversity),ZongpengLi(UniversityofCalgary)

Asacommonprocessingmethod,queryiswidelyusedinmanyareas,suchasgraphprocessing,machinelearning,statistics,etc.However,queriesareusuallypricedaccordingtovendor-specifiedfixedviews(API)ornumberoftransactions,whichignorestheheterogeneityofqueries(computingresourceconsumptionforqueryandinformationthattheanswerbrings)andviolatesthemonotoneprinciple.

Inthisworkwestudytherelationalquerypricingproblembytakingbothinformation(i.e.,data)valueandqueryresourceconsumptionsintoaccount.Wedesignefficientauctionsforquerypricing.Differentfromtheexistingquerypricingschemas,queryauctiondeterminesdatapricesthatreflectthedemand-supplyofsharedcomputingresourcesandinformationvalue(i.e.,pricediscovery).Wetargetqueryauctionthatrunsinpolynomialtimeandachievesnear-optimalsocialwelfarewithagoodapproximationratio,whileelicitstruthfulbidsfromconsumers.Towardsthesegoals,weadaptthepostedpricingframeworkingame-theoreticperspectivebycastingthequeryauctiondesignintoanIntegerLinearProgrammingproblem,anddesignaprimal-dualalgorithmtoapproximatetheNP-hardoptimizationproblem.Theoreticalanalysisandempiricalstudiesdrivenbyreal-worlddatamarketbenchmarkverifytheefficiencyofourqueryauctionschema.

Asacommonprocessingmethod,queryiswidelyusedinmanyareas,suchasgraphprocessing,machinelearning,statistics,etc.However,queriesareusuallypricedaccordingtovendor-specifiedfixedviews(API)ornumberoftransactions,whichignorestheheterogeneityofqueries(computingresourceconsumptionforqueryandinformationthattheanswerbrings)andviolatesthemonotoneprinciple.

Inthisworkwestudytherelationalquerypricingproblembytakingbothinformation(i.e.,data)valueandqueryresourceconsumptionsintoaccount.Wedesignefficientauctionsforquerypricing.Differentfromtheexistingquerypricingschemas,queryauctiondeterminesdatapricesthatreflectthedemand-supplyofsharedcomputingresourcesandinformationvalue(i.e.,pricediscovery).Wetargetqueryauctionthatrunsinpolynomialtimeandachievesnear-optimalsocialwelfarewithagoodapproximationratio,whileelicitstruthfulbidsfromconsumers.Towardsthesegoals,weadaptthepostedpricingframeworkingame-theoreticperspectivebycastingthequeryauctiondesignintoanIntegerLinearProgrammingproblem,anddesignaprimal-dualalgorithmtoapproximatetheNP-hardoptimizationproblem.Theoreticalanalysisandempiricalstudiesdrivenbyreal-worlddatamarketbenchmarkverifytheefficiencyofourqueryauctionschema.

BlockGraphChi:EnablingBlockUpdateinOut-of-coreGraphProcessingZhiyuanShao(HuazhongUniversityofScienceandTechnology),ZhenjieMei(HuazhongUniversityofScienceandTechnology),XiaofengDing(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology)

Inthepastseveralyears,lotsofout-of-coregraphprocessingsystemsarebuilttoprocessbiggraphdatasetsincomputersystemswithlimitedmainmemory.Duetotheiterativenatureofgraphalgorithms,mostofthesesystemsemploysynchronousexecutionmodeltoorganizethecomputation,i.e.,dividethecomputingintomultiplerounds,eachofwhichcorrespondstooneiterationofthegraphalgorithm.Inordertofullyutilizethediskbandwidth,thesesystemssequentiallyscanthewholegraphdatasetateachiteration.However,asthegraphdatasetunderprocessingmaybehuge,moreiterationsgenerallymeanslargerI/Ooverheads.Althoughasynchronousimplementationofthesynchronousexecutionmodelallowsmessagepassingwithinaniteration,theeffectivenessisstilllimited.Sinceinsuchmodel,atmostonemessageisallowedtobepassedfromonevertextoanother.

Inthispaper,weinvestigatetheideaofblockupdatinginthesynchronousexecutionmodelframeworkintheout-of-coregraphprocessingsystems.Withthisnewmodel,thesystemconductsgraphalgorithmontheloadedsubgraph(i.e.,block)toitslocalconvergence,andthenswitchestoothersubgraphstocontinuethisprocess,untiltheglobalconvergenceisreached.WeimplementthisnewmodelinGraphChi(theresultsystemiscalledBlockGraphChi),andproposeagraphpartitionmethod,namedasDMLP,tocooperatewiththisnewmodel.Bythisstudy,wefoundthatcomparedwiththeoriginalexecutionmodelofGraphChi:1)thenewmodelcangenerallyreducetheamountofiterations(andthustheI/Ooverheads)forgraphalgorithms,whiletheextentofreductiondependsonthemethodofgraphpartitioningandthepropertiesofthealgorithms;2)thenewmodelcandramaticallyreducetheoverallexecutiontimeofgraphtraversalalgorithms(byupto31.4x),andbetterpartitioningmethodleadstobetterperformance;3)thenewmodelhasmuchsmallereffectivenessonimprovingtheoverallperformanceoffix-pointalgorithms,suchasPageRank,duetotheincreasedcomputationaloverhead.

IncrementalParallelComputingusingTransactionalModelinLarge-scaleDynamicGraphStructuresAnandTripathi(UniversityofMinnesota,Minneapolis),RahulR.Sharma(UniversityofMinnesota),ManuKhandelwal(UniversityofMinnesota),TanmayMehta(UniversityofMinnesota),VarunPandey(UniversityofMinnesota)

Manygraphanalyticsproblemsbenefitfromtheuseofparallelcomputingtechniquestoreducetheexecutiontime,whichcanstillbequitehighforlargegraphproblems.Thegoalofourworkistoeliminatetheneedofre-executingananalyticsprogramwhenthegraphstructureismodifiedwithasmallsetofupdatesaftertheinitialexecutionoftheprogram.Towardsthisgoal,wepresentheretheresultsofourinvestigationofincrementalcomputationtechniquesindynamicgraph

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structuresusingatransactionalmodelofparallelprogramming.Inthismodel,computationtasksinananalyticsapplicationareexecutedinparallelasserializabletransactions.Thispaperdescribeshowincrementalcomputationtechniquesaresupportedbythismodelfordynamicgraphstructures.Weusetheproblemsoffindingconnected-componentsinagraphandthegraphcoloringproblemtoillustrateourapproachforincrementalcomputations.Usingexperimentalevaluations,weshowthebenefitsofthisapproach.

AgainstSigned-GraphDeanonymizationAttacks:PrivacyProtectionforSocialNetworksJianliangGao(CentralSouthUniversity),YuLiu(CentralSouthUniversity),PingZhong(CentralSouthUniversity),JianxinWang(CentralSouthUniversity)

Socialnetworksareusuallypresentedasgraphs.Butthetopologicalcharacteristicsofgraphscouldbeusedbyattackerstodeanonymizetargetentitiesinsocialnetworks.Existingworksmostlyhaveanassumptionthatattackerknowsonlythetargetentities'neighborhoodgraph.Thisassumptionmightresultinprivacyleakagebecauseoftheignoranceoflinkpropertybetweenentities.Inrealapplications,attackersmightre-identifyentitiesinsocialnetworksbasedonnotonlythelinksbetweenentities,butalsothepropertyoflinks.Inthispaper,wetakethepropertyoflinksintoconsiderationforthefirsttimewhenachieving$k$-anonymityforsocialnetworks,whichmeanstheattackerscannotre-identifyatargetwithconfidencehigherthan$1/k$.Thelinksarecatalogedaspositiveandnegative,whichiscalledsignedgraph.Inthisbackground,weproposea$k$-anonymizationschemetoprotecttheprivacyofkeyentitiesinsocialnetworks.Theproposedschememinimizestheamountofmodificationonoriginalgraphs,whichpreservestheutilityoftheoriginaldata.Extensiveexperimentsonrealdatasetsandsyntheticgraphillustratetheeffectivenessoftheproposedscheme.Theutilityofanonymizednetworksareremainedbydemonstratingwiththeresultsofvertexdegree,betweenness,closenessandtheirKolmogorov-Smirnov(K-S)test.

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CCN-CPS 2017 Workshop Abstracts

PoliciesGuidingCohesiveInteractionsamongInternetofThingswithCommunicationCloudandSocialNetworksHenryHexmoor(SouthernIllinoisUniversity)

CohesiveinteractionamongInternetofthingnodeswillbenefitfromformationofadhoccommunicationnetworkcloudsforrapidexchangeofinformationthatispertinentfortheirsuccessfulinteraction.Longenduringinteractionsamongsuchnodeswillbenefitfromadhocsociallylinkednetworksforcollaborationonsharedobjectives.Wepresentguidelinesforformingandusingtheseconstructsandpoliciesthatconstrainthemtorequirementsofspecificapplications.

EnhancedSecurityofBuildingAutomationSystemsThroughMicrokernel-BasedControllerPlatformsXiaolongWang(UniversityofSouthFlorida),RichardHabeeb(UniversityofSouthFlorida),XinmingOu(UniversityofSouthFlorida),SiddharthAmaravadi(KansasStateUniversity),JohnHatcliff(KansasStateUniversity),MasaakiMizuno(KansasStateUniversity),MitchellLNeilsen(KansasStateUniversity),RajRajagopalan(Honeywell),SrivatsanVaradarajan(HoneywellAerospaceAdvancedTechnologyLabs)

ABuildingAutomationSystem(BAS)isacomplexdistributedCyber-PhysicalSystemthatcontrolsbuildingfunctionalitiessuchasheating,ventilation,andaircondition-ing(HVAC),lighting,access,emergencycontrol,andsoon.ThereisagrowingopportunityandmotivationforBAStobeintegratedintoenterpriseITnetworkstogetherwithvariousnew""smart""technologiestoimproveoccupantcomfortandreduceenergyconsumption.Thesenewtechnologiescoexistwithlegacyapplications,creatingamixed-criticalityenvironment.Inthisenvironment,assystemsareintegratedintoITnetworks,newattackvectorsareintroduced.Thus,networkednon-criticalapplicationsrunningontheOSplatformmaybecompromised,leavingthecontrolsystemsvulnerable.Theindustryneedsareliablecomputingfoundationthatcanprotectandisolatetheseendangeredcriticalsystemsfromuntrustedapplications.

Thisworkpresentsanovelkernel-basedapproachtosecurecriticalapplications.Ourmethodusesasecurity-enhanced,microkernelarchitecturetoensurethesecurityandsafetypropertiesofBASinapotentiallyhostilecyberenvironment.WecomparethreesystemdesignandimplementationsforasimpleBASscenario:1)usingthemicrokernelMINIX3enhancedwithmandatoryaccesscontrolforinter-processcommunication(IPC),2)usingseL4,aformallyverified,capability-basedmicrokernel,and3)usingLinux,amonolithickernelOS.Weshowthroughexperimentthatwhenthenon-criticalapplicationsarecompromisedinbothMINIX3andseL4,thecriticalprocessesthatimpactthephysicalworldarenotaffected.WhereasinLinux,thecompromisedapplicationscaneasilydisruptthephysicalprocesses,jeopardizingthesafetypropertiesinthephysicalworld.ThisshowsthatmicrokernelsareasuperiorplatformforBASorothersimilarcontrolenvironmentsfromasecuritypointofview,anddemonstratesthroughexamplehowtoleveragethearchitecturetobuildarobustandresilientsystemforBAS.

HighlevelDesignofaHomeAutonomousSystemBasedonCyberPhysicalSystemModelingBasmanAlhafidh(FloridaInstituteofTechnology),WilliamH.Allen(FloridaInstituteofTechnology)

Theprocessusedtobuildanautonomoussmarthomesystemusingcyber-physicalsystems(CPS)principleshasreceivedmuchattentionbyresearchersanddevelopers.However,therearemanychallengesduringthedesignandimplementationofsuchasystem,suchasPortability,Timing,Prediction,andIntegrity.Thispaperpresentsanovelmodelingmethodologyforasmarthomesysteminthescopeofcyber-physicalinterfacethatattemptstoovercometheseissues.Wediscussahigh-leveldesignapproachthatsimulatesthefirstthreelevelsofa5CarchitectureinCPSlayersinasmarthomeenvironment.Adetaileddescriptionofthemodeldesign,architecture,andasoftwareimplementationviaNetLogosimulationprogramwillbepresented.Ourdesignprovidesanexamplefordevelopersonhowtoimplementanecosysteminahomeenvironmentaspartofasmartcities'infrastructurebasedonCPSdesignprinciples.

ACyberPhysicalBuses-and-DronesMobileEdgeInfrastructureforLargeScaleDisasterEmergencyCommunicationsMamtaNarang(AucklandUniversityofTechnology),WilliamLiu(AucklandUniversityofTechnology),JairoAGutierrez(AucklandUniversityofTechnology),LucaChiaraviglio(UniversityofRomeTorVergata)

Immediatelyafteradisaster,thenormaltelecommunicationinfrastructure,includingwiredandwirelessnetworks,isoftenseriouslycompromisedandcannotguaranteeregularcoverageandreliablecommunicationsservices.Thesetemporarily-missingcommunicationscapabilitiesarecrucialtorescuersandaffectedcitizensastherespondersneedtoeffectivelycoordinateandcommunicatetominimizethelossoflivesandproperty.Acyber-physicalsystem(CPS)iscomposedofintegratedcommunication,computationandphysicalobjects,andcyber-physicalvehiclesystems(CPVSs)areanemergingfieldduetotherapidadvancementsonreal-timecomputing,mobilecommunicationsandautonomouscontrolinintelligenttransportsystems.Inthispaper,weproposeacyber-physicalbuses-and-dronesmobiLeedgeinfrastructure(AidLife)fordisasteremergencycommunications,whichaimsatarapidlydeployableresilientsystemcapableofsupportingflexiblecommunicationstoservelarge-scaledisastersituationsbyutilizingtheexistingpublictransportsystem.Inparticularweenvisionaproposalwherepublicbusescanberecruitedtotemporarilyhostportablebasestation(BS)andcomputationunitsaswellaspowerresourcessoastoformabuses-basedmobileedgeinfrastructure,andalsoaccommodatedronestoextendtheircoveragetohard-to-reachareas.OurpreliminaryresultsshowthattheAidLifesystemcanguaranteeagoodcoveragetousers,evenwhenalargenumberofnormalBSsthataredamaged.

APerformanceComparisonofContainersandVirtualMachinesinWorkloadMigrationContextKumarGaurav(VMwareSoftwareIndiaPvtLtd),PavanKarkun(VMwareSoftwareIndiaPvtLTD),Y.C.Tay(NationalUniversityofSingapore)

Thispapergivesamathematicalframeworkfordecisionmakingaroundplacingandmigratingworkloadsinadata-centerwhereapplicationsarepackagedasOScontainersrunningonvirtualmachines.ThedecisionpointonVMmigrationvscontainerkill/restart,VMforkvscontainerspawnarestudiedhere.Weproposeamathematicalmodelforthemigrationofworkloadsaforementionedcasesandalsoforsharedmemorydecayincaseofforkingavirtualmachine.Experimentalresultsareanalyzedtodeterminethevalidityofthemodel.

TowardsService-OrientedMiddlewareforCyberPhysicalSystemsNaderMohamed(MiddlewareTechnologiesLab.),SanjaLazarova-Molnar(UniversityofSouthernDenmark)

Cyber-PhysicalSystems(CPS)providemanysmartfeaturesforenhancingphysicalsystemsandenvironments.Theyaredesignedwithasetofdistributedhardware,software,andnetworkcomponentsthatareembeddedinphysicalsystemsandenvironmentsorattachedtohumans.ManyCPSatdifferentscalesarebeingdevelopedforavarietyofapplicationsthatprovidevaluableinteractionsbetweenthecyberworldandthephysicalsystemsandenvironments.However,thesedevelopmentsfacemanychallengesduetothecomplexityoftheseapplications.Anappropriatemiddlewareisneededtoprovideinfrastructuralsupportandassist

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thedevelopmentandoperationsofdiverseCPSapplications.Thispaperstudiesutilizingtheservice-orientedmiddlewareapproachforCPSanddiscussestheadvantagesandrequirementsforsuchutilization.Inaddition,itproposesaservice-orientedmiddlewareforCPS,calledCPSWare.ThismiddlewareviewsallCPScomponentsasasetofservicesandprovidesaninfrastructuretodevelopandoperateCPSapplications.ThisapproachprovidessystemicsolutionsforsolvingmanycomputingandnetworkingissuesinCPS.ItalsoenablestheintegrationofCPSwithothersystemssuchasCloudComputingandFogComputing.Inaddition,asCPScanbedevelopedforvariousapplicationsatdifferentscales,thispaperprovidesaclassificationforCPSapplicationsanddiscusseshowCPSWarecaneffectivelydealwiththesecategories.

NetworkingandCommunicationinCyberPhysicalSystemsImadJawhar(UAEUniversity),JameelaAl-Jaroodi(RobertMorrisUniversity)

Cyber-physicalsystems(CPSs)areemergingasanewtechnology,whichisusedtoprovideseamlessinteractionbetweenthephysicalandcyberworlds.Thisnovelparadigmisanaturalevolutionandextensionofwirelesssensornetworks(WSNs)andcontrolmodelstoallowforeffectivemonitoringandcontrolofphysicalsystemsfromthecomputingenvironment.Inordertosupportthisinterfaceandallowsuchsmoothinteractions,efficientnetworkingandcommunicationbetweenthephysicalandcyberworldstakeaveryimportantandcriticalrole.Inthispaper,weidentifythevariousapplicationsandcategoriesofCPSsystems,andcharacterizetheassociateddatatrafficthatisgenerated.Wealsodiscussthedifferentprotocolsandrequirementsthatareneededatthevariousnetworkinglayersfortheseapplications.Subsequently,weidentifyimportantparameterssuchasbandwidth,delay,reliability,security,andmobility,whichareessentialinordertoallowforeffectiveandrobustoperationofthevariousCPSsystems.

OptimalDeploymentofChargingStationsforElectricVehicles:AFormalApproachAmarjitDatta(TennesseeTechnologicalUniversity),BrianLedbetter(TennesseeTechnologicalUniversity),MohammadAshiqurRahman(TennesseeTechnologicalUniversity)

Electricvehicles(EVs)areafascinatinginnovationofthemodernautomobileindustry.Duetotheirattractivefeaturesandagrowingworldwideenvironmentalawareness,thenumberofEVpurchasesisgrowingatanincreasingratedaybyday.AsthepriceofEVsisexpectedtodropinthenearfuture,alargenumberofnewEVswillhittheroadconsequently.However,ourcurrentinfrastructureisnotcapableofsupportingthisgrowingnumberofEVs.Weneedmorechargingstations,placedoptimallyacrossanarea,eachequippedwithmultiplechargingoutletstochargetheincomingEVsinareasonableamountoftime.Inthispaper,wepresentaformalframeworktooptimallydeploychargingstationsforEVsinagivenarea.TheframeworkdesignsthisverificationasanoptimizationproblemwherethegoalistooptimallyplacethechargingstationswithasufficientnumberofchargingoutletstoserveallEVsinagivenareawhilesatisfyingthelimitedbudgetandothersystemconstraints.Weevaluatetheproposedframeworkforitsanalysiscapabilityaswellasitsscalabilitybyexecutingexperimentsondifferentsynthetictestcases.

FormalVerificationofControlStrategiesforaCyberPhysicalSystemAmjadGawanmeh(KhalifaUniversityofScienceandTechnology),AliAlwadi(AucklandUniversityofTechnology),SaziaParvin(UniversityofNewSouthWales)

CyberPhysicalSystems(CPS)useemergingcomputing,communication,andcontrolmethodstomonitorandcontrolgeographicallydispersedcriticalsystemcomponentstoallowahighlevelofconfidenceabouttheiroperation.Simulationmethodsarefrequentlyusedintestingsuchcriticalsystemcomponents,however,itmightnotbeadequatetoshowtheabsenceoferrorsgiventhecomplexityofthesystemcomponentsundertest.Failureindetectingerrorsinsafetycriticalsystemscanleadtoacatastrophicsituation.Inthispaperweproposeanapproach,basedonsimulationandformalanalysis,forthereliabilityanalysisofCPS.WeillustratethisapproachonanindustrialcasestudythatdemonstratesseveralchallengingfeaturesinthedesignandimplementationofCPS.Experimentalresultsobtainedshowthattheproposedapproachisefficientlyusedinordertotestandverifythefourtanksprocesssystem,wheresimulationresultsshowthevalidityofapproximationandabstractionofthesystem,andformalanalysisisusedtovalidatethatseveraldesignrequirementsweresatisfiedinthecontrolstrategiesproposed.

LightweightDetectionandIsolationofBlackHoleAttacksinConnectedVehiclesSamiAlbouq(OaklandUniversity),ErikFredericks(OaklandUniversity)

ConnectedVehicles(CVs)canbeexposedtoblackholeattacksthatdeceivelegitimatenodesbyfalsifyinganattractiveroutetoadestinationnode.Thisoccurswhenanattackersendsapackettothesourcenodeconfirmingtheexistenceofafreshroute.Inthispaper,weproposeaBlackHoleDetectionProtocol(BlackDP)thatworksonahighwaydividedintoclustersandmonitoredbyRoadSideUnits(RSUs)todetectbothsingleandcooperativeblackholeattacks.EveryRSUistaskedwithperformingbothdetectionandisolationofblackholeattacksfortheirrespectivehighwaysectionafterauthenticationviolationsandsuspiciousrouteestablishmentactivitiesthathavebeenreportedbyalegitimatenode.ThedesigngoalofBlackDPistodecouplethedetectionprocessfrommobilenodesandconstructatrustedsemi-centricdetectionprocessthatcancollectneededinformationforlightweightdetectionandreliableisolationofmaliciousnodes.WevalidateBlackDPinasimulatedhighwayenvironmenttodemonstrateitseffectiveness.

AnewthreatassessmentmethodforintegratinganIoTinfrastructureinaninformationsystemBrunoDorsemaine(OrangeLabs),Jean-PhilippeGaulier(OrangeLabs),Jean-PhilippeWary(OrangeLabs),NizarKheir(Thales),PascalUrien(TelecomParisTech)

Inthispaper,weproposeanewapproachtomanagethethreatsbroughtbyanIoTinfrastructuretoaninformationsystem(IS).WefirstgiveastateofartforinformationsecuritypropertiesinIoTandISbasedonstandardssuchasISO16982andISO27005andapreviouslypublishedtaxonomy.Thenwedetailaninnovativemethod,basedontheevaluationofthreatsbroughtbyanIoTinfrastructureontoanIS.ItisrepresentedasaqualitativematrixbetweenIoTinfrastructurethreatsandtheSecuritypropertiesoftheIS.Themethodisthenappliedtotheusecaseofconnectedlightbulbs.Thankstothisapproach,itispossibletologicallyorganizethreatmanagementwhileintegratinganIoTinfrastructureintoanIS.

ASecurityFrameworkforSDN-enabledSmartPowerGridsUttamGhosh(TennesseeStateUniversity),PushpitaChatterjee(SRMRESEARCHINSTITUTE),SachinShetty(OldDominionUniversity)

Emergingsoftwaredefinednetworking(SDN)paradigmprovidesflexibilityincontrolling,managing,anddynamicallyreconfiguringsmartgridnetworks.ItcanbeseenintheliteraturethatconsiderablylessattentionhasbeengiventoprovidesecurityinSDN-enabledsmartgridnetworks.Mostoftheeffortsfocusonprotecting

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smartgridnetworksagainstvariousformsofoutsiderattacksonlybyprovidingconsistentaccesscontrol,applyingefficientandeffectivesecuritypolicies,andmanagingandcontrollingthenetworkthroughtheuseofacentralizedSDNcontroller.Furthermore,centralizedSDNcontrollersareplaguedbyreliabilityandsecurityissues.ThispaperpresentsaframeworkwithmultipleSDNcontrollersandsecuritycontrollersthatprovidesasecureandrobustsmartgridarchitecture.TheproposedframeworkdeploysalocalIDSinasubstationtocollectthemeasurementdataperiodicallyandtomonitorthecontrol-commandsthatareexecutedonSCADAslaves.AglobalIDSincontrolcentercollectsthemeasurementdatafromthesubstationsandestimatesthestateofthesmartgridsystembyutilizingthetheoryofdifferentialevolution.TheglobalIDSfurtherverifiestheconsequencesofcontrol-commandsissuedbySDNcontrollerandSCADAmaster.Analarmisgeneratedupondetectionofanattackerorunsteadystateofthesmartgridsystem.Theframeworkalsodeployslight-weightidentitybasedcryptographytoprotectthesmartgridnetworkfromoutsideattacks.PerformancecomparisonandinitialsimulationresulthavebeenpresentedtoshowthattheproposedframeworkiseffectiveascomparedtoexistingsecurityframeworksforSDN-enabledsmartgrids.

Real-timeMonitoringSteamGeneratorsusingaHybridImagingSystemMahmoudMeribout(PetroleumInstitute),ImranSaied(PetroleumInstitute),EsraAlHosani(AdcoGroup)

Thispaperpresentsahybriddeviceforreal-timemeasurementandimagingofsolidandliquidcontaminantsthatmayoccurinsteamgenerators.ThedeviceusesadedicatedNearInfra-Reddevicetodeterminethetypeofcontaminants(i.e.waterdropletsandironoxideparticles)andaTHzimagingsystemwhichmeasurestheamountofcontaminantsaswellasitsflowrate.TheNIRdevicecanalsodeterminetheconcentrationofcontaminantsatsub-mgaccuracywhenitsvalueisrelativelylowusingspectrometrytechniquecombinedwithprincipalcomponentanalysis(PCA).Threeprincipalcomponents(PC1,PC2,andPC3)wereenoughforthispurpose.ThePCAclassificationwasperformedusingtheleastsquaresupportvectormachine(LS-SVM)method.Incaseofrelativelyhighconcentration,theTHzimagingsystemwhichusesblock-basedmotionestimationalgorithmcandeterminethevelocityofindividualcontaminantparticlestocomputetheglobalmotionvector,theintensityanddirectionofwhichrepresentstheoverallflowrateandflowregimeofthecontaminants.TheusageofimageprocessingtechniquestogetherwithNIRspectrometryconstitutesanewpromisingstepinflowmetering.ThisisdemonstratedbytheextensiveexperimentswhichhavebeenconductedfordifferentscenariowheretheNIRsubsystemsystemcoulddeterminetheconcentrationofwaterdropletsandsolidcontaminantswithamaximumuncertaintyof+/-1.45%and+/-1.16%respectively.WiththeNIRsubsystem,pixel-levelaccuracyofmotionvectorwasachieved,whiletheconcentrationofsolidcontaminantsshowedconsistedproportionalitywiththeaveragepixelintensity.

SecuringbigDataEfficientlythroughMicroaggregationTechniqueandHuffmanCompressionShakilaMahjabinTonni(BangladeshArmyInternationalUniversityofScienceandTechnology),MohammadZahidurRahman(JahangirnagarUniversity),SaziaParvin(UniversityofNewSouthWales),AmjadGawanmeh(KhalifaUniversityofScienceandTechnology)

Cyber-PhysicalSystems(CPS)requiresbigdatacommunicationsaswellasintegrationfromseveraldistributedsources.Thisdatacanusuallybeinterconnectedwithphysicalapplications,suchaspowergridsorSCADAsystems.Inaddition,itcanbepubliclyaccessibleforusingbythirdpartyusersordatascientists.Therefore,itbecomesimperativetoensurethatthisbigdataiswellsecured.Microaggregationisanwidelyusedtechniquetoprotectadatasetthroughanonymityinordertopreventexposureofaperson'sidentity.Thisdatadisclosuremayalsoresultfromanunpredicteddatalinkagewithanotherdataset.As,mostofthesesurveydatasetsstorerecordsusingnumericalvalues,manyofthemicroaggregationtechniquesaredevelopedandtestedonnumericaldata.Thesealgorithmsarenotsuitableforthosedatawherebothnumericalandcategoricaldataarestored.Inthispaperwe'reproposingamicroaggregationtechniqueinordertoprovidedataanonymityregardlessofitstype.TherecordsareclusteredintoseveralgroupsusinganevolutionaryattributegroupingalgorithmandeachgrouprecordsarethenmicroaggregatedapplyingHuffmandatacompressionalgorithm.

ModelBasedEnergyConsumptionAnalysisofWirelessCyberPhysicalSystemsJingLiu(PekingUniversity),PingWang(PekingUniversity),JinlongLin(PekingUniversity),Chao-HsienChu(PennsylvanniaStateUniversity)

Wirelessmeshnetworksbegintobeusedasaninfrastructureofcyber-physicalsystems.Acriticalissueindevelopingwirelesscyberphysicalsystems(WCPSs)isthelimitedamountofenergyavailableinthenodes.Energyconsumptionanalysiscanhelpdesignertoconductapower-awaredesignprocess.Inthispaper,weproposeamodelbasedenergyconsumptionanalysisframeworkatarchitecturelevelforWCPSs.Weextracteventchainsfromthearchitecturemodel,withtheenergyconsumptionmodelforprocessingeachtypeofevent,wecanestimatetheenergyconsumptionforeachcontrolloopandeachnode,aswellastheoverallenergyconsumption.AlltheseenergyconsumptionindexescanhelpustodesignaperformanceandenergyconsumptionbalancedWCPS.

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HotPOST 2017 Workshop Abstracts

Router-basedBrokeringforSurrogateDiscoveryinEdgeComputingJulienGedeon(TechnischeUniversitätDarmstadt),ChristianMeurisch(TechnischeUniversitätDarmstadt),DishaBhat(TechnischeUniversitätDarmstadt),MichaelStein(TechnischeUniversitätDarmstadt),LinWang(TechnischeUniversitätDarmstadt),MaxMühlhäuser(TechnischeUniversitätDarmstadt)

In-networkprocessingpushescomputationalcapabilitiesclosertotheedgeofthenetwork,enablingnewkindsoflocation-aware,real-timeapplications,whilepreservingbandwidthinthecorenetwork.Thisisdonebyoffloadingcomputationstomorepowerfulorenergy-efficientsurrogatesthatareopportunisticallyavailableatthenetworkedge.Inmobileandheterogeneoususagecontexts,thequestionariseshowaclientcandiscoverthemostappropriatesurrogateinthenetworkforoffloadingatask.Inthispaper,weproposeabrokeringmechanismthatmatchesaclientwiththebestavailablesurrogate,basedonspecifiedrequirementsandcapabilities.Thebrokerisimplementedonstandardhomerouters,andthus,leveragestheubiquityofsuchdevicesinurbanenvironments.Tomotivatethefeasibilityofthisapproach,weconductacoverageanalysisbasedoncollectedaccesspointlocationsinamajorcity.Furthermore,thebrokeringfunctionalityintroducesonlyaminimalresourceoverheadontheroutersandcansignificantlyreducethelatencycomparedtodistant,cloud-basedsolutions.

ModelingtheSpreadofInfluenceforIndependentCascadeDiffusionProcessinSocialNetworksZeshengChen(IndianaUniversity-PurdueUniversityFortWayne),KurtisTaylor(IndianaUniversity-PurdueUniversityFortWayne)

Modelingthespreadofinfluenceinonlinesocialnetworksisimportantforpredictingtheinfluenceofindividualsandbetterunderstandingmanyscenariosinsocialnetworks,suchastheinfluencemaximizationproblem.Thepreviousworkonmodelingthespreadofinfluencemakestheassumptionthatthestatusesofnodesinanetworkareindependentofeachother,whichisapparentlynotcorrectforsocialnetworks.Thegoalofthisworkistoderiveanaccuratemathematicalmodeltocharacterizethespreadofinfluencefortheindependentcascadediffusionprocessinonlinesocialnetworks.Specifically,weapplythesusceptible-infected-recoveredepidemicmodelfromepidemiologytocharacterizetheindependentcascadediffusionprocessandderiveageneralmathematicalframework.Toapproximatethecomplexspatialdependenceamongnodesinanetwork,weproposeaMarkovmodeltopredictthespreadofinfluence.Throughtheextensivesimulationstudyoverseveralgeneratedtopologiesandarealcoauthorshipnetwork,weshowthatourdesignedMarkovmodelhasmuchbetterperformancethantheexistingindependentmodelinpredictingtheinfluenceofindividualsinonlinesocialnetworks.

ThankYouForBeingAFriend:AnAttackerViewonOnline-Social-Network-basedSybilDefensesDavidKoll(UniversityofGoettingen),MartinSchwarzmaier(UniversityofGoettingen),JunLi(UniversityofOregon),Xiang-YangLi(UniversityofScienceandTechnologyofChina),XiaomingFu(UniversityofGoettingen)

OnlineSocialNetworks(OSNs)havebecomearewardingtargetforattackers.OneparticularlypopularattackistheSybilattack,inwhichtheadversarycreatesmanyfakeaccountscalledSybilsinorderto,forinstance,distributespamormanipulatevotingresults.AfirstgenerationofdefensesystemstriedtodetecttheseSybilsbyanalyzingchangesinthestructureoftheOSNgraph---unfortunatelywithlimitedsuccess.Basedontheseeffortsasecondgenerationofsolutionsenrichesthegraph-structuralapproacheswithhigher-leveluserfeaturesinordertodetectSybilnodesmoreefficiently.Inthisworkweprovideanin-depthanalysisofthesedefenses.Wedescribetheircommondesignandworkingprinciples,analyzetheirvulnerabilities,anddesignsimpleyeteffectiveattackstrategiesthatanadversarycouldlaunchtocircumventthesesystems.InourevaluationwerevealthatanmiscreantcanexploitthecredulityofOSNusersandfollowatargetedattackstrategytosuccessfullyavoiddetectionbyallexistingapproaches.

EfficientDynamicServiceFunctionChainCombinationofNetworkFunctionVirtualizationWenkeYan(BeijingUniversityofPostsandTelecommunications),KonglinZhu(BeijingUniversityofPostsandTelecommunications),LinZhang(BeijingUniversityofPostsandTelecommunications),SixiSu(BeijingUniversityofPostsandTelecommunications)

NetworkFunctionVirtualization(NFV)andSoftwareDefinedNetwork(SDN)arerecentlyintroducedtoprovidethevirtualizationtechnologyfortacklingthedeploymentofnetworkservicefunctionsincorporatenetworks,broadbandaccessnetworks,andmorerecentlyindatacenters.Howtoenhancetheflexibility,efficiencyandeffectiveofservicefunctiondeploymentisfullofchallenge.AlthoughServiceFunctionChain(SFC)iscarriedouttosupporttheflexibilityofnetworkservices,itstillneedsonestepforwardtofulfilltheefficientandeffectivecombinationofnetworkservices.Inthispaper,weproposeanorthogonalcrossoverdifferentialevolution(OXDE)tooptimizeSFCcombinationwithrespecttoprocessingdelay,energyconsumption,andpacketlossrate.TheevaluationresultsshowthattheproposedOXDEalgorithmoutperformstheotheralgorithmsanditcanachievetheefficiencyandeffectivenessofSFCcombination.

WhenAugmentedRealitymeetsBigDataCarlosBermejo(TheHongKongUniversityofScienceandTechnology),ZhanpengHuang(TheHongKongUniversityofScienceandTechnology),TristanBraud(TheHongKongUniversityofScienceandTechnology),PanHui(TheHongKongUniversityofScienceandTechnology)

Weliveinanerawhereweareoverloadedwithdata,andthiscanbethekeyforgainingrichinsightsaboutourworld.Augmentedreality(AR)enablesusthepossibilitytovisualiseandanalysethegrowingtorrentofdatainainteractive,usablecanvas.Wecandisplaycomplexdatastructuresinsimplerandmoreunderstandablewaysthatwasnotpossiblebefore.BigDataisanewparadigmresultsfromthemyriaddatasourcessuchastransactions,Internet,socialnetworks,healthcaredevicesandsensornetworks.ARandbigdatahavealogicalmaturitythatinevitablywillconverge.ThetreadofharnessingARandbigdatatobreednewinterestingapplicationsisstartingtohaveatangiblepresence.Inthispaper,weexplorethepotentialtocapturevaluefromthemarriagebetweenARandbigdatatechnologies,followingwithseveralchallengesthatmustbeaddressedtofullyrealizethispotential.

SamplingBasedEfficientAlgorithmtoEstimatetheSpectralRadiusofLargeGraphsSamarAbbas(LahoreUniversityofManagementSciences),JuvariaTariq(LahoreUniversityofManagementSciences),ArifZaman(LahoreUniversityofManagementSciences),ImdadullahKhan(LahoreUniversityofManagementSciences)

Evaluatinganextremelyusefulgraphproperty,thespectralradius(largestabsoluteeigenvalueofthegraphadjacencymatrix),forlargegraphsrequiresexcessivecomputingresources.Thisproblembecomesespeciallychallenging,forinstancewithdistributedorremotestorage,whenaccessingthewholegraphitselfisexpensiveintermsofmemoryorbandwidth.Oneapproachtotacklethischallengeistoestimatethespectralradiusofthegraphwhilereadingonlyasmallportion

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ofthegraph.Inthispaperwepresentasamplingapproachtoestimatethespectralradiusoflargegraphs.Wedefineascoreforverticesthati)ismoreofacombinatorialnatureandiseasiertocomputeandii)hassolidtheoreticaljustificationshence,itcloselyapproximateavertex'scontributiontothelargesteigenvalueofthegraph.Usingthisscore,wemodelthesamplingproblemasabudgetedoptimizationproblemanddesignagreedyalgorithmtoselectasubgraphwhosespectralradiusapproachesthatofthewholegraph.Weprovideanalyticalboundoncomputationalcomplexityofouralgorithm.Wedemonstrateeffectivenessofouralgorithmonvarioussyntheticandreal-worldgraphsandshowthatouralgorithmalsoempiricallyoutperformsknowntechniques.Furthermore,wecomparethequalityofourresultstoestimatesobtainedfromwellknownupperandlowerboundsknowninthespectralgraphtheoryliterature.

ExtemporaneousMicro-MobileServiceExecutionWithoutCodeSharingZhengSong(VirginiaTech),MinhLe(UtahStateUniversity),Young-WooKwon(UtahStateUniversity),EliTilevich(VirginiaTech)

Inmobileedgecomputing,amobileorIoTdevicerequestsanearbydevicetoexecutesomefunctionalityandreturnbacktheresults.However,theexecutablecodemusteitherbepre-installedonthenearbydeviceorbetransferredfromtherequesterdevice,reducingtheutilityorsafetyofdevice-to-devicecomputing,respectively.Toaddressthisproblem,wepresentamicro-servicemiddlewarethatexecutesservicesonnearbymobiledevices,withatrustedmiddlemandistributingexecutablecode.Oursolutioncomprises(1)atrustedstoreofvettedmobileservices,self-containedexecutablemodules,downloadedtodevicesandinvokedatruntime;and(2)amiddlewaresystemthatmatchesservicerequirementstoavailabledevicestoorchestratethedevice-to-devicecommunication.Ourexperimentsshowthatoursolution(1)enablesexecutingmobileservicesonnearbydevices,withoutrequiringadevicetoreceiveexecutablecodefromanuntrustedparty;(2)supportsmobileedgecomputinginpracticalsettings,increasingperformanceanddecreasingenergyconsumption;(3)reducesthemobiledevelopmentworkloadbyreusingservices.

PreventingColludingIdentityCloneAttacksinOnlineSocialNetworksGeorgesA.Kamhoua(FloridaInternationalUniversity),NikiPissinou(FloridaInternationalUniversity),S.S.Iyengar(FloridaInternationalUniversity),JonathanBeltran(FloridaInternationalUniversity),CharlesKamhoua(AirForceResearchLaboratory),BrandonLHernandez(UTRGV),LaurentNjilla(AirForceResearchLaboratory)

Nowadays,OnlineSocialNetworks(OSNs)becomeoneofthemostcommonwayamongstpeopletofacilitatecommunication,thishasmadeitatargetforattackerstostealinformationfrominfluentialusers.ThishasbroughtnewformsofcustomizedattacksforOSNs.AttackerstakeadvantageoftheusertrustworthinesswhenusingOSN.Thisexploitationleadstoattackswithacombinationofbothclassicalandmodernthreats.Specifically,colludingattackershavebeentakenadvantageofmanyOSNsbycreatingfakeprofilesoffriendsofthetargetinthesameOSNorothers.Colludersimpersonatetheirvictimsandaskfriendrequeststothetargetintheaimtoinfiltrateherprivatecircletostealinformation.ThistypeofattacksaredifficulttodetectinOSNsbecausemultiplemalicioususersmayhaveasimilarpurposetogaininformationfromtheirtargeteduser.Inthispaper,toovercomethistypeofattack,wefirstaddresstheproblemofmatchinguserprofilesacrossmultipleOSNs,second,byusingbothtextualandfeaturesextractedfromuserprofileandbasedonsupervisedlearningtechniques,webuildaclassifier.Simulationandexperimentalresultsareprovidedtovalidatetheaccuracyofourfindings.

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IoTCA 2017 Workshop Abstracts

TowardsPrivacy-AwareSmartBuildings:Capturing,Communicating,andEnforcingPrivacyPoliciesandPreferencesPrimalPappachan(UniversityofCaliforniaIrvine),MartinDegelingy(CarnegieMellonUniversity),RobertoYus(UniversityofCaliforniaIrvine),AnupamDasy(CarnegieMellonUniversity),SrutiBhagavatulay(CarnegieMellonUniversity),WilliamMelichery(CarnegieMellonUniversity),PardisEmamiNaeiniy(CarnegieMellonUniversity),ShikunZhangy(CarnegieMellonUniversity),LujoBauery(CarnegieMellonUniversity),AlfredKobsa(UniversityofCaliforniaIrvine),SharadMehrotra(UniversityofCaliforniaIrvine),NormanSadeh(CarnegieMellonUniversity),NaliniVenkatasubramanian(UniversityofCaliforniaIrvine)

TheInternetofThings(IoT)ischangingthewayweinteractwithoursurroundingenvironmentindomainsasdiverseashealth,transportation,officebuildingsorourhomes.Insmartbuildingenvironments,informationcapturedaboutabuilding’sinfrastructureanditsinhabitantswillhelpdevelopservicesthatcanhelpusbecomemoreproductive,increaseourcomfort,enhanceoursocialinteractions,increasesafety,saveenergyandmore.Butbyrelyingonthecollectionandsharingofinformationaboutabuilding’sinhabitantsandtheiractivities,theseservicesalsoopenthedoortoprivacyrisks.Inthispaper,weintroduceaframeworkwhereIoTAssistantscaptureandmanagetheprivacypreferencesoftheirusersandcommunicatethemtoprivacy-awaresmartbuildings,whichenforcethemwhencollectinguserdataorsharingitwithbuildingservices.Weoutlineelementsofaninfrastructurenecessarytosupportsuchinteractionsandalsodiscussimportantprivacypolicyattributesthatneedtobecaptured.Thisincludeslookingatattributesnecessarytodescribe–(1)thedatacollectionandsharingpracticesassociatedwithdeployedsensorsandservicesinsmartbuildingsaswellas(2)theprivacypreferencesweneedtocapturetohelpusersmanagetheirprivacyinsuchenvironments.

DeployingData-DrivenSecuritySolutionsonResource-ConstrainedWearableIoTSystemHangCai(WorcesterPolytechnicInstitute),TianlongYun(WorcesterPolytechnicInstitute),JosiahHester(DartmouthCollege),KrishnaK.Venkatasubramanian(ClemsonUniversity)

WearableInternet-of-Things(WIoT)environmentshavedemonstratedgreatpotentialinabroadrangeofapplicationsinhealthcareandwell-being.SecurityisessentialforWIoTenvironments.LackofsecurityinWIoTsnotonlyharmsuserprivacy,butmayalsoharmtheuser’ssafety.ThoughdevicesintheWIoTcanbeattackedinmanyways,inthispaperwefocusonadversarieswhomountwhatwecallsensorhijackingattacks,whichpreventtheconstituentmedicaldevicesfromaccuratelycollectingandreportingtheuser’shealthstate(e.g.,reportingoldorwrongphysiologicalmeasurements).Inthispaperweoutlinesomeofourexperiencesinimplementingadatadrivensecuritysolutionfordetectingsensor-hijackingattackonasecurewearableinternet-of-things(WIoT)basestationcalledtheAmulet.Giventhelimitedcapabilities(computation,memory,batterypower)oftheAmuletplatform,implementingsuchasecuritysolutionisquitechallengingandpresentsseveraltradeoffswithrespecttoresourcesrequirements.WeconcludethepaperwithalistofinsightsintowhatcapabilitiesconstrainedWIoTplatformsshouldprovidedeveloperssoastomaketheinclusionofdata-drivensecurityprimitivesonsuchsystemseasy.

AMotifbasedIoTFrameworkforDataEfficiencyAkashSahoo(TexasA&MUniversity),RabiMahapatra(TexasA&MUniversity)

InternetofThings(IoT)hasallowedembeddeddevicestoconnecttothevastInternetnetworkworldwide.WithbillionsofIoTdeviceswaitingtobeconnected,itisnecessarytobuildefficientinfrastructuretohandlelargeamountofdataforefficientstorageandnetworktraffic.TheamountofdatacreatedattheIoTedgesisregardedasonethebiggestchallengesofIoT.Thispaperproposesamotif-basedencodingschemeforIoTframeworkthathelpstoreducedatageneratedbysensorsatedgenodes.Thissimpleencodingfeatureresidesinboththeserverandtheenddeviceslikeinserver-clientmodel.Ourexperimentsdemonstratedtheschemesbenefitsbyusingslowandfastbaudratesensorssuchastemperatureandaccelerometerrespectivelyasthecasestudies.Theresultsobtainedshowtheproposedmotifbasedframeworkreducesthedataredundancyuptotwoordersofmagnitudewhileretainingmorethan80%accuracytowardsmotifrecognition.

CoTWare:ACloudofThingsMiddlewareJameelaAl-Jaroodi(RobertMorrisUniversity),NaderMohamed(MiddlewareTechnologiesLab.),ImadJawhar(MidcompResearchCenter)

Therearemanyapplicationsthatrequireintegratingalargenumberofphysicalobjectsanddevicesinalarge-scaleInternetofThings(IoT)networks.Someexamplesoftheseapplicationsaresmartgrids,smartwaternetworks,andintelligenttransportationsystems.Theseapplicationsneedreal-timecontrols,powerfulandscalabledatastorageandprocessingcapabilities,andadvanceddataanalyticsmechanisms.OneofthepromisingtechnologiestosupportsuchapplicationsistheCloudofThings(CoT).CoTcanprovideaplatformforlinkinganIoTwithCloudComputing(CC).AnothertechnologythatcanbeutilizedforenhancingIoTapplicationsisFogComputing,whichextendsthetraditionalCloudComputingparadigmtotheedgeofthenetworktoenablebettersupportforoperatingenhancedservices.However,properintegrationandefficientutilizationofCoTandFogComputingforlarge-scaleIoTapplicationsisnotaneasytask.Thispaperproposesaservice-orientedmiddleware,calledCoTWare,tofacilitateeffectiveintegrationandutilizationofCoTandFogComputingforlarge-scaleIoTapplications.

SecuringtheInternetofThings:AMeta-StudyofChallenges,Approaches,andOpenProblemsMahmudHossain(UniversityofAlabamaatBirmingham),RagibHasan(UniversityofAlabamaatBirmingham),AnthonySkjellum(AuburnUniversity)

TheInternetofThings(IoT)isbecomingakeyinfrastructureforthedevelopmentofsmartecosystems.However,theincreaseddeploymentofIoTdeviceswithpoorsecurityhasalreadyrenderedthemincreasinglyvulnerabletocyberattacks.Insomecases,theycanbeusedasatoolforcommittingseriouscrimes.AlthoughsomeresearchershavealreadyexploredsuchissuesintheIoTdomainandprovidedsolutionsforthem,thereremainstheneedforathoroughanalysisofthechallenges,solutions,andopenproblemsinthisdomain.Inthispaper,weconsiderthisresearchgapandprovideasystematicanalysisofsecurityissuesofIoT-basedsystems.Then,wediscusscertainexistingresearchprojectstoresolvethesecurityissues.Finally,wehighlightasetofopenproblemsandprovideadetaileddescriptionforeach.WepositthatoursystematicapproachforunderstandingthenatureandchallengesinIoTsecuritywillmotivateresearcherstoaddressingandsolvingtheseproblems.IndexTerms—InternetofThings;SecurityIssue;AttackSur-face;AttackTaxonomy;IoTForensics.

InternetofThingsFrameworkforSmartLearningAnalyticsAliYavari(SwinburneUniversityofTechnology),RezaSoltanpoor(RMITUniversity)

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LearningAnalytics(LA)hasbecomeaprominentparadigminthecontextofeducationlatelywhichadoptstherecentadvancementsoftechnologysuchascloudcomputing,bigdataprocessing,andInternetofThings.LAalsorequiresanintensiveamountofprocessingresourcetogeneraterelevantanalyticalresults.However,thetraditionalapproacheshavebeeninefficienttotackleLAchallengessuchasreal-time,highper-formance,andscalableprocessingofheterogeneousdatasetsandstreamingdata.AnInternetofThings(IoT)scalable,distributedandhighperformanceframeworkhasthepotentialtoaddressmentionedLAchallengesbyefficientcontextualizationofdata.Inthispaper,SmartLearningAnalyticsconceptualmodelisproposedtoimprovetheeffectivenessofLAbyutilizinganIoT-basedplatformintermsofperformance,scalability,andefficiency.

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JCC 2017 Workshop Abstracts

HeterogeneousMalwareSpreadProcessinStarNetworkLiboJiao(TsinghuaUniversity),HaoYin(TsinghuaUniversity),DongchaoGuo(TsinghuaUniversity),YongqiangLyu(TsinghuaUniversity)

TheheterogeneousSISmodelforvirusspreadinanyfinitesizegraphcharacterizestheinfluenceoffactorsofSISmodelandcouldbeanalyzedbytheextendedN-Intertwinedmodelintroducedin[1].Wespecificallyfocusontheheterogeneousvirusspreadinthestarnetworkinthispaper.Theepidemicthresholdandtheaveragemeta-stablestatefractionofinfectednodesarederivedforvirusspreadinthestarnetwork.OurresultsillustratetheeffectofthefactorsofSISmodelonthesteadystateinfection.

CostReductioninHybridCloudsforEnterpriseComputingBiyuZhou(InstituteofComputingTechnology,ChineseAcademyofSciences),FaZhang(InstituteofComputingTechnology,ChineseAcademyofSciences),JieWu(TempleUniversity),ZhiyongLiu(InstituteofComputingTechnology,ChineseAcademyofSciences)

Hybridcloud-baseddeploymentisatrendincloudcomputingwhichenablesenterprisetobenefitfromcloudinfrastructureswhilehonoringprivacyrestrictionsonsomeservices.Enterpriseapplicationmigrationisaneffectivewaytoimprovetheefficiencyofusingthecloudinfrastructures.However,itisachallengingproblemtodecidewhichpartsoftheapplicationstomigrateandwheretomigrate.Inthispaper,wefocusontheproblemofplanningthemigrationofenterpriseapplicationsinhybridcloudinfrastructures.Unlikepreviousstudies,weconsiderageneralhybridcloudarchitecturethatinvolvesmultiplepubliccloudsratherthanonlyone.Ouraimistomaximizetheenterprisecostreductionundertheconstraintofuserexperienceintermsofresponsetime.Wefirstformulatethepplicationmigrationproblemasanoptimizationproblem.AwareofitsNP-hardness,wedesignanefficientmigrationframeworktoapproximatetheoptimumforalargeproblemsize.First,weleveragetheapplicationcharacteristictoreducethescaleoftheproblembydividingitintomultiplesmallersubproblems.Then,anefficientalgorithmbasedondynamicprogrammingisproposedtosolvethesmallscalesubproblems.Finally,weconstructafeasiblesolutiontotheoriginalproblem.Simulationresultsdemonstratethatourframeworkcanbringsignificantbenefitstoenterprises.

DC-RSF:ADynamicandCustomizedReputationSystemFrameworkforJointCloudComputingFanghuaYe(SunYat-senUniversity),ZibinZheng(SunYat-senUniversity),ChuanChen(SunYat-senUniversity),YurenZhou(SunYat-senUniversity)

Jointcloudcomputing(JointCloud),asabrand-newparadigmofcloudcomputing,aimsatbuildingacloudecosystem,inwhichendusersareagnostictocloudservicevendorsasapplicationsandservicesarebuiltuponvirtualclouds.IncaseoflowqualitycloudresourcesprovideddeliberatelyandinordertofacilitatethepersistentandsounddevelopmentofJointCloudecosystem,weproposeadynamicandcustomizedreputationsystemframework(DC-RSF)toevaluatethecredibilityofcloudservicevendors.AtthecoreofDC-RSFisthecustomizedanddynamiccredibilitymodel(CDCM),whichcalculatescreditvalueforeachcloudservicevendorbasedonservicerequirementsofendusersandcredentialattributesofcloudservicevendors.WefurtherincorporateaBlockchain-basedmoduleintoDC-RSFtopreventthecreditvaluefrombeingartificiallytampered.

WebServiceApplianceBasedonUnikernelKaiYu(NationalLabforParallelandDistributedProcessing),ChengfeiZhang(NationalLabforParallelandDistributedProcessing),YunxiangZhao(NationalLabforParallelandDistributedProcessing)

Mini-OSisatinyOS(operatingsystem)kerneldistributedwithXenProjectHypervisor.ItismainlyusedasanOSforstubdomainaimedatDom0disaggregationandalsoasteppingstoneforUnikerneldevelopment.WeimplementedasimplehttpserveronMini-OS,andbuiltMini-OSintoawebserviceappliance.WeevaluateditsperformancecomparedwiththesameimplementedserveronUbuntuPV(para-virtualization)DomU,andachievedabout39%performanceimprovement.TheresultsshowsthatMini-OScanbeawebserviceapplianceandhasagoodperformance.

AnalysisandEvaluationoftheGASModelforDistributedGraphComputationWangJinyan(NationalLabforParallelandDistributedProcessing),ZhangChengfei(NationalLabforParallelandDistributedProcessing)

Comparedwithdistributedgraphcomputation,traditionallysinglenodecomputationisunfittedinprocessinglargescalegraphdata.TheGAS(Gather,ApplyandScatter)Modelisauniversalvertex-cutgraphcomputationprogrammingmodelbasedonedge-centricprogramstosupportgraphalgorithms,whichprocessdistributedgraphcomputationaftergraphpartition.Inthispaper,weintroducethatthreeminor-stepsofGAS.WethenanalyzemorecompleteprocessofGASconsideringintra-nodecomputationandinter-nodecommunicationofdistributedgraphcomputation.Basedonouranalysis,weevaluatetheperformanceindifferentnodesofgraphanalysisalgorithmapplyingGASmodel.Theevaluationshowsthatthebottleneckiscomputationperformanceorcommunicationbandwidthdependingonnumberofnodes,whichisaninspirationofoptimizingtheGASmodel.

TrafficSignsDetectionBasedonFasterR-CNNZhongrongZuo(NationalLabforParallelandDistributedProcessing),KaiYu(NationalLabforParallelandDistributedProcessing),QiaoZhou(NationalLabforParallelandDistributedProcessing),XuWang(NationalLabforParallelandDistributedProcessing),TingLi(NationalLabforParallelandDistributedProcessing)

Inthispaper,weuseaadvancedmethodcalledFasterR-CNNtodetecttrafficsigns.Thisnewmethodrepresentsthehighestlevelinobjectrecognition,whichdon'tneedtoextractimagefeaturemanuallyanymoreandcansegmentimagetogetcandidateregionproposalsautomatically.Ourexperimentisbasedonatrafficsigndetectioncompetitionin2016byCCFandUISEEcompany.ThemAPvalueoftheresultis0.3449thatmeansFasterR-CNNcanindeedbeappliedinthisfield.Eventhoughtheexperimentdidnotachievethebestresults,weexploreanewmethodintheareaofthetrafficsignsdetection.Webelievethatwecangetabetterachievementinthefuture.

JCLedger:ABlockchainBasedDistributedLedgerforJointCloudComputing

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XiangFu(NationalUniversityofDefenseTechnology),HuaiminWang(NationalUniversityofDefenseTechnology),PeichangShi(NationalUniversityofDefenseTechnology),YingweiFu(NationalUniversityofDefenseTechnology),YijieWang(NationalUniversityofDefenseTechnology)

WiththedevelopmentofEconomicGlobalization,traditionalsingle-cloudproviderscannotmeettheneedsoftheexplosive,global,diversecloudservices.JointCloudaimsatempoweringthecooperationamongmultipleCloudServiceProviders(CSP)toprovidecross-cloudservices.OurworkinthispaperismainlyfocusedontheaccountingtechnologyforJointCloudcomputingandweproposetheJCLedger-ablockchainbaseddistributedledger.AnewparticipantCCP(CryptocurrencyProvider)isintroducedintotheJointCloudcollaborationenvironmenttoprovidethecryptocurrencytransferred.WehaveadetaileddescriptionofJCLedgermodel.WefurtheranalyzethefourmostimportantmechanismsforJCLedgerandprovidebasicperspectivesforin-depthanalysis.Finally,wediscusstheinnovationsofJCLedgerandourfutureworkinthisfield.

CorporationArchitectureforMultipleCloudServiceProvidersinJointCloudComputingPeichangShi(NationalUniversityofDefenseTechnology),HuaiminWang(NationalUniversityofDefenseTechnology),XikunYue(NationalUniversityofDefenseTechnology),ShilanYang(NationalUniversityofDefenseTechnology),ShangzhiYang(NationalUniversityofDefenseTechnology),YuxingPeng(NationalUniversityofDefenseTechnology)

Nowadays,cloudcomputingishardtoeffectivelysustaintheimplementationofthecommercialmodelofInternetServiceglobalization.Thereisagrowingtrendtobuildanenvironmentofcloudservice,withthecapacitytoserveanytimeandanywhere,bymutualcooperationbetweencloudserviceprovidersaroundtheworld.However,thistendencywillraiseakeyissuewhichishowtoprovideabenignenvironment,thatallowsself-collaborationandfaircompetition,fordifferentcloudserviceproviderswithdiversestakeholder.GuidedbytheconceptandstructureofService-OrientedArchitecture(SOA)service,thispaperproposesastructurenamedJointCloudCorporationEnvironment(JCCE),whichoffersamutualbenefitandwin-winJointCloudenvironmentforglobalcloudserviceproviders.JCCEcontainsthreecoreservices,whichareDistributedCloudTransaction,DistributedCloudCommunityandDistributedCloudSupervision.Also,facingwithdifferentcloudserviceparticipants,JCCEoffersthreemainservicemodesfortheirconsumption,supplyandcoordination.Thisstudyplaysasignificantroleinsupportingthesharingandself-collaborationofmultiplecloudentities,andpromotingthedevelopmentofcloudservicemarkethealthyandorderly.

SharingPrivacyDatainSemi-TrustworthyStoragethroughHierarchicalAccessControlYuzhaoWu(TsinghuaUniversity),YongqiangLyu(TsinghuaUniversity),QianFang(TsinghuaUniversity),GengZheng(TsinghuaUniversity),HaoYin(TsinghuaUniversity),YuanchunShi(TsinghuaUniversity)

Dataoutsourcingincloudisemergingasasuccessfulparadigmthatbenefitsorganizationsandenterpriseswithhigh-performance,low-cost,scalabledatastorageandsharingservices.However,thisparadigmalsobringsforthnewchallengesfordataconfidentialitybecausetheoutsourcedarenotunderthephysiccontrolofthedataowners.Theexistingschemestoachievethesecurityandusabilitygoalusuallyapplyencryptiontothedatabeforeoutsourcingthemtothestorageserviceproviders(SSP),anddisclosethedecryptionkeysonlytoauthorizeduser.Theycannotensurethesecurityofdatawhileoperatingdataincloudwherethethird-partyservicersareusuallysemi-trustworthy,andneedlotsoftimetodealwiththedata.WeconstructaprivacydatamanagementsystemappendinghierarchicalaccesscontrolcalledHAC-DMS,whichcannotonlyassuresecuritybutalsosaveplentyoftimewhenupdatingdataincloud.

AReliabilityBenchmarkforBigDataSystemsonJointCloudYingyingZheng(InstituteofSoftware,ChineseAcademyofSciences),LijieXu(InstituteofSoftware,ChineseAcademyofSciences),WeiWang(InstituteofSoftware,ChineseAcademyofSciences),WeiZhou(KSYUN),YingDing(ChangchunUniversityofScienceandTechnology)

JointCloudprovidesaflexibleandelasticcomputingresourceplatform.BigdatasystemssuchasMapReduceandSparkarewidelydeployedonthisplatformforbigdataprocessing.Theseframeworkshavehighscalability,buttheapplicationsrunningatopthemoftengenerateruntimeerrors,suchasoutofmemoryerrors,IOexceptions,andtasktimeouts.Forusers,theywanttoknowwhetherthedevelopedapplicationshavepotentialapplicationfaults.Forsystemdesignersandmanagers,theywanttoknowwhetherthedeployed/updatedframeworkshavepotentialsystemfaults.Currentperformancebenchmarkingcanchoosesuitablecloudsplatformforcustomers.However,theydonotconsiderreliabilityofapplicationsdeployedonthecloud.Inaddition,currentbenchmarksforbigdatasystemarealsoonlydesignedforperformancetesting.Tofillthisgap,weproposeareliabilitybenchmark,whichcontainsrepresentativeapplications,anabnormaldatagenerator,andaconfigurationcombinationgenerator.Differentfromperformancebenchmarks,thisbenchmark(1)generatesabnormaltestdataaccordingtotheapplicationcharacteristics,and(2)reducestheconfigurationcombinationspacebasedonconfigurationfeatures.Currently,weimplementedthisbenchmarkonSparkframework.Inourpreliminarytest,wefoundthreetypesoferrors(i.e.,outofmemoryerror,timeoutandwrongresults)infiveSQL,MachineLearning,andGraphapplications.

UCPR:UserClassificationandInfluenceAnalysisinSocialNetworkCongZha(TsinghuaUniversity),YongqiangLv(TsinghuaUniversity)

Therearevigorousdevelopmentsofsocialnetworkwhichaffectoutlifegreatly.Userinfluenceisanimportantreasontopro-motetheinteractioninsocialnetwork.Whenweanalyzeuserinfluence,singlevaluecan’tindicatetheuserinfluenceindifferentdomains.ThispaperputsforwardthedesignofUserClassi-ficationPageRank(UCPR)tosolvethisproblem.Firstly,weclassifyusersaccordingtothecontentwhichtheyforwarded.Then,weusespacemappingtosetupseveralsubnet.Finally,weanalyzeuserinfluenceineveryspecificsubnetbyDomainMappedNetwork(DMN)whichisbasedonPageRankalgorithmandweimprovethisalgorithmtoanalyzetheuserinfluenceindifferentdomains.Throughtheworkofthispaper,weusedavectortopresentuserinfluenceratherthanasinglenumberandwetestandverifiedthelong-taileddistributionsofsocialnet-workinexperiments.

AdaptiveRoutingAlgorithmforJointCloudVideoDeliveryZexunJiang(TsinghuaUniversity),HaoYin(TsinghuaUniversity)

AstheInternetkeepsgrowing,onlinevideohasbecomeagreatpartofthecurrentInternetdatatraffic,whichwilltakeover80%ofInternettrafficaccordingtoCiscosreport.Also,newandmoreheavyweightapplicationskeepdevelopingtofulfillpeople’sgrowingrequirements,like4kresolutionandvisualrealityvideos.However,onesingleserviceprovider,likeaContentDeliveryNetwork(CDN),cannotmeettheperformancerequirementscompletely.ToemploythepotentialofJointCloud,thispaperdesignsandimplementsanewrequestroutingalgorithmthatcanmakevideodeliveryutilizemultiplecloudsandservers.Onthepremiseofguaranteeingthequalityofvideoplaying,thisalgorithmminimizesthecostofserviceresourcesbasedondifferentinfrastructuresservicequality,cost,andcover

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areas.Basedonthisalgorithm,weimplementapracticalvideodeliverysystemusinglight-weight,flash-basedterminals.Andthissystemprovideslivevideoandvideo-on-demanddeliveryserviceforChinaFutureNetworksIndustrySummit2014onJune4th.Theactualuserdatawasgatheredandanalyzedtoverifytheeffectivenessofthisalgorithm.90.2%ofthetotalVODrequestswerecompletedsmoothlywithoutpause,andthevideotrafficwasoptimizedbythealgorithm.

TowardsEfficientResourceManagementinVirtualCloudsBoAn(PekingUniversity),JunmingMa(PekingUniversity),DonggangCao(PekingUniversity),GangHuang(PekingUniversity)

Theuseofmultiplecloudsbringsmanyadvantages:costoptimization,Quality-of-Service(QoS)improvements,highavailability,avoidanceofvendorlock-in,disasterrecoveryandsoon.However,currentlythecloudvendorislargelyproprietaryanddifferentcloudvendorshavetheirownheterogeneousinfrastructure,makingitdifficultforuserstoutilizeresourcefrommultiplecloudvendors.Asaresult,usershavetomanagedistributedapplicationsspanningmultiplecloudsandtakeintoconsiderationtheservicesmigrationforreasonslikebestcostefficiency.Inthispaper,weintroducesthenotionofVirtualCloudandfocusontheissuesrelatedtomulti-cloudresourcemanagementinVirtualCloud.VirtualCloudisacustomizedcloudbyaggregatingresourcesandservicesofdifferentcloudsandaimstoprovideenduserswithaspecificcloudworkingenvironment.Itwilleaseusers’burdenofresourceanddistributedapplicationmanagementaswellastheworkloadmigrationacrosscloud.

MonitoringandBillingofALightweightCloudSystemBasedonLinuxContainerYujianZhu(PekingUniversity),JunmingMa(PekingUniversity),BoAn(PekingUniversity),DonggangCao(PekingUniversity)

Nowadays,moreandmoreenterprisesandresearchinstituteschoosetobuildmini-datacentersanddeployprivatecloudenvironmentstomeetgrowingbusinessandresearchneeds.Tomakeuserscanrundifferentapplicationframeworksonthesamedatacenter,Caoetal.proposedanewservicemodelnamedClaaS(ClusterasaService)anddevelopedalightweightprototypesystemnamedDockletwhichisbasedonLXC(LinuxContainer).Dockletfacesaproblemofresourceswasteandabuseduetoourfreepolicy.ThispaperintroducesthemonitoringandbillingmodulesofDockletinordertosolvethisproblem.Monitoringmoduleprovidesusersandadministratorswithaclear,real-timeanddetailedmonitoringinterfacetounderstandthestatuesofrunningapplicationsandtheusageofphysicalresources.Billingmoduleusesthesedatatoreminduserstoreleaseunnecessaryresources.Anexperimentandobservationsshowthatourproposedmonitoringmethodiseffectiveandlightweightandourproposedbillingmodelincreasestheutilizationofphysicalresourcesofamini-datacenter.

Buildingemulationframeworkfornon-volatilememoryGuoliangZhu(NationalUniversityofDefenseTechnology),KaiLu(NationalUniversityofDefenseTechnology),XiaopingWang(NationalUniversityofDefenseTechnology)

Currently,researchersusesimulatorstoexperimenttheirinnovationonemergingnon-volatilememory.Unfortunately,simulationmethodisbothtime-consumingandarehardtodebug.Inthispaper,wepresentanon-volatilememoryemulatorwhichenablessystem-levelresearchonemergingmemory.Ouremulatorusesperformancemonitoringunitsonoff-the-shelfprocessorstoimplementanaccureteperformancemodel.

Seflow:EfficientFlowSchedulingforData-ParallelJobsQiaoZhou(NationalLabforParallelandDistributedProcessing),ZiyangLi(NationalLabforParallelandDistributedProcessing),PingZhong(CentralSouthUniversity),TianTian(NationalLabforParallelandDistributedProcessing),YuxingPeng(NationalLabforParallelandDistributedProcessing)

Data-paralleljobstransfermassiveamountsofdatabetweenaseriesofsuccessivestages.Thecoflowabstractionisproposedtorepresentagroupofparallelflowsbetweentwostagesandefficientlyimprovesstagelevelperformance.However,state-of-the-artcoflowschedulingtechniquesareagnostictothejobs’intercoflowsemanticsandthusaresuboptimalinreducingtheaveragejobcompletiontimes(JCT).Toaddressthisproblem,inthispaperwepresentthe“semanticflow”(seflow)abstractiontoexpressthejob-levelintercoflowsemantics.Aseflowcomprisesnotonlyallthecoflowsofajobbutalsotherelationshipbetweenthecoflows.Wedesignanefficientseflowschedulerwhichutilizestherichseflowsemanticsofjobstoachievebetterperformancethanseflow-agnosticschedulingfordataparalleljobs.

OnlineEncodingforErasure-CodedDistributedStorageSystemsFangliangXu(NationalUniversityofDefenseTechnology),YijieWang(NationalUniversityofDefenseTechnology),XingkongMa(NationalUniversityofDefenseTechnology)

Manylarge-scaledistributedstoragesystemsdeployerasurecodingtoprotectdatafromfrequentserverfailuresforcostreason.Inmostofthesesystems,newlyinserteddataisfirstreplicatedacrossdifferentstoragenodesandthenmigratedtoerasurecoded.Althoughthisofflineencodingmannercanimproveperformanceofdataaccessbeforeerasurecodingforsomesystems,ithelpslittleandwastesmanynetworkresourcesanddiskresourcesformanyothersystems.Inthisstudy,weproposeanonlineencodingmethod,whichencodesdataassoonasitisinsertedintothesystem.Byeliminatingthemigrationprocess,ouronlineencodingcansignificantlyreducenetworktransferanddataread;bycachingtheintermediateparityblocksintomemory,ouronlineencodingalsosignificantlyreducedatawrite.Analysisshowthatouronlineencodingcanreducedatatransferbymorethan25%,reducedatawriteby57%atleastandeliminatealldataread,comparedtotraditionalofflineencoding.

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PED 2017 Workshop Abstracts

WED-SQL:ARelationalFrameworkforDesignandImplementationofProcess-AwareInformationSystemsBrunoPadilha(UniversityofSaoPaulo),AndréLuisSchwerz(FederalUniversityofTechnology),RafaelLiberatoRoberto(FederalUniversityofTechnology)

Despitethesignificantevolutionofthedesignandimplementationofbusinessprocessmodels,atransactionalapproachthatevolvesanincrementalandadaptivestrategyremainsanimportantchallengetobeovercome.TraditionalframeworkssuchasBPEL,ProcessAlgebra,andPetriNetrequireanadditionalsoftwarelayerorsomethirdpartytoolkitstobeabletoenforceadata-statebasedtransactioncontrolanddealwithsemanticexceptions.However,thecomplexityofimplementationbasedonthesetraditionalframeworks,especiallytotreatexceptions,istoohigh.Inthispaper,wepresenttheWED-SQL,adistributedframeworkthatprovidesareliableandefficientwaytodesignandimplementbusinessprocesses.OurmaincontributionistheintegrationofWED-flowconceptsintothePostgresSQLRDBMS.ThisintegrationenablestheWED-SQLtotakefulladvantageoftransactionalpropertiesandalsobenefitfromtheSQLlanguagetospecifytheWED-flowdefinitions.

QueryingWorkflowLogsYanTang(UniversityofCaliforniaatSantaBarbara),JianwenSu(UniversityofCaliforniaatSantaBarbara)

Abusinessprocess(BPorworkflow)isanassemblyoftaskstoaccomplishabusinessgoal.Businessprocessmanagement(BPM)isastudytoprovidesupportforthedesign,configuration/implementation,enactmentandmonitoring,diagnose/analysis,andre-designofworkflow.Businessanalyticsorintelligence(BI)isanecessarysteptowardsre-design/improvement.ThetraditionalmethodologyforBIisthewellknownsequenceofETL,data/processwarehouse,andOLAPtools.Inthispaper,wefocusontheproblemofadhocqueryingprocessenactmentsfordata-centricbusinessprocesses.Wedevelopanalgebraicquerylanguagebasedon“incidents”toallowtheusertoformulateadhocqueriesdirectlyonworkflowlogs.Aformalsemanticsandanpreliminaryqueryevaluationalgorithmareprovided.

Ontheintegrationofevent-basedandtransaction-basedarchitecturesforSupplyChainsZhijieLi(IndianaUniversity–PurdueUniversityIndianapolis),HaoyanWu(IndianaUniversity–PurdueUniversityIndianapolis),BrianKing(IndianaUniversity–PurdueUniversityIndianapolis),ZinaBen-Miled(IndianaUniversity–PurdueUniversityIndianapolis),JohnWassick(TheDowChemicalCompany),JeffreyTazelaar(TheDowChemicalCompany)

Affordableandreliablesupplychainvisibilityisbecomingincreasinglyimportantasthecomplexityofthenetworkunderlyingsupplychainsisbecomingordersofmagnitudeshighercomparedtoadecadeago.Moreoverthisincreaseincomplexityisstartingtoreflectonthecostofgoodsandtheiravailabilitytotheconsumers.Thispaperaddressestwokeyissuesinthedistributionphaseofthesupplychain,namely,affordabilityandpseudoreal-timevisibilityoftruckloadactivities.Theproposedframeworkcreatesadigitalthreadthattracksthepseudoreal-timestatusoftheshipmentmakingthephysicaldistributionprocesscompletelytransparenttothestakeholders.Thearchitectureoftheframeworkisbasedonadynamichybridpeer-to-peernetworkandaprivate/publicblockchaindatamodelthatleveragesemergentsensortechnologies.

CacheDOCS:ADynamicKey-ValueObjectCachingServiceJulienGascon-Samson(UniversityofBritishColumbia),MichaelCoppinger(McGillUniversity),FanJin(McGillUniversity),JörgKienzle(McGillUniversity),BettinaKemme(McGillUniversity)

Cachingplaysanimportantroleinmanydomains,asitcanleadtoimportantperformanceimprovements.Akey-valuebasedcachingsystemtypicallystorestheresultsofpopularqueriesinefficientstoragelocation.Whilecachingenjoyswidespreadusageinthecontextofdynamicwebapplications,mostmainstreamcachingsystemsstorestaticbinaryitems,whichmakesthemimpracticalformanyreal-worldapplicationsthatwouldbenefitfromstoringdynamicitems.Inthispaper,weproposeCacheDOCS,adynamickey-valueobjectcachingservicethatallowsforcachingarbitraryobjects.Aspartofourmodel,CacheDOCSprovidesanAPIthatsupportstheexecutionofoperationsagainstcachedobjects,andallowsforclientstoseamlesslysubscribetokeeptheirlocalcopiesinsyncwithcachedremoteobjects.CacheDOCSsupportsmultipleupdatedisseminationstrategiesinordertooptimizeperformance,andproposesaversioningmechanismtoensureconsistency.WeimplementedafullversionofCacheDOCSandweranseveralperformance-relatedexperimentsunderthreeuse-casescenarios.

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PSBD 2017 Workshop Abstracts

Anovelgame-theoreticmodelforcontent-adaptiveimagesteganographyQiLi(HunanUniversity),XinLiao(HunanUniversity),GuoyongChen(HunanUniversity),LipingDing(GuangzhouBranchofInstituteofSoftware,ChineseAcademyofScience)

Content-adaptiveimagesteganographymeansthatsteganographerchoosessecurityembeddingpositionsbasedonimagetextures.Steganalystcanalsofocusondetectingthesepositionsaccordingtoimagetextures.Gametheoryispreferredtoanalyzetheabovesituation.However,inpreviousgamemodels,steganalystwillmistakenlyidentifythatnobitisembedded,whenthesecretbitisthesameastheleastsignificantbitofcoverimage.Inthispaper,anovelgametheoreticmodelbasedonsecondaryembeddingisproposedtocorrectthejudgmentdrawbackforabetterNashequilibriumbysteganalyst.However,steganalyst’schoicedisturbspreviousequilibriumandsteganographerwillchangehischoicetofindnewequilibriumbyGametheory.Co-occurrencematrixandpointdeviationdegreeareutilizedfordescribingsteganalyst’schoices.Theoccurrencenumberofeachpixelpairsiscalculatedtoconstituteco-occurrencematrix,andthenEuclideandistancebetweenonepointandadjacentpointsiscomputedtolocateembeddingpositions.Incontent-adaptiveimagesteganography,wecandrawaconclusionthatsteganographershouldselectembeddingpositionsfrombothimageedgeareasandsmoothareas.

AFine-grainedAccessControlSchemeforBigDataBasedonClassificationAttributesTengfeiYang(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),PeisongShen(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),XueTian(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),ChiChen(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences)

Inordertoprotectthesecurityandprivacyofbigdata,thecloudstorageserviceneedstoenforceeffectiveaccesscontrolmechanismonuserrequests.Attribute-BasedEncryptionisapromisingcryptographicaccesscontroltechniquetoensuretheend-to-endsecurityofdataincloud.However,theexistingABEresearchesmainlyfocusontheefficiencydecryption,whiletheflexibilityofpolicy,thecommunicationcost,andthemetadatamanagementofciphertextsarestillchallengingissuesinthebigdataenvironment.Inthispaper,forthefirsttime,weproposeanewdistributed,scalableandfine-grainedaccesscontrolschemebasedonclassificationattributesforthecloudobjectstorage.Theclassificationattributesandthresholdpoliciesareintegratedintoanaccessstructure,andthentheobjectsareencryptedwiththeintegratedaccessstructure.Theconstant-sizeciphertextcomponentsrelatedtoattributescanbemanagedasthecorrespondingmetadata.Asaresulttheencryptioncomplexityandciphertextstoragearereduced.Inaddition,wepresentanewlabel-basedaccesscontrolmodelwithmulti-authoritiestodescribethedetailedrelationshipsofentitiesinourscheme.Besides,theproposedschemeisprovedtobesecureunderlBDHEassumption,andthesystemimplementationdemonstratesthepracticalfeasibilityandgoodperformance.

Social-AwareDecentralizationforEfficientandSecureMulti-PartyComputationYuzheTang(SyracuseUniversity),SuchetaSoundarajan(SyracuseUniversity)

ThisworkstudiestheproblemofMPCschedulingthatis,identifyingasetofcomputingnodestoexecutesecuremulti-partycomputationprotocols(MPC)overadistributedprivatedataset.Ourprimarycontributionisinestimatingtheriskofcollusionbetweennodestowhomthecomputationisscheduled.Thisworkhaspotentialinenablingefficientprivacy-preservingdatasharinginemergingplatformsofbig-datafederation,inhealthcare,finance,andothermarketplaces.Inourmethods,weassumethattheMPCcomputingnodesexistinasocialnetwork,andpresenttwomodelsforestimatingtheriskofcollusion,aswellasalgorithmsforfindingtheMPCnodessuchthattheriskofcollusionisminimized.Weevaluateourmethodsonseveralreal-worldnetworkdatasets,andshowthattheyareeffectiveinminimizingtherisklevels.

StatisticalAnomalyDetectiononMetadataStreamsviaCommoditySoftwaretoProtectCompanyChristineChen(UniversityofPortland),JamesGurganus(MicroSystemsEngineering,Inc.)

Asacompanygrows,itsinfrastructurenaturallymustgrowtosupportit.Theresultingmountainsofinfrastructuremetadatacontainvaluableinformationonthehealthandwellbeingofthesystemsthroughoutthecompany.Forexample,anabnormallylowdiskwriteratetoafileservermayindicatethataregularlyscheduledtaskhasfailedtostart,oranabnormallyhighdiskwriteratemayindicatethepresenceofamaliciousthreatsuchasransomware.Thehypothesisofthiscasestudyisthatsuchmetadatastreamscanbeeffectivelyutilizedbyimplementingstatisticalanomalydetectionmethodsviacommoditysoftware(Splunk,inthiscase).Thesemethodsweretestedprimarilyonservermetadatainaransomwaresimulationandalsoonservermetadatafromfileserversandproductionserversinactiveuse.

Intheransomwaresimulation,thealertingsystemdetectedtheransomwarebehaviorfiveminutesafteranencryptioneventbeganinthesimulationenvironmentandalertedsteadilyforthedurationofthesimulation.Intheweek-longexperimentover11fileserversandproductionservers,atotalof1,484alertsweregenerated.Applyingsimplecorrelationtechniquescreatedamoreconcentratedinformationstreamwith77events.Theseresultsconfirmthevalueofmetadatainidentifyingsystemanomaliesandprovidinganotherlayerofdefenseagainstmaliciousthreats.Therelativelysimpleanomalydetectiontechniquesutilizedinthiscasestudyalsohighlighttheincreasingpracticalityofbehavioralanalytics—itcanonlybeamatteroftimebeforesuchtechniqueswillbeubiquitous.

Computationalimprovementsinparallelizedk-anonymousmicroaggregationoflargedatabasesAhmadMohamadMezher(UniversitatPolitècnicadeCatalunya),AlejandroGarcíaÁlvarez(UniversitatPolitècnicadeCatalunya),DavidRebollo-Monedero(UniversitatPolitècnicadeCatalunya),JordiForné(UniversitatPolitècnicadeCatalunya)

Thetechnicalcontentsofthispaperfallwithinthefieldofstatisticaldisclosurecontrol(SDC),whichconcernsthepostprocessingofthedemographicportionofthestatisticalresultsofsurveyscontainingsensitivepersonalinformation,inordertoeffectivelysafeguardtheanonymityoftheparticipatingrespondents.Theconcretepurposeofthisstudyistoimprovetheefficiencyofawidelyusedalgorithmfork-anonymousmicroaggregation,knownasmaximumdistancetoaveragevector(MDAV),tovastlyaccelerateitsexecutionwithoutaffectingitsexcellentfunctionalperformancewithrespecttocompetingmethods.Theimprovementsputforthinthispaperencompassalgebraicmodificationsandtheuseofthebasiclinearalgebrasubprograms(BLAS)library,fortheefficientparallelcomputationofMDAVonCPU.

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WoSC 2017 Workshop Abstracts

Ripple:HomeAutomationforResearchDataManagementRyanChard(ArgonneNationalLaboratory),KyleChard(UniversityofChicagoandArgonneNationalLab),JasonAlt(NationalCenterforSupercomputingApplications),DilworthParkinson(LawrenceBerkeleyNationalLaboratory),SteveTuecke(UniversityofChicagoandArgonneNationalLab),IanFoster(ArgonneNationalLaboratory&TheUniversityofChicago)

Explodingdatavolumesandacquisitionrates,plusevermorecomplexresearchprocesses,placesignificantstrainonresearchdatamanagementprocesses.Itisincreasinglycommonfordatatoflowthroughpipelinescomprisedofdozensofdif-ferentmanagement,organization,andanalysisstepsdistributedacrossmultipleinstitutionsandstoragesystems.Toalleviatetheresultingcomplexity,weproposeahomeautomationapproachtomanagingdatathroughoutitslifecycle,inwhichusersspecifyviahigh-levelrulestheactionsthatshouldbeperformedondataatdifferenttimesandlocations.Tothisend,wehavedevelopedRIPPLE,aresponsivestoragearchitecturethatallowsuserstoexpressdatamanagementtasksviaarulesnotation.RIPPLEmonitorsstoragesystemsforevents,evaluatesrules,andusesserverlesscomputingtechniquestoexecuteactionsinresponsetotheseevents.WeevaluateoursolutionbyapplyingRIPPLEtothedatalifecyclesoftworeal-worldprojects,inastronomyandlightsourcescience,andshowthatitcanautomatemanymundaneandcumbersomedatamanagementprocesses.

Pipsqueak:LeanLambdaswithLargeLibrariesEdwardOakes(UniversityofWisconsin-Madison),LeonYang(UniversityofWisconsin-Madison),KevinHouck(UniversityofWisconsin-Madison),TylerHarter(MicrosoftGraySystemsLab),AndreaC.Arpaci-Dusseau(UniversityofWisconsin-Madison),RemziH.Arpaci-Dusseau(UniversityofWisconsin-Madison)

Microservicesareusuallyfasttodeploybecauseeachmicroserviceissmall,andthuseachcanbeinstalledandstartedquickly.Unfortunately,leanmicroservicesthatdependonlargelibrarieswillstartslowlyandharmelasticity.Inthispaper,weexplorethechallengesofleanmicroservicesthatrelyonlargelibrariesinthecontextofPythonpackagesandtheOpenLambdaserverlesscomputingplatform.WeanalyzethepackagetypesandcompressibilityoflibrariesdistributedviathePythonPackageIndexandproposePipBench,anewtoolforevaluatingpackagesupport.WealsoproposePipsqueak,apackage-awarecomputeplatformbasedonOpenLambda.

LeveragingtheServerlessArchitectureforSecuringLinuxContainersNiltonBila(IBM),PaoloDettori(IBM),AliKanso(IBM),YujiWatanabe(IBM),AlaaYoussef(IBM)

Linuxcontainerspresentalightweightsolutiontopackageapplicationsintoimagesandinstantiatetheminisolatedenvironments.Suchimagesmayincludevulnerabilitiesthatcanbeexploitedatruntime.Avulnerabilityscanningservicecandetectthesevulnerabilitiesbyperiodicallyscanningthecontainersandtheirimagesforpotentialthreats.Whenathreatisdetected,aneventmaybegeneratedto(1)quarantineorremovethecompromisedcontainer(s)andoptionally(2)remedythevulnerabilitybyrebuildingasecureimage.Webelievethatsuchevent-drivenprocessisagreatfittobeimplementedinaserverlessarchitecture.InthispaperwepresentourdesignandimplementationofaserverlesssecurityanalyticsservicebasedonOpenWhiskandKubernetes.

ServerlessComputing:Design,Implementation,andPerformanceGarrettMcGrath(UniversityofNotreDame),PaulR.Brenner(UniversityofNotreDame)

Wepresentthedesignofanovelperformance-orientedserverlesscomputingplatformimplementedin.NET,deployedinMicrosoftAzure,andutilizingWindowscontainersasfunctionexecutionenvironments.Implementationchallengessuchasfunctionscalingandcontainerdiscovery,lifecycle,andreusearediscussedindetail.WeproposemetricstoevaluatetheexecutionperformanceofserverlessplatformsandconducttestsonourprototypeaswellasAWSLambda,AzureFunctionsandIBM’sdeploymentofApacheOpenWhisk.Ourmeasurementsshowtheprototypeachievinggreaterthroughputthanotherplatformsatmostconcurrencylevels,andweexaminethescalingandinstanceexpirationtrendsintheimplementations.Additionally,wediscussthegapsandlimitationsinourcurrentdesign,proposepossiblesolutions,andhighlightfutureresearch.

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NSF-JST 2017 Workshop Abstracts

AcceleratingBigDataInfrastructureandApplicationsKevinBrown(TokyoInstituteofTechnology),TianqiXu(TokyoInstituteofTechnology),KeitaIwabuchi(TokyoInstituteofTechnology),KentoSato(LawrenceLivermoreNationalLaboratory),AdamMoody(LawrenceLivermoreNationalLaboratory),KathrynMohror(LawrenceLivermoreNationalLaboratory),NikhilJain(LawrenceLivermoreNationalLaboratory),AbhinavBhatele(LawrenceLivermoreNationalLaboratory),MartinSchulz(LawrenceLivermoreNationalLaboratory),RogerPearce(LawrenceLivermoreNationalLaboratory),MayaGokhale(LawrenceLivermoreNationalLaboratory),SatoshiMatsuoka(TokyoInstituteofTechnology)

High-performancecomputing(HPC)systemsareincreasinglybeingusedfordata-intensive,or``BigData",workloads.However,sincetraditionalHPCworkloadsarecompute-intensive,theHPC-BigDataconvergencehascreatedmanychallengeswithoptimizingdatamovementandprocessingonmodernsupercomputers.Ourcollaborativeworkaddressesthesechallengesusingathree-prongedapproach:(i)measuringandmodelingextreme-scaleI/Oworkloads,(ii)designingalow-latency,scalable,on-demandburst-buffersolution,and(iii)optimizinggraphalgorithmsforprocessingBigDataworkloads.Wedescribethethreeareasofourcollaborationandreportontheirrespectivedevelopments.

DisasterNetworkEvolutionUsingDynamicClusteringofTwitterDataKrishnaKant(TempleUniversity),YilangWu(AizuUniversity),ShanshanZhang(TempleUniversity),JunboWang(AizuUniversity),AmitangshuPal(TempleUniversity)

Adhocsmartphonenetworkscanbeusedtoaugmentcommunicationsdegradedbydisastersprovidedthattheindividualadhocclusterscanreachsome``connectiongateways''togetouttotheInternetviaconnecteddevicesinthesurroundingarea(inadditiontoconnectivityviaanyspeciallydeployedemergencyequipment).Thedisconnectedareasarenotknownuntiltheyarebackonline;however,weneedamechanismtodeterminethemsothatthegatewaydevicecanbebestrecruitedtoprovidetheconnectivity.Thisneedstobedoneinadynamicenvironmentbecauseofdisasterrelatedmobility.Inthispaperweproposeamechanismtosolvethisproblembyestimatingregionsthatarelikelytobedensebutdisconnectedwithsignificantnumberofconnecteddevicesaroundthem.Becauseoflackofdirectinformationonpeople(orsmartphone)density,weattempttodothisbyanalyzingthetwitterdata.Byvirtueofitsefficiency,thealgorithmcanbeusedonadynamicallyevolvingdatasetandthusallowsdynamictracking.

Single-epochsupernovaclassificationwithdeepconvolutionalneuralnetworksAkisatoKimura(NTT),IchiroTakahashi(KavliIPMU,TheUniversityofTokyo),MasaomiTanaka(NationalAstronomicalObservatoryofJapan),NaokiYasuda(KavliIPMU,TheUniversityofTokyo),NaonoriUeda(NTT),NaokiYoshida(KavliIPMU,TheUniversityofTokyo)

SupernovaeType-Ia(SNeIa)playasignificantroleinexploringthehistoryoftheexpansionoftheUniverse,sincetheyarethebest-knownstandardcandleswithwhichwecanaccuratelymeasurethedistancetotheobjects.FindinglargesamplesofSNeIaandinvestigatingtheirdetailedcharacteristicshasbecomeanimportantissueincosmologyandastronomy.Thecurrentphotometricsupernovasurveysproducevastlymorecandidatesthancanbefollowedupspectroscopically,highlightingtheneedforeffectiveclassificationmethods.Existingmethodsreliedonaphotometricapproachthatfirstmeasurestheluminanceofsupernovacandidatespreciselyandthenfitstheresultstoaparametricfunctionoftemporalchangesinluminance.However,itinevitablyrequiresalotofobservationsandcomplexluminancemeasurements.Inthiswork,wepresentanovelmethodfordetectingSNeIasimplyfromsingle-shotobservationimageswithoutanycomplexmeasurements,byeffectivelyintegratingthestate-of-the-artcomputervisionmethodologyintothestandardphotometricapproach.Ourmethodfirstbuildsaconvolutionalneuralnetworkforestimatingtheluminanceofsupernovaefromtelescopeimages,andthenconstructsanotherneuralnetworkfortheclassification,wheretheestimatedluminancesandobservationdatesareusedasfeaturesforclassification.BothoftheneuralnetworksareintegratedintoasingledeepneuralnetworktoclassifySNeIadirectlyfromobservationimages.Experimentalresultsshowtheeffectivenessoftheproposedmethodandrevealclassificationperformancecomparabletoexistingphotometricmethodswithmanyobservations.

EnablingLargeScaleDeliberationusingIdeationandNegotiation-SupportAgentsKatsuhideFujita(TokyoUniversityofAgricultureandTechnology),TakayukiIto(NagoyaInstituteofTechnology),MarkKlein(MIT)

ThispaperdescribesanongoingJapan-USprojectthatisdevelopingthekindofadvancedcomputersupportforonlinecrowd-scaledeliberationthatisneededtoenablesmarterandmoreconnectedcommunities.Oursharedworkhasfocusedonaddressingboththeseproblems:(1)ideation:helpingcrowdsmoreeffectivelydeveloppotentialwin-winsolutions,and(2)decision-making:helpingcrowdsgettopareto-optimalityinthesolutionstheyselect.InJapan,adiscussionsupportsystemcalledCOLLAGREEthatfacilitatesfreetextdiscussionstoachieveconsensushasbeendeveloping.InUS,anonlinetoolcalledtheDeliberatoriumthatintegratesargumentationtheoryandsocialcomputingtechniquestoenablemoreeffectivecrowd-scaledeliberationhasbeendeveloping.Oneofourimmediatejointworkistointegratethefacilitatedfree-textdiscussionsofCOLLAGREEwiththestructureddeliberationsprovidedbytheDeliberatorium.Wewillalsodevelopautomatedagentsthatenablebetterideationaswellasbetterdecision-making.

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