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.

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. ✱ 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. 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. ✱ 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. 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. ✱ 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. 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. ✱ 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. 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. ✱ 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. 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. ISSN 0014-0171
284 23-3323 | Uen
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