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Ericsson Technology Review: Distributed cloud - A key enabler of automotive and industry 4.0 use cases

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Emerging use cases in industries where the first phases of the fourth industrial revolution are taking place, such as automotive and manufacturing, are creating new requirements for networks and clouds. At Ericsson, we believe that distributed cloud will be a key technology to support such use cases.

The latest Ericsson Technology Review article explains how distributed cloud technology exploits key features available in both 4G and 5G networks to enable a distributed execution environment for applications that ensures performance, short latency, high reliability and data locality. The flexibility of cloud computing is maintained at the same time that the complexity of the infrastructure is hidden, with application components placed in an optimal location that utilizes the key characteristics of distributed cloud.

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Ericsson Technology Review: Distributed cloud - A key enabler of automotive and industry 4.0 use cases

  1. 1. Local Regional Regional DCLocal DC MTSO MTSO MTSO H National D National siteLocal and regional sites Service exposure HD maps HD maps Data exposure for automotive services Access sites Hub site Video stream ECU sensors HD maps Video stream ECU sensors HD maps Mobile telepho switchin Intelligent driving Intelligent driving Advanced driver assistance Advanced driver assistance Huge amount of data ERICSSON TECHNOLOGY C H A R T I N G T H E F U T U R E O F I N N O V A T I O N | # 1 1 ∙ 2 0 1 8 DISTRIBUTEDCLOUD, AUTOMOTIVEAND INDUSTRY4.0
  2. 2. ✱ DISTRIBUTED CLOUD 2 ERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 20, 2018 Emerging use cases in the automotive industry – as well as in manufacturing industries where the first phases of the fourth industrial revolution are taking place – have created a variety of new requirements for networks and clouds. At Ericsson, we believe that distributed cloud is a key technology to support such use cases. CHRISTER BOBERG, MALGORZATA SVENSSON, BENEDEK KOVÁCS Both 4G and 5G mobile networks are designed to enable the fourth industrial revolution by providing high bandwidth and low-latency communication on the radio interface for both downlink (DL) and uplink (UL) data. Distributed cloud exploits these features, enabling a distributed execution environment for applications to ensure performance, short latency, high reliability and data locality. ■ Distributedcloudmaintainstheflexibilityof cloudcomputingwhileatthesametimehidingthe complexityoftheinfrastructure,withapplication componentsplacedinanoptimallocationthat utilizesthekeycharacteristicsofdistributedcloud. Theautomotivesectorandmanymanufacturing industriesalreadyhaveusecasesthatmakethem verylikelytobeearlyadoptersofdistributed cloudtechnology. Next-generationautomotiveservices andtheirrequirements Mobilecommunicationinvehiclesisincreasing inimportanceastheautomotiveindustryworks tomakedrivingsafer,smooththeflowoftraffic, consumeenergymoreefficientlyandlower emissions.Automatedandintelligentdriving, thecreationanddistributionofadvancedmaps withreal-timedata,andadvanceddrivingassistance usingcloud-basedanalyticsofULvideostreams areallexamplesofemergingservicesthatrequire vehiclestobeconnectedtothecloud.Theseservices alsorequirenetworksthatcanfacilitatethetransfer A KEY ENABLER OF AUTOMOTIVE AND INDUSTRY 4.0 USE CASES Distributed cloud
  3. 3. DISTRIBUTED CLOUD ✱ NOVEMBER 20, 2018 ✱ ERICSSON TECHNOLOGY REVIEW 3 ofalargeamountofdatabetweenvehiclesandthe cloud,oftenwithreal-timecharacteristicswithin alimitedtimeframewhilethevehicleisinactive operation. Highdatavolume Lookingattheautomotiveindustry,weoftenfocus onthereal-timeusecasesforsafety,asdefinedby V2X/C-ITS(vehicletoeverything/cooperative intelligenttransportsystem),wherereal-time aspectssuchasshortlatencyarethemostsignificant requirements.However,theautomotiveindustry’s newmobilityservicesalsoplacehighdemandson networkcapacityduetotheextremeamountofdata thatmustbetransportedtoandfromhighlymobile devices,oftenwithnear-real-timecharacteristics. Dataneedstobetransportedwithinalimitedtime window(~30min/day),withavaryinggeographical concentrationofvehiclesusingamultitudeof differentnetworktechnologiesandconditions. Themarketforecaststhataregenerallyreferred toindicatethattheglobalnumberofconnected vehicleswillgrowtoapproximately700millionby 2025andthatthedatavolumetransmittedbetween vehiclesandthecloudwillbearound100petabytes permonth.AtEricsson,however,weanticipatethat theautomotiveservicesofthenearfuturewillbe muchmoredemanding.Weestimatethatthedata trafficcouldreach10exabytesormorepermonthby 2025,whichisapproximately10,000timeslargerthan thepresentvolume.Gartnerrecentlyraisedthe expectationsfurtherinitslatestreport(June2018), estimatingthevolumetobeashighasoneterabyte permonthpervehicle[1]. Suchmassiveamountsofdatawillplacenew demandsontheradionetwork,asthemainpartis ULdata.Newbusinessmodelswillberequired,asa resultofthehighcostofhandlingmassiveamounts ofdata.AsexplainedintheAECC(AutomotiveEdge ComputingConsortium)whitepaper[2],thecurrent mobilecommunicationnetworkarchitecturesand conventionalcloudcomputingsystemsarenotfully optimizedtohandleallofthisdataeffectivelyona globalscale.Thewhitepapersuggestsmanypossible optimizationstoconsider–basedontheassumption thatmuchofthedatacouldbeanalyzedandfiltered atanearlystagetolimittheamountofdata transferred. Definition of key terms ❭❭ Distributed cloud is a cloud execution environment for applications that is distributed across multiple sites, including the required connectivity between them, which is managed as one solution and perceived as such by the applications. ❭❭ Edge computing refers to the possibility of providing execution resources (compute and storage) with the adequate connectivity (networking) at close proximity to the data sources. ❭❭ The fourth industrial revolution is considered to be the fourth big step in industry modernization, enabled by cyber-physical systems, digitalization and ubiquitous connectivity provided by 5G and Internet of Things (IoT) technologies. It is also referred to as Industry 4.0.
  4. 4. ✱ DISTRIBUTED CLOUD 4 ERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 20, 2018 Topology-awarecloudcomputingandstorageis anexampleofonesuchsolutionthatprovideswhat wecallaglobalautomotivedistributededgecloud. Thelimitationontheamountofdatathatcanbe effectivelytransportedoverthecellularnetwork mustnotbeallowedtoaffecttheserviceexperience negatively,asthatwouldhindertheevolutionofnew automotiveservices.Itisthereforenecessaryto increasecapacity,availabilityandcoverageaswellas findingappropriatemechanismstolimittheamount ofdatatransferred.Orchestratingapplicationsand theirdifferentcomponentsrunninginamultitudeof differentcloudsfromdifferentvendorsisoneofthe challenges.Vehiclesconnectingtonetworkswithout anexistingapplicationedgeinfrastructureis another. Theplacementofapplicationcomponentsat edgesdependsonthebehavioroftheapplication andtheavailableinfrastructureresources. Whendealingwithhighlymobiledevicesthat connecttoamultitudeofnetworks,itmustbe possibletomoveexecutionoftheedgeapplication automaticallywhenamoreappropriatelocation forthevehicleisdiscovered.Someapplications requiretransferofpreviouslyanalyzeddataand findingstothenewlocation,whereanewapplication componentinstancewillseamlesslytakeovertoserve themovingvehicle. Distributedcomputingonalocalizednetwork Wehavedevelopedtheconceptofdistributed computingonalocalizednetworktosolvethe problemsofdataprocessingandtrafficinexisting mobileandcloudsystems.Inthisconcept,several localizednetworksaccommodatetheconnectivity ofvehiclesintheirrespectiveareasofcoverage. AsshowninFigure1,computationpowerisadded totheselocalizednetworks,sothattheycanprocess Figure 1 High-volume data automotive services and their characteristics Local Regional Regional DCLocal DC MTSO MTSO MTSO H National DC National sitesLocal and regional sites Service exposure HD maps HD maps Data exposure for automotive services Access sites Hub sites Video stream ECU sensors HD maps Video stream ECU sensors HD maps Mobile telephone switching office Intelligent driving Intelligent driving Advanced driver assistance Advanced driver assistance Huge amount of data
  5. 5. DISTRIBUTED CLOUD ✱ NOVEMBER 20, 2018 ✱ ERICSSON TECHNOLOGY REVIEW 5 datalocally.Thisreducesthetotalamountofdata exchangedbetweenvehiclesandcloudswhile enablingtheconnectedvehiclestoobtainfaster responses.Theconceptischaracterizedbythree keyaspects:alocalizednetwork,edgecomputing anddataexposure. Alocalizednetworkisalocalnetworkthatcovers alimitednumberofconnectedvehiclesinacertain area.Thissplitsthehugeamountofdatatrafficinto reasonablevolumesperareaofdatatrafficbetween vehiclesandtheclouds. Edgecomputingreferstothegeographical distributionofcomputationresourceswithinthe vicinityoftheterminationofthelocalizednetworks. Thisreducestheconcentrationofcomputationand shortenstheprocessingtimeneededtoconclude atransactionwithaconnectedvehicle. Dataexposuresecuresintegrationofthedata producedlocallybyutilizingthecombinationofthe localizednetworkandthedistributedcomputation. Bynarrowingrelevantinformationdowntoa specificarea,datacanberapidlyprocessedto integrateinformationandnotifyconnectedvehicles inrealtime.Theamountofdatathatneedstobe exchangediskepttoaminimum. Privateandlocalconnectivity Aspartofthefourthindustrialrevolution,industry verticalsandcommunicationserviceproviders (CSPs)aredefiningasetofnewusecasesfor5G[3]. Privatedeploymentsand5Gnetworksprovidedby CSPstomanufacturingcompanies,smartcitiesand otherdigitalindustriesareonthehorizonaswell. However,therearetwomainchallengestomobile networkoperators’abilitytodeliver.Thefirstisthe toughlatency,reliabilityandsecurityrequirements ofthesenewusecases.Thesecondisfiguringout howtoshieldtheindustriesfromthecomplexity oftheinfrastructure,toenableeaseofusewhen programmingandoperatingnetworks. Secureprivatenetworkswith centralizedoperations Securityanddataprivacyarekeyrequirements forindustrialnetworks.Insomecases,regulations orcompanypoliciesstipulatethatthedatamust notleavetheenterprisepremises.Inothercases, someorallofthedatamustbeavailableatremote locationsforpurposessuchasproductionanalytics oremergencyprocedures.Atypicalindustrial environmenthasmultipleapplicationsdeployedand operatedbydifferentthirdparties.Whatthismeans inpracticeisthatthesameon-premises,cloud-edge instancethatafactoryalreadyusesforbusiness supportandITsystemswouldalsoneedtosupport theconnectivityforitsrobotstointeractwitheach other.Asaresult,thereisarequirementofmulti- tenancyforboththedevicesandtheinfrastructure. Tactileinternetandaugmentedreality Augmentedreality(AR)andmachinelearning(ML) technologiesarewidelyrecognizedasthemain pillarsofthedigitalizationofindustries[4],and researchsuggeststhatwidedeploymentof interactivemediaapplicationswillhappenon5G networks.Manyobserversenvisiontheworker oftomorrowassomeonewhoisequippedwith eye-trackingsmartglasses[5]andtactilegloves ratherthanscrewdriversets[6].Human-to-machine applicationsrequirelowlatencywhiledemanding highnetworkbandwidthandheavycompute resources.Runningthemonthedeviceitself wouldresultinhighbatteryconsumptionandheat dissipation.Atthesametime,latencyrequirements donotallowtherunningofthecompleteapplication inlargecentraldatabasesduetothephysicallimits oflightspeedinopticalfibers. INDUSTRYVERTICALS ANDCOMMUNICATION SERVICEPROVIDERSARE DEFININGASETOFNEW USECASESFOR5G
  6. 6. ✱ DISTRIBUTED CLOUD 6 ERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 20, 2018 AsimpleARapplicationanditsmaincomponents areshowninFigure2.Thecomponentsofthe applicationcouldbeexecutedeitheronthedevice itself,theedgeserverorinthecentralcloud. Deployingapplicationcomponentsatthenetwork edgemaymakeitpossibletooffloadthedevicewhile maintainingshortlatency.Edgecomputeisalso optimizingtheflowwhencoordinationisrequired– forexample,whenusingmultiplereal-timecamera feedstodeterminethe3Dpositionofobjects,also asshowninFigure2.Furthermore,advancedcloud softwareasaservice–ML,analyticsandDBsasa service,forexample–mayalsobeprovidedonthe edgesite. Ourdistributedcloudsolution Ericssonhasdevelopedadistributedcloudsolution thatprovidestherequiredcapabilitiestosupport theusecasesofthefourthindustrialrevolution, includingprivateandlocalizednetworks.Our solutionsatisfiesthespecificsecurityrequirements neededtodigitalizeindustrialoperations,with automotivebeingoneofthekeyusecases.Ericsson’s distributedcloudsolutionprovidesedgecomputing andmeetsend-to-endnetworkrequirementsaswell asofferingmanagement,orchestrationandexposure forthenetworkandcloudresourcestogether. AsshowninFigure3,wedefinethedistributed cloudasacloudexecutionenvironmentthatis geographicallydistributedacrossmultiplesites, includingtherequiredconnectivityinbetween, managedasoneentityandperceivedassuchby applications.Thekeycharacteristicofour distributedcloudisabstractionofcloud infrastructureresources,wherethecomplexityof resourceallocationishiddentoauserorapplication. Ourdistributedcloudsolutionisbasedonsoftware- definednetworking,NetworkFunctions Virtualization(NFV)and3GPPedgecomputing technologiestoenablemulti-accessandmulti-cloud capabilitiesandunlocknetworkstoprovideanopen platformforapplicationinnovations.Inthe managementdimension,distributedcloudoffers automateddeploymentinheterogeneousclouds. ThiscouldbeprovidedbymultipleCSPs,where workloadplacementispolicydrivenandbased onvariousexternalizedcriteria. Toenablemonetizationandapplicationinnovation, distributedcloudcapabilitiesareexposedon marketplacesprovidedbyEricsson,thirdparties andCSPs.Thedistributedcloudcapabilitiescanbe offeredaccordingtovariousbusinessandoperational Figure 2 An AR application and its modules optimized for edge computing Capturing Preprocessing Object detection feature extraction Recognition database match DB Display Tracking and annotation Position estimation Template matching IoT device/user equipment -20ms BW reduction -20ms/frame Computation heavy -20ms Computation heavy Multiple device data aggregation -100ms Requires access to central storage Edge site National site
  7. 7. DISTRIBUTED CLOUD ✱ NOVEMBER 20, 2018 ✱ ERICSSON TECHNOLOGY REVIEW 7 models.Oneexampleofapossiblescenarioisfora CSPtoofferconnectivityandacloudexecution environmenttoenterprisesasaservice.Inthiscase, aCSPmanagesthecomputationandconnectivity resources,butthesearelocatedattheenterprise premises.Theapplicationcharacteristicsdetermine theplacementofapplicationsatvariousgeolocations. InthecaseofAR/VRandimagerecognition applicationsusedbytechnicianstofixabroken powerstation,forexample,itwouldbemosteffective toplacethemclosetothebrokenpowerstation. Edgecomputing Ourdistributedcloudsolutionenablesedge computing,whichmanyapplicationsrequire. Wedefineedgecomputingastheabilitytoprovide executionresources(specificallycomputeand storage)withadequateconnectivityatclose proximitytothedatasources. Intheautomotiveusecase,thenetworkis designedtosplitdatatrafficintoseverallocations thatcoverreasonablenumbersofconnected vehicles.Thecomputationresourcesare hierarchicallydistributedandlayeredinatopology- awarefashiontoaccommodatelocalizeddataandto allowlargevolumesofdatatobeprocessedina timelymanner.Inthisinfrastructureframework, localizeddatacollectedvialocalandwidearea networksisstoredinthecentralcloudandintegrated Figure 3 Distributed cloud architecture Service and resource orchestration Any workload Access sites Local and regional DC sites National sites Anywhere in the network End-to-end orchestration Marketplace Service exposure Global clouds Public safety Automotive FWA Factory Video streaming Metering APP APP VNF VNF APP APP APP VNF VNF VNF VNF VNFVNF OURDISTRIBUTED CLOUDSOLUTIONENABLES EDGECOMPUTING,WHICH MANYAPPLICATIONS REQUIRE
  8. 8. ✱ DISTRIBUTED CLOUD 8 ERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 20, 2018 ontheedgecomputingarchitecturetoprovide real-timeinformationnecessaryforservicesof connectedvehicles. Theexactlocationsofthemicroandsmalldata centersmaybedependentontheCSP’snetwork topologyandtherequirementsoftheusecases– thisappliestocentralofficesites,basestationsand newDCsbuiltonindustrialsites.Thisinfrastructure shouldbeflexible,sothatitispossibletostartwitha fewsitesandgrowbyaddingnewsitesasrequired. Managementandorchestration Thedistributedcloudreliesonefficientmanagement andorchestrationcapabilitiesthatenableautomated applicationdeploymentinheterogeneousclouds suppliedbymultipleactors.Figure3illustrateshow theserviceandresourceorchestrationspansacross distributedandtechnologicallyheterogeneous clouds.Itenablesservicecreationandinstantiation incloudenvironmentsprovidedbymultiplepartners andsuppliers.Discovery,onboardingandauto- enrollmentofedgesareotherimportantcapabilities ofdistributedcloudmanagement. Whendeployinganapplicationoravirtual networkfunction(VNF),theplacementdecisions canbebasedonmultiplecriteria,wherelatency, geolocation,throughputandcostareafewexamples. Thesecriteriacanbedefinedeitherbyan applicationdeveloperand/oradistributedcloud infrastructureprovider,servingasinputtothe placementalgorithm.Onceatargetcloudhasbeen selected,theworkloadplacementcontinuesinany ofthesubordinatedclouds. Intheautomotiveapplicationsexample,the placementdecisioncouldbemadebasedonthe geolocationofthemovingcar,availabilityofthe computationresourcesandabilitytomeetregulatory requirementsattheedgesservingthemovingcar. TactileinternetandARapplicationsthatarevery sensitivetonetworklatencywhiledemanding highbandwidthandhighcomputingpower willbedeployedattheedgesthatcanfulfillthe requirements. Theserviceorchestrationmanagesthe distributedcloudresourcesaswellastheefficient distributionandreplicationoftheapplicationsthat utilizethedistributedcloudcomputationand connectivityresources.Theserviceandresource managementcapabilitiesarealsodeployedina distributedfashiontoenableefficientmanagement. Forexample,thescalingordatafunctionswillbe deployedclosetotheapplicationtheysupervise. Serviceexposure Theapplicationsdeployedinthedistributedcloud willpresenttheircapabilitiesthroughtheservice exposure.Withmulti-dimensionalexposure,eachof thelayersinthedistributedcloudstackwillexpose itscapabilities.Thecloudinfrastructurelayerand theconnectivitylayerwillexposetheirrespective capabilitiesthroughtheapplicationprogramming interface(s)(API(s)),whichwillthenbeusedby applicationdevelopersoftheindustriesmakinguse ofthemobileconnectivity.Bysettingdeveloper needsinfocus,theexposedAPI(s)willbeabstracted sothattheyareeasytouse. Evolutiontowardtheglobalmulti-operator distributedcloud Globalindustriessuchasautomotiverequire solutionsthatworkseamlesslyfromlocaltoglobal scale.Inlightofthis,theevolutiontowardtheglobal multi-operatordistributedcloudisnotrivialmatter. Tobepartofthegloballydistributedcloud,the edgecloudsthatCSPsprovideataccessandlocal sitesmustsupportastringentsetoffunctionsand APIs.ThisimpliesthatCSPsmustjoinforcesto createafederatedmodel.Doingsowillrequire significanteffort,withthefirststepbeingtoreachan agreementonthestandardmechanismstouse. GLOBALINDUSTRIES SUCHASAUTOMOTIVE REQUIRESOLUTIONS THATWORKSEAMLESSLY FROMLOCALTOGLOBAL SCALE
  9. 9. DISTRIBUTED CLOUD ✱ NOVEMBER 20, 2018 ✱ ERICSSON TECHNOLOGY REVIEW 9 Thesecondstepwillbetogainindustryacceptance forthemechanisms,beforefinallybeingableto implementthesolutionsandestablishthebusiness models. OnewaytoevolvethecloudedgesthatCSPs currentlysupplyistoprovideanenvironmentabove thecurrentinfrastructurethatishomogeneousfrom aconsumptionperspectivebutdiscoverablethrough APIsandorchestratedinthesamewayastheCSPs’ infrastructure.Thiswouldprovideanintermediate step,whereCSPswithoutanedgecloud infrastructurecouldbecomeapartoftheglobal scaledistributedcloud.Followingthisapproach, anindustryactorcouldconnecttoanyCSPaccess networkasopposedtobeinglimitedtocertainCSPs. Whilethesenetworkswillhavethesamefunctional scope,theywillnotbeabletoprovidefulledge characteristics.Thiswillalsoserveasacatalystfor otherCSPstojointheglobalscaledistributedcloud. Otherwise,theywillnotbecomepreferredsuppliers. Embracingindustryinitiatives andstandardizations Webelievethattheevolutiontowardtheglobal multi-operatordistributedcloudisdependentona fewkeyactions.First,wemusttakeactiontoaddress thefactthatthecurrentmobilecommunication networkarchitecturesandconventionalcloud computingsystemsarenotdesigned,orchestrated orexposedinawaythatcanhandletheindustries’ requirementseffectively.Wemustscrutinizethe systemarchitecturesandinvestigatenetwork deploymentsandpreferredprofilingtobetter accommodatetheoutlinedrequirements.The architectureevolutionwillbedrivenbytherelevant standardizationssuchas3GPPandETSI(European TelecommunicationsStandardsInstitute)NFV. Secondly,webelievethatitiscriticaltodrive industryalignmentbygettingreference implementationsofedgecloudsoftware.Thisiswhy Ericssonhasjoinedtheindustrycollaboration projectOPNFV(OpenPlatformforNFV)and ONAP(OpenNetworkAutomationPlatform)[7], whichprovidesthemanagementcapabilitiesof distributedcloud. Finally,webelievethatparticipatingin ecosystemsthatprovidetheopportunityfor interactionsbetweentheindustriesandvendorsis criticaltotheevolution.Thisisparticularlytruefor ecosystemsthatformulaterequirementsandways ofworking,defineusecases,agreeonacommon, easy-to-usereferenceimplementation,anddrive alignmentinstandardizationbodiesbasedonthose implementations.Examplesofsuchecosystemsare theAECCand5GAA(the5GAutomotive Association)forautomotiveand5G-ACIA(the5G AllianceforConnectedIndustriesandAutomation) [8],Industry4.0andtheIIoT(IndustrialInternetof Things)forthefourthindustrialrevolution. Conclusion Distributedcloudisacornerstoneoftheintelligent networksthatwillplayakeyenablingroleinthe fourthindustrialrevolution.Arobustdistributed cloudsolutionrequiresefficientandintelligent managementandorchestrationcapabilitiesthat spanheterogeneouscloudssuppliedbymultiple actors.Serviceexposurewillenablemonetization andapplicationinnovationthroughintegration withthemarketplacesand/orintegrationwiththe industries’ITsystems. Theevolutiontowardgloballydistributedcloud requiresactiontoaligntheindustryboththrough traditionalstandardizationsaswellasactive participationinopen-sourceprojectsaimedat providingreferenceimplementations.Ecosystems suchastheAECCplayanimportantroleby examiningthehigh-volumedatausecasesforthe automotiveindustry. ITISCRITICALTODRIVE INDUSTRYALIGNMENTBY GETTINGREFERENCE IMPLEMENTATIONSOFEDGE CLOUDSOFTWARE
  10. 10. ✱ DISTRIBUTED CLOUD 10 ERICSSON TECHNOLOGY REVIEW ✱ NOVEMBER 20, 2018 Further reading ❭❭ Ericsson Consumer & IndustryLab, 5G business value: A case study on real-time control in manufacturing, April 2018, available at: https://www.ericsson.com/assets/local/reports/5g_for_industries_ report_blisk_27062018.pdf ❭❭ Ericsson, Turn on 5G: Ericsson completes 5G Platform for operators, February 8, 2018, available at: https://www.ericsson.com/en/press-releases/2018/2/turn-on-5g-ericsson-completes-5g-platform-for-operators ❭❭ Ericsson, Going beyond edge computing with distributed cloud, available at: https://www.ericsson.com/ digital-services/trending/distributed-cloud ❭❭ Ericsson/KTH Royal Institute of Technology, Resource monitoring in a Network Embedded Cloud: An extension to OSPF-TE, available at: https://www.ericsson.com/assets/local/publications/conference- papers/03-cloud-ospf-camera-ready.pdf ❭❭ M2 Optics Inc., Calculating Optical Fiber Latency, January 9, 2012, Miller, K, available at: http://www. m2optics.com/blog/bid/70587/Calculating-Optical-Fiber-Latency ❭❭ Application function placement optimization in a mobile distributed cloud environment, Anna Peale, Péter Kiss, Charles Ferrari, Benedek Kovács, László Szilágyi, Melinda Tóth, in Studia Informatica - Issue no. 2/2018, pp37-52, available at: http://www.studia.ubbcluj.ro/arhiva/abstract_en. php?editie=INFORMATICA&nr=2&an=2018&id_art=15974 References 1. TelecomTV, Gartner says 5G networks have a paramount role in autonomous vehicle connectivity, June 21, 2018, available at: https://www.telecomtv.com/content/tracker/gartner-says-5g-networks-have-a- paramount-role-in-autonomous-vehicle-connectivity-31356/ 2. AECC White Paper, available at: https://www.ericsson.com/res/docs/2014/consumerlab/liberation-from- location-ericsson-consumerlab.pdf 3. Government Technology, Making 5G a Reality Means Building Partnerships — Not Just Networks, June 5, 2018, Descant, S, available at: http://www.govtech.com/network/Making-5G-a-Reality-Means- Building-Partnerships--Not-Just-Networks.html 4. Wired, Eye tracking is coming to VR sooner than you think. What now?, March 23, 2018, Rubin, P, available at: https://www.wired.com/story/eye-tracking-vr/ 5. Think Act, Digital factories – The renaissance of the U.S. automotive industry, Berger, R, available at: https://www.rolandberger.com/en/Publications/pub_digital_factories.html 6. Ericsson, Technology Trends 2018, Five technology trends augmenting the connected society, 2018, Ekudden, E, available at: https://www.ericsson.com/en/ericsson-technology-review/archive/2018/technology- trends-2018 7. Ericsson Technology Review, Open, intelligent and model-driven: evolving OSS, February 7, 2018, Agarwal, M; Svensson, M; Terrill, S; Wallin, J, available at: https://www.ericsson.com/en/ericsson-technology- review/archive/2018/open-intelligent-and-model-driven-evolving-oss 8. 5G-ACIA, 5G for Connected Industries and Automation, April 11, 2018, available at: https://www.5g-acia. org/publications/5g-for-connected-industries-and-automation-white-paper/
  11. 11. DISTRIBUTED CLOUD ✱ NOVEMBER 20, 2018 ✱ ERICSSON TECHNOLOGY REVIEW 11 theauthors Malgorzata Svensson ◆ is an expert in operations support systems. She joined Ericsson in 1996 and has worked in various areas within research and development. For the past 10 years, her work has focused on architecture evolution. Svensson has broad experience in business process, function and information modeling, information and cloud technologies, analytics, DevOps processes and tool chains. She holds an M.Sc. in technology from the Silesian University of Technology in Gliwice, Poland. Christer Boberg ◆ serves as a director at Ericsson’s CTO office, responsible for IoT technology strategies aimed at solving networking challenges for the industry on a global scale. He initially joined Ericsson in 1983 and has in his career within and outside Ericsson focused on software and system design as a developer, architect and technical expert. In recent years, Boberg’s work has centered on IoT and cloud technologies with a special focus on the automotive industry. As part of this work, he drives the AECC consortium together with industry leading companies. Benedek Kovács ◆ joined Ericsson in 2005 as a software developer and tester, and later worked as a system engineer. He was the innovation manager of the Budapest R&D site 2011-13, where his primary role was to establish an innovative organizational culture and launch internal startups based on worthy ideas. Kovács went on to serve as the characteristics, performance management and reliability specialist in the development of the 4G VoLTE solution. Today he is working on 5G networks and distributed cloud, as well as coordinating global engineering projects. He holds an M.Sc. in information engineering and Ph.D. in mathematics from the Budapest University of Technology and Economics in Hungary. Theauthorswould liketothank thefollowing peoplefortheir contributions tothisarticle: CarlosBravo, AlaNazari, StefanRuneson, OlaHubertsson, ThorstenLohmar andTomas Nylander. Terms and abbreviations AECC – Automotive Edge Computing Consortium | API – Application Programming Interface | APP – Application | AR – Augmented Reality | BW – Bandwidth | CSP – Communication Service Provider | DB – Database | DC – Data Center | ECU – Engine Control Unit |ETSI – European Telecommunications Standards Institute | FWA – Fixed Wireless Access | IoT – Internet of Things | ML – Machine Learning | MS – Millisecond | MTSO – Mobile Telephone Switching Office | NFV– Network Functions Virtualization | UL – Uplink | VNF – Virtual Network Function | VR – Virtual Reality | V2X/C-ITS– Vehicle-to-everything/ Cooperative Intelligent Transport System
  12. 12. ISSN 0014-0171 284 23-3323 | Uen © Ericsson AB 2018 Ericsson SE-164 83 Stockholm, Sweden Phone: +46 10 719 0000

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