Ericsson Technology Review - Issue 2, 2018

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 | V O L U M E 9 7 I 2 0 1 8 – 0 2
TECHNOLOGYTRENDS
AUGMENTINGTHE
CONNECTEDSOCIETY
FIXEDWIRELESS
ACCESSINLTEAND5G
DIGITAL
CONNECTIVITY
MARKETPLACES

FEATURE ARTICLE
Five technology trends augmenting
the Connected Society
Ericsson’s CTO Erik Ekudden presents the five technology trends that are
most relevant to the future development of network platforms capable of
supporting the continuous evolution of industries and societies around the world.
The use of machines to augment human intelligence will play a key role.
30
CONTENTS ✱
08 OPEN, INTELLIGENT AND MODEL-DRIVEN: EVOLVING OSS
Ericsson is leveraging the open-source approach to build open and intelligent
operations support systems (OSS) that are designed to support autonomic
networks and agile services. Our concept benefits from our ability to combine the
open-source approach with our unique network and domain knowledge.
20 LOW LATENCY, HIGH FLEXIBILITY: VIRTUAL AQM
Bufferbloat – the excessive buffering of packets – can cause significant delays in
mobile networks for the application consuming the data, which is why it is so
important for the network to be able to detect and properly manage it. Virtual Active
Queue Management is an innovative approach to buffer management that is
deployed as a centralized network function.
42 DIGITAL CONNECTIVITY MARKETPLACES TO ENRICH
5G AND IoT VALUE PROPOSITIONS
The ongoing digitalization of industry represents a key growth opportunity for the
telecom sector. The main challenge is to fully understand the needs of a new market.
Our solution is to establish a platform model where capabilities from many providers
can be effectively packaged and exposed in attractive ways to buyers from different
industries.
52 COGNITIVE TECHNOLOGIES IN NETWORK
AND BUSINESS AUTOMATION
While the terms artificial intelligence and machine learning are often used
synonymously, achieving automation that goes beyond low-level control of
network parameters also requires machine reasoning. The best way to create
the intelligent agents that are necessary to automate operational and
business processes is to capitalize on the strengths of both technologies.
64 LEVERAGINGLTEAND5GNRNETWORKSFOR
FIXEDWIRELESSACCESS
The high-speed mobile broadband coverage that LTE and 5G New Radio (NR)
enables has created a commercially attractive opportunity for operators to use
fixed wireless access (FWA) to deliver broadband services to previously unserved
homes and small and medium-sized enterprises around the world.
Induced
models
Inferred
knowledge
Knowledge
transfer
Knowledge
extraction
Training
examples
Expert
knowledge
P
F
A
Reasoning
Planning
Actions
Machine
learning
(Numeric)
Machine
reasoning
(Symbolic)
52
Consumer
Producer
Consumer Consu
Digital connectivity platf
Digital connectivity marke
Cloud services market
Ope
se
Bu
se
Application &
enablement
market
Information
market
Connectivity services market
42
08
Dequeuing
scheduler
QFI and
DSCP tagged
IP packets
GBR QOS
flows
Non-GBR
QOS flows
Flow
queueing
Flow
hashing
20
64
30
64

#02 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ #02 2018 7
Ericsson Technology Review brings you
insights into some of the key emerging
innovations that are shaping the future of ict.
Our aim is to encourage an open discussion
about the potential, practicalities, and benefits
of a wide range of technical developments,
and provide insight into what the future
has to offer.
a d d r e s s
Ericsson
se-164 83 Stockholm, Sweden
Phone: +46 8 719 00 00
p u b l i s h i n g
All material and articles are published on the
Ericsson Technology Review website:
www.ericsson.com/ericsson-technology-review
p u b l i s h e r
Erik Ekudden
e d i t o r
Tanis Bestland (Nordic Morning)
tanis.bestland@nordicmorning.com
e d i t o r i a l b o a r d
Håkan Andersson, Anders Rosengren,
Mats Norin, Erik Westerberg,
Magnus Buhrgard, Gunnar Thrysin,
Håkan Olofsson, Dan Fahrman, Robert Skog,
Patrik Roseen, Jonas Högberg,
John Fornehed and Sara Kullman
f e at u r e a r t i c l e
Five technology trends augmenting the
Connected Society by Erik Ekudden
a r t d i r e c t o r
Liselotte Eriksson (Nordic Morning)
p r o d u c t i o n l e a d e r
Susanna O’Grady (Nordic Morning)
l ay o u t
Liselotte Eriksson (Nordic Morning)
i l l u s t r at i o n s
Nordic Morning Ukraine
c h i e f s u b e d i t o r
Ian Nicholson (Nordic Morning)
s u b e d i t o r s
Paul Eade and Penny Schröder-Smith
(Nordic Morning)
issn: 0014-0171
Volume: 97, 2018
■ technology development keeps getting
faster and more interconnected, with new innovations
appearing every day. As a result, we’re swiftly moving
toward the realization of the “Augmented Connected
Society” – a world characterized by ubiquitous
internet access for all, self-learning robots and truly
intuitive interaction between humans and machines.
But how can our industry best prepare for this future?
In my role as CTO, I have the challenging and
exhilarating annual task of identifying the five
technology trends of the future that are (or will be)
most relevant to our industry. You can find my insights
and reflections on this year’s trends on page 30.
The augmentation of human intelligence is one
of the key themes in this year’s trends article.
Creating the highly automated environment that
network operators and digital service providers
will need in the future requires the support of
intelligent agents that are able to work
collaboratively. The two proofs of concept
presented in the Cognitive Technologies article
in this issue demonstrate how the combination
of machine reasoning and machine learning
techniques makes it possible to create intelligent
agents that are able to learn from diverse inputs,
and share or transfer experience between contexts.
I believe that fixed wireless access (FWA) is likely to play
an important role on our journey toward a world
characterized by ubiquitous internet access for all.
Already today, LTE and 5G New Radio are opening up
significant commercial opportunities for operators to
use FWA to bring the internet to many of the more than
TRANSFORMING
TO FIT THE
FUTURE REALITY
one billion households around the world that are still
unconnected, as well as to many small and medium-
sized businesses. The FWA article in this issue
highlights the key principles for combined mobile
broadband and FWA deployments, and presents
a use case that illustrates the recommended
deployment approach.
In my discussions with people from different
industries this past year, I’ve noticed a growing
awareness that telecom infrastructure can bring
significant value to the digital transformation of
industries. However, many providers need help
to understand the requirements of this new
market and to create solutions for it. The Digital
Connectivity Marketplaces article in this issue
explains our concept for a platform model in
which capabilities from many providers can be
effectively packaged and exposed in attractive
ways to buyers from different industries.
There’s no doubt that operations support systems
(OSS) have a critical role to play in the future of our
industry. Check out the OSS article in this issue to
learn about our future OSS concept, which is built on
a solid implementation architecture that enables the
use of industrydefined interfaces and open-source
modules, as well as integration with full component
compatibility.
Finally, I want to encourage those of you who are
concerned about bufferbloat to check out the
Virtual Active Queue Management (vAQM) article
in this issue. In our innovative concept, vAQM
is centralized and applied per user and per flow,
adapting to link rate fluctuation and considering
the current bottleneck link bandwidth to the user.
Our testing has shown that when vAQM is
centralized upstream, rather than being deployed
in the bottleneck nodes as it is in classic AQM,
there is a substantial reduction in bufferbloat.
I hope you get a lot of value out of this issue of
Ericsson Technology Review, and that the articles
can serve as the basis for stimulating future-focused
discussions with your colleagues and business
partners. If you would like to share a link to the whole
magazine or to a specific article, you can find both
PDF and HTML versions at https://www.ericsson.
com/en/ericsson-technology-review
✱ EDITORIAL EDITORIAL ✱
THE AUGMENTATION OF HUMAN
INTELLIGENCE IS ONE OF THE KEY THEMES
IN THIS YEAR’S TRENDS ARTICLE
ERIK EKUDDEN
SENIOR VICE PRESIDENT
AND GROUP CTO

8 #02 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ #02 2018 9
EVOLVING OPERATIONS SUPPORT SYSTEMS ✱✱ EVOLVING OPERATIONS SUPPORT SYSTEMS
toolchainsthatsupportautomation.Microservice
architecturesupportstheDevOpsparadigmand
enableshighlyscalable,extendableandflexible
systems.
Ericsson’sapproachtoevolvingOSSisbasedon
combiningthesetechnologiesandsolutionstocreate
amodernOSSarchitecturethatisoptimizedtomeet
therequirementsofserviceprovidersinthemidto
longterm.
OurOSSarchitectureprinciples
ThekeyprinciplesguidingEricsson’sOSS
architectureconceptarethatitisserviceoriented,
thattheautomationisanalyticsandpolicydriven,
andthatitusesvirtualizationandabstractionof
networkfunctions.Byfollowingtheseprinciples,
wecanevolveOSStobecomeprogrammableand
abletoleveragetheinterfacesofferedbythe
productiondomains.
Terms and abbreviations
AI–artificialintelligence|API–applicationprogramminginterface|BSS–businesssupportsystems|
ETSI–EuropeanTelecommunicationsStandardsInstitute|MANO–ManagementandOrchestration|
MI–machineintelligence|ML–machinelearning|NFV–NetworkFunctionsVirtualization|ONAP–Open
NetworkAutomationPlatform|OSS–operationssupportsystems|PNF–PhysicalNetworkFunction|
SDN–software-definednetworking|SLA–ServiceLevelAgreement|TOSCA–TopologyandOrchestration
SpecificationforCloudApplications|UDM–UnifiedDataManagement|VNF–VirtualNetworkFunction
MALGORZATA
SVENSSON,
MUNISH AGARWAL,
STEPHEN TERRILL,
JOHAN WALLIN
The long-standing paradigm of bundling
vendor and domain-specific management
with network functions has been challenged
in recent years by a new approach that is built
on the concept of an open and model-driven
platform. This new approach leverages open
source and uses standardized interfaces to
enable a horizontal management platform.
■Today’sserviceprovidersexpectrapidnetwork
deployments,agileintroductionofnewservices
andcost-efficientoperationandmanagement–
requirementsthatareevenmoreimportantwhen
planningforfuture5Gcapabilities.Recent
technologytrendsmakeitpossibletoredefine
traditionalsupportsystemsatthesametimeas
theyenablegreaterefficiencyindevelopmentand
operations.
Forexample,model-drivenautomationeliminates
theneedformanualinteractionbyexternalizing
behavioralaspectsofspecificdomainsfrom
applicationlogic.Real-timeandnear-real-time
analyticsredefinetheimplementationandscopeof
supportsystemfunctionslikeperformance,fault
management,assuranceandoptimization,where
analyticsmodelsactonthecollectedandcorrelated
data,producinginsightsthatarefarricherinscope
thantheoutcomeofthetraditionalfunctions.
Machineintelligence(MI)algorithmsenable the
transitionfromreactivetoproactivedecisionmaking.
TheDevOpsparadigmsimplifiesdevelopment
andoperationalprocesses.Itrequiresdevelopment
anddeployment-friendlysoftwarearchitectureand
The simplicity required to deploy and manage services in future network and
infrastructure operations is driving the need to rethink traditional operations
support systems (OSS). To unleash the potential of 5G, Network Functions
Virtualization (NFV) and software-defined networking (SDN), future OSS
need to be open, intelligent and able to support model-driven automation.
OPEN, INTELLIGENT AND
model-driven:
evolvingOSS
Definitions of key concepts
〉〉 Analytics is the discovery, interpretation and communication of meaningful patterns in data. It is the
umbrella for AI, ML and MI.
〉〉 Cloud native is a term that describes the patterns of organizations, architectures and technologies that
consistently, reliably and at scale take full advantage of the possibilities of the cloud to support cloud-
oriented business models.
〉〉 Microservice is a small service supporting communication over network interfaces. Several such services
constitute an application. Each microservice covers a limited and coherent functional scope and is
independently manageable by fully automated life-cycle machinery. A microservice is small enough to be
the responsibility of a single, small development team.
〉〉 OSS business layer is a conceptual layer of the OSS that guides the service provider through the
operational support subset of its business process.
〉〉 OSS control layer is a layer of the OSS containing functionality that controls and manages production
domains.
〉〉 Policy is a function that governs the choices in the behavior of a system. Policy can use a declarative or
imperative approach.
〉〉 Programmability is the ability to externally influence the behavior of a system in a defined manner.
〉〉 Resource is a means to realize a service, and can be either physical or non-physical.
〉〉 Service is a means to deliver value to a customer or user; it should be understandable to a customer.
Service is also a representation of the implementation aspects to deliver the value.

Abstraction,programmability
anddomain-specificextensions
Networksarecomplex.Asthephysicalresources
withinthemtransformintovirtualresourcessuch
asVirtualNetworkFunctions(VNFs),virtual
switches,virtualnetworksandvirtualcloud
resources,theywillexposeonlywhatisnecessary
andbemanagedinauniformway.Thisleadstoa
logicalrepresentationofresourcesandnetworks,
whichisreferredtoasabstraction.The
capabilitiesofthesevirtualresourcesareexposed
throughtheirinterfaces,whiletheirbehavioris
influencedbytheseinterfaces.Thisisreferredto
asprogrammability.Themethodologyof
abstractionandprogrammabilityiscontinually
andrecursivelyappliedatalllevelsoftheOSSand
networks,fromtheresources’interfacestothe
exposedservices.
Abstractionandprogrammabilityarethemain
prerequisitesforuniformmanagementof
automation,easingtheshiftfrommanaging
networkstomanagingautomation.Theyenable
simplificationbyexposingonlytherequired
capabilitiesofadomain,whilestillsupportingan
efficientinteractionbetweenmanagementsystem
scopes,andmanagementsystemsandnetworks.
Regardlessofthegeneralizationofthebehavior
betweenvariousproductiondomains,thedomain-
specificaspectsremain.Someexamplesof
domain-specificextensionsare:managingand
optimizingradiofrequency,designingandhandling
datacentercapacity,ordesigningL2/L3service
overlay.Specificproductiondomaincompetencies
andprocessesarerequiredtoprovidethenecessary
capabilitiesintheseexamples,eventhoughthe
capabilitiesarerealizedbythesameOSS.
Fromautomaticmanagementto
managementofautomation
Thegoalofautomationistoprovideazero-touch
network,whichmeansthatautomationmoves
fromautomatingviascriptingwhatcanbedone
manually,toanautonomicnetworkthattakescare
ofitselfandhandlespreviouslyunforeseen
situations.Thischangestheapproachto
management,frommanagingnetworksmanually
tomanagingautomation.
Akeypartofmanagingautomationisthecontrol
loopparadigm,asshowninFigure1.Thisisachieved
bydesigningtheinsightsandpoliciesrequiredfora
usecase,andthedecisionsthatneedtobemade.
Insightsaretheoutcomewhendataisprocessedby
analytics.Policiesrepresenttherulesgoverningthe
decisionstobemadebythecontrolloop.Thedecisions
areactuatedbyCOM(Control,Orchestration,
Management),whichinteractswithproduction
domainsbyrequestingactionssuchasupdate,
configureorheal.Thecontrolloopsareimplemented
bymultipleOSSfunctionsandcanactina
hierarchicalway.
Byleveraginganalytics,MIandpolicy,control
loopsbecomeadaptiveandarecentraltoassuring
andoptimizingthedeployedservicesandresources.
MIandautonomicnetworks
Automationcanbefurtherenhancedbyapplying
MI,whichisthecombinationofmachinelearning
(ML)andartificialintelligence(AI).MIadaptsto
thesituationinanetworkandlearnstoprovidethe
bestinsightsforagivennetworksituation.
AsthelevelofMIincreases,theroleofpolicyshifts
fromgovernanceofthedecisionstobemade,to
ensuringthattheinsightsproducedfromMIare
appropriate.Inthissense,MIisanenablerfor
autonomicnetworks.
AUTOMATIONCANBE
FURTHERENHANCEDBY
APPLYINGMI,WHICHISTHE
COMBINATIONOFMACHINE
LEARNINGANDARTIFICIAL
INTELLIGENCE
Figure 1 Analytics and policy-driven automation: closed control loop
Analytics Policy
Control
orchestration
management
Insights Decisions
Actions
Data
OSS business layer
OSS control layer
Production domains
Design Onboard Test Deploy Operate Retire
PNF PNF VNF VNF VNF VNF VNF
Cloud system
infrastructure Transport

Figure 2 Model-driven OSS architecture
Network slice
life-cycle
management
Studio suite VNF scaling SLA assurance ...RAN
optimization
OSS business layer
OSS control layer
OSS information layer
Network slice
deployment model
VNF scaling policy
model
RAN optimization
policy model
Design Onboard Test Deploy Operate Retire
Orche-
stration
PolicyAnalyticsCatalog Inventory Mediation
Model-drivenarchitecture
Tosecureefficientandconsistentmanagement,
thebehavioralaspectsofspecificdomainsare
externalizedfromapplicationlogictomodels.
Therearemodelsforconfiguration,orchestration,
policyrulesandanalytics.
Resourceandservicelife-cyclemanagementisa
goodexampleofamodel-drivenapproach.
AsshowninFigure2,themodelsdescribethe
servicesandtheresources.Thesemodelsare
designedandonboardedinastudiosuite.Theyare
thenusedtotest,deploy,operateandretirethe
servicesandtheresources.Themodelsarebased
onstandardmodelingapproaches.Forexample,
deploymentmodelsaredescribedusingHEAT
(specifically,orchestrationinOpenStack[1])–and
TOSCA[2].The“operate”processcancover
activitiessuchasscale,upgrade,heal,relocate,stop
andstart.
Theconfigurationandinstrumentationofthe
executingresourceandserviceinstancesrequire
resourceandserviceviewsexpressedalsoas
models.IETFYANG[3]hasemergedasthe
mainstreammodelinglanguageforthispurpose.
Policyrulesetsdefinethebehavioralaspectsof
serviceassurance,resourceperformanceand
scaling.Sincethemodelsarethewaytocapture
businesslogicandintellectualproperty,theywill
becomesellableobjectsthemselvesandbesubject
tocommercialagreements.
Figure 3 Ericsson’s OSS architecture
Service and
resource life-cycle
management
User service and
resource life-cycle
management
Assurance and
optimization
Interaction management
Studiosuite
ServicesandAPIexposure
Capacity management
OSS catalogs and inventories
Mediation and analytics
Production
domains
BSS
Knowledge
management
Security
management
Procurement
Forecasting
Development
Marketplace
Packet
core
Communi-
cation
services
Cloud system
infrastructure TransportRAN UDM
Modulararchitectureand
implementationcapabilities
TheoptimalOSSofthefuturewillbebasedbothon
theprinciplesoutlinedaboveandonmodular
architectureandimplementationcapabilities.
Studiosuite
Figure3illustratesthestudiosuite,whichprovides
asingle,coherentenvironmenttodesign,test,
operate,controlandcoordinateallthebusiness,
engineeringandoperationalprocessesofaservice
provider.Thisisdonebyexpressingtheprocesses,
interfaceextensions,policies,deploymentsand
configurationsasmodelsandusingthemfor
automationofalltheOSSfunctionsandthe
managedproductiondomains.Thestudiosuite
frameworkincludesbusiness,engineeringand
operationalstudios.
Thebusinessstudioistheenvironmentwhere
businessandoperationalprocessesaremaintained.
Thebusinessstudioautomaticallyorchestratesthe
processmodelsbyinvokingoperationsonthe
appropriatefunctionsintheOSSstack.
Theengineeringstudioprovidesamodel-driven
platformwheretheOSSfunctionsandtherelevant
informationandinformationmodelsassociated
withthosefunctionsareconstructedandextended.
Itletsauser(humanormachine)realizevarious
managementtasksbyextendingtheOSSfunctions
withuse-casespecificmodelsforconfiguration,

orchestration,dataandpolicies.Itprovidessoftware
developmentkitsfordefiningtheextensionmodels
andtheninstantiatestheneededcomponents,
provisionsthecomponentswiththemodelsand
wiresthemtogethertorealizethedesiredusecase.
AnexamplecouldbeasetofMLfunctionsthat
providescalablemachinerythatcanbeassignedto
multipleusesbyapplyingdifferentconfigurationand
datamodels.
Theoperationalstudioofferscapabilitiesto
manageresourcesandservicesthroughouttheirlife
cycle.Resourcesrepresenttheinfrastructureandthe
networksofproductiondomains.Servicesusethe
infrastructureandnetworkstodelivervalueto
customers.Oneofthefirstactivitiesintheresource
andservicelifecycleistoconstructaspecificationof
aresourceoraservice,thenassigntherequired
policyrulesandconfigurationsdefiningthebehavior
ofthenetwork,ortheinfrastructureortheservice.
Oncetheyaredefined,thespecifications,policies
andconfigurationsarestoredintheOSScatalogs
anddistributedinrealtimetobeusedbytheOSS
functions.
Managingservicesandresources
Serviceandresourcelife-cyclemanagement
functionsprovideaprogrammablewayofmanaging
theservicesandresourcesoftheproduction
domainsbasedonspecifications,policiesand
configurationsstoredinthecatalogs.Thecore
capabilitiesareorchestrationandpolicyengines
thatparsethedefinedmodelstorealizeallthelife-
cyclestepsoftheservicesandresources.Thepolicy
andorchestrationenginesautomatethenetwork
managementbytakingovertheburdenoflow-level
decisionmakingandexecutionoflife-cycle
operationsfromhumanusers.
Thesefunctionsaretriggeredbyeventsgenerated
bysurroundingfunctionslikeinteraction
management,assuranceandoptimization.They
providebothclosedloopoptimizationcapabilities
(wheredecisionmakingisdonebasedonpoliciesand
MI)andanopenloopoptimizationfeature(wherea
humanuserdecidesbasedonasetofautomatic
recommendations).Thisdynamic,model-drivenand
real-timeinteractioniswhatwillmakethisfuture
OSSsolutiondifferentfromthetraditionalone.
Wecanalreadyseetheneedforseamless
interactionbetweenOSSsoftwaredevelopmentand
operations,whichsuggeststhatthecoreDevOps
capabilitiesofcontinuousintegration,delivery,
releaseanddeploymenton-demandwillbean
integralpartofthefutureOSS.
OSSinventorieswillbeusedtostoreinstantiated
resourcesandservices.Theytrackthepresence,
capacityandconfigurationofresourcesandservices,
aswellastheirrelationshipstootherresourcesand
services.Theyprovidethedataforcapacity
management,whichenablescapacityplanning,
designandmonitoringovertime.Byusinganalytics
incapacitymanagement,itispossibletomakeitwork
inrealtimeandbepredictiveandself-learning.
Thepurposeoftheuserserviceandresourcelife-
cyclemanagementfunctionistomanageresourcesand
servicesthathavebeenassignedtoauser,tenantor
subscriber.Thiscapabilityalsosupportsuser,tenant
andsubscriberprovisioning.Theclosedandopen
controllooparchitecturepatternsapplyhereaswell.
Theuserserviceandresourcelife-cyclemanagement
functionhasthesamepropertiesastheserviceand
resourcelife-cyclemanagementfunction.
Mediationprovidesanopenandadaptable
interfacetocollectdata,processeventsgeneratedby
theproductiondomains,anddotherequired
translation,transformationandfiltering.Italso
providesasetofflexibleinterfacestoinvokelife-cycle
operationsonthenetworksandinfrastructuresofthe
productiondomains.
Theassurancefunctionmakesitpossibletocheck
thatresourcesandservicesbehaveastheyhavebeen
designed,publishedandoffered.Assurancemakes
useofanalyticstodiscoverinsightsaboutthe
performanceofservicesandresources.Policy
enginesarethentriggeredtoactontheinsightsto
determineactionsthatensurethedesired
performancelevelofferedonservicesandresources.
OurOSSarchitectureisdesignedtouse
interactionmanagement,servicesandapplication
programminginterface(API)exposuretointeract
withexternalfunctions.Afewexamplesofthose
externalfunctionsaredevelopment,marketplaces,
businesssupportsystems,advancedsecurityand
knowledgemanagementsystems,procurementand
forecasting.
TheproductiondomainexampleshowninFigure
3includesaRAN,packetcoreandcommunication
services,UnifiedDataManagement,cloudsystem
infrastructure,fixedaccessandtransportnetworks.
OurOSSmanagesmulti-vendornetworkand
infrastructure.
TheOSSfunctionsarebasedonanAPI-first
approachthatmandatesthatanAPIbedefined
beforethefunction-exposingservicescanbe
implemented.Theapproachenablesparallel
developmentandprovidestheabilitytoadaptto
externalimplementations.TheAPImanagement
frameworkfacilitatestheeffectiveuseofAPIsduring
development,discoveryofinternalAPIendpointsat
runtimeandefficientlycontrolledexposureofAPIs
toexternalapplications.
Cloudnative
Cloudnativeisthenewcomputingparadigmthatis
optimizedformodern,distributedsystems
environments,capableofscalingtotensofthousands
ofself-healing,multi-tenantnodes.Cloudnative
systemsarecontainerpackaged,dynamically
managedandmicroserviceoriented.TheEricsson
OSSofthefuturewillbebasedoncloud-native
architecturetoenablebetterreuse,simplified
operationsandimprovedefficiency,agilityand
maintainability.
Themicroservicearchitectureenablesfine-grained
reusebyhavingseveralsmallcomponentsinsteadofa
singlelargeone.Bybeinglooselycoupledand
backwardcompatible,theservicescanbedeveloped
independently,enablingefficientDevOps.Italso
allowseachservicetochoosethemostefficient
developmentandruntimetechnologyforitspurpose.
Themicroservicearchitecturemakesitpossibleto
composevariousbusinesssolutions,thendeploythem
automatically,significantlyreducingdeploymentcost
andtimetomarket.Thearchitecturewillusethe
cloud-nativeecosystemasaportabilitylayerto
supportmultipledeploymentoptions.
Security
Supportingmultipledeploymentmodelsrequires
multiplesecuritycapabilitieslikecredential
management,securecommunication,encryption,
authenticationandauthorization,whichcanbe
selectedbasedonthedeployment.OurOSS
architecturewillincludethecapabilitiesneededto
supportthemultipledeploymentmodelsandto
protectthemodels.
Embracingindustryinitiatives
DuetoNFVandprogrammablenetworkssuchas
SDN,thereishugeindustryinterestinautomationto
decreasecomplexityandachieverapidintroduction
ofservicesandresources.Thishasresultedinseveral
significantindustryinitiatives,mostnotably

ETSI-MANO[4]andtheOpenNetwork
AutomationPlatform(ONAP)[5].
Initiativesthatcallforstandardizationprovidean
environmenttodevelopstrongconceptsandidentify
importantinterfaces,whileopensourceprovides
referenceimplementations,aswellas
implementationinterfaces.
ONAP
Bybringingtogethertheresourcesofitsmembers,
ONAPhasspedupthedevelopmentofaglobally
sharedarchitectureandimplementationfor
networkautomation.ONAPprovidesbothdesign
timeandruntimereferenceimplementations,
coveringservicedesignandcreation,catalog,
inventory,orchestrationandcontrol,policiesand
analytics.Italsoincludesservicemanagementand
automationcapabilitiesforPhysical/Virtual
NetworkFunctions(PNFs/VNFs),transportand
cloudinfrastructure.
Ericsson’smodularandmodel-drivenOSS
approachiswellalignedwithONAPandextendsthe
ONAPvisiontoautomationbeyondnetwork
managementtoincludeinteractionmanagement,
workforcemanagement,userserviceandresource
life-cyclemanagement,advancedservice
optimizationandassuranceasafewexamples.
TointeractwithONAP,weareintheprocessof
adoptingthenecessaryinterfacesandmodel
definitionsinourproductportfolio(bothinOSSand
networkfunctions).EricssoniscommittedtoONAP
andheavilyengagedwithitacrossabroadrangeof
topics.WeaimtousetheONAP-providedopen
sourceimplementationsinaccordancewith
Ericsson’sarchitectureprinciples.
Managingmultipleproductiondomainsrequiresa
model-drivenarchitecturebecauseachieving
domain-specificbehaviorrequirestheapplicationof
appropriatemodelsonacompatibleOSSplatform.
Sinceamodel-drivenarchitectureisessentialto
driveautomation,weareworkingtowardensuring
thatourmodelsareusablewithinONAP.
Conclusion
AsbothanestablishedinfrastructureandOSS
supplier,EricssonhasavisionforOSSinwhich
autonomicnetworksenableintelligentand
automatedserviceandresourcelife-cycle
management.Weareusinganalytics,MI,policy
andorchestrationtechnologiestocreatetheOSSof
thefuture,andtooffergreaterefficiencyin
developmentandoperations.WeapplyaDevOps
paradigmtooursoftwaredevelopmenttokeepus
closetocustomersandspeedupoursoftware
delivery.
Asourapproachismodel-driven,OSSbehavioris
externalizedfromthemanagementplatformand
materializedthroughacombinationofmodelsand
configurations.Asaresult,thedevelopedmodelsare
independentoftheexecutionplatform.
OurOSSconceptismodularbydesignandfollows
microservicearchitectureprinciplesthatmakeit
possibletoreplacesoftwarecomponentswithopen
sourceimplementations.Theconceptisbuiltona
solidimplementationarchitecturethatenablesthe
useofindustry-definedinterfacesandopensource
modules,aswellasintegrationwithfullcomponent
compatibility.
Ericssonhasembracedopensource
implementationsthroughactivecontributionsand
usage,aswellasdrivingimportant
standardizations.Wearecommittedtodriving
industryalignmentthatwillhelpcreateahealthy
ecosystem.
Further reading
〉〉 Ericsson Technology Review, Technology trends driving innovation – five to watch, 2017, Ekudden, E:
https://www.ericsson.com/en/publications/ericsson-technology-review/archive/2017/technology-trends-2017
〉〉 Ericsson Technology Review, Generating actionable insights from customer experience awareness,
September 2016, Niemöller, J; Sarmonikas, G; Washington, N: https://www.ericsson.com/en/publications/
ericsson-technology-review/archive/2016/generating-actionable-insights-from-customer-experience-awareness
〉〉 EricssonTechnologyReview,DevOps:fuelingtheevolutiontoward5Gnetworks,April2017,Degirmenci,
F;Dinsing,T;John,W;Mecklin,T;Meirosu,C;Opsenica,M:https://www.ericsson.com/en/publications/ericsson-
technology-review/archive/2017/devops-fueling-the-evolution-toward-5g-networks
〉〉 Ericsson,Gearingupsupportsystemsforsoftware-definedandvirtualizednetworks,June2015: https://www.
ericsson.com/en/news/2015/6/gearing-up-support-systems-for-software-defined-and-virtualized-networks-
〉〉 Ericsson,Zerotouchnetworkswithcloud-optimizednetworkapplications,Darula,M;Más,I: https://www.
ericsson.com/assets/local/narratives/networks/documents/zero-touch-networks-with-the-5g-cloud-optimized-
network-applications.pdf
〉〉 EricssonTechnologyReview,Architectureevolutionforautomationandnetworkprogrammability,November
2014,Angelin,L;Basilier,H;Cagenius,T;Más,I;Rune,G;Varga,B;Westerberg,E: https://www.ericsson.com/
en/publications/ericsson-technology-review/archive/2014/architecture-evolution-for-automation-and-network-
programmability
References
1. OpenStack,HeatOrchestrationTemplate(HOT)Guide,availableat:https://docs.openstack.org/heat/ocata/
template_guide/hot_guide.html
2. OASIS,OASISTopologyandOrchestrationSpecificationforCloudApplications(TOSCA)TC,availableat:
https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=tosca
3. InternetEngineeringTaskForce(IETF),October2010,YANG–ADataModelingLanguagefortheNetwork
ConfigurationProtocol(NETCONF),availableat:https://tools.ietf.org/html/rfc6020
4. ETSI,NetworkFunctionsVirtualization,availableat:http://www.etsi.org/technologies-clusters/technologies/nfv
5. ONAP, available at: https://www.onap.org/

18 ERICSSON TECHNOLOGY REVIEW ✱ #02 2018
✱ EVOLVING OPERATIONS SUPPORT SYSTEMS
Malgorzata
Svensson
◆ is an expert in OSS. 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.
She has broad experience in
business process, function
and information modelling;
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.
Munish Agarwal
◆ is a senior specialist in
implementation architecture
for OSS/BSS. He has 19
years of experience in
telecommunications working
with product development,
common platforms and
architecture evolution. He
has hands-on experience
with mediation, rating,
order management and
interactive voice response.
Agarwal holds a Bachelor
of Technology degree
from the Indian Institute of
Technology Kharagpur.
Stephen Terrill
◆ has more than 20 years
of experience working
with telecommunications
architecture, implementation
and industry engagement.
His work has included both
architecture definition and
posts within standardization
organizations such as
ETSI, 3GPP, ITU-T (ITU
Telecommunication
Standardization Sector) and
IETF (Internet Engineering
Task Force). In recent years,
his work has focused on the
automation and evolution
of OSS, and he has been
engaged in open source on
ONAP’s Technical Steering
Committee. Terrill holds an
M.Sc., a B.E. (Hons.) and a
B.Sc. from the University of
Melbourne, Australia
Johan Wallin
◆ is an expert in network
management. He joined
Ericsson in 1989 and has
worked in several areas in
RD in various positions
covering design, product
management and system
management. He has broad
experience of systems
architecture in various
mobile telephony systems
including GSM, TDMA and
CDMA. For the past 10
years, he has been working
with network management
architectures from an overall
Ericsson perspective. He
holds an M.Sc. in computer
engineering and computer
science from the Institute
of Technology at Linköping
University, Sweden.
theauthors
Theauthors
wouldliketo
acknowledge
thecontribution
theircolleagues
JohnQuilty,Bo
Åström,Ignacio
Más,JanFriman,
andJacoFourie
madetothe
writingofthis
article.

✱ VIRTUAL ACTIVE QUEUE MANAGEMENT VIRTUAL ACTIVE QUEUE MANAGEMENT ✱
findtheidealsendrateforagivenend-to-endlink.
Mobilenetworksareprovisionedwithlargebuffers
tohandleburstytraffic,userfairnessandradio
channelvariability.Eventhoughtheseoversized
bufferspreventpacketloss,theoverallperformance
degrades,asbufferbloathasmajorimpactson
congestioncontrolatendpoints.Itmisleadsloss-
basedcongestioncontrolalgorithms,resulting
inpacketovershootingonthesenderside.Over-
bufferingalsoresultsinlargejitterofend-to-end
latency,whichpotentiallybringsintimeoutevents
andeventuallyleadstothroughputdegradation.
Applicationimpact
Highlatencyandjittercausesignificantproblems
forinteractiveapplicationssuchasVoIP,gaming
andvideoconferencing.Webbrowsingisalso
highlydependentonlatency,andpageloadtimes
growlinearlyasround-triptime(RTT)increases.
Videostreamingwithadaptivebitratethrougha
continuoussequenceofsmallHTTPdownloads
sufferswhenpacketsarelost.Undergood
conditions,videoistransferredoverthenetwork
atthesamerateasitisplayedback.Ifthenetwork
can’tkeepupwithvideoplayback,theplayer
usuallyswitchestoalowervideoraterepresentation
andreasonablevideoqualitycanbeachieved
withlessbandwidth.
Themainchallengesinvideostreamingare
packetlossandexcessivebuffering.Whenapacket
islost,thevideoplayerstopsreceivingdatauntilthe
lostpackethasbeenretransmitted,eventhoughdata
packetscontinuetoarrive.Videoplayersnormally
buffervideosegments,allowingthemtocontinue
playingfromtheirbufferwhiledownloadingvideo
segmentsthatmightinvolveretransmissions.
Bufferingisnormallyusedforvideoondemandand
timeshiftedvideobutislimitedforlivevideo.
ACK–acknowledgement|AMBR–aggregatemaximumbitrate|AQM–ActiveQueueManagement|CoDel
–ControlledDelay|CPE–Customer-premisesequipment|DN–DataNetwork|DNS–DomainNameSystem
|DSCP–DifferentiatedServicesCodePoint|DRR–DeficitRoundRobin|EPC–EvolvedPacketCore| EPS
–EvolvedPacketSwitchedSystem|FQ–FlowQueue|FQ-CoDel–FlowQueueControlledDelay|GBR–
guaranteedbitrate|IETF–InternetEngineeringTaskForce|PDNGW–publicdatanetworkgateway|PDU–
protocoldataunit|QFI–QoSFlowIdentifier|QUIC–QuickUDPInternetConnections|RTT–round-triptime|
SDF–ServiceDataFlow|SMF–SessionManagementFunction| SYN–synchronization|TCP–Transmission
ControlProtocol|UE–userequipment|UPF–userplanefunction|vAQM–virtualActiveQueueManagement
MARCUS IHLAR,
ALA NAZARI,
ROBERT SKOG
To minimize the effects of bandwidth
fluctuations in mobile networks, radio base
stations are typically provisioned with large
buffers. However, since excessive buffering of
packets causes a large perceived delay for the
application consuming the data, it is important
for the network to detect and properly manage
cases in which buffering becomes excessive.
■ActiveQueueManagement(AQM)isawell-
establishedtechniqueformanagingthefrequent
andsubstantialfluctuationsinbandwidthand
delaythatarecommoninnetworks.Ithasbeen
appliedtonetworkbuffersformanyyearswiththe
purposeofdetectingandinformingdatasenders
aboutincipienttrafficcongestion.TraditionalAQM
tendstoworkwellinfixednetworks,wherebottle-
necklinkbandwidthvarieslittleovershorttime
durations.However,thedeploymentoftraditional
AQMisnotwidespreadinmobilenetworks.
Theproblemofbufferbloat
Bufferbloatisanundesirablephenomenon
prevalentinpacketnetworksinwhichexcessive
bufferingresultsinunnecessarilylargeend-to-
endlatency,jitterandthroughputdegradation.It
occursduetolargequeuesthatabsorbahuge
amountoftrafficinacongestedpathandusually
causesalongqueuingdelay.Topreventpacket
losses,networksnodesrelyonlargebuffers.
Bufferbloatoccursmostlyatedgenetworknodes
anddefeatsthebuilt-inTCPcongestionavoidance
mechanism,whichreliesondroppedpacketsto
Virtual Active Queue Management is an innovative approach to buffer
management that does not require deployment in the network nodes where
the buffering takes place. Instead, it is deployed as a centralized network
function, which allows for simpler configuration and increased flexibility
compared to traditional Active Queue Management deployments.
LOW LATENCY, HIGH FLEXIBILITY
Virtual
AQM

However,ifmultipleTCPpacketsarelost,TCP
mayslowdownitssendingratetowellbelowthe
datarateofthevideo,forcingtheplayerintothe
rebufferingstate.
Inshort,bufferbloatdegradesapplication
performanceandnegativelyimpactstheuser
experience.Thereisthereforeapressingneed
tocontrollatencyandjitterinordertoprovide
desirableQoStousers.
Mitigation
TraditionalAQMmitigatesbufferbloatby
controllingqueuelengththroughdroppingor
markingpacketsfromthebottleneckbufferwhen
itbecomesfullorwhenthequeuingdelayexceeds
athresholdvalue.AQMreactstocongestionby
droppingpacketsandtherebysignalingtothe
senderthatitshouldrestrictthesendingdatarate
duetotheimminentoverload.Theshortwaittime
alsoimprovestheresponsetimeforthetransport
protocolerrorhandling.
ControlledDelay
ControlledDelay(CoDel)isanemergingAQM
schemedesignedbytheIETF(Internet
EngineeringTaskForce)[1].Itsgoalistocontain
queuinglatencywhilemaximizingthethroughput.
CoDelattemptstolimitbufferbloatandminimize
latencyinsaturatednetworklinksbydistinguishing
goodqueues(theonesthatemptyquickly)from
badqueuesthatstaysaturatedandslow.CoDel
featuresthreemajorinnovationsthatdistinguish
itfrompriorAQMs.First,itusesthelocalminimum
queueasameasureofthestanding/persistent
queue.Second,itusesasinglestate-tracking
variableoftheminimumdelaytoseewhere
itisrelativetothestandingqueuedelay.
Third,insteadofmeasuringqueuesizeinbytes
orpackets,itmeasuresthepacketsojourntime
inthequeueandusesitasametrictodetect
incipientcongestion.
CoDelworksasfollows:uponarrival,apacket
isenqueuedifthereisroominthequeue.While
enqueuing,atimestampisaddedtothepacket.
Whendequeuing,thepacketsojourntimeis
calculated.CoDeliseitherinadroppingstateor
anon-droppingstate.Ifthepacketsojourntime
remainsabovethetargetvalue(default5ms)fora
specifiedintervaloftime(default100ms),CoDel
entersthedroppingstateandstartsdropping
packetsattheheadofqueue.WhileCoDelisin
droppingstate,ifthepacketsojourntimefallsbelow
thetargetorifthequeuedoesnothavesufficient
packetstofilltheoutgoinglink,CoDelleavesthe
droppingstate.
TheCoDelparameterstargetandintervalare
fixedparametersandtheirvalueshavebeenchosen
basedontheobservationsfromseveralexperiments.
Intervalisusedtoensurethatthemeasured
minimumdelaydoesnotbecometoostale.Itshould
besetatabouttheworst-caseRTTthroughthe
bottlenecktogiveendpointssufficienttimetoreact.
Thedefaultvalueis100ms.Thetargetparameteris
theacceptablequeuedelayforthepackets,including
capacitytocopewithtrafficspikes.Atargetof5ms
workswellinmostsituations;lowervaluescan
reducethethroughput.Ifthetargetismorethan
5ms,thereisminorornoimprovementinutilization.
FlowQueueControlledDelay
FlowQueueControlledDelay(FQ-CoDel)[2]isa
standardAQMalgorithmthatensuresfairnessin
CoDel.ItisusefulbecauseAQMalgorithms
workingonsinglequeuesexacerbatethetendency
ofunfairnessbetweentheflows.FQ-CoDelisa
hybridschemethatclassifiesflowsintooneof
multiplequeues,applyingCoDeloneachqueue
andusingmodifiedDeficitRoundRobin(DRR)
schedulingtosharelinkcapacitybetweenqueues.
WithFQ-CoDel,itispossibletoreduce
bottleneckdelaysbyseveralordersofmagnitude.
ItalsoprovidesaccurateRTTestimatesto
elephantflows,whileallowingpriorityaccessto
shorterflowpackets[3].
IncombinationwithimprovementsinLinux
networkstacks,CoDelandFQ-CoDelcan
eliminatetheproblemofbufferbloatonEthernet,
cable,digitalsubscriberlinesandfiber,resultingin
networklatencyreductionsoftwotothreeordersof
magnitude,aswellasimprovementsingoodput[4].
Inmobilenetworks,however,AQMneedsto
adjusttolinkbandwidthfluctuationthatvariesfor
differentusers.Thisisduetothefactthatbottleneck
linkbandwidthcanvarysignificantlyinmobile
networksevenovershorttimedurations,which
causesqueuebuild-upthatcanaddsignificant
delayswhenlinkcapacitydecreases.
OurinnovativeconceptforvirtualAQM(vAQM)
isappliedperuserandperflow,adaptingtolinkrate
fluctuationandconsideringthecurrentbottleneck
linkbandwidthtotheuser.Bottlenecklink
bandwidthisdeterminedbycorrelatingthenumber
ofbytesinflightwithRTTmeasurements.vAQMis
similartoFQ-CoDelbutitusesthemeasuredtime-
varyinglinkbandwidthtotheuserasthedequeuing
rateinsteadofnon-varyingdeficit/quantumasin
FQ-CoDel.
VirtualAQM
TraditionalAQMistypicallydesignedtooperate
onqueuesdirectly.However,duetodeployment
constraintsandotherfactors,itisnotalways
straightforwardtodeploynewAQMsatthe
networkedgeswherecongestionislikelytobe
experienced.Onewayofgettingaroundthis
problemistomovethebottlenecktoamore
centralpointinthenetworkandapplytheAQM
there.Inafixednetwork,itiseasytoachievethis
byshapingtraffictoarateslightlybelowthe
bottleneckpeakcapacity.Sincemobilenetworks
exhibitvastlydifferentpropertiesoffluctuating
bandwidth,amorerefinedapproachisrequired
toapplycentralizedAQM.
VirtualAQMconsistsoftwoseparatebut
interrelatedcomponents:abottleneckmodeler
andaqueuemanager.AseparatevAQMinstance
isrequiredforeachbottleneck.InLTE,this
correspondsroughlytoabearer(dedicatedor
default),whilein5GitcorrespondstoaProtocol
DataUnit(PDU)sessionoraQoSflow.Toeffectively
modelthebottleneck,thevAQMmeasurestheRTT
ofpacketsbetweenitselfandtheuserequipment
(UE),aswellastheamountofinflightdata.
VirtualAQMmaintainsfourvariables:PathRTT,
AverageRTT,TargetQueueDelayandTarget
RTT.PathRTTrepresentsthepropagationdelay
betweenthevAQMandtheUEatthereceivingend,
withoutqueuingdelay.Itiscalculatedbyfeeding
RTTmeasurementsamplestoaminfilter.Duetothe
varyingnatureofradionetworks,thePathRTTmust
beoccasionallyrevalidated.
AverageRTTisanexponentiallyweighted
movingaverageofthemeasuredRTT,which
representsthecurrentobserveddelay.Theuseof
aweightedmovingaveragereducestheimpactof
inherentnoiseintheRTTsignal,whichtypically
arisesfrompathlayercharacteristicssuchasRadio
LinkControllayerretransmissions.
Acertainbottleneckqueuedelaymustbe
toleratedtoensuresmoothdeliveryofdataoverthe
radio.TargetQueueDelayrepresentsthedelaythat
istoleratedontopofPathRTTbeforevAQMtakes
somesortofaction.TargetQueueDelayisdefinedas
afractionofPathRTT.ThesumofthePathRTTand
theTargetQueueDelayistheTargetRTT.
Thesefourvariablesareusedtodetermine
whetherqueuingistakingplaceinnetwork
bottlenecks.Theinformationcanbeusedintwo
distinctways:eitherasdirectinputtoanAQM
algorithmorasinputtoabitrateshaper.
OURINNOVATIVE
CONCEPTFORVIRTUAL
AQMISAPPLIEDPERUSER
ANDPERFLOW
WITHFQ-CODEL,
ITISPOSSIBLETOREDUCE
BOTTLENECKDELAYSBY
SEVERALORDERSOF
MAGNITUDE

vAQMwithdynamicbitrateadjustment
InvAQMwithdynamicbitrateadjustment,the
trafficshaperdynamicallyadjustsitsdequeuing
ratebasedonthemeasuredsendingrateandthe
relationbetweenAverageRTTandPathRTT.The
rateofincomingdataismeasuredandappliedtoa
maxfilter,whiletheaverageRTTislessthanor
equaltoTargetRTT.WhenAverageRTTexceeds
theTargetRTT,theshaperwillsetthedequeue
ratetothelargestbitrateobservedwhileAverage
RTTwasbelowtarget.Furthermore,the
differencebetweenAverageRTTandTargetRTT
isusedasanadditionalboundingfactoronthe
dequeuerate.
Internalqueueswillstarttoformifpacketsarrive
atthevAQMatahigherratethanthedequeuerate.
Insuchcases,astandardAQMalgorithmsuchas
FQ-CoDelcanbeappliedtothosequeues.Path
RTTcanbeusedasinputtotheAQMalgorithmto
determinetheCoDelintervalandothervariables.
Figure1demonstrateshowvAQMassigns
differentflowstodifferentqueuesmanagedby
CoDelanddequeueswithDRRusingthecurrent
ratetotheUE.
vAQMwithoutbitrateadjustment
ThevAQMwithoutbitrateadjustmentapproach
sacrificestheflowisolationthatisachievedbyFQ
forreducedimplementationandruntime
complexity.ThisapproachusestheRTT
measurementsasdirectinputtoanAQM
algorithmwithoutapplyingtheshapingstep.
TheAQMmakesitsdrop/markdecisionsbased
ontherelationbetweenAverageRTT,PathRTT
andtheTargetQueueDelay.IftheAverageRTT
continuouslyexceedstheTargetRTTforan
intervalamountoftime,theAQMentersa
droppingstate,likethatinCoDel.Theintervalis
determinedasafunctionofPathRTT.Ifmultiple
flowsaretraversingthesamebottleneck,adrop
willoccurontheflowthatisdeterminedtobethe
mostsignificantcontributorofqueuedelay.Thisis
effectivelydeterminedbycomparingtheflight
sizesoftherespectiveflows.
Handlingofnon-responsiveflows
ThebasicprincipleofAQMisthatitprovides
feedbacktothecongestioncontrolmechanismat
thesendingendpoint.However,thereisa
possibilitythatsenderswillnotreactasexpected
tothesignalsthattheAQMprovides.ThevAQM
candetectsuchflowsandapplyflow-specific
shapingtoalevelwheretheflowdoesnotgenerate
congestioninthebottleneck.Thisfeaturemakesit
safetodeployandexperimentwithnew
congestioncontrolalgorithms,asitreducesthe
potentialharmtothenetworkandotherflows
sharingthesamebottleneck.
RateandRTTmeasurement
VirtualAQMreliesonpassivemeasurements
ofthroughputandRTTtodetectcongestion.
TCPisunencryptedatthetransportleveland
canbemeasuredtriviallybystoringsequence
numbersandobservingacknowledgements
(ACKs)onthereversepath.
Whenthetransportlayernetworkprotocol
QUIC(QuickUDPInternetConnections)isused,
acryptographicenvelopehidesmosttransport
mechanisms.Mostnotably,theACKframeis
completelyhiddenfromon-pathobservers.
Aspecificmechanismthatallowspassiveon-path
measurementofRTTisbeingproposedinQUIC
standardization[5].Thismechanismmakesuseof
asinglebitintheQUICshortheader,thevalueof
thebitcycleswithafrequencycorrespondingto
theRTTbetweentwocommunicationsendpoints.
However,atthetimeofwriting,neitherthe
QUICprotocolnorthedescribedmechanism
isafinishedstandard.
TheadditionalbenefitsofvAQM
ThebenefitsoftraditionalAQMarewellknown,
namely:fastdeliveryofshort-livedflowsand
increasedperformanceoflatency-critical
applications.Thereisanincreasedresponsiveness
toshort-livedflowsduetoschedulingatflowlevel
andminimalqueuedelay.Forexample,Domain
NameSystem(DNS)responsesandTCPSYN/
ACKpacketsdonothavetoshareaqueuewith
aTCPdownloadofbulkdata.
Inrecentyears,ithasbecomeincreasingly
commonfordifferenttypesofapplicationsto
shareasinglebottleneckbuffer.Latency-critical
applicationsthatruninparallelwithbulkdownload
applicationssuchasvideostreamingorfile
downloadstypicallyexperiencesevereperformance
degradation.Byensuringthatthestandingqueuesat
thebottleneckaresmallornon-existent,largeflows
willnotdegradelatency.
InadditiontothebenefitsoftraditionalAQM,
vAQMisadaptivetolinkratefluctuationand
considersthecurrentbottlenecklinkbandwidthto
theuser.Italsoprovidesthebenefitsofcentralized
AQMandmodularityforincreasedflexibility.
vAQMisabletosimplifythedeploymentofAQM
inanoperatornetworkbecauseembeddingitina
single(aggregation)nodeissufficienttoenablethe
managementoftheentirenetworkdownstream
fromthepointofdeployment.Thismeansthat
legacyroutersandradioschedulersdonotneed
tobeupgradedtoincludeAQM.WhilevAQM
currentlyusesaschemeresemblingFQ-CoDel,it
isbuilttosupportpluggableAQMmodules,sothat
advancesinthefieldofAQMandcongestioncontrol
canhaverapidimpactinlivenetworks.
vAQMtesting
WehavetestedourvAQMconceptonthe
EricssonlabLTEnetwork,embeddingitina
multi-serviceproxythatinspectstrafficto
determineoptimalbottlenecklinkcapacitytoUEs.
Figure 1: vAQM with dynamic bitrate adjustment and flow separation
DRR scheduling
Flows hashed into separate queues
Dequeue rate determined
by measurement of RTT
and flight size
vAQM
Different flows
...
AQM algorithm per queue
Origin
server
Origin
server
Origin
server

Weconductedthetestingaccordingtotwo
multiusercellscenarios:atfull-signalstrength
andatweak-signalstrength.
Thetestapplicationconsistedofloadinga
GoogleDrivepageanddownloadinga16MBimage.
Weusedthreemobiledevicestogeneratethe
backgroundtrafficforthemultiusercell,with
eachofthemdownloading1GBfiles,sothat
wecouldtestlatencyunderloadformultiplestreams.
ThepagewasfetchedusingbothTCPandQUIC,
andtheeNodeBwasconfiguredwithadrop-tail
queue.Theserverwasconfiguredtousethe
CUBICcongestioncontrolalgorithmforboth
TCPandQUIC.
Weranthetestsforeachscenariointhree
differentways:
〉〉 TCPwithvAQMdisabled
〉〉 QUICwithvAQMdisabled
〉〉 TCPwithvAQMenabled
Theresults,showninFigure2,demonstratea
reductionofthemedianRTTbyroughlythree
timeswithvAQMenabledinthestrong-signal
scenario.Intheweak-signalscenario,themedian
RTTwithvAQMisnotmuchdifferentfromthe
strong-signalscenario,whereasthetrafficwithout
AQMdisplaysincreasedlatencywitha95th
percentileRTTofapproximatelyonesecond
forbothQUICandTCPtraffic.
vAQMinpacketcore
Figure3illustratesvAQMintwoscenarios:Packet
DataNetworkGateway(PDNGW)ofEvolved
PacketCore(EPC)in4GandintheUserPlane
Function(UPF)of5Gcore.
In5G,vAQMwillbedeployedasanetworkfunction
bytheUPFofthe5Gcore.Itwillbeconfiguredper
PDUsession,perSDF(ServiceDataFlow)/QoS
flow–thatis,theguaranteedbitrate(GBR)ornon-
GBRQoSflow.Thenon-GBRQoSflowsofone
PDUsessionwillbeaggregatedandhandledby
onevAQM,whereaseachGBRQoSflowwillbe
allocatedavAQMwithadedicatedvirtualbuffer.
TheSessionManagementFunction(SMF)
willconfigurevAQManditsvirtualbuffersforthe
QoSflowsduringthePDUsessionestablishment/
modificationphase.Configurationwilloccurwhen
SMFprovidestheUPFwithaQoSEnforcement
RulethatcontainsinformationrelatedtoQoS
enforcementoftrafficincludinganSDFtemplate
(inotherwords,packetfiltersets),theQoS-related
informationincludingGBRandmaximumbitrate,
andthecorrespondingpacketmarkinginformation
–inotherwords,theQoSFlowIdentifier(QFI),
thetransport-level-packet-markingvalue.vAQM
isself-learning,makingitfullyautomatedwithout
anyparametrization.Whensendingthepackets
onthePDUsessiontotheUE,vAQMwillread
outthepacketsfromthequeues,recalculatethe
ratetotheUEandadjustthebottleneck.Incases
Figure 2: vAQM in full signal strength testing scenario (top) and in weak signal
strength scenario (bottom)
TCP QUIC
Median 95th percentile
TCP vAQM
RTT (ms)
200
400
600
800
1000
0
TCP QUIC
Median 95th percentile
TCP vAQM
RTT (ms)
200
400
600
800
1000
0
Figure 3: vAQM at PDN GW of EPC and at UPF of 5GC
4G UE
PDN
EPC
5GC
Data network
Local Data
Network
4G RAN
Serving GW
PDN GW
vAQM
5G RAN
4G RAN
5G RAN
Wi-Fi
access
5G UE
4G UE
5G UE
Wi-Fi UE
CPE
Fixed
access
vAQM at edge UPF per
session and per QoS flow for
local breakout PDU session
vAQM at centralized UPF
per PDU session and per
QoS-flow
vAQM per PDN connection
and per EPS bearer
UPF
vAQM
UPF
vAQM

wherethePDUsessionhasalocalbreakout,vAQM
isconfiguredattheedgeUPFservingthelocal
breakout;inotherwords,attheuplinkclassifieror
branchingpoint.
Indownlink,asshowninFigure4,theUPFbinds
theIPflowsofthePDUsessiontoSDFs/QoSflows
basedontheSDFtemplatesusingtheIPpacketfilter
set(forexample,5tuples).TheUPFthenenforces
thePDUsessionaggregatemaximumbitrate
(AMBR)acrossallnon-GBRQoSflowsofthePDU
session.ItalsoenforcestheGBRfortheGBRQoS
flows.TheUPFfinallytagsthepacketswithQoS
FlowIdentities(QFIs)andhandsoverthepacketsto
vAQM,whichisresponsiblefortheQoSflow.
ThedifferentvAQMinstancesassociated
withQoSflowscancontainimplementations
ofcompletelydifferentAQMalgorithmsorbe
configuredwithdifferentdefaultparameters.
Conclusion
Theongoingevolutionofnetworksandapplications
isincreasingtherequirementsonend-to-end
performance.Specifically,itisimportanttobe
abletoservedataathighrateswithoutcausing
unnecessarydelayduetobloatedbuffers.Our
testinghasshownitispossibletomitigate
bufferbloatsubstantiallythroughtheuseofavirtual
formofAQM(vAQM)thatiscentralizedupstream
ratherthanbeingdeployedinthebottleneck
nodes,asitisinclassicAQM.Thisapproachgreatly
simplifiesthedeploymentandconfigurationof
AQMinmobilenetworks,anditensuresconsistent
behavioracrossbottlenecknodes,whichinmany
casesaresuppliedbyamultitudeofvendors.Inlight
ofthesebenefits,vAQMwillbeanimportantuser
planefunctionin5Gcore.
Further reading
〉〉 Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi (conference paper), Toke
Høiland-Jørgensen et al., USENIX ATC (2017), available at: https://www.usenix.org/system/files/conference/
atc17/atc17-hoiland-jorgensen.pdf
〉〉 Ending the Anomaly: Achieving Low Latency and Airtime Fairness in WiFi (slide set), Toke Høiland-
Jørgensen et al., USENIX ATC (2017), available at: https://datatracker.ietf.org/meeting/99/materials/slides-
99-iccrg-iccrg-presentation-1/
References
1. ControlledDelayActiveQueueManagement,RFC8289,January2018,availableat:
https://tools.ietf.org/html/rfc8289
2. TheFlowQueueCoDelPacketSchedulerandActiveQueueManagementAlgorithm,RFC8290,January
2018,availableat:https://www.ietf.org/mail-archive/web/ietf-announce/current/msg17315.html
3. CoDelOverview,availableat:https://www.bufferbloat.net/projects/codel/wiki
4. MakingWiFiFast(blog),DaveTäht,availableat:http://blog.cerowrt.org/post/make-wifi-fast/
5. TheAdditionofaSpinBittotheQUICTransportProtocol,IETFDatatracker(2017),availableat:
https://datatracker.ietf.org/doc/draft-trammell-quic-spin
Figure 4: vAQM for the PDU session QoS flows at the UPF
Dequeuing
scheduler
QFI and
DSCP tagged
IP packets
GBR QOS
flows
Non-GBR
QOS flows
DN
Flow
queueing
Flow
hashing
UE-AMBR
policing on
non-GBR SDFs
MBR policing
(MBR for GBR SDFs
and session AMBR
for non-GBR SDFs)
Packet filter
sets
PDU session
IP flows
PF1
PF2
PF3
PF4
PF5
PF6
SDF1SDF1
SDF2SDF2
SDFSDF3
Marcus Ihlar
◆ is a system developer
in the field of traffic
optimization and media
delivery. He joined Ericsson
in 2013 and initially did
research on information-
centric networking.
Since 2014, he has been
working on transport
layer optimization and
media delivery. Ihlar also
participates actively in
IETF standardization in the
Transport Area Working
Group. He holds a B.Sc.
in computer science from
Stockholm University in
Sweden.
Ala Nazari
◆ is a media delivery
architecture expert. He
joined Ericsson in 1998 as
a specialist in datacom,
working with GPRS, 3G
and IP transport. More
recently, he has worked as
a senior solution architect
and engagement manager.
Prior to joining Ericsson,
Nazari spent several years
at Televerket Radio working
with mobile and fixed
broadband and transport. He
holds an M.Sc. in computer
science from Uppsala
University in Sweden
Robert Skog
◆ is a senior expert in the
field of media delivery. After
earning an M.Sc. in electrical
engineering from KTH Royal
Institute of Technology
in Stockholm in 1989, he
joined Ericsson’s two-year
trainee program for system
engineers. Since then, he
has mainly been working in
the service layer and media
delivery areas, on everything
from the first WAP solutions
to today’s advanced user
plane solutions. In 2005,
Skog won Ericsson’s
prestigious Inventor of the
Year Award.
theauthors

#02 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ #02 201830 31
✱ FEATUREARTICLE FEATUREARTICLE ✱
Rapid advancements in the use of machines to augment human intelligence are creating a
new reality in which we increasingly interact with robots and intelligent agents in our daily
lives, both privately and professionally. The list of examples is long, but a few of the most
common applications today are found in education, health care, maintenance and gaming.
My vision of the future network is an intelligent platform that enables this new reality
by supporting the digitalization of industries and society. This network platform consists
of three main areas: 5G access, automation through agility, and a distributed cloud.
A set of intelligent network applications and features is key to hiding complexity from
the network’s users, regardless of whether they are humans or machines.
The ability to transfer human skills in real time to other humans and machines located
all around the world has the potential to enable massive efficiency gains. Autonomous
operation by machines with self-learning capabilities offers the additional advantage of
continuous performance and quality enhancements. High levels of cooperation and trust
between humans and machines are essential. Building and maintaining trust will require
decision transparency, high availability, data integrity and clear communication of intentions.
The network platform I envision will deliver truly intuitive interaction between humans
and machines. In my view, there are five key technology trends that will play critical roles
in achieving the vision:
by erik ekudden, cto
#1 The realization of zero touch
#2 The emergence of the Internet of Skills
#3 Highly adaptable, cyber-physical systems
#4 Trust technologies for security assurance
#5 Ubiquitous, high-capacity radio
five technology
trends augmenting
the connected
society

✱ FEATUREARTICLE
the realization
of zero-touch
#1
TH E Z ERO -TO U CH networks
of the future will be characterized by
the fact that they require no human
intervention other than high-level
declarative and implementation-
independent intents. On the road to
zero touch both humans and machines
will learn from their interactions.
This will build trust and enable the
machines to adjust to human intention.
Computeandintelligencewillexistinthe
device,inthecloudandinvariousplacesin
thenetwork.Thenetworkwillautomatically
computetheimperativeactionstofulfill
givenintentsthroughaclosedloop
operation.Today’scomplexnetworks
aredesignedforoperationbyhumansand
thecomplexityisexpectedtoincrease.As
machinelearningandartificialintelligence
continuetodevelop,efficientlyintegrating
learningandreasoning,thecompetence
levelofmachineintelligencewillgrow.
AUGMENTATIONOFHUMAN
INTELLIGENCE
Therealizationofzerotouchisaniterative
processinwhichmachinesandhumans
collaboratereciprocally.Machinesbuild
intelligencethroughcontinuouslearning
andhumansareassistedbymachines
intheirdecision-makingprocesses.
Inthiscollaboration,themachines
gatherknowledgefromhumansandthe
environmentinordertobuildmodels
ofthereality.Structuredknowledgeis
createdfromunstructureddatawiththe
supportofsemanticwebtechnologies,
suchasontologies.Themodelsare
createdandevolvedwithnewknowledge
tomakeinformedpredictionsandenhance
automateddecisionmaking.
Tomaximizehumantrustandimprove
decisionquality,thereisaneedfor
transparencyinthemachine-driven
decision-makingprocess.Itispossible
togaininsightsintoamachine’sdecision
processbyanalyzingitsinternalmodeland
determininghowthatmodelsupported
particulardecisions.Thisservesasabasis
forgeneratingexplanationsthathumans
canunderstand.Humanscanalsoevaluate
decisionsandprovidefeedbacktothe
machinetofurtherimprovethelearning
32 33
process.Theinteractionbetweenhumans
andmachinesoccursusingnatural
languageprocessingaswellassyntactical
andsemanticanalysis.
ROBOTSANDAGENTSCOLLABORATE
WITHHUMANS
Inacollaborativescenario,arobotwill
beabletoanticipatehumanintentions
andrespondproactively.Forexample,an
assembly-linerobotwouldautomatically
adaptitspacetotheskillsofitshuman
coworkers.Suchinteractionsrequire
theintroductionofexplainableartificial
intelligencetocultivatehumantrustin
robots.Robotswillworkalongsidehumans
toaidandtolearn.Robotscanalsointeract
withotherdigitalizedcomponentsor
digitaltwinstoreceivedirectfeedback.
However,furtheradvancementsinrobot
designandmanufacturingwillbeneeded
toimprovetheirdexterity.
Asoftwareagentinazero-touch
networkactsinthesamewayasahuman
operator.Theagentshouldbeabletolearn
theroleinrealtime,aswellasthepattern
andtheproperactionsforagiventask.
Inparticular,itshouldbeabletohandle
awiderangeofrandomvariationsinthe
task,includingcontaminateddatafrom
therealworldthatoriginatesfrom
incidentsandmistakes.Theseagents
willlearnthroughacombinationof
reinforcementlearning(wheretheagent
continuallyreceivesfeedbackfrom
theenvironment)andsupervised/
unsupervisedlearning(suchas
classification,regressionandclustering)
frommultipledatastreams.Anagentcan
bepre-trainedinasafeenvironment,
aswithinadigitaltwin,andtransferred
toalivesystem.Domainknowledgeisa
keysuccessfactorwhenapplyingagents
tocomplextasks.
Techniquessuchasneuralnetworks
offersignificantadvantagesinlearning
patterns,butthecurrentapproachistoo
rigid.Differentialplasticityisanother
techniquethatlookspromising.
#02 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ #02 2018
FEATUREARTICLE ✱

the emergence
of the internet
ofskills
#2
TH E I NTERN E T O F S KI LL S
allows humans to interact in real time
over great distances – both with each
other and with machines – and have
similar sensory experiences to those
that they experience locally. Current
application examples include remote
interactive teaching and remote repair
services. A fully immersive Internet of
Skills will become reality through a
combination of machine interaction
methods and extended
communication capabilities. Internet
of Skills-based systems are
characterized by the interplay of
various devices with sensing,
processing and actuation capabilities
near the user.
Currentsystemslacktheaudio,visual,
hapticandtelecommunicationcapabilities
necessarytoprovideafullyrealistic
experience.ToenabletheInternetofSkills,
theinterplaybetweenhumansandrobots,
andbetweenhumansandvirtualcontent,
isofparticularimportance.Bothindustry
andconsumersareshowinggreatinterest
andopennessinusingthesenew
capabilities.
HUMANSKILLSDELIVERED
WITHOUTBOUNDARIES
Anauthenticvisualexperiencerequires
real-time3Dvideocapturing,processing
andrendering.Thesecapabilitiesmakeit
possibletocreatea3Drepresentationof
thecapturedworldandprovidethe
experienceofbeingimmersedinaremote
orvirtualenvironment.Whiletoday’suser
devicesdon’tyetprovidethenecessary
resolution,fieldofview,depthperception,
wearabilityandpositioningcapabilities,
thequalityandperformanceofthese
technologycomponentsissteadily
improving.
Spatialmicrophoneswillbeusedto
separateindividualsoundsourcesinthe
spacedomain.Thisimpliesthattherewill
beanincreasedamountofdataneededto
capturetheaudiospatialaspects.Spatial
audiorenderingperformanceisverymuch
tiedtoefficienthead-relatedfiltermodels.
Newformatsforexchangingspatialaudio
streamshavebeenspecifiedand
compressiontechniquesarebeing
developed.
Hapticcomponentsallowuserstofeel
shapes,textures,motionandforcesin3D.
Deviceswillalsotrackthemotionsand
forcesappliedbytheuserduring
interaction.Withcurrenttechnologiesthe
userneedstowearorholdaphysical
device,butfutureultrasoundbasedhaptic
deviceswillofferacontact-freesolution.
Standardizationeffortsforhaptic
communicationwillallowforaquicker
adoptionofhapticcapabilities.
INSTANTINTERACTION
ANDCOMMUNICATION
Communicationbetweenhumansand
machineswillbecomemorenatural,tothe
pointthatitiscomparabletointerpersonal
interaction.Naturaluserinterfacessuchas
voiceandgesturewillbecommonplace.
Theuseofvision-basedsensorswillallow
foranintuitivetypeofinteraction.Tobetter
understandhuman-machineinteraction
thereisaneedtoevolvetheunderstanding
ofkinesiology,ergonomics,cognitive
scienceandsociology,andtoincorporate
themintoalgorithmsandindustrialdesign.
Thiswouldmakeiteasiertoconveya
machine’sintentbeforeitinitiatesactions,
forexample.
Largevolumesof3Dvisualinformation
imposehighnetworkcapacitydemands,
makingultra-lowlatencyandhigh
bandwidthcommunicationtechnologies
essential.Enablingthebestuser
experiencerequirestheuseofnetwork
edgecomputerstoprocessthelarge
volumesof3Dvisual,audioandhaptic
information.Thissetupsavesdevice
batterylifetimeandreducesheat
dissipation,aswellasreducingnetwork
load.
✱ FEATUREARTICLE
34 35#02 2018 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ #02 2018
FEATUREARTICLE ✱

highly
adaptable
cyber-physical
systems
#3
✱ FEATUREARTICLE
A CYB ER- PHYS I CAL system
is a composite of several systems of
varying natures that will soon be
present in all industry sectors. It is a
self-organizing expert system created
by the combination of model of models,
dynamic interaction between models
and deterministic communication.
A cyber-physical system presents a
concise and comprehensible system
overview that humans can understand
and act upon.
Themainpurposeofacyber-physical
systemistocontrolaphysicalprocessand
usefeedbacktoadapttonewconditions
inrealtime.Itbuildsupontheintegrationof
computing,networkingandphysical
processes.Anexampleofacyber-physical
systemisasmartfactorywhere
mechanicalsystems,robots,rawmaterials,
andproductscommunicateandinteract.
Thisinteractionenablesmachine
intelligencetoperformmonitoringand
controlofoperationsatallplantlevels.
SYNERGISTICINTEGRATIONOF
COMPUTATION,NETWORKINGAND
PHYSICALPROCESSES
Themainchallengeistheorchestration
ofthenetworkedcomputationalresources
formanyinterworkingphysicalsystems
withdifferentlevelsofcomplexity.Cyber-
physicalsystemsaretransformingtheway
peopleinteractwithengineeredsystems,
justastheinternethastransformedthe
waypeopleinteractwithinformation.
Humanswillassumeresponsibility
onawideroperatingscale,supervising
theoperationofthemostlyautomated
andself-organizingprocess.
Acyber-physicalsystemcontains
differentheterogeneouselementssuchas
mechanical,electrical,electromechanical,
controlsoftware,communicationnetwork
andhuman-machineinterfaces.Itisa
challengetounderstandtheinteractionof
thephysical,cyberandhumanworlds.
Systemmodelswilldefinetheevolutionof
eachsystemstateintime.Anoverarching
modelwillbeneededtointegrateallthe
respectivesystemmodelswhilecontem-
platingallpossibledynamicinteractions.
Thisimpliesacontrolprogramthatdelivers
adeterministicbehaviortoeach
subsystem.Currentdesigntoolsneedto
beupgradedtoconsidertheinteractions
betweenthevarioussystems,their
interfacesandabstractions.
MODELOFMODELSCREATES
THECYBER-PHYSICALSYSTEM
Withinthecyber-physicalsystemall
systemdynamicsneedtobeconsidered
throughamodelthatinteractswithallthe
sub-models.Manyfactorsimpactthe
dynamicsoftheinteractionsbetweenthe
systems,includinglatency,bandwidthand
reliability.Forawirelessnetwork,factors
suchasthedevicelocation,thepropagation
conditionsandthetrafficloadchangeover
time.Thismeansthatnetworksneedtobe
modeledinordertobeintegratedinthe
modelofmodels.
Thetimeittakestoperformataskmay
becriticaltoenableacorrectlyfunctioning
system.Physicalprocessesare
compositionsofmanythingsoccurringin
parallel.Amodeloftimethatisconsistent
withtherealitiesoftimemeasurementand
timesynchronizationneedstobe
standardizedacrossallmodels.
EXAMPLE:INDUSTRY4.0
Thefactoryofthefutureimplementsthe
conceptofIndustry4.0,whichincludesthe
transformationfrommassproductionto
masscustomization.Thisvisionwillbe
realizedthroughlarge-scaleindustrial
automationtogetherwiththedigitalization
ofmanufacturingprocesses.
Humansassumetheroleofsupervising
theoperationoftheautomatedandself-
organizingproductionprocess.Inthis
contextitwillbepossibletorecognizeall
thesystemmodelsthatneedtointeract:
• Physicalandroboticsystemssuchas
conveyors,roboticarmsandautomated
guidedvehicles
• Controlsystemssuchasrobot
controllersandprogrammablelogic
controllersforproduction
• Softwaresystemstomanageallthe
operations
• Bigdataandanalytics-based
softwaresystems
• Electricalsystemstopower
machinesandrobots
• Communicationnetworks
• Sensorsanddevices.
Themastermodelconsistsofandinteracts
withallthelistedprocessesabove,resulting
intherealizationofthefinalproduct.
FEATUREARTICLE ✱

trust
technologies
for security
assurance
#4
TRUS T TECH N O LO G I E S will
provide mechanisms to protect
networks and offer security assurance
to both humans and machines.
Artificial intelligence and machine
learning are needed to manage the
complexity and variety of security
threats and the dynamics of networks.
Rapidly emerging confidential
computing – together with possible
future multi-party computation – will
facilitate secure cloud processing of
private and confidential data.
Performance and security demands
are driving the development of
algorithms and protocols for identities.
Theuseofcloudtechnologiescontinuesto
grow.Billionsofnewdeviceswithdifferent
capabilitiesandcharacteristicswillallbe
connectedtothecloud.Manyofthemare
physicallyaccessibleandthusexposed
andvulnerabletoattackortobeing
misusedasinstrumentsofattack.Digital
identitiesareneededtoproveownership
ofdataandtoensurethatservicesonly
connecttoothertrustworthyservices.
Flexibleanddynamicauditingand
complianceverificationarerequiredto
handlenewthreats.Furthermore,thereisa
needforautomatedprotectionthatadapts
tooperatingmodesandperformsanalytics
onthesysteminoperation.
PROTECTIONDRIVENBY
ARTIFICIALINTELLIGENCE
Artificialintelligence,machinelearningand
automationarebecomingimportanttools
forsecurity.Machinelearningaddresses
areassuchasthreatdetectionanduser
behavioranalytics.Artificialintelligence
assistssecurityanalystsbycollectingand
siftingthroughthreatinformationtofind
relevantinformationandcomputing
responses.However,thereisaneedto
addressthecurrentlackofopen
benchmarkstodeterminethematurityof
thetechnologyandpermitcomparisonof
products.
Whilethecurrenttrendistocentralize
dataandcomputation,security
applicationsfortheInternetofThingsand
futurenetworkswillrequiremore
distributedandhierarchicalapproachesto
supportbothfastlocaldecisionsand
slowerglobaldecisionsthatinfluencelocal
policies.
CONFIDENTIALCOMPUTING
TOBUILDTRUST
Confidentialcomputingusesthefeatures
ofenclaves–trustedexecution
environmentsandrootoftrust
technologies.Codeanddataiskept
confidentialandintegrityprotectionis
enforcedbyhardwaremechanisms,which
enablestrongguaranteesthatdataand
processingarekeptconfidentialinthe
cloudenvironmentandprevent
unauthorizedexposureofdatawhendoing
analytics.Confidentialcomputingis
becomingcommercialincloudsystems.
Researchisunderwaytoovercomethe
remainingchallenges,includingimproving
theefficiencyofthetrustedcomputing
base,reducingcontextswitchoverheads
whenportingapplicationsandpreventing
sidechannelinformationleakage.
Multi-partycomputationenables
partiestojointlycomputefunctionsover
theircombineddatainputswhilekeeping
thoseinputsprivate.Inadditionto
protectingtheconfidentialityoftheinput
data,multi-partyprotocolsmust
guaranteethatmaliciouspartiesarenot
abletoaffecttheoutputofhonestparties.
Althoughmulti-partycomputationis
alreadyusedinspecialcases,itslimited
functionalityandhighcomputation
complexitycurrentlystandinthewayof
wideadoption.Timewilltellifitbecomes
aspromisingasconfidentialcomputing.
PRIVACYREQUIRESSECURE
IDENTITIES
Digitalidentitiesarecrucialtomaintaining
ownershipofdataandforauthenticating
andauthorizingusers.Solutionsthat
addressidentitiesandcredentialsfor
machinesareequallyimportant.The
widespreaduseofwebandcloud
technologieshasmadetheneedfor
efficientidentitysolutionsevenmore
urgent.Inaddition,betteralgorithmsand
newprotocolsforthetransportlayer
securityprovideimprovedsecurity,lower
latencyandreducedoverhead.Efficiency
isparticularlyimportantwhen
orchestratingandusingidentitiesformany
dynamiccloudsystems,suchasthose
realizedviamicroservices,forexample.
Whenquantumcomputerswithenough
computationalpowerareavailable,all
existingidentitysystemsthatusepublic-
keycryptographywilllosetheirsecurity.
Developingnewsecurealgorithmsforthis
post-quantumcryptographyeraisan
activeresearcharea.

TH E WI RELE SS access network
is becoming a general connectivity
platform that enables the sharing of
data anywhere and anytime for anyone
and anything. There is good reason to
believe that rapidly increasing data
volumes will continue in the
foreseeable future. Ultra-reliable
connectivity requires ultra-low
latency, which will be needed to
support demanding use cases.
The focus will be on enabling high
data rates for everyone, rather than
support for extremely high data rates
that are only achievable under specific
conditions or for specific users.
Afewtechnologieswillneedtobe
enhancedinordertocreateaubiquitous,
high-capacityradionetwork.Thecommon
denominatorforthesetechnologiesistheir
capabilitytoenableandutilizehigh
frequenciesandwidebandwidth
operations.Coverageisaddressed
throughbeamformingandflexibilityin
deviceinterworking.Thechallengeisto
supportdatavolumesanddemanding-
trafficusecases,withoutacorresponding
increaseincostandenergyconsumption.
DEVICESACTASNETWORKNODES
Toenhancedevicecoverage,performance
andreliability,simultaneousmulti-site
connectivityacrossdifferentaccess
technologiesandaccessnodesisrequired.
Wirelesstechnologywillbeusedforthe
connectivitybetweenthenetworknodes,
asacomplementtofiber-basednetworks.
Devicecooperationwillbeusedtocreate
virtuallargeantennaarraysonthedevice
sidebycombiningtheantennasofmultiple
devices.Theborderlinebetweendevices
andnetworknodeswillbemorediffuse.
Massiveheterogenousnetworkswill
haveamuchmoremesh-likeconnectivity.
Advancedmachinelearningandartificial
intelligencewillbekeytothemanagement
ofthisnetwork,enablingittoevolveand
adapttonewrequirementsandchangesin
theoperatingenvironment.
NOSURPRISE–EXPONENTIAL
INCREASEDDATARATES
Meetingfuturebitratedemandswill
requiretheuseoffrequencybandsabove
100GHz.Operationinsuchspectrumwill
enableterabitdatarates,althoughonlyfor
short-rangeconnectivity.Itwillbean
implementationchallengetogenerate
substantialoutputpowerandhandleheat
dissipation,consideringthesmall
dimensionsofTHzcomponentsand
antennas.Spectrumsharingwillbefurther
enabledbybeamforming,whichismade
possiblebythehighfrequency.
Integratedpositioningwillbeenabledby
high-frequencyandwide-bandwidth
operationincombinationwithverydense
deploymentsofnetworknodes.High-
accuracypositioningisimportantfor
enhancednetworkperformanceandisan
enablerfornewtypesofend-userservices.
Thepositioningofmobiledevices,both
indoorandoutdoor,willbeanintegrated
partofthewirelessaccessnetworks.
Accuracywillbewellbelowonemeter.
ANEWTRADE-OFFBETWEEN
ANALOGANDDIGITALRADIO
FREQUENCYHARDWARE
Forthepast20yearstherehasbeen
acontinuoustrendtowardmoving
functionalityfromtheanalogtothedigital
radiofrequencydomain.However,the
trendisreversedforverywideband
transmissionatveryhighfrequencies,
incombinationwithaverylargenumber
ofantennas.Thismeansthatanew
implementationbalanceandinterplay
betweentheanaloganddigitalradio
frequencydomainswillemerge.
Increasinglysophisticatedprocessingis
alreadymovingovertotheanalogdomain.
Thiswillsoonalsoincludeutilizing
correlationsbetweendifferentanalog
signalsreceivedondifferentantennas,for
example.Thecompressionrequirements
ontheanalog-to-digitalconversionis
reduced.Thesplitbetweenanalogand
digitalradiofrequencyhardware
implementationwillchangeovertimeas
technologyandrequirementsevolve.
ubiquitous,
high-capacity
radio
#5

✱ DIGITAL CONNECTIVITY MARKETPLACES DIGITAL CONNECTIVITY MARKETPLACES ✱
consumenetworksjustasotherICTcapabilities
areconsumed.Thebusinessmodelofthefuture
willlookmorelikecurrentaaSmodelsforotherparts
oftheICTsector. Thecurrenttelecombusinessmodel
mustevolvesignificantlytoworkinthisnewmarket
realitytoplacetelecomcompaniesinabetterposition
tocapitalizeonnewbusinessopportunities.
Thecaseforbusinessmodeltransformation
Formanyyears,thetelecomindustryhassuccessfully
beenoperatinginwhatisbestdescribedasavertical
businessmodel.Inthismodel,telecomoperators
havedeliveredend-to-end(E2E)standardized
servicestoconsumersunderlong-termcontracts
andcompetedwithinnationalboundaries.
Thecollaborationbetweencommunication
serviceprovidershasgenerallybeenlimitedto
interoperabilityandroamingforthepurpose
ofglobalservicereachandtodrivetechnology
andmarketscale.Itisreasonabletoexpectthe
traditionalmodusofcollaborationtocontinue
toservetheindustryintheyearsahead,butitis
alsoclearthatthisframeworkisnotfittoexplorethe
fullvalueofnetworkinfrastructure.Richconfiguration
variancesandmorecomplexserviceswillinmany
casespushdemandbeyondthecapabilityofasingle
aaS–asaService|B2B–business-to-business|B2C–business-to-consumer|BSS–businesssupportsystems
|DCP–DeviceConnectionPlatform|E2E–end-to-end|IoT–InternetofThings|OSS–operationssupport
systems|SLA–ServiceLevelAgreement
MALGORZATA
SVENSSON,
LARS ANGELIN,
CHRISTIAN OLROG,
PATRIK REGÅRDH,
BO RIBBING
Digitalization is undoubtedly one of the most
significant forces for change of our time,
characterized by a wide variety of smart
devices and applications that touch all
possible areas of life. Now, new technologies
such as the Internet of Things (IoT), artificial
intelligence, wearables and robotics are
driving an accelerating digital transformation
in which core processes across virtually all
businesses and industries are becoming more
distributed,connectedandreal-timeoptimized.
■Businessesneednetworksthatcanprovide
themwiththefoundationtooperatethenew
transformingbusinessmodels.Digitaltransformation
requiresnetworkcapabilitiestosupportabroad
spectrumofdifferentuserscenarios.Coverage,
speed,latencyandsecurityaresomeofthekey
parameters,alongwithnewnetworkfunctions
foreffectiveprocessingofdataanddistributionof
applicationsandfunctionsfaroutinthenetwork.
5Gbringstheabilitytorealizeawidevariety
ofconnectivityandnetworkservicestomeet
theperformancerequirementsoftomorrow’s
digitalindustries.
Withnetworkscapableofsupportingan
unlimitedsetofservicesandusecases,ithasbecome
increasinglyclearthattraditionalmass-market
serviceswillsoonbeathingofthepast.Looking
forward,networksmustbeviewedasahorizontal
foundationorplatformonwhichbusinessescan
One of the key growth opportunities for the telecom industry is to provide
network capabilities that support the digital transformation underway
in most businesses and industries. Already today, we have a powerful
technology foundation in place, and this will become even stronger with 5G.
Now is the ideal time to evolve the business side of the equation toward
platform business models, which will enable the telecom industry to prosper
in multisided business ecosystems as well.
Digital
marketplacesTO ENRICH 5G AND IoT
VALUE PROPOSITIONS
connectivity
Definitions
〉〉 Consumer:auserassociatedwithacompanythatpurchasesservices/productsexposedbytheplatform/
marketplace.Aconsumercanbeanindividualand/oranorganization.
〉〉 Producer:asupplierofservices/productstoaplatform/marketplace.Aproducercanbeanindividualand/
oranorganization.
〉〉 Platformprovider:anorganizationthatmanagestheplatform/marketplace.
〉〉 Platform/marketplace:theentitythatexposestheservicesoftheplatform.Theexposedservicesarea
compositionofplatformservicesandservicessuppliedbyproducers.
〉〉 Digitalconnectivitymarketplace:themarketplacethatoffersvariousconnectivityservicesandconnectivity
serviceenablerstoenterprises.TheenterprisesmayrequireIoTcapabilities.
〉〉 Cloudservices:asetofservicesincludingstorageandcomputation.
〉〉 Digitalization:theuseofdigitaltechnologiestochangeabusinessmodelandprovidenewrevenueandvalue-
producingopportunities.Itistheprocessofmovingtoadigitalbusiness.

provider,aswellasbeyondthescopeofwhatis
possibletorealizewithtraditionalinteroperability
andfederationmodels.
Inlightofthis,itisnecessarytoevolvetoward
anewcollaborationframework–onethatoffers
serviceprovidersanenvironmentwheretheycan
dynamicallyaggregatecapabilitiesfrommultiple
sourcesintothetailoredsolutiontheyrequireto
enabletheiruniquedigitalizationjourney.
Suchamodelmustexcelinservingtheuniqueand
varyingneedsofconsumersaseffectivelyaspossible
andincludevaluecontributionfrominnovation
partners.Itmustalsomeetanewsetofrequirements
toeffectivelycreateandcapturevalue.Someofthe
mostcriticalaspectsare:
〉〉 simplicityforserviceproviderstodefineandconfigure
theirownconnectivityandnetworksolutionregardless
ofunderlyinginfrastructureandplayerboundaries.
〉〉 opennessforserviceproviderstoeasilyintegrateand
dynamicallyadjusttheirsolution,includingtheneeded
management,intotheirdigitalbusinessprocesses.
〉〉 well-structuredexposureofenhancedconnectivity
andnetworkcapabilitiessuchasdistributedcloud,
edgeanalytics,andsoon,forrapiduse-caseinnovation
andvaluecreation.
〉〉 easyexchangeofdigitalassetssuchassafeandsecure
tradeofdataacrossdifferentplayers.
〉〉 attractivecomplementforoperatorstoexplorecurrently
unaddressablebusiness/marketsbeyondthevertical
businesstheytraditionallyruntoday.
〉〉 creationofanenvironmentwhereinnovationthrives
tothebenefitofallbusinessmodelparticipants.
Toeffectivelymeettheseandotherrequirements,it
isobviousthattheestablishmentofsuchabusiness
modelisbeyondtheabilityofasingleplayeror
company.Instead,itmakesmoresensetolookata
businessthatisdesignedlikeaplatformwherea
multitudeofplayers,producersandconsumerscan
coexistandgainmutualbusinessoutcomes.Inthis
scenario,theplatformisbothamarketplacethat
supportsthebusinessofitsstakeholdersinthemost
effectivewayandalsoaninfrastructurethat
facilitatesandsimplifiesthesemultisided
relationshipswitharangeofkeyfunctionalities.
Multisidedconnectivityplatforms
Multisidedplatformsbringtogethertwoormore
distinctbutinterdependentgroupsofconsumers
andsuppliers.Suchplatformsareofvaluetoone
group(consumers,forexample)onlyiftheother
group(suppliers,inthiscase)isalsopresent.A
platformcreatesvaluebyfacilitatinginteractions
andmatchmakingbetweendifferentgroups.A
multisidedplatformgrowsinvaluetotheextent
thatitattractsmoreusersanditmatchmakesone
consumer’sneedswithservicesandproducts
offeredbymultiplesuppliers;aphenomenonknown
asthenetworkeffect.
Thetelecomindustryalreadyoffersavastvariety
ofconnectivityservices,and5Gtechnologies
willacceleratethedevelopmentofnewones[1]
characterizedbythedifferentcapabilitiesthatthe
variousindustriesrequire.Theconnectivityservices
of5Gwillberealizedbymeansofnetworkslicing.
Webelievethattailoredconnectivityserviceswith
diversecharacteristicswillbenecessaryfor
enterpriseswithintheindustriestodigitalizetheir
operationalandbusinessprocesses[2].
Digitalizationofenterprises’businessand
operationrequiresnotonlyconnectivityservicesbut
alsovariouscloud-basedserviceslikecomputation
andstorage,wherecompanies’operationsand
businessapplicationscanbehosted,deployedand
operated,orwhereenterprisescanbenefitfromthe
cloudservices’capabilitiesofferedinthesoftwareas
servicebusinessmodel.
Thefullpotentialof5Gconnectivityandcloud
servicescanbereachedwhentheyareexposed
onfullydigitalmultisidedconnectivityplatforms
(marketplaces),asshowninFigure1.Inthisway,the
services–andconsequentlytheindustries–will
benefitfromthepresenceofavastrangeofother
servicessuchasinformationandapplication
enablementservices,aswellasbusinessand
operationsupportcapabilities.Theconnectivity
marketplacewillcomplementthefederationmodel
forconnectivityandbridgethegapwherethe
existingfederationmodelisnotenough.
Thedigitalconnectivitymarketplacewillenable
newbusinessmodelsbyprovidingcapabilities
fortheexchangeofinformationandconnectivity
enablingservicestoecosystemsofconsumers
andsuppliers.
MULTISIDEDPLATFORMS
BRINGTOGETHERTWOORMORE
DISTINCTBUTINTERDEPENDENT
GROUPSOFCONSUMERSAND
SUPPLIERS
Figure 1: Digital connectivity marketplace: multisided platform
Consumer
Producer
Platform
provider
Consumer Consumer Consumer
Digital connectivity platform
Digital connectivity marketplace
Cloud services market
Operation support
services market
Business support
services market
Application
enablement
market
Information
market
Connectivity services market

Ourvisionofthefuturedigitalconnectivity
marketplaceisthatitwillofferinteractionbetween
producersandconsumersbyexposingthe
connectivityandcloudservicesononeside,and
collectingtherequirementsfromconsumersand
matchmakingthemagainstthecapabilitiesofthe
servicesontheother.Theplatformwillfacilitatethe
interactionsbyprovidingbusinessandoperation
supportservicestobothproducersandconsumers.
Theservicesexposedinthemarketplacecouldbe
suppliedbymultipleproducersowningthenetworks
andinfrastructuresrequiredtoproducethese
services.Theconsumersrepresentingvarious
industrieswillrequireconnectivityandcloud
servicestodigitalizetheirbusinessand
operationalprocesses[2].
Consumerservicesofferedbyproducers
Asdiscussedabove,thefuturemultisided
connectivitymarketplacewilloffercapabilitiesto
exposeconnectivityandcloudservicesthatmay
beprovidedbymultipleproducers.Inthecontext
of5G thefollowingtypesofconnectivityservices
maybeexposed:massivemachinetype
communication(alsoknownasmassiveIoT),
mission-criticalmachinetypecommunication
(alsoknownasmission-criticalIoT)andextreme
mobilebroadbandservices.Therewillbeaneed
tocomplementtheconnectivityservicesprovided
byotherstobeabletoofferthedesiredvalue
propositionandsatisfyindustrydemands.
Securitywillremainoneofthetoppriorities
associetiescontinuetoconnecteverythingfrom
autonomoustruckstoelevatorsandventilation
systems.Isolationtechniquessuchasautomatic
networkslicingarelikelytoplayakeyrolein
reducingsecurityrisks,togetherwithmachine-
learning-basedingressfirewalls,possiblyembedded
asvirtualnetworkfunctionsinoperators’networks.
Makingtheplatformattractive
andefficienttooperate
AtEricsson,weforeseethedigitalmarketplaceas
thefoundationofanecosystemofmanyconsumers
andprovidersfromamultitudeofindustries.
Thisimpliesthatindustrieswillrequirecapabilities
toexpresstheirrequirementsinlanguagesand
contextsthatarespecifictotheirbusinessand
operationalenvironments,asopposedtoneeding
telcoandcloud-domainknowledgetobeableto
chooseordefinetherequirementsonthe
connectivityandcloudservices.
Themultisidedcharacterofaplatform–andits
matchmakingabilities–willmakeitwellsuited
toprovidethesecapabilities.Theplatformwill
matchindustry-specificrequirementswith
servicecapabilities.
Thematchmakingfunctionusestherelatively
oldresearchareaofgraphtheory,boostedbyits
explosioninusageandimplementationinsocial
networksinrecentyears.ThemodelinFigure2
showsanexampleofhowconsumerrequirements,
modeledasgraphedges,arelinkedwithservices,
thentechnologiesandconsequentlysubscriptions.
Bydefiningeachserviceasavertexinagraph
whereedgesconstituteaconsumer/producer
relationship,itispossibletomodelcomplexvalue
chainsorrelationshipsbetweenservices.
Givenasetofrequirementsmodeledasvertexes
withconsumptionneedsas“danglingedges,”
wecanfindanefficientsubsetofthegraphwhich
fulfillstheseneeds,ifatallpossible.Byalsomodeling
non-functionalaspectssuchaslocation,latency
andthroughput,wegetagenericallyapplicable
mechanism.Thisgivesrisetotremendous
possibilitieswhereamultitudeoflooselyconnected
playerstogetherenableanefficientE2Eservice,
asadynamicallyconstructedvaluechain,capable
ofreachingfarbeyondwhatiscurrentlypossible.
Therearemanyimplementationoptions,including
capabilitiesforlife-cyclemanagementtoensurethat
consumersandproducers,joinedbymarketforces
andtheplatform,provideasuitablemixofstability
andinnovation.
Figure 2: Graph model: how industry requirements can be linked with connectivity services
Matchmaking
Subscription
Application
Capability
Technology
Technology
Capability
Capability
Assets
Service
Subscription Producer
Producer
Consumer Subscription
x
Subscription
y
Producer
y
Data
volume
Throughput
Latency
Producer
x
Subscription
Subscription
z
4G
Connectivity
Vehicle
Logistics
Edge
compute
5G
SECURITYWILLREMAIN
ONEOFTHETOPPRIORITIES
ASSOCIETIESCONTINUE
TOCONNECT

Datalakesandanalyticsareusedtoexecute
matchmaking,asshowninFigure3.Analytics
serviceswillmakethefutureplatformdatadriven,
astheywillbeexposedforusebyeitherconsumers
orproducerstohelpthemevolvetheirservices.
Atthesametime,theconsumersorproducers
providetheplatformwithextendedinsightintohow
producers’servicesareused,potentiallyhelping
improvetherecommendationssupportingservice
discoveryandmatchmaking.
Acommonsolutionforprovidingidentification
isyetanotherimportantcomponentoftheplatform
totrulysupportscaleandinnovativecombinations.
Federationofidentitiesisakeyservicewhen
consideringnewservicecombinations.Authorization
foraconnectionbetweentwothingswilllikelytake
bothsoftwarefingerprintingandbehavioralanalysis
intoaccountifthisfunctionalityisavailableina
“thingmanagement”systeminatrustedrelationship.
Securitysolutionswillenablethesafeandsecure
tradeofdatabetweendifferentplayersandthe
exchangeofdigitalassets[3].Trustfacilitieswill
enablesecuretransactionsbetweenconsumers
andproducers.Analyticsinsightintheshapeof
fraudmanagement,combinedwithanovelform
ofanalyticsinsightpredictinglikelihoodofsuccessful
serviceactivation,willbethekeyfortheplatform
providertobeabletomanageriskwhenbridging
thetrustdivide.
Serviceexposurewillsecureopennessfor
consumerstoeasilyintegrateanddynamicallyadjust
theirsolutionsandservices,includingtheneeded
management,intotheirdigitalbusinessprocesses.
Businesssupportplatform
Thebusinesssupportplatformcomprisesmanythings
forbothproducersandconsumers:theirentirelife
cycle,interactionsandmanagementwhileinabusiness
relationshipwiththeplatform;theentireservicelife
cycleoftheonboardedproducerandconsumer
servicessets;andself-servicesfortheaforementioned.
Oneofthemostcriticalcapabilitiesiscontracting,
duetothefactthatthemixofservices,SLAsand
pricingschemesiscomplexandcontractsare
frequentlynegotiatedandrenegotiated.Thebusiness
supportplatformsimplifiesbusinessbyusingonly
bilateralcontractsbetweenparties–thatis,a
business-to-business(B2B)contractbetween
anytwoofthethree:theproducer,theconsumer
andthemarketplaceprovider.B2Ccontractsare
applicableiftheconsumerisaprivateindividual
andnotanenterprise.Whilebusinessvaluechains
areoftenreferredtoasB2B2x,thecontracting
arrangementsupportingthemisasetoflinked
B2BandB2Ccontracts.
Inaddition,themarketplaceallowsforapartyto
actasaconsumertopurchaseservices,orchestrate
themintoanewservice,andoffertheorchestrated
serviceontheplatformasaproducer.Apartyin
aproducerrolecanalsoactasaconsumerforits
resourceneeds.ThisresultsinaseriesofB2Cand
B2Bcontracts,whichistheessenceofavibrant
ecosystembuiltontheplatform.Thecontractingand
contractmanagementprocessesarefullyautomated.
Operationssupportplatform
Theconsumerandproducerservicesthatenablethe
networkcapabilitiessuchasdistributedcloud
andedgeanalyticswillbeexposedusingservice
exposure,therebyenablinginnovationand
valuecreation.
Thedependenciesbetweenservicesare
automaticallyinferredthroughservice-level
assurance,andthecombinedanalyticsofthe
differentservicesprovidesinsightintoperformance
atseverallevels.Thismayallowfine-tuningofSLAs
basedonheuristicsandhelpfuelinnovationby
managingandprovidingtransparencywithregard
torisk.Anetworkorserviceoutagemustbedealt
withswiftly,astheymaybeusedinconsuming
enterprises’mission-criticalproduction.Automatic
healing,basedonorchestrationandpolicies,isone
waytotomitigateoutagesituations.
Theserviceconfigurationandactivationwillmake
itpossibleforenterprisestodefineandconfigure
theirconnectivityservicessettingssimplyand
independentlyofunderlyinginfrastructureand
playerboundaries.
DeviceConnectionPlatform
Oneofthefirstrealizationsofamultisidedplatform
isEricsson’sDeviceConnectionPlatform(DCP).
Thisisaplatformwheremobilenetworkconnectivity
servicesareexposedtobeconsumedbyapplications.
Itsfundamentalpurposeistomakeconnectivity
availableforenterprisestoincludeintheirofferings
andprovidethemeanstomanagethisconnectivity.
Twoexamplesoftypicalenterprisesinthiscontext
wouldbeanoriginalequipmentmanufacturer
ofsomesort(suchasanautomotivecompanyor
acameramanufacturer)oraserviceproviderofa
servicewhereadeviceisanintegralpartofthe
offering(suchasautilitycompanyusingautomatic
meterreadingorapoint-of-salesterminalprovider).
Asmobilenetworksarealwaysofageographically-
limitednature,normallybycountryorlicensearea,
thereisaneedtoharmonizethewaytheenterprise’s
connectivityisusedacrossseveralnetworks.Ideally,
theenterpriseneedstohavethesameconnectivity
experiencewhereveritlaunchesandrunsitsservice.
Theonlypracticalwaytoachievethisistostandardize
onacentralizedplatform,whichbringstogether
theconnectivityofallprovidersandexposesitin
auniformway.Thismakestheexperiencetruly
harmonized,intermsoffunctionalityaswellasservice
levelsandoperationalprocedures.Furthermore,it
alsominimizesthecostofintegrationbetweenthe
enterprisesystemsandtheaccessnetworks,saving
moneybothinitiallyandcontinuously,aswellas
shorteningtimetomarketfortheenterprise’sservice.
Conclusion
Theongoingdigitalizationofbusinessesand
industriesisoneofthekeygrowthopportunities
Figure 3: Digital connectivity platform
Matching engine
Serviceexposure
Service activation Service configuration
Service capability matchmaking
Consumer/producer service management
Consumer/producer managementInteraction management
IdentitymanagementSecuritymanagement
Assetmanagementintegration
CloudserviceintegrationConnectivityserviceintegration
Service assurance
Business support systems
Operations support systems
Data lake and analytics

Further reading
〉〉 Ericsson white paper, November 2015, Operator service exposure – enabling differentiation and
innovation, available at: https://www.ericsson.com/en/white-papers/operator-service-exposure--enabling-
differentiation-and-innovation
〉〉 Ericsson Technology Review, April 2017, Tackling IoT complexity with machine intelligence, available at:
https://www.ericsson.com/en/publications/ericsson-technology-review/archive/2017/tackling-iot-complexity-
with-machine-intelligence
〉〉 Ericsson press release, December 2017, 2018 hot consumer trends: technology turns human, available at:
https://www.ericsson.com/en/press-releases/2017/12/2018-hot-consumer-trends-technology-turns-human
References
1. Ericssonwhitepaper,January2017,5G–enablingthetransformationofindustryandsociety,availableat:
https://www.ericsson.com/en/white-papers/5g-systems--enabling-the-transformation-of-industry-and-society
2. Ericsson white paper, October 2017, Telecom IT for the digital economy, available at:
https://www.ericsson.com/en/publications/white-papers/telecom-it-for-the-digital-economy
3. EricssonTechnologyReview,November2017,End-to-endsecuritymanagementfortheIoT,availableat:
https://www.ericsson.com/en/ericsson-technology-review/archive/2017/end-to-end-security-management-for-the-iot
Malgorzata
Svensson
◆ is an expert in operations
support systems (OSS).
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.
Patrik Regårdh
◆ is head of strategy
for Solution Area OSS,
where his work focuses
on market development,
industry dynamics and
driving strategies and
initiatives for Ericsson’s
digital business. He joined
the company in 1994 and
his previous positions have
ranged from strategy and
business development to
account management.
Currently based at the global
headquarters in Stockholm,
he has also worked
extensively in Brazil, Thailand
and Germany. Regårdh holds
an M.Sc. from KTH Royal
Institute of Technology in
Stockholm, Sweden.
Christian Olrog
◆ is an expert in cloud
service delivery architecture.
He joined Ericsson in 1999
and has been a technical
leader and architect in many
different areas, ranging
from embedded client
development and wireless
enterprise connectivity with
a security focus to cloud and
operations support systems/
business support systems
(OSS/BSS). Olrog currently
sits in the Technology
Industry group at Business
Area Digital Services at
Ericsson. He holds an M.Sc.
in general physics from KTH
RoyalInstituteofTechnology.
Lars Angelin
◆ is an expert in BSS at
Business Area Digital
Services. He has more
than 30 years of experience
in the areas of concept
development, architecture
andstrategieswithinthe
telco and education
industries. He joined
Ericsson in 1996 as a
research engineer, and in
2003 he moved to a position
as concept developer in the
machine-to-machine and
OSS/BSS areas. Since 2006,
Angelin has been focusing on
BSS – specifically business
support, enterprise
architectures and the
software architectures to
implement the systems.
He holds an M.Sc. in
engineering physics and
a Tech. Licentiate in tele-
traffic theory from the
Faculty of Engineering at
Lund University in Sweden,
and an honorary Ph.D. from
Blekinge Institute
of Technology, Sweden.
Bo Ribbing
◆ is head of Product
Management for
Connectivity Management.
He joined Ericsson in 1991
and has worked in the
networks business since
1995. During this time, he has
gained broad international
experience, particularly
from Latin America and Asia
Pacific, and has held several
management positions.
Ribbing holds an M.Sc.
in applied physics from
Linköping University
in Sweden.
theauthors
Theauthors
wouldliketo
thankFrans
deRooijforhis
contributionto
thisarticle.
forthetelecomindustry.Telecominfrastructure,
encompassingbothcurrentnetworksandsoon-to-be-
launched5G,israpidlyevolvingintoaverypowerful
resourcethatcanbringsignificantvaluetothedigital
transformationofmostindustries.Lookingahead,
someofthetelecomindustry’skeyvalueleversare:
qualitydifferentiatedconnectivity;distributed
computingandstorage;analyticsofdataflows;
andsecuritysolutions.Withthetechnologyalready
inplace,themainchallengeistofullyunderstand
theneedsofanewbreedofconsumers,andusethat
informationtoorganizethebusiness,developnew
relationshipsandestablishefficientoperations.
AtEricsson,webelievethatthebestwayto
overcomethischallengeistoestablishaplatform
modelwherecapabilitiesfrommanyproviderscan
beeffectivelypackagedandexposedinattractive
waystobuyersfromdifferentindustries.This
approachcreatesanewandmuch-neededrole
forthetelecomindustryastheproviderofthe
marketplaceandbringsopportunitiesformultisided
businessrelationsandtransactionstoprosper.
Ononesideoftheplatformarethebusinesses
andindustriesthatareconsumingservicesfrom
theunderlyinginfrastructure,andontheotherside
areprovidersofbothnetworkassetsandenabling
functions.Theplatformorganizestherelationships
foroptimalfitandusefulnessfortheplayersinvolved.
Insodoing,theplatformprovidesanumberofuseful
functionswithoneofthekeyvaluesbeingthescale
ofbusinessandthenetworkingeffectthatithasto
offer.Insteadofcompetingforthesameconsumers
withthesameoffer,operatorsthatrestructure
accordingtoalogicthatenablesfullparticipation
inanecosystemsplatformwillfindthemselves
wellpositionedtofullycapitalizeonnewmarket
opportunities.

Ericsson Technology Review - Issue 2, 2018

Ericsson Technology Review - Issue 2, 2018

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Ericsson Technology Review - Issue 2, 2018