移動邊緣計算中計算卸載和資源分配協(xié)同優(yōu)化策略設(shè)計與實現(xiàn)

移動邊緣計算中計算卸載和資源分配協(xié)同優(yōu)化策略設(shè)計與實現(xiàn)移動邊緣計算中計算卸載和資源分配協(xié)同優(yōu)化策略設(shè)計與實現(xiàn)

摘要：隨著移動互聯(lián)網(wǎng)的快速發(fā)展，移動設(shè)備在人們的日常生活中扮演著越來越重要的角色。然而，移動設(shè)備的計算能力和存儲能力有限，無法滿足越來越復(fù)雜的應(yīng)用需求。同時，移動網(wǎng)絡(luò)的帶寬和延遲也限制了移動設(shè)備在處理大規(guī)模數(shù)據(jù)時的效率。因此，移動邊緣計算應(yīng)運(yùn)而生，使用邊緣節(jié)點的計算資源和存儲資源來處理數(shù)據(jù)。計算卸載和資源分配是移動邊緣計算的關(guān)鍵問題之一。本文針對移動邊緣計算中計算卸載和資源分配問題，提出一種協(xié)同優(yōu)化策略，并實現(xiàn)了相應(yīng)的算法。首先，通過對移動設(shè)備的資源需求和邊緣節(jié)點的負(fù)載情況進(jìn)行監(jiān)測和分析，建立了一種資源分配模型；然后，為了降低計算卸載帶來的通信開銷，提出了一種基于機(jī)器學(xué)習(xí)的計算卸載決策算法。最后，在移動邊緣計算環(huán)境下，通過仿真實驗驗證了所提出算法的可行性和有效性。

關(guān)鍵詞：移動邊緣計算；計算卸載；資源分配；機(jī)器學(xué)習(xí)；協(xié)同優(yōu)化

ABSTRACT:Withtherapiddevelopmentofmobileinternet,mobiledevicesareplayinganincreasinglyimportantroleinpeople'sdailylives.However,thecomputingpowerandstoragecapacityofmobiledevicesarelimitedandcannotmeettheincreasinglycomplexapplicationrequirements.Atthesametime,thebandwidthandlatencyofmobilenetworksalsolimittheefficiencyofmobiledevicesinhandlinglarge-scaledata.Therefore,mobileedgecomputing(MEC)hasemerged,whichusesthecomputingandstorageresourcesofedgenodestoprocessdata.Computingoffloadingandresourceallocationareoneofthekeyissuesinmobileedgecomputing.Inthispaper,weproposeacollaborativeoptimizationstrategyforcomputingoffloadingandresourceallocationinmobileedgecomputingandimplementthecorrespondingalgorithm.First,aresourceallocationmodelisestablishedbymonitoringandanalyzingtheresourcerequirementsofmobiledevicesandtheloadofedgenodes.Then,acomputingoffloadingdecisionalgorithmbasedonmachinelearningisproposedtoreducethecommunicationoverheadcausedbycomputingoffloading.Finally,thefeasibilityandeffectivenessoftheproposedalgorithmareverifiedthroughsimulationexperimentsinthemobileedgecomputingenvironment.

KEYWORDS:mobileedgecomputing;computingoffloading;resourceallocation;machinelearning;collaborativeoptimizationMobileedgecomputing(MEC)isapromisingtechnologythatenablestheoffloadingofcomputationallyintensivetasksfrommobiledevicestonearbyedgenodes.Thisapproachcanreducetheenergyconsumptionandresponsetimeofmobiledevices,aswellaspromoteefficientresourceutilization.However,theoffloadingdecisionalgorithmiscriticaltoachievingthesebenefits.

Theprimarychallengeoftheoffloadingdecisionalgorithmistobalancetheresourcerequirementsofmobiledevicesandtheloadofedgenodes.Mobiledeviceshavelimitedcomputationalresourcesandbatterypower,whileedgenodeshavelimitedprocessingcapacityandenergyresources.Moreover,theheterogeneityofmobiledevicesandedgenodesandthedynamicchangesofnetworkconditionsfurthercomplicatetheproblem.

Toaddressthischallenge,acomputingoffloadingdecisionalgorithmbasedonmachinelearningisproposed.Thealgorithmutilizesacollaborativeoptimizationframeworktojointlyoptimizetheresourceallocationandoffloadingdecision.Theresourceallocationisbasedontheresourcerequirementsofmobiledevicesandtheresourceavailabilityofedgenodes.Theoffloadingdecisionisbasedonthepredictedexecutiontimeandenergyconsumptionofdifferentoffloadingstrategiesusingmachinelearningmodels.

Specifically,theproposedalgorithmincludesthreemainsteps.Firstly,theresourcerequirementsandavailabilityareestimatedbasedontheinformationcollectedfrommobiledevicesandedgenodes.Secondly,themachinelearningmodelsaretrainedusinghistoricaldataandusedtopredicttheexecutiontimeandenergyconsumptionofdifferentoffloadingstrategies.Finally,acollaborativeoptimizationalgorithmisemployedtodeterminetheoptimaloffloadingstrategy,takingintoaccounttheresourceallocationandpredictedperformance.

SimulationexperimentsareconductedtoevaluatethefeasibilityandeffectivenessoftheproposedalgorithminaMECenvironment.Theresultsshowthatthealgorithmcanachievesignificantimprovementsintermsofenergyconsumption,responsetime,andresourceutilization,comparedtoexistingapproaches.TheproposedalgorithmcanbepotentiallyappliedtovariousMECscenariosandprovideamoreefficientandintelligentoffloadingdecisionmechanismformobiledevicesMEC(MobileEdgeComputing)hasbecomeanimportantresearchdirectioninthefieldofmobilecomputinginrecentyears.Itisanewcomputingparadigmthatbringscomputingandstorageresourcesclosertotheedgeofthenetwork,enablingmobiledevicestoaccesspowerfulcomputingcapabilitieswhilereducinglatencyandimprovingnetworkefficiency.Withtheincreasingdemandformobileservices,theoptimizationofresourceallocationandtaskoffloadinginMECenvironmentshasbecomeachallengingresearchtopic.

Inthiscontext,anovelalgorithmfortaskoffloadingandresourceallocationinMECenvironmentshasbeenproposed,whichtakesintoaccounttheenergyconsumption,responsetime,andresourceutilizationofmobiledevices.Theproposedalgorithmisbasedonamulti-objectiveoptimizationmodel,whichconsidersthetrade-offsbetweenthesethreefactorsandobtainsasetofPareto-optimalsolutions.Areinforcementlearningalgorithmisthenusedtoselectthemostappropriatesolutionbasedonthecurrentenvironmentanddeviceperformance.

SimulationexperimentshavebeenconductedtoevaluatetheperformanceoftheproposedalgorithminaMECenvironment.Theresultsshowthatthealgorithmoutperformsexistingapproachesintermsofenergyconsumption,responsetime,andresourceutilization.Theproposedalgorithmachievesahigherenergyefficiencyandalowerresponsetimeformobiledevices,whilealsoimprovingtheutilizationofavailableresources.

Moreover,theproposedalgorithmisapplicabletoawiderangeofMECscenariosandcanprovideanefficientandintelligentoffloadingdecisionmechanismformobiledevices.Thealgorithmcanoptimizethetaskoffloadingandresourceallocationinreal-time,consideringthedynamicchangesinthenetworkenvironment,theworkloadofmobiledevices,andtheavailabilityofcomputingandstorageresourcesattheedgeofthenetwork.

Inconclusion,theproposedalgorithmrepresentsasignificantcontributiontothefieldofmobilecomputingandMEC.ItprovidesacomprehensiveandeffectivesolutiontotheresourceallocationandtaskoffloadingprobleminMECenvironments,takingintoaccounttheenergyefficiency,responsetime,andresourceutilizationofmobiledevices.ItisexpectedthattheproposedalgorithmwillbewidelyadoptedinpracticalapplicationsandcontributetothedevelopmentofMEC-basedmobileservicesInadditiontothepracticalapplicationsoftheproposedalgorithminMECenvironments,thereareseveraltheoreticalcontributionsthatcanbemadetothefieldofmobilecomputing.Firstly,thealgorithmprovidesamoreefficientandeffectivewayofusingthelimitedresourcesofmobiledevicestoperformresource-intensivetasks.Thisisparticularlyimportantinsituationswheretheresourcesofthemobiledevicearelimited,suchasinlow-powerdevicesordeviceswithlimitedmemoryorprocessingpower.

Secondly,thealgorithmtakesintoaccounttheenergyefficiencyofmobiledeviceswhenmakingresourceallocationandtaskoffloadingdecisions.Thisisimportantinlightoftheincreasinguseofmobiledevicesandthecorrespondingincreaseinenergyconsumption.Byoptimizingtheallocationofresourcesandminimizingtheamountofenergyconsumedbymobiledevices,theproposedalgorithmmakesasignificantcontributiontothedevelopmentofsustainablemobileservices.

Thirdly,theproposedalgorithmconsiderstheresponsetimeofmobiledeviceswhenmakingresourceallocationandtaskoffloadingdecisions.Thisisimportantinsituationswherethelatencyofthemobiledevicecanimpactthequalityandreliabilityoftheservicebeingprovided.Byoptimizingtheallocationofresourcesandminimizingtheresponsetimeofmobiledevices,theproposedalgorithmensuresthatmobileservicesaredeliveredinatimelyandreliablemanner.

Finally,theproposedalgorithmmakesasignificantcontributiontothefieldofMECbyprovidingacomprehensiveandeffectivesolutiontotheresourceallocationandtaskoffloadingproblem.Thisisanimportantresearchtopicinthefieldofmobilecomputing,andtheproposedalgorithmprovidesanovelandeffectiveapproachthatcanbeusedin