学术英语论文.docx
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学术英语论文.docx
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学术英语论文
NANCHANGUNIVERSITY
课程名称:
学术英语
题目:
AStudyofEnergyEfficient_
CloudComputingPoweredby
_WirelessEnergyTransfer___
英语班级:
理工1615班
专业/年级:
物联网工程161班
姓名/学号:
(47)
二零一八年六月
AStudyofEnergyEfficientMobileCloudComputingPoweredbyWirelessEnergyTransfer
Abstract
Achievinglongbatterylivesorevenself-sustainabilityhasbeenalongstandingchallengefordesigningmobiledevices.Thisstudypresentsanovelsolutionthatseamlesslyintegratestwotechnologies,mobilecloudcomputingandmicrowavepowertransfer(MPT),toenablecomputationinpassivelow-complexitydevicessuchassensorsandwearablecomputingdevices.Specifically,consideringasingle-usersystem,abasestation(BS)eithertransferspowertooroffloadscomputationfromamobiletothecloud;themobileusesharvestedenergytocomputegivendataeitherlocallyorbyoffloading.AframeworkforenergyefficientcomputingisproposedthatcomprisesasetofpoliciesforcontrollingCPUcyclesforthemodeoflocalcomputing,timedivisionbetweenMPTandoffloadingfortheothermodeofoffloading,andmodeselection.GiventheCPU-cyclestatisticsinformationandchannelstateinformation(CSI),thepoliciesaimatmaximizingtheprobabilityofsuccessfullycomputinggivendata,calledcomputingprobability,undertheenergyharvestinganddeadlineconstraints.Furthermore,thisstudyrevealsthatthetwosimplesolutionstoachievetheobjecttosupportcomputationloadallocationovermultiplechannelrealizations,whichfurtherincreasesthecomputingprobability.Last,thetwokindsofmodessuggestthatthefeasibilityofwirelesslypoweredmobilecloudcomputingandthegainofitsoptimalcontrol.Andthefutureaspecttostudyissimplytobeanswer.
Keywords:
wirelesspowertransfer;energyharvestingcommunications;mobilecloudcomputing;energyefficientcomputing
Introduction
Mobilecloudcomputing(MCC)asanemergingcomputingparadigmintegratescloudcomputingandmobilecomputingtoenhancethecomputationperformanceofmobiledevices.TheobjectiveofMCCistoextendpowerfulcomputingcapabilityoftheresource-richcloudstotheresource-constrainedmobiledevices(e.g.,laptop,tabletandsmartphone)soastoreducecomputationtime,conservelocalresources,especiallybattery,andextendstoragecapacity.Toachievethisobjective,MCCneedstotransferresource-intensivecomputationsfrommobiledevicestoclouds,referredtoascomputationoffloading.Thecoreofcomputationoffloadingistodecideonwhichcomputationtasksshouldbeexecutedonthemobiledeviceoronthecloud,andhowtoschedulelocalandcloudresourcetoimplementtaskoffloading.TheexplosivegrowthofInternetofThings(IOT)andmobilecommunicationisleadingtothedeploymentoftensofbillionsofcloud-basedmobilesensorsandwearablecomputingdevicesinnearfuture(Huang&Chae,2010).Prolongingtheirbatterylivesandenhancingtheircomputingcapabilitiesaretwokeydesignchallenges.Theycanbetackledbytwopromisingtechnologies:
microwavepowertransfer(MPT)forpoweringthemobilescomputation-intensivetasksfromthemobilestothecloudandmobilecomputationoffloading(MCO).Twotechnologiesareseamlesslyintegratedinthecurrentworktodevelopanoveldesignframeworkforrealizingwirelesslypoweredmobilecloudcomputingunderthecriterionofmaximizingtheprobabilityofsuccessfullycomputinggivendata,calledcomputingprobability.TheframeworkisfeasiblesinceMPThasbeenproveninvariousexperimentsforpoweringsmalldevicessuchassensorsorevensmall-scaleairplanesandhelicopters.Furthermore,sensorsandwearablecomputingdevicestargetedintheframeworkareexpectedtobeconnectedbythecloud-basedIOTinthefuture,providingasuitableplatformforrealizingMCO.
Materials
MCOhasbeenanactiveresearchareaincomputersciencewhereresearchhasfocusedondesigningmobile-cloudsystemsandsoftwarearchitectures,virtualmachinemigrationdesigninthecloudandcodepartitioningtechniquesinthemobilesforreducingtheenergyconsumptionandimprovingthecomputingperformanceofmobiles.Nevertheless,implementationofMCOrequiresdatatransmissionandmessagepassingoverwirelesschannels,incurringtransmissionpowerconsumption.Theexistenceofsuchatradeoffhasmotivatedcross-disciplinaryresearchonjointlydesigningMCOandadaptivetransmissionalgorithmstomaximizethemobileenergysavings.Astochasticcontrolalgorithmwasproposedforadaptingtheoffloadedcomponentsofanapplicationtoatime-varyingwirelesschannel.Furthermore,multiusercomputationoffloadinginamulti-cellsystemwasexploredbyShinohara(2014),wheretheradioandcomputationalresourceswerejointlyallocatedformaximizingtheenergysavingsunderthelatencyconstraints.
AccordingtoSwan(2012),thethreshold-basedoffloadingpolicywasderivedforthesystemwithintermittentconnectivitybetweenthemobileandcloud.Lastly,theCPU-cyclefrequenciesarejointlycontrolledwithMCOgivenamoreskilledandincreasinglyappropriate
wirelesschannel.TheframeworkisfurtherdevelopedinthecurrentworktoincludethenewfeatureofMPT(Kostaetal.,2012).Thisintroducesseveralnewdesignchallenges.Amongothers,thealgorithmicdesignoflocalcomputingandoffloadingbecomesmorecomplexundertheenergyharvestingconstraintduetoMPT,whichpreventsenergyconsumptionfromexceedingtheamountofharvestedenergyateverytimeinstant.AnotherchallengeisthatMPTandoffloadingtimesharethemobileantennaandthetimedivisionhastobeoptimized.
Nowthetechnologyisbeingfurtherdevelopedtopowerwirelesscommunications.Thishasresultedintheemergenceofanactivefieldcalledsimultaneouswirelessinformationandpowertransfer(SWIPT).TheMPTtechnologyhasbeendevelopedforpoint-to-pointhighpowertransmissioninthepastdecades(Brown,1984).Furthermore,existingwirelessnetworkssuchascognitiveradioandcellularnetworkshavebeenredesignedtofeatureMPT.MostpriorworkonSWIPTaimsatoptimizingcommunicationtechniquestomaximizetheMPTefficiencyandsystemthroughput.Incontrast,thecurrentworkfocusesonoptimizingthelocalcomputingandoffloadingunderadifferentdesigncriterionofmaximumcomputingprobability(Huang&Lau,2014).
MethodsandResults
Considerasingle-usersystemcomprisingonemulti-antennabasestation(BS)usingtransmit/receivebeamformingfortransferringpowertoasingle-antennamobileorrelayingoffloadeddatafromthemobiletothecloud.Tocomputeafixedamountofdata,themobileoperatesinoneofthetwoavailablemodes:
Localcomputingandoffloading:
inthemodeoflocalcomputing,MPToccurssimultaneouslyascomputingbasedonthecontrollableCPU-cyclefrequencies.Nevertheless,inthemodeofoffloading,thegivencomputationdurationisadaptivelypartitionedforseparateMPTandoffloadingsincetheysharethemobileantenna(Shinohara,2014).AssumethatthemobilehastheknowledgeofstatisticsinformationofCPUcyclesandchannelstateinformation(CSI).Theindividualmodesaswellasmodeselectionareoptimizedformaximizingthecomputingprobabilityundertheenergyharvestinganddeadlineconstraints.Fortractability,themetricistransformedintoequivalentones,namelyaveragemobileenergyconsumptionandmobileenergysavings,forthemodesoflocalcomputingandoffloading,respectively.Comparedwiththepriorwork,thecurrentworkintegratesMPTwiththemobilecloudcomputing,whichintroducesnewtheoreticalchallenges.Inparticular,theenergyharvestingconstraintarisingfromMPTmakestheoptimizationproblemforlocalcomputingnon-convex.Totacklethechallenge,theconvexrelaxationtechniqueisappliedwithoutcompromisingtheoptimalityofthesolution.ItisshowninthesequelthatthelocalcomputingpolicyisaspecialcaseofthecurrentworkwherethetransferredpowerissufficientlyhighbySwan(2012).Furthermore,thecaseofdynamicchannelformobilecloudcomputingisexplored.Approximationmethodsareusedforderivingthesimpleandclose-to-optimalpolicies.
Mobilemodeselection:
Theaboveresultsarecombinedtoselectthemobilemodeformaximizingthecomputingprobability.Givenfeasiblecomputinginbothmodes,theonlyone
yieldingthelargerenergysavingsispreferredandtheselectioncriterionisderivedintermsofthresholdsontheBStransmissionpoweraswellasthedeadlineforcomputing(Huangetal.,2012).
Optimaldataallocationforadynamicchannel:
Last,theaboveresultsareextendedtothecaseofadynamicchannel,modeledasindependentandidenticallydistributed.blockfading,andnon-causalCSIatthemobile(acquiredfrome.g.,channelprediction).Theproblemofoptimizinganindividualmobilemode(localcomputingoroffloading)isformulatedbasedonthemaster-and-slavemodelusingthesamemetricasthefixed-channelcounterpart(Kumar&Liu,2013).
Conclusion
Wirelessandmobilecomputingtechnologiesprovidemorepossibilitiesforaccessingservicesconveniently.Mobiledeviceswillbeimprovedintermsofpower,CPU,andstorage.Mobilecloudcomputinghasemergedasanewparadigmandextensionofcloudcomputing.
Bytwokindsofavailablemodes,wecanpurelyknowoftheEnergyEfficientMobileCloudComputing.ThroughmystudyfortheMobileCloudComputing,wearehereexposingtwosimplesolutionstosolvethisproblem.Althoughmyresearchisprettybasic,itstillbenefittheprocessofthedevelopmentformobilecloudcomputingandhowtomakeitenergyefficient.Webelievethatexploringotheralternatives,suchasintroducingamiddlewarebasedarchitectureusinganoptimizingoffloadingalgorithm,c
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