iot based agriculture as a cloud and big data service · iot based agriculture as a cloud and big...

DOI: 10.4018/JOEUC.2017100101

Journal of Organizational and End User ComputingVolume 29 • Issue 4 • October-December 2017

Copyright©2017,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited.

IoT Based Agriculture as a Cloud and Big Data Service:The Beginning of Digital IndiaSukhpal Singh Gill, CLOUDS Lab, School of Computing and Information Systems, University of Melbourne, Melbourne, Australia

Inderveer Chana, Computer Science and Engineering Department, Thapar University, Patiala, India

Rajkumar Buyya, CLOUDS Lab, School of Computing and Information Systems, University of Melbourne, Melbourne, Australia

ABSTRACT

Cloudcomputinghastranspiredasanewmodelformanaginganddeliveringapplicationsasservicesefficiently.Convergenceofcloudcomputingwithtechnologiessuchaswirelesssensornetworking,InternetofThings (IoT)andBigDataanalyticsoffersnewapplications’of cloud services.Thispaperproposesacloud-basedautonomicinformationsystemfordeliveringAgriculture-as-a-Service(AaaS)throughtheuseofcloudandbigdatatechnologies.TheproposedsystemgathersinformationfromvarioususersthroughpreconfigureddevicesandIoTsensorsandprocessesitincloudusingbigdataanalyticsandprovidestherequiredinformationtousersautomatically.TheperformanceoftheproposedsystemhasbeenevaluatedinCloudenvironmentandexperimentalresultsshowthattheproposedsystemoffersbetterserviceandtheQualityofService(QoS)isalsobetterintermsofQoSparameters.

KEywORDSAgriculture as a Service, Autonomic Management, Big Data, Cloud Computing, Internet of Things

1. INTRODUCTION

EmergenceofICT(InformationandCommunicationTechnologies)playsanimportantroleintheagriculture sectorbyproviding services throughcomputer-basedagriculture systems (SinghandChana,2015).Buttheseagriculturesystemsarenotabletofulfilltheneedsoftoday’sgenerationduetoprocessingoflargeamountofdata,lackofimportantrequirementslikeprocessingspeed,datastoragespace,reliability,availability,scalabilityetc.andevenresourcesusedincomputer-basedagriculturesystemsarenotutilizedefficiently.Agriculture-as-a-Service(AaaS)applicationsexhibitBigdatacharacteristics.Forexample,thevolumeofagriculturedatasetcapturedbyenvironmentssuchasOpenGovernmentDataPlatformIndia(data.gov.in,2015),IndiaAgricultureandClimateDataSet(Sanghietal.),andregionallandandclimatemodellinginChina(Shangguanetal.,2012)canbeinorderof1000000recordswithsizeof3.5GB.Thedataiscominginlargedatavarietyandvolumefrombothusersintheformofimageslikedamagedcropimagesduetoweather,insectsetc.anddevicesthroughInternetofThings(IoT)sensorsandsatellites(GPSsystems)thatsendweatherrelatedimages.Asaresultofregularcapturingandcollectionofdatasets,theygrowwiththevelocity

1


2

of80.72KB/minuteormore(data.gov.in,2015).Tosolvetheproblemofexistingagriculturesystems,thereisaneedtodevelopacloud-basedservicethatcaneasilymanagedifferenttypesofagriculturerelated-databasedondifferentdomains(crop,weather,soil,pest,fertilizer,productivity,irrigation,cattle,andequipment)throughthesesteps:i)gatherdatafromvarioussensorsthroughpreconfigureddevices,ii)classifythegathereddata(heterogeneous,highvolumeofbigdata)intovariousclassesthrough analysis, iii) store the classified information in cloud repository for future use, and iv)automaticdiagnosisoftheagriculturestatus.Aslargenumberofusersareusingagriculturesystemsoperatingonlargedatasetssimultaneously,thereisaneedofhighlyscalableandelasticdistributedcomputingenvironmentsuchascloudcomputing.Inaddition,cloud-basedautonomicinformationsystemshouldbeable to identify theQoS(QualityofService)requirementsofuserrequestandresourcesshouldbeallocatedefficientlytoexecutetheuserrequestbasedontheserequirements.

Themainaimofthispaperis todesignarchitectureofAgriculture-as-a-Service(AaaS)thatmanagesvarioustypesofagriculture-relateddatabasedondifferentdomains.Thisisrealizedthroughthefollowingobjectives:i)proposeanautonomicresourcemanagementtechniquewhichisusedtoa)gathertheinformationfromvarioususersthroughpreconfigureddevices,IoTsensors,GPS(GlobalPositioningSystem),etc.b)extract theattributes,c)analyze the informationbycreatingvariousclassesbasedontheinformationreceived,d)storetheclassifiedinformationincloudrepositoryforfutureuseande)diagnosetheagriculturestatusautomaticallyandii)performresourceallocationautomaticallyatinfrastructurelevelafteridentificationofQoSrequirementsofuserrequest.

The rest of the paper is organized as follows. Section 2 presents related work of existingagriculturessystems.ProposedarchitectureispresentedinSection3.Section4presentsAutonomicResourceManagement.Sections5describetheexperimentalsetupandpresenttheresultsofevaluation.Section6presentsconclusionsandfuturescope.

2. RELATED wORK

Existingresearchreportedthatfewagriculturesystemshavebeendevelopedwithlimitedfunctionality.Relatedworkofexistingagriculturesystemshasbeenpresentedinthissection.

2.1. Existing Agriculture SystemsRanyaetal.(2013)presentedALSE(AgricultureLandSuitabilityEvaluator)tostudyvarioustypesof land to find theappropriate land fordifferent typesof cropsbyanalyzinggeo-environmentalfactors. ALSE used GIS (Global Information System) capabilities to evaluate land using localenvironmentconditionsthroughdigitalmapandbasedonthisinformationdecisionscanbemade.Raimoetal.(2010)proposedFMIS(FarmManagementInformationSystem)usedtofindtheprecisionagriculturerequirementsforinformationsystemsthroughweb-basedapproach.AuthoridentifiedthemanagementofGISdataisakeyrequirementofprecisionagriculture.Sorensenetal.(2010)studiedtheFMIStoanalyzedynamicneedsoffarmerstoimprovedecisionprocessesandtheircorrespondingfunctionalities.Furthertheyreportedthatidentificationofprocessusedforinitialanalysisofuserneeds ismandatory foractualdesignofFMIS.Zhao (2002)presentedananalysisofweb-basedagriculturalinformationsystemsandidentifiedvariouschallengesandissuesstillpendinginthesesystems.Duetolackofautomationinexistingagriculturesystem,thesystemistakinglongertimeandisdifficulttohandledynamicneedsofuserwhichleadstocustomerdissatisfaction.Sorensenetal.(2011)identifiedvariousfunctionalrequirementsofFMISandinformationmodelispresentedbasedontheserequirementstorefinedecisionprocesses.TheyidentifiedthatcomplexityofFMISisincreasingwithincreaseinfunctionalrequirementsandfoundthatthereisaneedofautonomicsystemtoreducecomplexity.Yuegaoetal.(2004)proposedWASS(Web-basedAgriculturalSupportSystem)andidentifiedfunctionalities(information,collaborativeworkanddecisionsupport)andcharacteristicsofWASS.Basedoncharacteristics,authorsdividedWASSintothreesubsystems:production,research-educationandmanagement.


3

Reddyatel.(1995)proposedGISbasedDSS(DecisionSupportSystem)frameworkinwhichSpatialDDShasbeendesignedforwatershedmanagementandmanagementofcropproductivityatregionalandfarmlevel.GISisusedtogatherandanalyzethegraphicalimagesformakingnewrulesanddecisionsforeffectivemanagementofdata.Shitalaetal.(2013)presentedmobilecomputingbasedframeworkforagriculturistscalledAgroMobileforcultivationandmarketingandanalysisofcropimages.Further,AgroMobileisusedtodetectthediseasethroughimageprocessingandalsodiscussedhowdynamicneedsofuseraffectstheperformanceofsystem.Seokkyunetal.(2013)proposedcloudbasedDiseaseForecastingandLivestockMonitoringSystem(DFLMS)inwhichsensornetworkshasbeenusedtogatherinformationandmanagesvirtually.DFLMSprovidesaneffectiveinterfaceforuserbutduetotemporarystoragemechanismused,itisunabletostoreandretrievedataindatabasesforfutureuse.TheproposedQoS-awareCloudBasedAutonomicInformationSystem(AaaS)hasbeencomparedwithexistingagriculturesystemsasdescribedinTable1.

AlltheaboveresearchworkshavefocusedondifferentdomainsofagriculturewithdifferentQoSparameters.Noneoftheexistingagriculturesystemsconsidersself-managementofresources.Duetolackofautomationofresourcemanagement,servicesbecomeinefficientwhichfurtherleadsto customer dissatisfaction. The proposed system is a novel QoS-aware cloud based autonomicinformationsystemandconsidersvariousdomainsofagricultureand,allocatesandmanagestheresourcesautomaticallywhichisnotconsideredinotherexistingagriculturesystems.

3. AGRICULTURE-AS-A-SERVICE ARCHITECTURE

Theexistingagriculturesystemsarenotabletofulfilltheneedsoftoday’sgenerationduetolackinginimportantrequirementslikeprocessingspeed,datastoragespace,reliability,availability,scalabilityetc.Evenresourcesusedincomputerbasedagriculturesystemsarenotutilizedefficiently.Tosolvetheproblemofexistingagriculturesystems, there isaneed todevelopacloud-basedautonomicinformation system that delivers Agriculture-as-a-Service. This section presents architecture ofcloud-basedautonomicinformationsystemforagricultureservicecalledAaaSthatmanagesvarious

Table 1. Comparisons of existing agriculture systems with proposed system (AaaS)

Agriculture System Mechanism QoS-aware

(Parameter) Domains Data Classification

Resource Management

Big Data

ALSE(Elsheikhetal.,2013)

Non-Autonomic Yes(Suitability) Soil Yes No No

FMIS(Nikkilaetal.,2010)

Non-Autonomic No PestandCrop No No No

WASS(Huetal.,2004)

Non-Autonomic No Productivity No No No

AgroMobile(Prasadetal.,2013)

Non-Autonomic

Yes(Dataaccuracy) Crop Yes No No

DFLMS(Jeongetal.,2013)

Non-Autonomic No Crop No Yes No

ProposedSystem(AaaS) Autonomic

Yes(Cost,Time,ResourceUtilization,Latency,ThroughputandAttackDetectionRate)

Crop,Weather,Soil,Pest,FertilizerandIrrigation

Yes Yes Yes


4

typesofagriculture-relateddatabasedondifferentdomains.ArchitectureofAaaSisshowninFigure1.QoSparameters(executiontimeandcost)mustbeidentifiedbeforetheallocationofresources.AaaSisthekeymechanismthatensuresthattheresourcemanagercanservelargeamountofrequestswithoutviolatingSLAtermsanddynamicallymanagestheresourcesbasedonQoSrequirementsidentifiedbyQoSmanager.TheservicesofAaaShasbeendividedintothreetypes:SaaS(SoftwareasaService),PaaS(PlatformasaService)andIaaS(InfrastructureasaService).InSaaS,auserinterfaceisdesignedinwhichuserscaninteractwithsystem.Anekaisa.NET-basedapplicationdevelopmentPaaS,whichisusedasascalablecloudmiddlewaretomakeinteractionbetweencloudsubsystem and user subsystem. In IaaS, an autonomic resource manager manages the resourceautomaticallybasedontheidentifiedQoSrequirementsofaparticularrequest.ThearchitectureofAaaScomprisesoftwosubsystems:i)userandii)cloud.

3.1. User SubsystemThissubsystemprovidesauserinterface,inwhichdifferenttypeofusersinteractwithAaaStoprovideandgetusefulinformationaboutagriculturebasedondifferentdomains.Ninetypesofinformationofdifferentdomainsinagriculturehasbeenconsidered:crop,weather,soil,pest,fertilizer,productivity,irrigation, cattle, andequipment.Usersarebasicallyclassified in threecategories: i) agricultureexpert,ii)agricultureofficer,andiii)farmer.TheagricultureexpertsharesprofessionalknowledgebyansweringfarmerqueriesandupdatestheAaaSdatabasebasedonthelatestresearchdoneinthefieldofagriculturewithrespecttotheirdomain.Agricultureofficersarethegovernmentofficialsthatprovidethelatestinformationaboutnewagriculturepolicies,schemes,andrulespassedbythegovernment.FarmerisanimportantentityofAaaSwhocantakemaximumadvantagebyaskinghisqueriesandgettingautomaticreplyafteranalysis.Userscanmonitoranydatarelatedtotheirdomainandgettheirresponsewithoutvisitingtheagriculturehelpcenter.ItintegratesthedifferentdomainsofagriculturewithAaaS.Thequeriesreceivedfromuser(s)areforwardedtocloudrepositoryforupdatesandresponsesendsbacktoparticularuserontheirpreconfigureddevices(tablets,mobilephones,laptopsetc.)viainternet.

3.2. Cloud SubsystemThis subsystemcontains theplatform inwhich agriculture service is hostedon a cloud.Detailsabout users and agriculture information are stored in a cloud repository in different classes fordifferentdomainswithuniqueidentificationnumber.Theinformationismonitored,analyzed,andprocessedcontinuouslybyAaaS.Theanalysisprocessconsistsofvarioussubprocesses:selection,datapreprocessing,transformation,classificationandinterpretationasshowninFigure1.Differentclassesforeverydomainandsubclassesforfurthercategorizationofinformationhavebeendesigned.Instoragerepository,userdataiscategorizedbasedondifferentpredefinedclassesofeverydomain.Thisinformationisfurtherforwardedtoagricultureexpertsandagricultureofficersforfinalvalidationthroughpreconfigureddevices.Further,anumberofuserscanusecloud-basedagricultureservicesotheQoSmanagerandautonomicresourcemanagerincloudsubsystemhavebeenintegrated.QoSmanageridentifiestheQoSrequirementsbasedonthenumberandtypeofuserqueriesasdiscussedinpreviousresearchwork(Jeongetal.,2013;SinghandChana,2015;Singhetal.,2015).BasedonQoSrequirements,autonomicresourcemanageridentifiesresourcerequirementsautomaticallyandallocatesandexecutestheresourcesatinfrastructurelevel.Performancemonitorisusedtoverifytheperformanceofsystemandalsomaintainitautomatically.Ifthesystemwillnotbeabletohandletherequestautomaticallythenthesystemgeneratesanalert.

3.2.1. Cloud-Based Agriculture ServiceCloud-basedagricultureserviceprovidesauserplatformthroughwhichusercanaccessagricultureserviceasshowninFigure2.Firstly,agricultureserviceallowsusertocreateprofileforinteractionwithAaaS.Afterprofilecreation,theuserisrequiredtoprovidehispersonaldetailsalongwiththe


5

detailsofinformationdomain.AaaSanalysestheinformationtoverifywhetherthedataiscompleteornotforfurtherprocessingbyperformingvariouschecks.Furtherdataisprocessedandredundancyofdataisremovedanddataisusedtoselectdomaintowhichdatabelongs.Informationisclassifiedproperlyinorderwithuniqueidentificationnumber.Thisinformationisforwardedtoagricultureexpertsandagricultureofficersforfinalvalidationthroughpreconfigureddevices.Aftersuccessfulvalidationofinformation,itisstoredinAaaSdatabase.Ifuserwantstoknowtheresponseoftheirquery,thensystemwillautomaticallydiagnosetheuserqueryandsendtheresponsebacktothatuser.

Figure 1. Agriculture-as-a-Service architecture

Figure 2. Functional aspects of AaaS


6

3.2.2. Detailed MethodologyAaaSallowsuserstouploadthedatarelatedtodifferentdomainsofagriculturethroughpreconfigureddevicesandclassifiedthembasedonthedomainsspecifiedindatabase.SubtasksofinformationgatheringandprovidedinAaaSare:i)selection,ii)preprocessing,iii)transformation,iv)classificationandv)interpretation.Inselection,targetdatasetsarecreatedbasedontherelevantinformationthatwillfurtherbeconsideredforanalysisinnextsubprocess.Inpreprocessing,differentusershavedifferentinformationregardingagriculture.Todevelopafinaltrainingset,thereisneedofpreprocessingstepsbecausedatamightcontainsomemissingsampleornoisecomponents.InAaaS,datapreprocessingcontainsfourdifferentsubprocesses:i)datacleaning,ii)dataintegration,iii)dataconversionandiv) data reduction. Data transformation provides an interface between data analysis subprocess(classification)anddatapreprocessing.Afterdatapreprocessing,thisprocessconvertsthelabeleddataintoadequateformatsuitableforclassification.Inclassification,AaaSclassifytheagricultureinformationofdifferentusersofdifferentdomainsbasedontheextracteddata.K-NN(k-NearestNeighbor)classificationmechanismhasbeenused in this researchwork to identify thedifferentclasslabelsofusers.K-NNissupervisedmachinelearningtechniquewhichisusedtoclassifytheunknowndatausingtrainingdatasetgeneratedbyit.K-NNusedtoidentifytheproductivitylevelthroughTrainingInstanceDataset(TID).Figure3describestheK-NNAlgorithm.

InK-NNalgorithm,distanceiscomputedfromonespecificinstancetoeverytraininginstancetoclassifythatunknowninstance.Bothk-nearestneighborandkminimumdistanceisdeterminedandoutputclasslabelisidentifiedamongkclasses.Duringtrainingphase,K-NNAlgorithmutilizestrainingdata.Figure4illustratestheclassificationprocessusedinthisresearchwork.

K-NNmodelisusedtoidentifytheproductivitylevelthroughTrainingInstanceDataset(TID).Fivelevelsofproductivity(A-E)havebeenfixedasshowninTable2.Thelevel‘A’indicatestheproductivityisveryhighwhilelevel‘E’indicatestheproductivityisverylow.Basedonthegiveninformation,TIDidentifiestheclassinwhichgivendatabelongs.

TestdataisaninputofthismodelanditiscomparedwithTIDandidentifiestheclassinwhichdatalaidusingfollowingrule:

Rule:If {CropName˄ Temperature˄ SoilTexture˄ Season˄ Pesticide˄ Fertilizer}thenProductivity

Thefinalstepistointerprettheagriculturedatasubmittedbydifferentusersofdifferentdomainswhichhelpsusertounderstandtheclassifieddatasets.AaaSiscapabletodiagnosetheagriculturestatusbasedontheinformationenteredbyuserandsendthediagnosedagriculturestatustoparticularuser

Figure 3. Pseudo code of K-NN algorithm


7

automatically.Sixattributeshavebeenconsidered:CropName,Temperature,SoilTexture,Season,PesticideandFertilizerandoneoutput:Productivity.Basedonthesesixattributes,AaaSdesignsrules.ValuesforsixvariablesareconsideredasTID.Forexample,refertoTable3.

AaaSusestheruleshowninTable3tofindtheproductivitylevelusingTID(seeTable4).Similarly,anytypeofqueryrelatedtodifferentdomainscanbeaskedbyusersandAaaSexecutes

theuserqueryandsendresponsebacktoparticularuserautomaticallybasedontherulesdefinedinAaaSdatabase.ThroughAaaS,userscaneasilydiagnosetheagriculturestatusautomatically.

3.2.3. Infrastructure Management (IaaS)EfficientmanagementofinfrastructureincloudismandatorytomaintaintheperformanceoftheAgri-Info.Itcomprisesoftwosubunits:QoSManagerandResourceManager.

Figure 4. Classification process

Table 2. Productivity Levels

Productivity Level Description

A VeryHighProductivity

B HighProductivity

C NeutralProductivity

D LowProductivity

E VeryLowProductivity

Table 3. User wants to retrieve the productivity level using AaaS

User Query

Crop Name Temperature Soil

Texture Season Pesticide Fertilizer Productivity

Soybean 21-27°C SlityLoamClay Winter Organochlorine Urea ?


8

3.2.3.1. QoS ManagerUsersubmitsarequesttoAgri-Infotoretrievesomespecificagriculturerelatedinformation.Agri-InfoidentifiestheQoSparametersrequiredtoprocesstheuserrequestthroughanalysisbasedonuserrequest.BasedonthekeyQoSrequirementsofaparticularuserrequest,theQoS Managerputstheuserrequestintocriticalandnon-criticalqueuesthroughQoSassessment.ForQoSassessment,QoS Managerwillcalculatetheexecutiontimeofuserrequestandfindtheapproximateuserrequestcompletion time. If the completion time is lesser than the desired deadline then it will executeimmediatelywiththeavailableresourcesandreleasetheresource(s)backtoresourcemanagerforanother execution otherwise calculate extra number of resources required and provide from thereservedstockforcurrentexecution.3.2.3.2. Resource ManagerFurther,tworesourceschedulingpolicies(SinghandChana,2015)areusedtoscheduletheresourcesforexecutionofuserqueries:timebasedandcostbasedschedulingpolicy.Time based scheduling policyworksasperfollowing:First,theallocationagentbeginstocomputetheDeadlineTimeoftheuserrequestinthegivenbudget.Allocateresourcesbasedontime,theuserrequestwhichhasshortestDeadlineTimewillexecutefirst.Ifthetworequestshavesamedeadlinetimethenthatrequestwillexecutefirstthathaslesserexecutiontime.TheallocationagentthenschedulesalltherequestswithsmallestexecutiontimerequesttotheresourcesthatprovidehighQoS.TherulesfortimebasedschedulingpolicyaredescribedinTable5alongwiththeirconditions.

Cost based scheduling policyworksasperfollowing:First,theallocationagentbeginstocomputethecostofeachrequestthensort,asthepriorityisgiventotherequestwhichhasmaximumbudget.Ifthetworequestshavesamebudgetthenthatrequestwillexecutefirstthathaslesserexecutiontime.TheallocationagentthenschedulesalltherequestswithhighbudgetrequesttotheresourcesthatprovidehighQoS.Finally,allotherrequestsarescheduledontheavailableresourcesset.TherulesforcostbasedschedulingpolicyaredescribedinTable6alongwiththeirconditions.

4. AUTONOMIC RESOURCE MANAGEMENT

WorkingofautonomicelementofAgri-InfoisbasedonIBM’sautonomicmodelthatconsidersfourstepsofautonomicsystem:i)monitor,ii)analyze,iii)planandiv)executeasshowninFigure1.The

Table 4. AaaS response utilized to in order to find the productivity level using TID

AaaS Response

Crop Name Temperature Soil

Texture Season Pesticide Fertilizer Productivity

Soybean 21-27°C SlityLoamClay Winter Organochlorine Urea C

Table 5. Rules of time based resource scheduling

Request Pending Urgency Add Resource Request

Yes Yes Reserve Submit

Yes No Available Submit

No - - Finish


9

objectiveofresourceprovisioninginautonomicresourcemanagementistoprovisiontheresourcestoprocessuserrequests.Therequestssubmittedshouldbeexecutedwithintheirbudgetanddeadline.Requestssubmittedbyusertoresourceprovisionerarestoredasbulkofworkloadsfortheirexecution.AllthesubmittedworkloadsareanalyzedbasedontheirQoSrequirements.Basedonimportanceoftheattribute,weightsforeverycloudworkloadarecalculated.Afterthat,workloadsareclusteredbasedonk-meansbasedclusteringalgorithmforbetterresourceprovisioning(Singhetal.,2015).Ifthevalueofworkloadsexecuteswithindeadlineandbudgetand[ResourceConsumptionandRequestsMissedislesserthanThresholdValue]thenitwillprovisionresourcesotherwisegeneratealertforanalysestheworkloadagain.

Aftersuccessfulprovisioningofresources,ResourceScheduler(RS)takestheinformationfromtheappropriateworkloadafteranalyzingthevariousworkloaddetailswhichuserrequestdemanded(SinghandChana,2015).KnowledgeBasecontainsdetailsofalltheresourcesavailableinresourcepoolandreserveresourcepool.BasedonCloudconsumerdetails,RSassignsresourcesandexecutesCloudworkloads.Duringexecutionofaparticularcloudworkload,theResourceExecutor(RE)willcheckthecurrentworkload.Iftheresourcesaresufficientforexecutionthenitwillcontinuewithexecutionotherwiserequestformoreresources.IfthevalueofResourceConsumptionandRequestsMissedislesserthanthresholdvalue,thenREwillexecuteworkloadsotherwiseREwillgeneratealert.AftersuccessfulexecutionofCloudworkloads,REreleasesthefreeresourcestoresourcepoolandREisreadyforexecutionofnewcloudworkloads.Duringexecutionofuserrequests,performanceismonitoredcontinuouslyusingsubunitperformancemonitortomaintaintheefficiencyofAgri-Infoandgeneratesalertincaseofperformancedegradation.Alertscanbegeneratedintwoconditionsgenerally:i)ifresourceconsumptionismorethanthresholdvaluesofresourceconsumptiontoexecuteuserrequest(Action:Reallocatesresources)andii)ifthenumberofmissedrequestsaregreaterthanthethresholdvalue(Action:PredictQoSRequirementsAgain).Sameactionisperformedtwice,ifAgri-Infofailstocorrectitthensystemwillbetreatedasdown.Componentsofautonomicsystemaredescribedbelow:

4.1. SensorsSensorsgettheinformationaboutperformanceofothernodesusinginthesystemandtheircurrentstate.Firstly,theupdatedinformationfromprocessingnodesistransfertomanagernodethenmanagernode transfers this information to sensors.Updated information includes informationaboutQoSparameters(executiontime,executioncostandresourceutilizationetc.).

4.2. MonitorInitially, Monitors are used to collect the information from sensors for monitoring continuouslyperformancevariationsbycomparingexpectedandactualperformance,andmonitorsthevalueof

Table 6. Rules of cost based resource scheduling

Request Pending RA > 0 Et > Wd BA > Pr Status

Yes True True True AddResource

Yes False True True AddResource

No - - - Finish

Yes True False True Finish

Yes True True False Finish

RA

= Resource Available, Et

= Estimated Time, Pr

= Resource Price, Wd

= Desired Deadline and BA

= Available Budget. Details of both time and cost based scheduling policy is given in previous research work (Singh and Chana, 2015).


10

resourceconsumptionandmissedrequests.ActualinformationaboutperformanceisobservedbasedQoSparametersandtransfersthisinformationtonextmoduleforfurtheranalysis.

4.3. Analysis and PlanAnalyzeandplanmodulestartanalyzingtheinformationreceivedfrommonitoringmoduleandmakeaplanforadequateactionsforcorrespondingalert.FollowingformulaisusedtocalculateResourceConsumption(Equation1):

ResourceConsumption

i

n

� ==∑1

Actual�Resource�Usage

Predicted�Reesource�Usage

(1)

whereActual Resource Usage isusageofresourcetoexecuteparticularnumberofuserrequestsand Predicted Resource Usage isresourceusageestimatedbeforeactualexecutionandnisthenumberofresources.Assumed: Predicted Resource Usage Actual Resource Usage≤

.Value

of ResourceConsumption . ismorethan1generallybecause Actual Resource Usage ismorethan Predicted Resource Usage butideallyitwillbe1whenbothareequal.Inthisresearchrk,maximum values for ResourceConsumption has been fixed and that is called threshold value.Followingformulaisusedtocalculatenumberofrequestsmissed Requests

Missed( ) inaparticularperiodoftime(Equation2):

RequestsMissed

= [Number of Requests Executed Successfully – Number of Requests Missed Deadline] (2)

Forsuccessfulexecutionofresources,valueofRequestsMissed

islesserthanthresholdvalue.Algorithm1isusedtoanalysestheperformanceofmanagementofresources.

Withthehelpof(Equation1)and(Equation2),resourceconsumptioniscalculatedandallocatestheresourcesforexecutionandthencomparestheresourceconsumptionwiththresholdvalue Th

c( ) .IfresourceconsumptionislessthanthresholdvalueandvalueofRequests

Missedislessthanthreshold

value Thm( ) thenexecutionofresourcescontinuesotherwisenoresourceisallocatedandprocess

ofreallocationisstartedusingAlgorithm1.Aftermeetingthiscondition,resourcesareallocatedfor

Algorithm 1. Analyzing and Panning Unit (AU)


11

furtherexecutionandvalueofresourceconsumptionand RequestsMissed

arecheckedperiodically.Incaseofmorevaluethanthreshold,alertwillbegeneratedbyperformancemonitor.

4.4. ExecutorExecutorimplementstheplanafteranalyzingcompletely.Toreducetheexecutiontimeandexecutioncostandimproveresourceutilizationisamainobjectiveofexecutor.Basedontheoutputgivenbyanalysisandexecutortracksthenewuserrequestsubmissionandresourceaddition,andtaketheactionaccordingtorulesdescribedinknowledgebase.

4.5. EffectorEffectorisusedtoexchangeupdatedinformationanditisusedtotransferthenewpolicies,rulesandalertstoothernodeswithupdatedinformation.

5. PERFORMANCE EVALUATION

Theaimofthisperformanceevaluationistodemonstratethatitisfeasibletoimplementanddeploytheagricultureasaserviceonrealcloudresources.ToolsusedforsettingupcloudenvironmentforperformanceanalysisareMicrosoftVisualStudio2010(SaaS),Aneka(PaaS),SQLServer2008,andCitrixXenServer(IaaS).Anekahasbeeninstalledalongwithitsrequirementsonallthenodesthatprovidecloudservice.Nodesinthissystemcanbeaddedorremovedbasedontherequirement.AaaSisinstalledonmainserverandtestedonvirtualcloudenvironmentthathasbeenestablishedatCLOUDS Lab, University of Melbourne, Australia.Differentnumberofvirtualmachineshavebeeninstalledondifferentservers,anddeployedtheAaaStomeasurethevariations.Inthisexperimentalsetup,threedifferentcloudplatformsareused:SoftwareasaService(SaaS),PlatformasaService(PaaS)andInfrastructureasaService(IaaS)asshowninFigure5.

AtSaaS level,MicrosoftVisualStudioisusedtodevelope-agriculturewebservicetoprovideuserinterfaceinwhichusercanaccessservicefromanygeographicallocation.AtPaaS level,AnekacloudapplicationplatformisusedasascalablecloudmiddlewaretomakeinteractionbetweenIaaS

Figure 5. Deployment of components at runtime and their interaction


12

andSaaS,andcontinuallymonitor theperformanceof thesystem.AtIaaS level, threedifferentservers(consistofvirtualnodes)havebeencreatedthroughCitrixXenServerandSQLServerhasbeenusedfordatastorage.SchedulerasshowninFigure5,runsatIaaSlevelonCitrixXenServer.ComputingnodesusedinthisexperimentworkarefurthercategorizedintothreecategoriesasshowninTable7.Theexecutioncostiscalculatedbasedonuserrequestanddeadline(ifdeadlineistooearly(urgent)itwillbemorecostlybecausethereisaneedofgreaterprocessingspeedandfreeresourcestoprocessparticularrequestwithurgency).Thereisindividualpriceisfixed(artificially)fordifferentresourcesbecausealltheresourcesareworkingincoordinationmannertofulfillthedemandofuser(demandofuserischangingdynamically).

Experimentsetupusing3serversinwhichfurthervirtualnodes(12=6(Server1)+4(Server2)+2(Server3))arecreated.EveryvirtualnodehasdifferentnumberforExecutionComponents(ECs)toprocessuserrequestandeveryEChastheirowncost(C$/ECtimeunit(Sec)).Table1showsthecharacteristicsoftheresourcesusedandtheirExecutionComponent(EC)accesscostpertimeunitinClouddollars(C$)andaccesscostinC$ismanuallyassignedforexperimentalpurposes.TheaccesscostofanECinC$/timeunitdoesnotnecessarilyreflectthecostofexecutionwhenECshavedifferentcapabilities.TheexecutionagentneedstotranslatetheaccesscostintotheC$foreachresource.Suchtranslationhelpsinidentifyingtherelativecostofresourcesforexecutinguserrequestsonthem.Duetolimitednumberofresources,costincreaseswithincreaseinuserrequests.Costisvaryingintwodifferentcases:i)relaxeddeadlineandii)tightdeadline.Inbothcases,whenthedeadlineislow(e.g.200secs),thenumberofuserrequestsprocessedincreasesasthebudgetvalueincreases.Whenahigherbudgetisavailable,theexecutionagentusesexpensiveresourcestoprocessmoreuserrequestswithinthedeadline.Alternatively,whenschedulingwithalowbudget,thenumberofuserrequestsprocessedincreasesasthedeadlineisrelaxed.DifferentnumberofexperimentshasbeenperformedbycomparingAaaS(QoS-awareAutonomic)asdiscussedinSection 4withnon-autonomicresourcemanagementtechnique(non-autonomic)inwhichnoautonomicschedulingmechanismisconsideredwhileallocatingresourcestoprocesstheuserrequests.

5.1. DatasetsDatasets used in this research work are downloaded from the Open Government Data PlatformIndia(data.gov.in,2015),IndiaAgricultureandClimateDataSet(Sanghietal.),andregionallandandclimatemodellinginChina(Sanghietal.)canbeintheorderof1000000records,withsizeof3.5GB.Thedataiscominginlargedatavarietyandvolumefrombothusersintheformofimageslikedamagedcropimagesduetoweather,insectsetc.anddevicesthroughInternetofThings(IoT)sensorsandsatellites(GPSsystems)thatsendweatherrelatedimages.Asaresultofregularcapturingandcollectionofdatasets,theygrowwiththevelocityof80.72KB/minuteormore(Sanghietal.).FivedifferenttablesusedtoprocessthedifferenttypesofdataasdescribedinTable8toTable12.

Table 7. Configuration Details of Cloud Environment

Resource_Id Configuration Specifications Operating System

Number of Virtual Node

Number of ECs

Price (C$/EC Time

Unit)

R1 IntelCore2Duo-2.4GHz

1GBRAMand160GBHDD Windows 6 18 2

R2 IntelCorei5-2310-2.9GHz

1GBRAMand160GBHDD Linux 4 12 3

R3 IntelXEONE52407-2.2GHz

2GBRAMand320GBHDD Linux 2 6 4


13

Table 8. Crop Information

CropId Crop Name

Crop Type Soil Texture Min

LandGrowing Period

Seed Type Price Quantity

C1 Rice Kharif SlityClay 5Acre 3Months Wet 1200Rs./Kg 2Kg/Acre

C2 Maize Rabi SlityLoamClay 4Acre 4Months Dry 1600Rs./Kg 1Kg/Acre

C3 Wheat Zaid LoamClay 3Acre 3Months Wet 1000Rs./Kg 2Kg/Acre

C4 Sugarcane Cash Slity 4Acre 6Months Dry 800Rs./Kg 6Kg/Acre

Table 9. Weather information

Crop Name Temperature Season Pressure (CFM) Wind Speed Rainfall Location

Rice 15-18°C Winter 0.75to1.5 16Km/h 300–650mm Ambala

Maize 17-22°C Summer 0.05to0.5 12Km/h 100–150mm Amritsar

Wheat 25-30°C Rainy 1.5to5.2 17.3Km/h 200–250mm GangaNagar

Sugarcane 35-40°C Summer 1to10 8Km/h 400–600mm Pathankot

Table 10. Soil information

Soil Texture Bulk Density Inorganic

MaterialOrganic Material Water Air Color Structure Infiltration

SlityClay 2.60to2.75gramspercm3 Sandandclay

Plantandanimalresidues

25% 28% Brown Plate-like 15mm/hour

SlityLoamClay

2.7to2.75gramspercm3 SandandSlit Animal

residues 22% 18% Red Prism-like 10mm/hour

LoamClay 2.60to2.75gramspercm3 ClayandSlit Plantresidues 37% 21% Brown Blocklike 18mm/hour

Slity 2.60to2.75gramspercm3

Sand,SlitandClay

Plantandanimalresidues

31% 29% Black Spherelike 22mm/hour

Table 11. Pest information

Crop Type

Crop Disease Effect Treatment Pesticide Name Solubility

in Water Price Outcome

Kharif Bacterialbrownspot

Degradesoilfertility

ReduceIrrigation Carbonate Yes Rs.

1500/LImproveProductivity

Rabi Zonateeyespot

Degradeproductivity

DistributeSoil Organophosphate No Rs.

2200/LImprovesoilfertilization

Zaid Dwarfbunt

Increaseriskofotherdisease

Sprayirrigation Parathyroid Yes Rs.

2300/LReduceriskofotherdiseases

Cash Ergot Degradeproductivity

DripIrrigation Parathyroid Yes Rs.

1800/LReduceproductivity


14

5.2. Performance MetricsThefollowingmetricsareusedtocalculatetheexecutioncost,executiontime,resourceutilization,latency,detectionrateandscalabilityforprocessinguserrequestsastakenfrompreviouswork(SinghandChana,2015;Singhetal.,2015;SinghandChana,2016):

Execution TimeisaratioofdifferenceofrequestfinishtimeWFi( ) andrequeststarttimeWStart

i( ) tonumberofrequests.FollowingformulaisusedtocalculateExecutionTime(ET)(Equation3):

ETWF WStart

nii

ni i=−

=∑

1

(3)

wheren isthenumberofrequeststobeexecuted.Execution Costisdefinedasthetotalamountofcostspentperonehourfortheexecutionof

requestandmeasuredinCloudDollars(C$).Followingformulaisusedtocalculateexecutioncost(C)(Equation4):

C ET Pricei

= × (4)

Latencyisadefinedasadifferenceoftimeofinputcloudworkloadandtimeofoutputproducedwithrespecttothatworkload.FollowingformulaisusedtocalculateLatency(Equation5):

Latency timeof output producedafterexecution timei

i

n

= −=∑1

� � � � � �� of inputof cloudworkload( ) (5)

wherenisnumberofworkloads.Resource Utilizationisdefinedasaratioofactualtimespentbyresourcetoexecuteworkload

to total uptime of resource for single resource. Following formula is used to calculate resourceutilization(Equation6):

ResourceUtilizationactual timespentbyresourcet

ii

n

��

==∑1

ooexecuteworkload

total uptimeof resource

� �

� � �

(6)

wherenisnumberofworkloads.

Table 12. Fertilizer information

Crop Type Fertilizer Name Nutrient Composition Price

Kharif Urea Nitrogeninformofurea(amide)(N) 7000Rs./10Kg

Rabi Ammonium-Nitrate AmmoniacalNitrogen,NitrogenNitrateandUreaNitrogen 9100Rs./10Kg

Zaid Ammonium-Sulphate AmmoniacalnitrogenandSulpher 6200Rs./10Kg

Cash Urea-Ammonium AmmoniacalnitrogenandNeutralammoniumcitrateSolublephosphate 13200Rs./10Kg


15

Securityismeasuredintermsofdetectionrate.Experimenthasbeenconductedwithdifferenttypeofattacks(DoS,R2L,U2RandProbing)anddifferenttoolsusedtolaunchdifferentattacksaremetasploitframeworkforDoS,HydraforR2L,NetCatforL2RandNMAPforprobing.Detection Rateistheratiooftotalnumberoftruepositivestothetotalnumberofintrusions(Sorensenetal.,2010):

DetectionRateTotal NumberofTruePositives

Total Numbe

=

rrof Intrusions (7)

Scalabilityismeasuredintermsofthroughput.Itistheratiooftotalnumberofworkloadstothetotalamountoftimerequiredtoexecutetheworkloads.Followingformulaisusedtocalculatethroughput(Equation8):

ThroughputW

Totalamountofn�

Total�Number�of�Workloads�=

( )� � �ttimerequired toexecutetheworkloads W

n� � � � � �( )

(8)

Experimenthasbeenconductedwith180userrequestsforverificationofexecutioncost,executiontime,resourceutilization,latency,detectionrateandscalability.Withincreasingthenumberofuserrequests,thevalueoflatencyisincreasing.ThevalueoflatencyinQoS-awareautonomicsystemislesserascomparedtonon-autonomicbasedresourceschedulingatdifferentnumberofuserrequestsasshowninFigure6.Themaximumvalueoflatencyis193secondsandminimumvalueoflatencyis59secondsinQoS-awareautonomicresourcemanagementtechnique.AveragelatencyinQoS-awareautonomicis15.22%lesserthannon-autonomicresourcemanagementtechnique.Thevalueofaveragecost forbothQoS-awarecloudbasedautonomic resourcemanagement techniqueand

Figure 6. Effect of change in number of user requests on latency

5.3. Experimental Results -- Based on Modelling and Simulation using CloudSim


16

non-autonomicresourcemanagementiscalculatedwithdifferentnumberofuserrequestsasshowninFigure7.Averagecostisincreasingwithincreaseinnumberofuserrequests.At180userrequests,averagecostinQoS-awareautonomicis25.36%lesserthannon-autonomicresourcemanagementtechnique.QoS-awareautonomicperformsexcellentwithdifferentnumberofuserrequests.ExecutioncostinQoS-awareautonomicis27.65%lesserthannon-autonomicresourcemanagementtechnique.

AsshowninFigure8,theexecutiontimeisincreasingwithincreaseinnumberofuserrequests.At 90 user requests, execution time in QoS-aware autonomic resource management techniqueis 24.66% lesser than non-autonomic resource management technique. After 120 user requests,executiontimeincreasesabruptlyinnon-autonomicresourcemanagementtechniquebutQoS-awareautonomicperformsbetter thannon-autonomic technique.Averageexecution time inQoS-awareautonomicis18.960%lesserthannon-autonomicresourcemanagementtechnique.Withincreasingthenumberofuserrequests,thepercentageofresourceutilizationisincreasing.ThepercentageofresourceutilizationinQoS-awareautonomicresourcemanagementtechniqueismoreascomparedtonon-autonomicresourcemanagement(non-autonomic)atdifferentnumberofuserrequestsasshowninFigure9.Themaximumpercentageofresourceutilizationis94.66%at180userrequestsinQoS-awareautonomicbutQoS-awareautonomicperformsbetterthannon-autonomictechnique.AverageresourceutilizationinQoS-awareautonomicis31.96%morethannon-autonomicresourcemanagementtechnique.

Scalability ismeasured in termsof throughput.Numberof software,networkandhardwarefaults(faultpercentage)hasbeeninjectedtoverifythethroughputoftheproposedsystemwith100userrequests.Figure10showsthecomparisonofthroughputofbothQoS-awareautonomicresourcemanagementapproachandnon-QoSbasedresourcemanagementtechnique(non-autonomic)at100userrequestsanditisclearlyshownthatQoS-awareautonomicperformsbetterthannon-autonomic.Inthisexperiment,ithasbeenfoundthemaximumvalueofthroughputatfaultpercentage45%i.e.QoS-awareautonomichas26%morethroughputthannon-autonomic.Detectionrateincreaseswithrespecttotimeanditconsidersthenumberofblockedanddetectedattacks.Fornewattackorintrusiondetection,databaseisupdatedwithnewsignaturesandnewpolicesandrulesaregeneratedtoavoid

Figure 7. Effect of change in number of user requests on execution cost

Volume 29 • Issue 4 • October-December 2017

17

sameattack.Experimenthasbeenconductedforknownattacks;itisclearlyshowninFigure11thatQoS-awareautonomicperformsbetterthansnortanomalydetector(non-autonomic).Furthersignaturesofsomeknownattackshavebeenremovedfromdatabasetoverifytheworkingofproposedsystem.

Table13describesthecomparisonofexecutiontimeusedtoprocessdifferentnumberofworkloads(90and180)oncloudenvironmentforproposedsystemwithdifferentnumberof

Figure 8. Effect of execution time with change in number of user requests

Figure 9. Effect of change in number of user requests on resource utilization

Journal of Organizational and End User Computing


18

VirtualMachines(VMs).ThenumberofVMsusedtoexecutetheworkloadswasincrementedgraduallyshowinghowthetotalexecutiontimewasreducedwhenmoreVMswereaddedtothecloud.WithonevirtualnoderunningonServerR1,executionof45workloadsfinishedin436.12seconds.With12virtualnodes(6runningonR1,4runningonR2and2runningonR3),theapplicationtook276.16seconds.Itisnotedthattheexecutiontimeisreducedwithaddingadditionalvirtualnodes.

Figure 10. Throughput [100 user requests] vs. Fault percentage (%)

Figure 11. Detection rate vs. Attacks


19

5.4. Statistical Analysis

StatisticalsignificanceoftheresultshasbeenanalyzedbyCoefficientofVariation Coff ofVar. .( ) ,astatisticalmethod.Coff ofVar. . isstatisticalmeasureofthedistributionofdataaboutthemeanvalue.Coff of Var. . isusedtocomparetodifferentmeansandfurthermoreofferanoverallanalysisofperformanceofthetechniqueusedforcreatingthestatistics.Itstatesthedeviationofthedataasaproportionofitsaveragevalue,andiscalculatedasfollows(Equation9):

Coff ofVarSD

. . �M

= ×100 (9)

whereSD isastandarddeviationandM ismean.Coff ofVar. . ofexecutiontimeandhavebeenstudied of QoS-aware autonomic resource management technique and non-autonomic resource

Table 13. Total execution time of a bulk of cloud workloads distributed in three servers

Number of WorkloadsVirtual Nodes

Total Workers Execution Time (Seconds)R1 R2 R3

45 1 0 0 1 436.12

45 1 1 0 2 428.69

45 2 1 0 3 418.97

45 2 2 0 4 407.55

45 3 2 0 5 398.17

45 4 2 0 6 380.30

45 4 2 1 7 361.66

45 4 3 1 8 345.18

45 5 3 1 9 331.21

45 5 3 2 10 315.03

45 5 4 2 11 299.97

45 6 4 2 12 276.16

90 1 0 0 1 1803.11

90 1 1 0 2 1771.18

90 2 1 0 3 1759.66

90 2 2 0 4 1736.15

90 3 2 0 5 1691.77

90 4 2 0 6 1668.96

90 4 2 1 7 1636.11

90 4 3 1 8 1625.19

90 5 3 1 9 1578.21

90 5 3 2 10 1551.68

90 5 4 2 11 1529.11

90 6 4 2 12 1503.11


20

managementtechniqueasshowninFigure12andFigure13.RangeofCoff ofVar. . (0.25%-1.69%)for execution timeand (0.37% -1.96%) for cost approves the stabilityofQoS-aware autonomicresourcemanagementtechniqueasshowninFigure12andFigure13.SmallvalueofCoff ofVar. . signifies QoS-aware autonomic resource management technique is more efficient in resourceschedulinginthesituationswherethenumberofuserrequestshaschanged.ValueofCoff ofVar. . decreasesasthenumberofuserrequestsisincreasing.

6. CONCLUSION AND FUTURE DIRECTIONS

Cloud-basedautonomicinformationsystem(AaaS)foragricultureservicehasbeenpresented,whichmanagesthevarioustypesofagriculture-relateddatabasedondifferentdomainsthroughdifferentuserpreconfigureddevices.K-NN(k-NearestNeighbor)classificationmechanismisusedtoclassifytheagriculturedata.Further,classifieddataisinterpretedanduserscaneasilydiagnosetheagriculturestatusautomaticallythroughAaaS.Inaddition,AaaSusestworesourceschedulingpolices(timeandcost)forefficientresourceallocationatinfrastructurelevelafteridentificationofQoSrequirementsofuserrequest.Theperformanceofproposedsystemhasbeenevaluatedincloudenvironmentand

Figure 12. CoV for execution time with each scheduling algorithm

Figure 13. CoV for execution cost with each scheduling algorithm


21

experimentalresultsshowthattheproposedsystemperformsbetterintermsofexecutiontime,cost,resourceutilization,latency,scalabilityandsecurity.Infuture,theproposedtechniquecanbeextendedbyincorporatingotherQoSparameterslikenetworkbandwidth,availability,customersatisfaction,computingcapacityetc.Proposedtechniquecanbeextendedbydevelopingpluggablescheduler,inwhichresourceschedulingcanbechangedeasilybasedontherequirements.

ACKNOwLEDGMENT

Oneoftheauthors,Dr.SukhpalSinghGill[PostDoctorateFellow],gratefullyacknowledgestheCLOUDS Lab, School of Computing and Information Systems, The University of Melbourne,Australia,forawardinghimtheFellowshiptocarryoutthisresearchwork.


22

REFERENCES

AgricultureData,GovernmentofIndia.(n.d.).Retrievedfromhttps://data.gov.in/catalogs/sector/Agriculture-9212

Elsheikh,R.,MohamedShariff,A.R.B.,Amiri,F.,Ahmad,N.B.,Balasundram,S.K.,&Soom,M.A.M.(2013).AgricultureLandSuitabilityEvaluator(ALSE):Adecisionandplanningsupporttoolfortropicalandsubtropicalcrops.Computers and Electronics in Agriculture,93,98–110.doi:10.1016/j.compag.2013.02.003

Hu,Y.,Quan,Z.,&Yao,Y.(2004).Web-basedAgriculturalSupportSystems.Proceeding of the Workshop on Web-based Support Systems(pp.75-80).

IndiaAgricultureAndClimateDataSet.(n.d.).Retrievedfromhttps://ipl.econ.duke.edu/dthomas/dev_data/datafiles/india_agric_climate.htm

Jeong,S.,Jeong,H.,Kim,H.,&Yoe,H.(2013).CloudComputingbasedLivestockMonitoringandDiseaseForecastingSystem.International Journal of Smart Home,7(6),313–320.doi:10.14257/ijsh.2013.7.6.30

Narayana Reddy, M., & Rao, N. H. (1995). GIS Based Decision Support Systems in Agriculture. NationalAcademyofAgriculturalResearchManagementRajendranagar.

Nikkilä, R., Seilonen, I., & Koskinen, K. (2010). Software architecture for farm management informationsystems inprecisionagriculture.Computers and Electronics in Agriculture,70(2), 328–336.doi:10.1016/j.compag.2009.08.013

Prasad,S.,Peddoju,S.K.,&Ghosh,D.(2013).AgroMobile:ACloud-BasedFrameworkforAgriculturistsonMobilePlatform. International Journal of Advanced Science and Technology,59,41–52.doi:10.14257/ijast.2013.59.04

Ruixue,Z.(2002).StudyonWeb-basedAgriculturalInformationSystemDevelopmentMethod.Proceedings of the Third Asian Conference for Information Technology in Agriculture,China(pp.601-605).

Shangguan,W.,Dai,Y.,Liu,B.,Ye,A.,&Yuan,H.(2012,February29).Asoilparticle-sizedistributiondatasetforregionallandandclimatemodellinginChina.Geoderma,171,85–91.doi:10.1016/j.geoderma.2011.01.013

Singh,S.,&Chana,I.(2015).QoS-awareAutonomicResourceManagementinCloudComputing:ASystematicReview.ACM Computing Surveys,48(3),1–46.doi:10.1145/2843889

Singh,S.,&Chana, I. (2015).QRSF:QoS-aware resourcescheduling framework incloudcomputing.The Journal of Supercomputing,71(1),241–292.doi:10.1007/s11227-014-1295-6

Singh,S.,&Chana,I.(2015).Q-aware:Qualityofservicebasedcloudresourceprovisioning.Computers & Electrical Engineering,47,138–160.doi:10.1016/j.compeleceng.2015.02.003

Singh,S.,&Chana,I.(2016).EARTH:Energy-awareAutonomicResourceSchedulinginCloudComputing.Journal of Intelligent and Fuzzy Systems,30(3),1581–1600.doi:10.3233/IFS-151866

Sørensen,C.G.,Fountas,S.,Nash,E.,Pesonen,L.,Bochtis,D.,Pedersen,S.M.,&Blackmore,S.B.et al.(2010).Conceptualmodelofafuturefarmmanagement informationsystem.Computers and Electronics in Agriculture,72(1),37–47.doi:10.1016/j.compag.2010.02.003

Sørensen,C.G.,Pesonen,L.,Bochtis,D.D.,Vougioukas,S.G.,&Suomi,P.(2011).Functionalrequirementsforafuturefarmmanagementinformationsystem.Computers and Electronics in Agriculture,76(2),266–276.doi:10.1016/j.compag.2011.02.005

https://data.gov.in/catalogs/sector/Agriculture-9212

http://dx.doi.org/10.1016/j.compag.2013.02.003

https://ipl.econ.duke.edu/dthomas/dev_data/datafiles/india_agric_climate.htm

https://ipl.econ.duke.edu/dthomas/dev_data/datafiles/india_agric_climate.htm

http://dx.doi.org/10.14257/ijsh.2013.7.6.30



http://dx.doi.org/10.14257/ijast.2013.59.04

http://dx.doi.org/10.14257/ijast.2013.59.04

http://dx.doi.org/10.1016/j.geoderma.2011.01.013

http://dx.doi.org/10.1145/2843889

http://dx.doi.org/10.1007/s11227-014-1295-6

http://dx.doi.org/10.1016/j.compeleceng.2015.02.003

http://dx.doi.org/10.3233/IFS-151866




23

Sukhpal Singh Gill joined Computer Science and Engineering Department of Thapar University, Patiala, India, in 2016 as a Faculty. Presently, Dr. Gill is working as Post Doctorate Fellow at CLOUDS Lab, School of Computing and Information Systems, The University of Melbourne, Australia. Dr. Gill obtained the Degree of Master of Engineering in Software Engineering from Thapar University, as well as a Doctoral Degree specialization in “Autonomic Cloud Computing” from Thapar University. Dr. Gill received the Gold Medal in Master of Engineering in Software Engineering. Dr. Gill is a DST Inspire Fellow [2013-2016] and worked as a SRF-Professional on DST Project, Government of India. He has done certifications in Cloud Computing Fundamentals, including Introduction to Cloud Computing and Aneka Platform (US Patented) by ManjraSoft Pty Ltd, Australia and Certification of Rational Software Architect (RSA) by IBM India. His research interests include Software Engineering, Cloud Computing, Internet of Things and Fog Computing. He has more than 40 research publications in reputed journals and conferences.

Inderveer Chana joined Computer Science and Engineering Department of Thapar University, Patiala, India, in 1997 as Lecturer and is presently serving as Professor in the department. She is Ph.D. in Computer Science with specialization in Grid Computing, M.E. in Software Engineering from Thapar University and B.E. in Computer Science and Engineering. Her research interests include Grid and Cloud computing and other areas of interest are Software Engineering and Software Project Management. She has more than 100 research publications in reputed Journals and Conferences. Under her supervision, more than 40 ME thesis and seven Ph.D thesis have been awarded and five Ph.D. thesis are on-going. She is also working on various research projects funded by Government of India.

Rajkumar Buyya is a Fellow of IEEE, Professor of Computer Science and Software Engineering and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is also serving as the founding CEO of Manjrasoft, a spin-off company of the University, commercialising its innovations in Cloud Computing. He has authored over 500 publications and four text books. He is one of the highly cited authors in computer science and software engineering worldwide (h-index 110+, 60000+ citations). He has served as the founding Editor-in-Chief (EiC) of IEEE Transactions on Cloud Computing and now serving as Co-EiC of Journal of Software: Practice and Experience.

iot based agriculture as a cloud and big data service · iot based agriculture as a cloud and big...

Documents