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Mahua Bhattacharya
Latika Kharb
Deepak Chahal (Eds.)
6th International Conference, ICICCT 2021
New Delhi, India, May 8, 2021
Revised Selected Papers
EditorialBoardMembers JoaquimFilipe
PolytechnicInstituteofSetúbal,Setúbal,Portugal
AshishGhosh
IndianStatisticalInstitute,Kolkata,India
RaquelOliveiraPrates
FederalUniversityofMinasGerais(UFMG),BeloHorizonte,Brazil
LizhuZhou
TsinghuaUniversity,Beijing,China
Moreinformationaboutthisseriesat http://www.springer.com/series/7899
MahuaBhattacharya
6thInternationalConference,ICICCT2021 NewDelhi,India,May8,2021
RevisedSelectedPapers
Editors
MahuaBhattacharya
ABVIndianInstituteofInformation TechnologyandManagement Gwalior,India
DeepakChahal
JaganInstituteofManagementStudies
Delhi,India
LatikaKharb
JaganInstituteofManagementStudies
Delhi,India
ISSN1865-0929ISSN1865-0937(electronic) CommunicationsinComputerandInformationScience
ISBN978-3-030-88377-5ISBN978-3-030-88378-2(eBook) https://doi.org/10.1007/978-3-030-88378-2
© SpringerNatureSwitzerlandAG2021
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Preface TheInternationalConferenceonInformation,CommunicationandComputingTechnology(ICICCT2021)washeldonMay8,2021,inNewDelhi,India.ICICCT2021 wasorganizedbytheDepartmentofInformationTechnology,JaganInstituteof ManagementStudies(JIMS)Rohini,NewDelhi,India.Theconferencereceived83 submissionsandafterrigorousreviews20paperswereselectedforthisvolume.The acceptanceratewasaround16.6%.Thecontributionscamefromdiverseareasof informationtechnologycategorizedintotwotracks,namely(1)Communicationand NetworkSystemsand(2)ComputationalIntelligenceTechniques
TheaimofICICCT2021wastoprovideaglobalplatformforresearchers,scientists,andpractitionersfrombothacademiaandindustrytopresenttheirresearchand developmentactivitiesinalltheaspectsofcommunicationandnetworksystemsand computationalintelligencetechniques.
WethankallthemembersoftheOrganizingCommitteeandtheProgramCommitteefortheirhardwork.WeareverygratefultoMahuaBhattacharya,ABVIndian InstituteofInformationTechnologyandManagement,India,asgeneralchair,andSami Muhaidat,KhalifaUniversity,AbuDhabi,UAE,asprogramchair.Wearealsograteful toourkeynotespeakers,MarcinPaprzycki,SystemsResearchInstitute,PolishAcademyofSciences,Poland,andMariaGanzha,WarsawUniversityofTechnology, Poland.WewouldliketothankFerdousAhmedBarbhuiya,IndianInstituteof Technology(IIT)Guwahati,India,assessionchairforTrack1,andNarendraLondhe, NationalInstituteofTechnology,Raipur,India,assessionchairforTrack2.
WealsothankalltheTechnicalProgramCommitteemembersandrefereesfortheir constructiveandenlighteningreviewsonthemanuscripts,Springerforpublishingthe proceedingsintheCommunicationsinComputerandInformationScience(CCIS) series,andalltheauthorsandparticipantsfortheirgreatcontributionsthatmadethis conferencepossible.
August2021LatikaKharb DeepakChahal
Organization GeneralChair
MahuaBhattacharyaABVIndianInstituteofInformationTechnology andManagement,India
ProgramChair
SamiMuhaidatKhalifaUniversity,AbuDhabi,UAE
KeynoteSpeakers
MarcinPaprzyckiSystemsResearchInstitute,PolishAcademy ofSciences,Poland MariaGanzhaWarsawUniversityofTechnology,Poland
ConferenceSecretariat
PraveenAroraJaganInstituteofManagementStudies,India
SessionChairforTrack1
FerdousAhmedBarbhuiyaIndianInstituteofTechnology(IIT)Guwahati,India SessionChairforTrack2
NarendraLondheNationalInstituteofTechnology,Raipur,India ProgramCommitteeChairs
LatikaKharbJaganInstituteofManagementStudies,India
DeepakChahalJaganInstituteofManagementStudies,India
TechnicalProgramCommittee
RastislavRokaSlovakUniversityofTechnology,Slovakia SiddhivinayakKulkarniMITWorldPeaceUniversity,India
P.ChennaReddyJawaharlalNehruTechnologicalUniversityAnantapur, India
RazaliYaakobUniversitiPutraMalaysia,Malaysia
NoorAfizaMohdAriffinUniversitiPutraMalaysia,Malaysia
MaltiBansalDelhiTechnologyUniversity,India
M.BabuReddyKrishnaUniversity,India AhmadKhanCOMSATSUniversityIslamabad,Pakistan MohdAbdulAhadJamiaHamdard,India RizwanRehmanDibrugarhUniversity,India ShahabShamshirbandIranUniversityofScienceandTechnology,Iran AtulGonsaiGosaiSaurashtraUniversity,India ShamimulQamarKingKhalidUniversity,SaudiArabia P.Subashini Avinashilingam
AvinshilingamInstituteforHomeScienceandHigher EducationforWomen,India ParthaPakrayNationalInstituteofTechnology,Assam,India AzurahUniversitiTeknologiMalaysia,Malaysia AnazidaUniversitiTeknologiMalaysia,Malaysia ChanWengHoweUniversitiTeknologiMalaysia,Malaysia
C.ShobaBinduJNTUACollegeofEngineering,India
S.PallamSettyAndhraUniversity,India K.MadhaviJNTUACollegeofEngineering,India JanakaWijekoonSriLankaInstituteofInformationTechnology, SriLanka
J.HanumanthappaUniversityofMysore,India K.ThabotharanUniversityofJaffna,SriLanka KamalEldahshanAl-AzharUniversity,Egypt TonySmithUniversityofWaikato,NewZealand Abdel-BadeehSalemAinShamsUniversity,Egypt
KhalidNazimSattarAbdulMajmaahUniversity,SaudiArabia H.S.NagendraswamyUniversityofMysore,India
S.R.BoselinPrabhuAnnaUniversity,India
S.RajalakshmiSriChandrasekharendraSaraswathiViswa Mahavidyalaya,India AnastasiosPolitisTechnologicalandEducationalInstituteofCentral Macedonia,Greece SubhashChandraYadavCentralUniversityofJharkhand,India UttamGhoshVanderbiltUniversity,USA WafaaShalashKingAbdulazizUniversity,SaudiArabia EtimadFadelKingAbdulazizUniversity,SaudiArabia OleksiiTyshchenkoUniversityofOstrava,CzechRepublic HimaBinduMaringantiNorthOrissaUniversity,India FroilanD.MoboPhilippineMerchantMarineAcademy,ThePhilippines LatafatA.GardashovaAzerbaijanStateOilAcademy,Azerbaijan WenjianHuFacebook,USA
MuhammadUmairRamzanKingAbdulazizUniversity,SaudiArabia AreejAbbasMalibaryKingAbdulazizUniversity,SaudiArabia DilipSinghSisodiaNationalInstituteofTechnologyRaipur,India
P.R.PatilPSGVPMandal’sD.N.PatelCollegeofEngineering, India
JoseNeumanSouzaFederalUniversityofCeara,Brazil NerminHamzaKingAbdulazizUniversityJeddah,SaudiArabia
R.ChithraK.S.RangasamyCollegeofTechnology,India HomeroToralCruzUniversityofQuintanaRoo,Mexico
J.VijiGripsyPSGRKrishnammalCollegeforWomen,India
BoudhirAnouar Abdelhakim
AbdelmalekEssaâdiUniversity,UAE
MuhammedAliAydinIstanbulCerrahpaşaUniversity,Turkey SuhairAlshehriKingAbdulazizUniversity,SaudiArabia DaliborDobrilovicUniversityofNoviSad,Serbia
A.V.PetrashenkoNationalTechnicalUniversityofUkraine,Ukraine
AliHussainSriSaiMadhariInstituteofScienceandTechnology, India
A.NagarajuCentralUniversityRajasthan,India Cheng-ChiLeeFuJenCatholicUniversity,Taiwan ApostolosGkamasUniversityEcclesiasticalAcademyofVella ofIoannina,Greece
M.A.H.AkhandKhulnaUniversityofEngineering&Technology, Bangladesh
SaadTalibHassonUniversityofBabylon,Iraq
ValeriMladenovTechnicalUniversityofSofia,Bulgaria
KateRevoredoViennaUniversityofEconomicsandBusiness,Austria DimitrisKanellopoulosUniversityofPatras,Greece
SamirKumar
Bandyopadhyay UniversityofCalcutta,India
BaljitSinghKhehraBBSBEC,India NitishPathakBVICAM,India
MdGaparMdJoharManagementScienceUniversity,Malaysia
KathemreddyRamesh Reddy VikramaSimhapuriUniversity,India
ShubhnandanSinghJamwaUniversityofJammu,India
SurjeetDalalSRMUniversityDelhi-NCR,India
S.VasundraJawaharlalNehruTechnologicalUniversity, Anantapur,India
ManojPatilNorthMaharashtraUniversity,India
RahulJohariGGSIPU,India
AdeyemiIkuesanUniversityofPretoria,SouthAfrica
PinakiChakrabortyNetajiSubhasUniversityofTechnology,India SubrataNandiNationalInstituteofTechnology,Durgapur,India VinodKeshaoraoPachghareCollegeofEngineering,Pune,India
A.V.SenthilKumarHindusthanCollegeofArtsandScience,India KhalidRazaJamiaMiliaIslamia,India
G.VijayaLakshmiVikramaSimhapuriUniversity,India
ParameshachariB.D.TGSSSInstituteofEngineeringandTechnology forWomen,India
E.GraceMaryKanagaKarunyaUniversity,India
SubalalithaC.N.SRMUniversityKanchipuram,India NiketaGandhiMachineIntelligenceResearchLabs,USA
T.SobhaRaniUniversityofHyderabad,India ZunnunNarmawalaNirmaUniversity,India AniruddhaChandraNationalInstituteofTechnology,Durgapur,India AshwaniKushKurukshetraUniversity,India ManojSahniPanditDeendayalPetroleumUniversity,India PromilaBahadurMaharishiUniversityofManagement,USA GajendraSharmaKathmanduUniversity,Nepal RabindraBistaKathmanduUniversity,Nepal RenukaMohanrajMaharishiUniversityofManagement,USA EduardBabulakInstituteofTechnologyandBusiness,CzechRepublic ZoranBojkovicUniversityofBelgrade,Serbia PradeepTomarGautamBuddhaUniversity,India ArvindSelwalCentralUniversityofJammu,India AtifFaridMohammadUniversityofNorthCarolinaatCharlotte,USA MaushumiBarooahAssamEngineeringCollege,India PremPrakashJayaramanSwinburneUniversityofTechnology,Australia KalmanPalaggiUniversityofSzeged,Hungary J.VijayakumarBharathiarUniversity,India JacekIzydorczykSilesianUniversityofTechnology,Poland PamelaL.ThompsonUniversityofNorthCarolina,Charlotte,USA ArkaProkashMazumdarMalaviyaNationalInstituteofTechnologyJaipur,India R.GomathiBannariAmmanInstituteofTechnology,India ZunnunNarmawalaNirmaUniversity,India DiptenduSinhaRoyNationalInstituteTechnology,Meghalaya,India NitinKumarNationalInstituteofTechnology,Uttarakhand,India B.SurendiranNationalInstituteofTechnology,Puducherry,India ParismitaSarmaGuwahatiUniversity,India ManasRanjanKabatVSSUniversityofTechnology,India AnujGuptaChandigarhEngineeringCollege,India Md.AlimulHaqueVeerKunwarSinghUniversity,India AbdullahM.AlBinAliTaibahUniversity,SaudiArabia SubhojitGhoshNationalInstituteofTechnology,Raipur,India RohiniSharmaPanjabUniversityChandigarh,India AlessioBottrighUniversityofEasternPiedmont,Italy SunitaSarkarAssamUniversity,India SonalChawlaPanjabUniversity,India AnuragJainGuruGobindSinghIndraprasthaUniversity,India MattKretchmarDenisonUniversity,USA SharadSaxenaThaparInstituteofEngineeringandTechnology,India DushyantKumarSinghMotilalNehruNationalInstituteofTechnology Allahabad,India R.I.MinuSRMInstituteofScienceandTechnology,India M.MuraliSRMInstituteofScienceandTechnology,India RajeshMehtaThaparInstituteofEngineeringandTechnology,India VibhavPrakashSinghMotilalNehruNationalInstituteofTechnology Allahabad,India
Contents CommunicationandNetworkSystems
PerformanceEnhancementinBigDatabyGuidedMapReduce..........3 HimadriSekharRay,AnuragChakraborty,andRadibKar
MultipathTCPSecurityIssues,ChallengesandSolutions...............18 KhushiPopatandViralVinodKapadia
Challenge-ResponseBasedDataIntegrityVerification(DIV)andProof ofOwnership(PoW)ProtocolforCloudData.......................33 BasappaB.KodadaandDemianAntonyD’Mello
Multi-layerParallelizationinTransportationManagementSoftware........44 AntonIvaschenko,SergeyMaslennikov,AnastasiaStolbova, andOlegGolovnin
APostQuantumSignatureSchemeforSecureUserCertificationSystem....52 SwatiRawalandSahadeoPadhye
ComparativeAnalysisofServerlessSolutionsfromPublic CloudProviders...........................................63 DarshanBaid,PallaviMurghaiGoel,PragyaBhardwaj,AsthaSingh, andVishuTyagi
ComparativeAnalysisofSecurityProtocolsinIoT...................76 SaifSaffahBadrAlazzawiandTamannaSiddiqui
SecureDataTransmissionTechniquesforPrivacyPreservingComputation OffloadingBetweenFogComputingNodes........................87 YashKetanPatel,KrunalDipakbhaiPatel,andPayalChaudhari
MinimizePenaltyFeesDuringReconfigurationofaSetofLight-TreePairs inanAll-OpticalWDMNetwork...............................102 AmanvonFerdinandAtta,GillesArmelKeupondjoSatchou, JoëlChristianAdépo,andSouleymaneOumtanaga
ComputationalIntelligenceTechniques
Web-BasedReal-TimeGestureRecognitionwithVoice................119 GhadekarPremanandPralhad,S.Abhishek,TejasKachare, OmDeshpande,RushikeshChounde,andPrachiTapadiya
AMapReduceApproachtoAutomaticKeyFileUpdates forSPT(Squeeze,PackandTransfer)Algorithm....................132 ShivPreetandAmandeepBagga
MeasurementofLiquidLevelUsingPredictionMethodology............141 TruptiNagrare
PerformanceAnalysisofNamedEntityRecognitionApproaches onCode-MixedData........................................153
SreejaGaddamidiandRajendraPrasath
QuantumBasedDeepLearningModelsforPatternRecognition..........168 PrakharShrivastava,KapilKumarSoni,andAkhtarRasool
EmpiricalLawsofNaturalLanguageProcessingforNeuralLanguage GeneratedText............................................184
SumedhaandRajeshRohilla
StoryGenerationfromImagesUsingDeepLearning..................198 AbrarAlnami,MiadaAlmasre,andNorahAl-Malki
DetectingText-BullyingonTwitterUsingMachineLearningAlgorithms....209 AbdullahYahyaAbdullahAmerandTamannaSiddiqui
PredictingtheStockMarketTrend:AnEnsembleApproachUsing ImpactfulExploratoryDataAnalysis.............................223 NusratRouf,MajidBashirMalik,andTasleemArif
CanMachineLearningPredictanEmployee’sMentalHealth?...........235 GaganmeetKaurAwalandKunalRao
PredictingtheDecompositionLevelofForestTreesThrough EnsemblingMethods........................................248
S.JeyabharathyandPadmapriyaArumugam
CommunicationandNetworkSystems PerformanceEnhancementinBigData byGuidedMapReduce HimadriSekharRay(B) ,AnuragChakraborty ,andRadibKar JadavpurUniversity,Kolkata,India
Abstract. Inrecentyears,duetotheemergenceoftheInternetandcommunicationtechnologydataisgeneratedatanincreasingrate.Everythingaroundus isconnectedtotheinternetandseemstogeneratedata.Healthcareisoneofthe biggestboomingdataproducingsector.Toefficientlyfetchtherequiredinformationformthelargevolumeofdata,itisrequiredtostorethedatainadistributed fashionsothattheretrievaltimeshouldbeverylessandforthat,amassivestorageclusteroftensofthousandsofconnectedmachineswithbuilt-inanalytics toolsarerequiredtosystematicallystoreandfastretrieveofthedata.Hadoop,a well-knownanalyticalapplication,dividesthedataintosmallblocksandwiththe helpofHadoopDistributedFileSystem(HDFS),itdistributesamongtheclusternodeforstorageandfutureprocessing.Ifthereisaneedtorunaqueryona specificitem,Hadooprunsitsqueryinallofitsthousandsofnodesforfetching aparticulardataset,whichincreasestheoverallexecutiontime.Toovercomethis issue,wehavemadeanapplicationNodeGuidedMap-Reduce(NGMR)based onNode.jsinadistributedclusterwithmovablenodes,wherethisissuehasbeen resolved.Tobuildandrunthisapplicationandmakeresearchonit,ahugeamount ofcapitalisrequiredtobuildadatastorageservice.Duetothislimitation,wehave madeasmallscalemodelofthereplicaofalargestorageclusterusinglow-end computationalpowerandlimitedresourceARM-basedsingle-boardcomputers (RaspberryPi).Theworkwasdoneoverthepastfewyearsistheimplementationoftheproposedschemetoenhancetheperformanceinbigdatahandlingby GuidedMap-ReducewiththehelpofNode.jsandtopresentandcreateefficient cost-effectivedatastorageandretrievalsystemforthehealth-careindustry.
Keywords: BigData · Opensourcehardware · Raspberrypi · BATMAN-adv · Distributedprocessing Node.js Map-Reduce Healthcare Movablenodes Cluster · NodeguidedMap-Reduce(NGMR)
1Introduction Withtherecentadvancementofdistributedprocessing,healthcaresectorisoneofthe largedata-producingsectorswhichproduceahugenumberofdataeveryday.Oneof themajorchallengesistomakealargescaledistributedstoragesystemtoefficiently store,processandretrievethenecessaryhealthrecords.Sinceitsearlybeginning,the HadoopMap-Reducehasbecomeapopulartoolforstoring,managingandprocessing themassiveamountofdata.HadoopMap-Reducehascomeupwithahighlyeffective
©SpringerNatureSwitzerlandAG2021
M.Bhattacharyaetal.(Eds.):ICICCT2021,CCIS1417,pp.3–17,2021. https://doi.org/10.1007/978-3-030-88378-2 1
frameworkforstoringandanalysingBigdata.Butthistypeofframeworkdistributes datatoallofitsnodesandlaterattheprocessingtime,theuserprovidedMap-reduce queriesalsoexecuteonallnodes.Thus,sometimesagivenqueriesmaybeprocessed onsomeofthenodeswherenodatarelatedtotheusergivenqueryisstored.This phenomenonincreasestheoverallprocessingtime.Therefore,in[1],wehaveproposed adistributedstorageplatformandMap-Reducemodelthatovercometheproblemfaced intheexistingsystems.Basedontheproposedmodel,wehavedevelopedaframework NodeguidedMapReduce(NGMR)usinggoogleMap-Reduceparadigm.
Thepaperdescribesanefficientbig-datahandlingapplicationNGMR.TheframeworkNGMRguidestheMap-Reduceprocesstorunonaspecificsetofclusternodes amongallnodesinthecluster.Fordevelopingandexperimentingwiththehealth data,singleboardcomputerswithopen-sourcehardwareareusedasdistributednodes. Allnodesareconnectedininfrastructure-lessADHOCmobilenetworkestablishedby BATMAN-ADV[6].NGMRisdevelopedwithNode.js,afreeopen-sourceserverenvironmentthatrunsonJavaScriptontheserverandcanrunonvariousplatformslike Windows,Linux,MacOSetc.
Therestofthepaperisorganizedasfollows.Section 2 discussestherelatedworks. Section 3 givesabriefideaaboutthefilesystemoftheNodeGuidedMapReduce (NGMR)application.Section 4 dealswiththesetupprocedureofNodeGuidedMap Reduceapplicationcluster.InSect. 5 thetestscenariosandsystemsetuparedescribed forcomparingNGMRfilesystemwithHadoopfilesystem.InSect. 6 datauploading procedureandevaluationresultarepresenttocomparetheperformancewithexisting frameworkalongwiththeexecutionprocessofuserdefinedqueriesareelaborated. Section 2 givesabriefideaoftherelatedpresentdayworksandfinallySect. 7 concludes andprovidesthepossiblefuturedirections(Table 1).
Table1. ListofAbbreviations
Abbreviation StandsFor
NGMR NodeGuidedMap-Reduce
MR Map-Reduce
HDFS HadoopDistributedFileSystem
JSON JavaScriptObjectNotation
2RelatedWork
WiththerecentadvancementoftheBigDatasolutions,scientistshavefocusedonthe applicationoftheBigDataconceptfollowingtheMap-Reduceparadigm.TheBigData storagemodelbasedonMap-Reducetextcategorizationhasbeenusedforforecasting datainthenextperiodoftimeinaparticularregion[9].Thetwoapproachesarecompared,i.e.Map-ReduceandGPU-Reducetocalculatetheperformancemeasurementfor searchingindexfileindatabasequeryprocessing.Variousstudiesaremadetocheckthe
performanceofNoSQLdatabasesystemusingtheMap-Reduceprogrammingmodel withsingleandmultiplenodes.ItisshownthatNoSQLwithMAP-Reduceprogrammingmodelprovidesbetterperformancegaincomparedtorelationaldatabasesystems [10].
TheHadoopframework[8]handlesdistributedfilesystemwiththehelpofHDFS (Hadoopfilesystem).HDFSrunsonmaster/clientarchitecture.AnHDFSclusterconsists ofasingleNameNodeorMasterNodeandnumberofDataNodes.DataNodestoresthe datafilesinthecluster,however,MasterNodedoesnotstoreanydatafiles.TheMaster Nodemanagesthefilesystemnamespaceandregulatesaccesstofileswhicharestored intheDataNodes.Besides,thereisasecondaryNameNode,usually,onepercluster, whichmanagesstorageattachedtothenodesandhelpstheMasterNode.Ifforsome reason,theMasterNodeisdown,SecondaryNameNodecantaketheresponsibilityof handlingthetaskoftheMasterNode.HDFSexposesafilesystemnamespaceandallows userdatatobestoredinfiles.Internally,afileissplitintooneormoreblocksandthese blocksarestoredinasetofDataNodes.Bydefault,HDFSmaintainsthedatablocksize as128MB.TheNameNode/MasterNodeexecutesfilesystemnamespaceoperations, likeopening,closing,andrenamingfilesanddirectories.Italsodeterminesthemapping ofblocksontoDataNodes.TheDataNodesareresponsibleforservingreadandwrite requests,blockcreation,deletion,andreplicationuponinstructionfromtheNameNode. AgraphicalrepresentationofHDFSarchitectureisshowninFig. 1.
AtthetimeofdataprocessinginHDFS,theHadoopframeworksendstheusergiven analyticscodetoallofitsnodestoruntheuser-givenqueries.Sometimesitmayhappen thatthegivenqueryrunsonsuchanodewherenodataisreturnedasoutputofthequery execution.Therefore,processingonsuchnodesincreasestheoveralldataretrievaltime. Ourworkisdifferentfromtheabove-mentionedresearchworks,becauseweconsider thespecificissuesrelatedtothestorageandretrievaloftheBigDataandcarrythe experimentalstudyregardingtheperformanceofNGMRandthewell-knownbigdata solutiononverylowcommodityhardware.
3NGMRFileSystem NGMR(NodeguidedMap-Reduce)filesystemisdevelopedasanefficientdistributed filesystembasedontheframeworkproposedin[1].Theframeworkfollowsmaster
Fig.1. HDFSArchitecture
clientarchitecturewherethemasterserveriscalledtheNGMRMasterNodeandthe ClientNodesarecalledtheNGMRClientNodeorNGMRClusterUnits.Theseunits communicatewitheachotherwithRESTarchitecture.
TheNGMRMasterNodemaintainsseveraldatastructuresformanagingvarious tasks.Forstorage,theMasterNodekeepsthemetadatainformationinaJSONfilefor eachofthelargefilesstoredinthecluster.ForMap-Reduceexecution,itmaintainsthe information,like IdleState, In-progressstate, CompletedState ofeachjob.TheNGMR MasterNodealsostorestheNGMRClientNodeinformationlikenames,hostnames, totalusablespace,totalavailablespaceandtotalusedspaceinNGMRfilesystem,last heartbeatmessagereceivingtimefromeachnode,deadnodeandlivenodestatusetc. TheMasterNodealsostoresthedetailsoftheintermediatefileregionsproducedbyeach oftheMaptasks.TheinformationispushedtotheClientNodethathasMap-Reducetask inprogress.ClientNodealsomaintaintwotypesofdatastructures,oneistomaintain themetadataforeachdatablockofeachfileandoneisforstoringthedatablockitself.
NGMRfilesystemmaintainstwolevelsofindexing.TheMasterNodelevelindexing dealswiththenamesofthefiles/documents,respectiveclusterIDsanddataunitlevel indexingreferences.
TherawindexingfilestructureoftheMasterNodeisshowninFig. 2 below.
Fig.2. Indexingfilestructureofmasternode
TheClientNodelevelindexingstorestheinformationofallblocks,theirsizes, locations,androotfilenames.Similarlytherawstructureoftheclusterunitindexing fileisshowninFig. 3,
Fig.3. Indexingfilestructureofclusternode
3.1DataFragmentation NGMRfilesystemisdesignedfordistributedstorageandparallelprocessingsystems. Thefilesystemgivestheenduserfullflexibilitytofragmentthebigdatafileaccording toanypolicy,suchasverticalfragmentationorhorizontalfragmentationorboth,based ontheselectedsingleormultiplekeyattributesfromthedatafile.Asingleorasetof keyattributesfromthebigdatafilearechosenasadistributionkeytofragmentand distributethedatatotheNGMRclusternodes.
NGMRfragmentsthedatabyreadingtherecordsonebyone,bufferingthemina temporarymemorycreatedoneachdistinctoccurrencesofrecordsbasedonthechosen attributes.BufferingofdatatakesplaceontheNGMRMasterNodeandwhenthebuffer sizereachesaparticularsize,thedataissenttoaNGMRClientNode.Bydefault,the NGMRMasterNodecreatesabufferof64MBsizeforeachvalueofthekey(orsetof keys)attribute.Sizeofthebufferissameasthesizeofthedatablockandcanbechanged bytheuser.Whenthebufferisfull,thedataistransferredfromtheNGMRMasterNode totheClientNodeasa64MBdatablockforstoring.
ForeachsuchtransferactionofthebuffereddatatotheNGMRClientNode,aunique identificationforthatfragmentedfileisrecordedinaJSONfileintheNGMRMaster Nodethatisstoredasmastermetadata.AnotherJSONfileisstoredontheClientNode thatiscalledchildmetadata.ThemetadataattheMasterNodenotonlyguidesthequery executiononthedesiredClientNode,butitalsoplaysavitalroleinfailurerecovery.
3.2FaultTolerancebyDataDistributionUsingZoneDivision NGMRfilesystemisdesignedinsuchawaythattherecoveryisachievedsmoothly.A singleattributeorsetofattributesareinputtofragmentthebigdatafile.Thuseveryset offragmentsbasedonakeycanactasareplicaofanothersetoffragmentsbasedona differentkey.
Theabovepolicyaddressesanotherchallengerelatedtorecoveryofthefragmented blocksstoredindifferentclusterunits,incaseoffailureofsomenodes.Thismayalso helpincaseofmobilenodeswhensomenodesarenotwithinthecommunicationrange. So,themainideaisnottoreplicatetheexactcopyofthefragmentstootherlocations,but torearrangeeverycopybasedonthechosenkeyattributesandtoplacethemoptimally, sothatnodataislostduringnodefailureorwhennodeisnotreachableinthecluster. ThesetofClientNodescontainingallthedatablocksfragmentedonthebasisofa particularkeyattributeistermedasa Zone [1].Similarly,otherZonesmaybecreated basedonanotherkey.EveryZonemustcontainallthefragmentsbasedonthesame key,sothatevenifaunitofaparticularZonefails,thefragmentsstoredinanyofthe remainingZonescanbeaccumulatedtorecoverthebigdatafileandthatparticularkey basedfragmentsofthefailednodeareregeneratedbythealgorithmitselfensuringfailure recoverywithoutdataloss.
Asimpleexamplemaybegivenusingourdata-model.Thedatablocksfragmented basedonthe“location”attributeaskeyarestoredinClientnodesone,twoandthree. Inthiscase,thedatablocksfragmentedbasedonthediseaseswillbestoredinClient nodesotherthanone,twoorthree(forexampleinfourandfive).Soifanyoneorallof theClientnodesone,twoandthreearedownornotreachable,thedatacanberecovered fromnodesfourandfiveandre-fragmentedbasedonthe“location”attribute.Themain ideaistodistributeallthefragmentsacrossthenodesinsuchaway,thatifwetakethe unionofallblocksfromthesenodes,itwillreturntheoriginaldatafile.
4SettingUpanNGMRCluster Thehugeamountofhealthdatacannotbestoredinsingleconventionalstorageasthe sizeofthebigdatafileistoolargetofitinasinglesystem.Itshouldbedistributed
forfastprocessingofuser-givenqueries.Fortheexperimentwiththebigdata,large Datacentersarerequiredalongwithlargecapacityserverstobuildtheinfrastructure. Significantcapitalisrequiredtoproducethereplicaofsuchinfrastructurewhichmay sometimebealimitationforresearch.
BasedonthediscussionpresentedinSect. 4,aRaspberryPibasedclusterhasbeen deployedforthiswork.Theadvantageofthisdeploymentisthat,whileexperimenting withtheNGMRfilesystem,wehavealsoobservedtheeffectofmobilityonsuchsystems.
4.1RaspberryPi-Node.jsCluster
Raspberrypiisalow-costsingleboardcomputerwhichisveryefficientformaking storagecluster[2].Wehavemadea16nodestorageclusterhavingoneMasterNode with32GBstorageeach.TheconfigurationoftheMasterNodeishigherthantheClient Nodes.IthasaQuad-coreCortex-A72(ARMv8)64-bitSoC@1.5GHz.processorwith 4GBLPDDR4-3200SDRAM.andtheClientNodeshaveARMCortex-A531.4GHz processorwith1GBSRAMoneachnode.
Thenodesareconnectedinaself-forming,self-healing,andself-organizationadhoc meshnetworkusingBATMAN-adv(BetterApproachToMobileAd-hocNetworking [7])routingprotocol,designedanddevelopedtodealwiththenetworksthatarebased onunreliablelinks.Asthenodesaremovablesometimetheroutescanbebrokenand needtoreestablishthenewroute.Alltheclusternodeswillmaintainalistofneighbors totheothernodesinthenetwork.Anodewillselectthenexthopbasedonsomefactors likelesspacketloss,highsignalstrength,transmitquality(TQ)etc.Eachnodeperceives andmaintainsonlytheinformationaboutthebestnexthoptowardsallothernodes.The nodesaresetupusingstaticIPandconnectedwith5.2GHzintegratedWLANtogain accessfromMasterNode.
HandlinglargefilesisnotnewtoJavaScript.Infact, inthecorefunctionalityofNode.js,thereareseveral standardsolutionsforreadingfromandwritingtofiles. Themoststraightforwardsolutionistostreamthedata in(andout).Thisoptionfailsbecausethelargesetof dataisnotexpectedtobestaticdata(“dataatrest”).
Henceeventstreamingischosenforthiswork.Theeventstreamingfunctionseffectivelyforthesituationwherethereisaconstantflowofdata(“datainmotion”)andthe actionneedstobetakeninreal-timeassoonaspossible(Figs. 4 and 5).
Fig.4. Replicaofmobilenode
Node.jsrequesthandlingthreadstructure
5ExperimentalSetup Inthissection,wediscusstheexperimentalsetupforcomparingourproposedNGMR filesystemwithHDFS.Beforediscussingtheexperiments,wediscussthedatamodel usedinthisworkandthedatagenerationtechniqueforcarryingouttheexperiments.
5.1DataModel
Inthispaper,weusethedata-modelpresentedin[5].Theuseddatamodelisshown inFig. 6.However,datamembersarenotshownhere.A Person canbea Doctor , ora Patient.Thepatientcanregisteroneormore complaints regardingtheillness.A complaintistreatedwithinoneormore treatmentepisodes bydoctors.Atreatment episodemayconsistofoneormore visits.Ina visit,thedoctorprescribesoneormore investigations.Theinvestigationisoftwotypes, continuousmonitoring and discrete monitoring.Diagnosiscanbeconcludedinavisit.
Fig.5.
Fig.6. Useddatamodel
5.2DataGeneration
Wehavesetupkioskbasedhealthcentresintheruralareas.Thekioskbasedhealth centresaredrivenbythemedicalprofessionalswiththehelpofcloudbasedapplication KiORH(KioskOperatedRuralHealthcare)[4].Theapplicationprovidesanintegrated environmentforgatheringsymptomsandotherinformationaboutapatientvisitingthe kiosk,anduploadingthedetailstocloudforreal-timetreatmentbyaremotelylocated doctor.Thedatacollectedfromtheruralpatientthroughtheapplicationisstoredincloud storage.Thiscollecteddatavolumehasbeenusedforthisresearchwork.Although,the datavolumewasnotverylarge,ithasbeenusedtogeneratelargevolumeofsyntheticdata byduplicating,restructuringandreusing.Thesyntheticdatasetisproducedfollowing theabove-mentioneddatamodel.
Westartedwithtenpatientshaving805diagnosisrecordsandgraduallyincreased thedatasizebyaddinglocations,permutingdoctorsandincreasingdiagnosisrecords.
ThedatacountandsizesaregiveninTable 2
5.2.1SettingupHadoopCluster InSect. 6,wediscussedaboutNGMRcluster.Aspartofourexperiment,wehavealso builtaHadoopclusterwiththesamesetofRaspberryPinodes.
ForstoringthedataandexecutingtheuserqueriesusingHadoop,wehaveinstalled Hadoopinall17nodes(Raspberrypi).OnenodeismadeaMasterNode,whichhasa higherconfigurationandothernodesaresetupasDataNodes.Thenodesareconnected usingadhocnetwork.WehaveusedthelatestversionofHadoop(V3.3.0).Forthe executionofuser-givenqueriesandtosupportHadoopexecution,Java8isusedasa programminglanguageandforJVM.
Inthenexttwosections,wepresenttwosetsofexperimentsconductedontheHadoop clusterandontheNGMRclusterandcomparetheresults.
Table2. DataSizeandcount
6ExperimentalResults Inthissection,theresultsoftheexperimentscarriedoutontheNGMRclusterare presentedandcomparedwiththeresultsobtainedfromtheexperimentscarriedouton HDFScluster.
6.1DataUploadingTime Inourmodel,healthdataisprimarilystoredincloudstorage.Atthefirststep,thislarge amountofunstructuredhealth-dataisfragmentedanddistributedontotheclusternodes. Next,theuserqueriesareexecutedonthedistributeddatastoredontheclusternodes.
Thefirstexperimentismadebyfragmentingthedatawith“City”asakey.Thedata consistsoftotal53numberofcities.Thesystemprimarilymakesanequaldistributionof citiesforeachclusternode.Table 3 showsthedistributionofcityineachclusternodes.
WehavemeasuredthedatauploadingtimeforHDFS,aswellasforNGMRfile system.Wehavegraduallyincreasedthefilesizefrom10to2.60Lakhsrecordsand clusternodecountfrom1to16.Whennumberofpatientcountislow,theuploading timeinNGMRfilesystemisslightlyhigherthanHDFS,askey-wisedivisiontakesplace beforedatadistributionstarts.Butwhenthedatasizeandnumberofnodesincrease,both requirenearlythesameamountoftime.ThedatauploadtimesareshowninFigs. 7, 8, 9, 10, 11 and 12.
Table3. Citiesineachclusternode
Fig.7. Datauploadtimefor1nodecluster
Fig.8. Datauploadtimefor2nodescluster
Fig.9. Datauploadtimefor4nodescluster Fig.10. Datauploadtimefor8nodes cluster
Fig.11. Datauploadtimefor16nodecluster Fig.12. NGMRdatauploadtime1–16nodes cluster
6.2ExecutionofUserDefinedQueries HadoopQueryExecution: Hadoopframeworkperformsparallelcomputationacross thelargeclusterhavingasingleMasterNode(JobTracker)andmultipleClientNodes (TaskTrackers)usingmap-reducearchitecture.Map-reducehasthreebasiccomponents thatperformdistinctoperationsonthedata,i.e.Mapper,CombinerandReducer.
• Mapper: Mapperisappliedoninputkey-valuepairsofdata,whichrunsonevery blockofdataoneachnodes.Itgeneratesintermediatekeyvaluepairsforcombiner.
• Combiner: Combinercanproduceanynumberofkeyvaluepairs.Butallkey-value pairsmustbeinsametypeofthemapperoutput.Partitionersareresponsiblefor dividingtheintermediatekeyspaceandassigningintermediatekeyvaluepairsto reducer.
• Reducer: Reducergathersalltheoutputofreducerfromallnodesinkey-valueformat, thenshuffleandsortthemandstoredinHDFSandcanbeextractedfromMasterNode.
WhenauserprovidesaqueryasaMap-ReduceProgram,theJobTrackersendsthe userquery(TheMRProgram)toalltheclusternode(DataNode),andprovidethenode blockdetailstotheframework.
JobtrackermonitorstheprogressofMap-Reducetaskandcoordinatetheexecution ofmapandreducethetaskoftheTaskTrackerswithrunoneverynode.Itcanhappenthat theusergivenquerywillnotreturnanydataafterexecutionofMRjobfromaparticular node,still,theTaskTrackerwillrunandinitiatereducetask.Thetaskreturnszeroreduced setofdata.ThisprocessincreasesthetotalexecutiontimeoftheMap-Reducetask.
Wetakeanexampleofexecutinghealth-relatedqueriesusingtheabove-mentioned dataset.Thedatasetisdistributedbasedonakeynamedas‘city’.AsHadoopdistributes
thedatasetintheHadoopcluster,theJobTrackerinitiatestheMap-Reducetaskonall clusternodeswiththehelpofTaskTracker,andeachTaskTrackerrunstheMRcodeto allnodes.Theremaybesomenodeswherenodataisstoredrelatedtotheusergivenkey values.TaskTrackerrunsthequeryonthosenodeblockswhereitreturnszerokey-value pairdata.
InFig. 13,theend-userneedsthedetailsforakey-value k1.Inthiscase,theMapReduceenginewillrunonallblocksonallnodesasshowninthefigure.
Fig.13. HadoopMRprocessonhealthrecordsofmultiplecities
Theusergivenquerymaybe“FindthedetailsofthepatientswhobelongtoKolkata andsufferingfromtuberculosis”.Asourdata-modeldoesnotstorebothinformationin thesamefile,wehavetoruntwoMRjobs.Atfirst,HadooprunsanMRjobtofind thedetailsofthepatientswhobelongtotherespectivecities.Theseresultsarestored intheHDFSfirstasanoutputandthenthepatientdataissortedandoutputisfiltered bythediseasesTheoverallexecutiontimeisthesumofthetwoMRjobsexecuted inHadoop.However,theentireprocessrequireslongertimeduetotheoverheadas mentionedearlier.Therefore,abetterstrategywillbetodiscardtherecordscontaining keyvaluesotherthanthosementionedinthequeryeitherintheMapphaseorinthe Reducephase.
Node.jsQueryExecution: InNGMR,thestoreddataareprocessedinparallelbothin theClusterunitsandinsidetheClusterunitsdependingupontheirprocessorcores.In theNGMRframework,insteadofrunningtheMRanalyticscodeinalltheClusterunits, thecoderunsinasmallsubsetoftheClusterunits.TheNGMRsystemguidestheMR codetoruninaspecificsetofselectednodes.
• GuidedMapReduce: WhenauserplacesaqueryintheNGMRframework,the NGMRMasterNodecheckswhichoftheblocksonwhichnodesarereallynecessary forexecutionofthegivenMRquery.Thenframeworkfollowsthesplit-apply-combine strategyinthesamewayasatypicalmap-reduceprogrammingparadigmonthedata blocksdistributedinthecluster.TheuserprovidesthecodewritteninNode.js.The frameworkreadsthequeryandextractsthekeyattributesandtheirvalueswhichare requiredforexecutingthequery.TheMasterNodethenreadsthemastermeta-data storedforthefilereferredbytheuser.Fromthemastermeta-data,MasterNodegets
thereferenceoftheClusterunitsalongwiththemeta-datastoredintheClusterunits. TheframeworkreadstheClusterunitmeta-datareferredtobytheMasterNodeand getsthereferencesofthedata-blockswhichneedtobeprocessedforexecutionof theusergivenquery.TheframeworksendstheMRcodetotheClusterunitswhere therequesteddata-blocksarestored.Themaptaskisthenexecutedinthesameway astheconventionalMap-Reduceprogrammingmodel,however,itworksonlyonthe selectedClusterunitblocks.
Afterthemapprocess,intheNGMRframework,thereducetaskisexecutedintwo phases,firstphasewillbeexecutedineachClusterunitwhichhasalreadycompletedthe maptask,andthenthesecondwilltakeplaceinsidetheMasterUnit.Oncethefirstphase ofreducetaskiscompleted,thekey-valuepairsofoutputdataaresenttotheMaster Nodeforfurtherreduction.Afterthesecondphaseofreducetasks,theframeworkstores theoutputdataasfinalresultsintheNGMRfilesystem.Figure 14 showstheguided Map-reduceprocessinNGMRframework.
Fig.14. NGMRframeworkMRprocessonhealthrecordsofmultiplecities
• Parallelexecutioninprocessorcore: NGMRusesanNPMpackagenamedas ‘mapred’.ThispackagefollowstheGooglemap-reducespecification.Theinput shouldbeakey-valuepairedarray-likemap(key1,value1),sothatasetofintermediate key-valuelists(key2,value2)arecreatedforreduce.Thereduceprogramacceptsthe intermediatekeyandasetofvaluesforthatkeylikereduce(key2,list(value2))and producesasmallersetofvalueslikelist(value2).Mapredpackagehastheprovision touseallcoresoftheprocessororusercanfixhowmanycoresshouldbeusedbythe systemtoprocesstheMRcode.
TheOSrunstheI/Oinparallelandsendsthedatatosingle-threadedJS.Ourcode consistsofsmallportionsofsynchronousblocksthatrunfastandpassthedatatofiles andstreams.SoourJavaScriptcodedoesnotblocktheexecutionofotherpiecesof JavaScript.AlotmoretimeisspentwaitingforI/OeventstohappenthanJavaScript codebeingexecuted.NGMRNode.jsframeworkjustinvokesthefunctionsanddoes notblocktheexecutionofotherpiecesofcode.Itwillgetnotifiedthroughthecallback
whenthepreviouscodeexecutionisover,andthesystemwillreceivetheresult.Node.js doesnotevaluatethenextcodeblockintheeventqueueuntilthepreviousonefinishes executing.So,wesplitourcodeintosmallersynchronouscodeblocksandcallthe callbackfunctiontoinformNode.jsthatpreviouscodeexecutionisoverandthatitcan continueexecutingpendingthingsthatareinthequeue.
6.2.1Discussion Astheexperimentshavebeenconductedstartingfromlowvolumeofdata,inasinglenodecluster,thenodestopsrespondingwhenwestartincreasingthedatavolume.Incase ofHadoop/HDFS,aClusterunitgetsdownwhennumberofpatientrecordscrosses40 thousand(size531MB).ButwhenanodeclusteristestedwithNGMRfile-system,it workswithmorethan2Lakhs20thousand(2.92GB)dataasshownFig. 15.Incaseof twonodecluster,withHDFSwehavebeenabletoexecutethequerywith80thousand (1.1GB)records,butwithNGMRfilesystem,thepatientcountcanbeincreasedupto morethan2LakhsFig. 16.Thisway,ithasbeenobservedthatourdevelopedNGMR filesystemhasalwaysbeenabletouploaddatainhighervolumecomparedtoHadoop HDFSwithinthesametimeduration.TheresultsareshowninFigs. 17, 18 and 19. Hadoopdistributesthedatatoallitsclusternodes,butNGMRnotonlydistributes,but alsotracksalltheblocklocationsusingtwolayerindexingprocess(Fig. 20).
Fig.15. Executiontimeoftheusergiven querieswith1nodecluster
Fig.17. Executiontimeoftheusergiven querieswith4nodescluster
Fig.16. Executiontimeoftheusergiven querieswith2nodescluster
Fig.18. Executiontimeoftheusergiven querieswith8nodescluster
Executiontimeoftheusergiven querieswith16nodescluster
NGMRexecutiontime1–16Nodes
AtthetimeofexecutionofMRcode,Hadoopexecutiontimeincreasesdrastically whendatasizeisincreased.ThereasonbehindthisbehavioristhatHadooprunsitsMR codeonallnodes.IncaseofNGMR,MRcodeisrunonasmallsetofClusterunits.So executiontimeismuchlessthanthepreviouscase.
Wehavecarriedoutourexperimentwithmobilenodes.Sothetimerequiredfor datauploadingandexecutionofMRcodeisalwayshigherincaseofHadoop,because Hadoopframeworkwaitsifthenodeisdownornotresponding.IfHadoopdoesnotget anyresponsewithinacertaintime,itchecksthereplicationfactorandsendstheMR codetoanothernodewherethesameblockofdataisstored.Figure 18 showsthatat datacount60k,theHadoopframeworkwaitsforalongertimeasnodesarenotwithin therange.ButincaseofNGMR,itwaitsfor3sandchecksforanothernode(unit). AsNGMRrunsitMRcodeonasmallsetofClusterunits,itisgenerallypossiblethat respectivenodesarealwayswithintherangeandexecutiondoesnottakelongertime.If allrequestedunitsaredown(ornotwithintherange),thenitimmediately(afterwaiting for3s)startsexecutiononotherblocksofdatastoredinotherunitsofthenextzone aftergettingreferencesfrommastermeta-datastoredintheMasterNode.
7ConclusionandFutureWork Inthispaper,theimplementationofNodeGuidedMap-Reducetechniquehasbeen discussed.Thisresearchworkisdonebasedonthestorageandretrievalschemeproposed inourearlierwork[1].Theapplicationofthistechniquedecreasesthedataretrievaland userqueryexecutiontimebydistributingtheBigDatatoselectednodes,andexecuting thequeryontheseselectednodesonlyandnottocoverallthenodesattachedinthecluster. TheexperimentalresultsarecomparedwithHadoopanditisseenthatthedeveloped techniquegivesabetterperformancewithrespecttodataretrievalandqueryexecution time.Currently,thedataaredistributedtothenodesaftergettingthepreferencesof theuser.Tomakeitmoreefficient,wearepresentlyworkingontheautomaticnode selectionalgorithmbasedonthekeyattributesoftheBigdatafiledependingonthe usergivenqueries.Thetestbedwhichhasbeenusedisaraspberrypimobilecluster. Ournextendeavoristodeployahigh-endclusterwithmovablenodes.Infuture,we aimtocarryoutourexperimentwithalarge,high-endclusterwithmobilenodesand implementautomaticclusternodeselectionalgorithmforexecutionoftheuserqueries formoreefficientdataretrieval.
Fig.19.
Fig.20.
Acknowledgement. WewouldliketoacknowledgeoursupervisorDrNandiniMukherjee,Professor,DepartmentofComputerScienceandEngineering,JadavpurUniversity,forherguidance, assistanceandtimelysuggestionwhichhelpsustocompletethisresearchwork.
References 1.Ray,H.S.,Mukherjee,S.,Mukherjee,N.:Performanceenhancementinbigdatahandling. In:2020InternationalConferenceonContemporaryComputingandApplications(IC3A), Lucknow,India,pp.17–22(2020). https://doi.org/10.1109/IC3A48958.2020.233261
2.Kim,C.,Son,S.:Astudyonbigdataclusterinsmartfactoryusingraspberry-pi.In:2018 IEEEInternationalConferenceonBigData(BigData),Seattle,WA,USA,pp.5360–5362 (2018). https://doi.org/10.1109/BigData.2018.8622539
3.Asb,Andrews,Liz.:Introducingturbomode:upto50\%moreperformanceforfree,19 September2012. https://www.raspberrypi.org/blog/introducing-turbo-mode-up-to-50-moreperformance-for-free/.Accessed23Aug2020
4.Mukhopadhyay,P.,Roy,H.S.,Mukherjee,N.:E-healthcaredeliverysolution.In:2019 11thInternationalConferenceonCommunicationSystemsandNetworks(COMSNETS), Bengaluru,India,pp.595–600(2019). https://doi.org/10.1109/COMSNETS.2019.8711429
5.Ray,H.S.,Naguri,K.,Sen,P.S.,Mukherjee,N.:ComparativeStudyofQueryPerformance inaRemoteHealthFrameworkusingCassandraandHadoop.HEALTHINF(2016)
6.Liu,L.,Liu,J.,Qian,H.,Zhu,J.:PerformanceevaluationofBATMAN-Advwirelessmesh networkroutingalgorithms.In:20185thIEEEInternationalConferenceonCyberSecurity andCloudComputing(CSCloud)/20184thIEEEInternationalConferenceonEdgeComputingandScalableCloud(EdgeCom),Shanghai,pp.122–127(2018). https://doi.org/10.1109/ CSCloud/EdgeCom.2018.00030
7.Mesh.(n.d.). https://www.open-mesh.org/projects/open-mesh/wiki/BATMANConcept. Accessed27Aug2020
8.AnishaGnanaVincy,V.G.,Karthija,T.,Sunil,J.:Understandinghadoopframeworkthrough single-nodeclusterinstallation.In:2019InternationalConferenceonRecentAdvancesin Energy-efficientComputingandCommunication(ICRAECC),Nagercoil,India,pp.1–4 (2019). https://doi.org/10.1109/ICRAECC43874.2019.8995060
9.Qing,W.,Yue,Y.,Yi,Y.,Liang,W.:Amethodofpre-sentencetextbasedonMap/Reduce storageandindexingclassification.In:2014IEEE5thInternationalConferenceonSoftware EngineeringandServiceScience,Beijing,pp.195–199(2014). https://doi.org/10.1109/ICS ESS.2014.6933543
10.Yuzuk,S.,Aktas,M.G.,Aktas,M.S.:Ontheperformanceanalysisofmap-reduceprogrammingmodelonin-memoryNoSQLstorageplatforms:acasestudy.In:2018InternationalCongressonBigData,DeepLearningandFightingCyberTerrorism(IBIGDELFT), ANKARA,Turkey,pp.45–50(2018). https://doi.org/10.1109/IBIGDELFT.2018.8625300
MultipathTCPSecurityIssues,Challenges andSolutions KhushiPopat(B) andViralVinodKapadia
DepartmentofComputerScienceandEngineering,FacultyofTechnologyandEngineering, TheMaharajaSayajiraoUniversityofBaroda,Vadodara,India {khushi.popat-cse,viral.kapadia-cse}@msubaroda.ac.in
Abstract. MultipathTCP(MPTCP)isabidirectionalbytestreamtransportlayer protocolintroducedbyInternetEngineeringTaskforce(IETF)whichprovides numerousbenefitssuchashigherthroughput,reliability,faulttolerance,backward compatibilityandloadbalancingbysupportingmulti-homingthatallowsuseof multiplepathsfordatatransferoversinglenetworkconnectionstillitisvulnerable tomanysecurityintrusionssuchasDenialofService,sessionhijacking,SYN Floodingetc.Inthispaper,thevulnerabilitiesofMPTCPleadingtosomepotential attacksandtheiravailablesolutionsarefocused.Currentlymanysolutionsare availablebutsomeofthemincreasetheoverheadofMPTCP,somearevulnerable totime-shiftedattackandrestsarenottestedproperly.Theimplementationand experimentdetailsofADD_ADDRandEavesdropperininitialhandshakeattacks performedonMPTCPversion0andversion1,detailedanalysisofalltheavailable solutions,limitationsofthesesolutionsanddirectiontoofferthesolutionover theselimitationsarecoveredinthispaper.
Keywords: MPTCP Sessionhijacking Man-in-the-middleattack ADD_ADDRvulnerability · Time-shiftedattack · Hashbasedsolution · Chained basedsolution
1Introduction
TransmissionControlProtocol(TCP)[1]isthewidelyusedtransportlayerprotocol designedforprovidingprocesstoprocessdeliveryofpacketbetweencommunicating hostoverIPnetwork.TCPbindstheIPaddressesofcommunicatinghostsoverconnectionasshowninFig. 1 (b)whichleadstoconnectiondropiffailureoccursduring communication.Ifthehosttriestoswitchtoanothernetworkinterfaceduringon-going communicationwiththeserver,thecurrentTCPconnectionwillbedroppedasdevice willgetconnectedtoanothernetworkandnewIPaddresswillbeassignedtoit.TCP doesn’tutilizemorethansinglenetworkinterfacesoversingleconnection.Forexample, MobiledeviceshaveWi-FiandLTEnetworkinterfacesbutatatimeonecanbeconnectedonlytooneinterface.ToovercometheserestrictionsofTCP,MPTCP[2, 3]was standardizedbyIETFwiththegoaltoincreasetheutilizationofnetworkresourcesby supportingroutingofpacketsovermultipledisjointpathsforsingleconnectionasshown
©SpringerNatureSwitzerlandAG2021 M.Bhattacharyaetal.(Eds.):ICICCT2021,CCIS1417,pp.18–32,2021. https://doi.org/10.1007/978-3-030-88378-2 2
inFig. 1 (a),increasesreliabilitybyprovidingbackuppathsandimprovesperformance bycombiningbandwidthsofdifferentpaths.EachMPTCPpath(sub-flow)behaveslike singleTCPconnectionatnetworklayerandusesregularTCPSocketAPIsatapplicationlayer[2].Themobiledevices,laptops,serversetc.withmulti-homingfacilitiescan beequippedwithMPTCPtoofferthehigherbandwidthandreliability.Manymobile devicescompanieslikeAPPLE,Samsung,LGetc.havestartedusingMPTCPwiththeir smartphoneinvariousapplicationstoprovideQualityofService(QoS).Datacenteris anotherusecaseofMPTCPinwhichTCPcanbereplacedtomanagetheloadthrough multipledisjointpaths.
DespiteMPTCPprovidesmanyattractivebenefitsoverTCPsuchashigherrobustness,higheravailability,higherperformanceandbackwardcompatibilitywithcurrent Internetapplications,itsmulti-homing(connectedtomorethanonenetworks)support usingthevariousoptionsinTCPheaderarevulnerabletomanysuspiciousattackssuchas sessionhijacking,flooding,Denialofservicesetc.[4].ADD_ADDR,oneoftheoptions usedinTCPheadertoinformthepeerhostabouttheavailableIP,isvulnerabletosession hijackingattack.ADD_ADDRoptionplayscrucialroleinwirelessnetworksasdueto themobilityofdevices,itisrequiredtoinformthepeerhostaboutthechangeofnetwork IPfrequently.Inversion1ofMPTCP,theHashbasedMessageAuthentication(HMAC) isusedtoauthenticatethepeertoaddthenewaddresswithADD_ADDRoptionbutthe keysusedtocalculatetheHMACareexchangedinclearformduringtheinitial3-way handshakeofMPTCP.However,itsecurestheadditionofsubflows,itdoesn’tofferthe protectionforkeyexchangesduringinitialhandshakewhichagainopensthedoorsfor attackers.
MPTCPincreasesthenetworkperformancebytransferringdataacrossmultiple availablepathsbutthisfeatureallowsattackerstotransfermaliciouscodesacrossdifferentpaths.Thus,ondifferentpathsdifferentSignatureBasedIntrusionDetectionSystems willbedeployedbutnoneofthemwillhaveaccessoffullmaliciouscodewhichprevents themtoidentifytheSignatureBasedIntrusion[5].
Fig.1.
