VESTED INTERESTS DRIVE DIVERGENCEIN E Cthe benefit of edge computing, and edge computing enables new...

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Tolaga ResearchHarness the Power of Intelligence

VESTED INTERESTS DRIVE DIVERGENCE IN EDGE

COMPUTING

June 2018

Executive Summary

Author: Dr Phil Marshall

http://www.etsi.org/technologies-clusters/technologies/multi-access-edge-computing

https://opencord.org/

http://openedgecomputing.org/index.html


https://www.edgexfoundry.org/


https://www.openfogconsortium.org/


https://aws.amazon.com/greengrass/



https://azure.microsoft.com/en-us/services/iot-hub/

https://www.ge.com/digital/predix/edge-to-cloud


https://www.honeywellaidc.com/solutions/platforms/mobility-edge

https://www.elektroniknet.de/international/detailed-concept-for-edge-computing-153449.html



Vested interests define edgeThere are familiar moments in history whenseemingly modest innovations result in tectonicshifts in the business models of entire industries.Notable examples include the iPhone in January2007, or more importantly its touch screentechnology, and Amazon’s AWS cloudInfrastructure-as-a-Service (IaaS) solution, whichwas launched almost a year earlier. Both theseinnovations combined with Internet and mobilitytechnology have played a tremendous role indefining cloud services and will impact the future ofedge computing.

Cloud computing enables applications to operate inmassive data centers, with centralized architecturesprovided by web-scale companies like Amazon,Facebook, Google, and Microsoft. Commonly cloudapplications are peered directly with user-friendlyend-point devices. This peering has generallypushed value creation away from communicationnetworks, and towards end-point devices and clouddata-centers. This, to benefit web-scale providersand device manufacturers, largely at the expense ofnetwork operators.

Cloud computing has enabled tremendous growthin consumer and enterprise-led digital services andthe proliferation of connected devices. This growthis stressing current systems and creatingopportunities for edge computing solutions, withdistributed (as opposed to centralized) computearchitectures.

Edge computing is not a new concept and is hasbeen widely adopted for:

● Smart consumer electronics devices provided bycompanies like Samsung and Whirlpool.

● Connected enterprise and industrial infrastructureequipment (such as programmable logic

controllers (PLC)) provided by companies likeABB, Honeywell, Siemens General Electric andRockwell, and;

● Content distribution network (CDN) solutions thatare operated by companies like Akamai, Amazon,Fastly and Verizon.

Although centralized (cloud) and distributed (edge)architectures are complementary, edge computinghas the potential to disrupt the business models ofincumbent players depending how it isimplemented. Edge computing has a broaddefinition which allows for compute functionalityanywhere from outside a data-center to endpointdevices. Network operators favor opportunities tocollocate edge computing and networkinfrastructure with the aim of bringing valuecreation back to network environments. Operatorsare also using edge computing for their ownnetwork advancements, such as cloud RAN, andother virtualized network functions. In contrast,web-scale companies are providing edge computesolutions that are targeted towards enterprise IoT.Commonly these solutions seek to maintain valueat the ecosystem peripheries where web-scalecompanies have greater strength. Other players likeembedded system providers have edge computestrategies that tend to concentrate value within theend-point devices themselves.

The divergent approaches towards edge computingare reflected in a variety of initiatives that are beingpursued by industry players. Key initiatives areinvestigated in this report with a focus towards theirsalient characteristics relative to key technology andmarket drivers.



Why edge computing and whenis it needed?Edge and centralized cloud computing will coexistfor the foreseeable future. Today centralized cloudservices are prevalent. For these services there isgenerally an economic cost for migrating to edgecompute platforms. While data-center inspireddesign-principles are being used for edgecomputing, they come with added expense andcomplexity and lack the economies of scale ofcentralized cloud platforms. Notable factors thatconstrain the the adoption of edge computinginclude the following:

● Existing services are designed and deployed incentralized cloud compute environments.Since these services have been designed in theabsence of edge computing, a classic “chickenand egg” dilemma commonly occurs. Thisdilemma constrains market development, evenfor services that are better suited to edgecompute architectures, because it encouragesstatus quo design principles.

● Edge compute implementations can be costlyand complex to scale. All things being equal,the cost and complexity for workloads operatingin centralized environments are less than thoseoperating in distributed edge computingenvironments. As a result, designs typically startwith centralized architectures and only becomedistributed with edge computing if thecentralized architectures prove inadequate.However, design philosophies that emphasizecentralized architectures have their flaws, since allthings are not necessarily equal. This isparticularly the case as digital services proliferateand become increasingly demanding, and edgecomputing solutions mature, and;

● Ecosystem Friction. The ecosystems required foredge computing are generally more complicatedthan centralized cloud-based architectures.Collaboration is often needed amongstecosystem players. While this collaboration maybe based on seemingly benevolent partnerships,it is likely to change as players jockeying forcompetitive advantage with incentive-ledideologies.

Even though edge computing is confronted withmarket constraints, there are specific servicerequirements that drive its adoption. Theserequirements include, reduced service latency,security and privacy, bandwidth management, localservice control, service continuity, advancedanalytics and automation, device energy efficiency,and operations in constrained environments.

Managing latency with edgecomputingToday most digital services can cope with theconnection latencies associated with centralizedcloud services. These latencies tend to be in theorder of 100’s of milliseconds. This is aconsequence of delays in transmission medium (e.g.fiber-optic cable) and in the gateway, routing andserver functions. Connection latencies can beimproved with optimal routing and server platformarchitectures. However, latencies are fundamentallylimited by the speed of light over the physicaltransmission medium between the client and serverdevices. For example, light travels at approximately203 meters/microsecond in typical fiber links (i.e.the fiber has a refractive index of approximately1.47). This defines the theoretical latency limits for agiven geographical separation between a clientdevice and its application server.



The tolerable latencies for several services aresummarized in Exhibit 1. In particular, Exhibit 1identifies those services that don’t require edgecomputing, benefit from edge computing, or forwhich edge computing is mandatory. Most digitalservices deployed today can cope with theconnection latencies associated with centralized

cloud services. Many services can be improved withthe benefit of edge computing, and edgecomputing enables new services, such as thoseassociated with autonomous transportation,haptics, robotics, augmented and virtual reality(AR/VR) and real-time manufacturing.

Exhibit 1: Edge computing enables new service categories that depend on low latency connectivity

Tolaga conducted a series of latency measurementsfor Amazon’s AWS-EC2. Amazon has 55 datacenters (so called Availability Zones) clustered in 18regions, see Exhibit 2. In addition, Amazon has beenexpanding its edge compute infrastructure across allthe regions where it operates. To investigatelatencies over large geographical distances,measurements were conducted with a client devicelocated in Auckland, New Zealand. A modified“TraceRoute” algorithm was used and connectionlatencies were measured relative to their theoreticalminima.

For the measurements conducted, the theoreticalminimum round-trip latencies ranged between 22and 178ms and the measured round-trip latenciesranged between 92 and 360ms. Based on theseresults, the incremental latencies relative to thetheoretical minima ranged between 14 and 208ms.These results demonstrated that not only protractedlatencies are problematic for latency sensitiveservices, but also the variations in latency that canoccur between sessions.

Sample Use Case LatencyTolerance

Implementation

TraditionalArchitectureSuffficient

Data file download or backup >10s

Batch video surveillance,home automation, web searchsensor readings

1-10s

Interactive website,Smart-building operationsRemote analytics

<100ms

Challenging withcentralized architectureand strong justificationfor edge computing

Virtual reality, autonomoustransport, interactive gaminghaptics, robotics, real-timemanufacturing

1-10ms Edge computingmandatory

http://aws.amazon.com/



Exhibit 2: Amazon EC2 round-trip connection latency measurement results based on a modified“TraceRoute” algorithm

Edge computing bringsbandwidth reliefAs connected devices proliferate data trafficvolumes are growing at an unprecedented rate. Thenumber of connected devices globally is forecast toincrease from 18 to 30 billion between 2018 and2023, see Exhibit 3. Tolaga estimates that onaverage each device will generate between 40 and105 kB/s of useful data, which corresponds to 2.5-

3.6 pB/s (1015 Bytes/second) of useful data cross the

30 billion connected devices forecast in 2023. Lessthan ten percent of the useful data from connecteddevices is transported to the cloud today, largelybecause of network transport costs andcomplexities. Edge computing can be used toprocess device data locally and alleviate transportcosts. Furthermore, since access networks constitutebetween 60 and 80 percent of network transportcosts, we believe that when edge computing is usedfor bandwidth management it will predominantlyhave on-device and on-premise implementations.

3

3 3 32

6

333

3

2

23 21

4

3

3Planned Deployment

AWS Regional Deployment

Auckland - N. CaliforniaTheoretical Min 106msMin Measured 120msMax Measured 198ms

Auckland - OhioTheoretical Min 132msMin Measured 154msMax Measured 180ms

Auckland - N. VirginiaTheoretical Min 136msMin Measured 184msMax Measured 190ms

Auckland - SydneyTheoretical Min 22msMin Measured 92msMax Measured 98ms

Auckland - MumbaiTheoretical Min 120msMin Measured 298msMax Measured 360ms

Auckland - SeoulTheoretical Min 94msMin Measured 160msMax Measured 206ms

Auckland - FrankfurtTheoretical Min 178msMin Measured 278msMax Measured 288ms



Edge computing for security,privacy and local service control

As society becomes increasingly dependent ondigital services and connected devices, the scopeand magnitude of security vulnerabilities are on theincrease. High profile attacks are nowcommonplace, and executed by bad-actors who areaided by the prevalence of malicious software andmotivated by the greater impact of their attacks(see our recent publication, Taking CommunicationNetwork Security to New Heights).

Even centralized servers with the most sophisticatedprotection are vulnerable to malicious attacks andrequire security operations to rapidly identify andrespond to attacks when they occur. For example,when the Mirai malware software was launched in2017, it has wreaked havoc on the Internet withdistributed denial of service (DDoS) attacks. These

attacks and many others capitalize on centralizedweb server architectures to concentrate their impactand are much less effective in targeting distributededge compute environments.

As more “things” become connected, privacy andanonymity becomes harder to protect. Server-lessedge computing based artificial intelligence hasbeen implemented by companies including Amazon(e.g. Alexa) and Google (e.g. Nest) and startups likeIC Realtime and Light House AI. These solutionsenable private and sensitive device data to beprocessed and analyzed locally in the end-pointdevices. Similar solutions are incorporated in smart-CCTV-camera technology and provided by start-upcompanies like Horizon Robotics and Intellivision.With these solutions, key CCTV images (such asthose associated with anomalous or relevantactivity) are processed locally and only meta-datadescribing the images is sent to cloud servers.

Exhibit 3: Connected device forecast and useful data volumes

2017 2018 2019 2020 2021 2022 2023

16.218.1

20.623.5

26.3

30.1

34.2

5

10

15

20

25

30

35

40

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0.790.94

1.22

1.58

1.97

2.48

3.06

peta Bytes/secbillion devices

Aggregate useful device data

Total connected devices

http://www.tolaga.com/Documents/Publications/2018/Security/tolagaResearchNokiaSRAEvaluationFeb2018.pdf

http://www.tolaga.com/Documents/Publications/2018/Security/tolagaResearchNokiaSRAEvaluationFeb2018.pdf

http://www.tolaga.com/home/tolagaDocument?doc=ConsumerIoTDDoS

https://developer.amazon.com/alexa?cid=a

https://play.google.com/store/apps/details?id=com.google.android.apps.chromecast.app&hl=en

https://icrealtime.com/

https://www.light.house/

http://en.horizon.ai/

https://www.intelli-vision.com/about-us/



Service continuity in constrainedenvironments with energyefficient devicesAlthough network coverage is always improving,there is and will continue to be network coverageholes, areas where network bandwidth isinadequate and expensive, and where fringecoverage result in severely compromised devicebattery life. Extreme cases include operations inremote mining and oil and gas plants. Other lessextreme cases include asset tracking and remoteinfrastructure monitoring, in areas where networkavailability is intermittent.

Edge compute technology with local area networkcapabilities can be used to enable service continuityin areas where wide area coverage is lacking.Depending on technical and commercial priorities,the edge compute solutions might use:

● Proprietary platforms that are developed in-house and managed by internal IT or OTorganizations.

● Proprietary platforms provided by industrialcompanies like ABB, GE (GE Predix), Honeywell(Mobility Edge) and Siemens (Industrial Edge),with vertically integrated solutions that aretargeted towards particular markets.

● Proprietary platforms that are hosted bycompanies like Amazon Green-Grass andMicrosoft Azure IoT, and;

● Standards based platforms provided byenterprise IT and telecom service providers

Analytics and automationAnalytics and automation is being implementedthroughout entire connectivity ecosystems.However, as connected devices evolve with diverse

scope and functionality, the associated ecosystemsgrow in complexity. Edge computing is used tolocalize the functionality and reduce theimplementation complexities for analytics andautomation. Rather than requiring the coordinationof many companies across complex ecosystems, theentire functionality can be supported by the edgecompute platform of a single company. Forexample, analytics and automation is at the heart ofthe edge computing solutions provided byindustrial infrastructure providers like ABB, GeneralElectric, Honeywell, Rockwell and Siemens.Although the Amazon Greengrass and MicrosoftAzure IoT platforms have started out with a focustowards enabling service continuity for IoTapplications, they are rapidly evolving toincorporate functionality for analytics andoperational automation.

Vertical markets have uniqueedge computing demandsThe demand drivers for edge computing and theassociated functionality that is needed, variesamongst industry verticals and the specificsolutions being addressed. Vertical market studieswill be investigated in upcoming Tolaga reports,and notable verticals and services are summarizedin Exhibit 4. The services that are identified for eachof the verticals are those that would benefit (and insome cases require) edge computing capabilities. Inaddition, to existing services, new services andfeatures are likely to emerge once edge computingsolutions become available. These include solutionsfor augmented and virtual reality and autonomoussystems, local data collection and analytics,machine learning and artificial intelligence, andpersistent connectivity in remote areas such as oiland gas fields.



https://www.elektroniknet.de/international/detailed-concept-for-edge-computing-153449.html





Exhibit 4: Vertical market services and solutions that depend on edge computing

Edge computing initiatives areproliferatingMany initiatives and framework architectures arebeing developed for edge computing. Thesecommonly have biases that reflect the interests oftheir sponsors and reflect the diverseimplementation scenarios for edge computingsolutions. The edge compute initiatives andframework architectures are supported byseemingly mutually beneficial partnerships toaddress ecosystem complexities. We expect thatthese partnerships will change as the marketmatures and players seek competitive advantage.Notable edge computing initiatives can be dividedinto several categories, including those beingpursued by the communications industry,proprietary solutions and other initiatives andframeworks which have broader marketapplicability.

Communications industry leadsnetwork-centricityThe communications industry is theoretically wellpositioned to capitalize on edge computing sinceit’s networks are inherently distributed. Forcommunication network operators, edgecomputing provides:

● Opportunities to drive network-centric valuecreation by aligning solutions with networkecosystems.

● Opportunities to relieve the network demands byoffloading traffic at the edge, and;

● Support for next generation network demands,such as virtual network functions (VNF) and cloudRAN solutions.

ResidentialConsumer

Retail

Smart City

TelecomInfrastructure

Automotive

Energy

ManufacturingNatural ResourcesHealthcare(Hospitals/Clinics)

Security | Smart Appliances | Infotainment | Assisted Living |Energy Management

Digital Signage | Instore Experience | Proximity Marketing |Real Time Analytics | Supply Chain Optimization

Video Surveillance | Smart Lighting | Traffic Signaling | SmartBuildings | Public Safety | Public Venue Services | Utilities

NFV/SDN | Cloud RAN | MEC | CORD | Managed Security

Autonomous | Assisted Driving | Operations and Maintenance| Security

Micro Generation | Renewables | Transmission | Distribution| Operations and Maintenance

Asset Tracking | Remote Operations | Automation | Diagnosticsand Maintenance | Security EnforcementContinuous Patient Monitoring | Remote Patient Care |CognitiveAssistance | Physical Therapy

Other Agriculture | Government |Banking and Securities | Health andFitness (Wearables) | Hospitality | Logistics etc.



There are several well-known edge computinginitiatives being spearheaded by thecommunications industry, which include Multi-Access Edge Computing (MEC), CORD (CentralOffices Re-Architected for Data-Centers), andControl User Plane Separation (CUPS).

Multi-Access Edge Computing (MEC)Multi-Access Edge Computing (MEC) is an ETSI-3GPP standard that was originally developed with amobile industry focus. It was spearheaded by Nokiaand has been adopted by many other playersacross the communication industry value chain.

MEC currently has 19 active work items, with thesupport of 21 companies. Exhibit 5 summarizes thenumber of work items that each of these companiesare participating in.

In September 2017, MEC announced a collaborativepartnership with the OpenFog Consortium. Whilethis collaboration makes sense, we believe that MECmust attract greater participation from verticalindustry players, such as ABB, Bosch, GeneralElectric, Honeywell, Philips, Rockwell, Siemens andWhirlpool.

Exhibit 5: Contributors to active MEC Work Items in June 2018

Nok

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Voda

fone

Hua

wei

Inte

l

Viav

i Sol

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ns ZTE

HPE

NEC

Inte

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Sagu

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orks

Sony

Virtu

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(Vee

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NTT

DoC

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ITR

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Ital

ia

Tele

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ca

ADVA

Opt

ical

Ixia

Juni

per

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com

4

0

8

12

16Number

ofWorkItems

Today MEC functionality is primarily anchored with3GPP based mobile access technologies. However,MEC has several active Work Items that incorporateother access technologies beyond mobile. EarlyMEC standardization efforts have focused onexposing radio network performance managementcapabilities and device location information, forapplications to use. MEC supports RESTful centricAPIs to orchestrate and manage applications. Thestated use cases for MEC include the following:

● Consumer orientated: gaming,augmented/assisted reality, remote desktop,cognitive assistance etc.

● Network operator and third party services: bigdata preprocessing, active device locationtracking, cognitive assistance, connected vehiclesand;

● Network performance related services: qualityof experience, content/DNS caching, and networkperformance and video optimization.

http://www.etsi.org/technologies-clusters/technologies/multi-access-edge-computing



Exhibit 6 illustrates a generic network architecturethat incorporates MEC. Devices connect via multi-access environments to MEC platforms that containthe edge compute functions. The MEC platformscan reside anywhere within edge networks and cansupport the needs of multiple applications. Inputparameters from networks (such as bandwidthutilization) and devices (such as their location) areused to support the capabilities provided by theMEC.

Currently MEC solutions are focused on applicationenablement (i.e. discovery, security and

communication) with standardized APIs (based onOpenAPI with standardized network and contextexposure) and mobile access-centric managementand orchestration frameworks. As the scope forMEC has broadened towards multi-accessfunctionality, so has the associated work items, toinclude Wi-Fi and fixed access technologies,support for a broader range of virtualizedenvironments (including NFV), dedicated Vehicle-to-X capabilities, direct interaction with chargingplatforms and regulated systems such as lawenforcement.

Exhibit 6: Generic Multi-Access Edge Computing (MEC) Architecture

Connected Devices MEC Mobile Core InternetMulti-Access

MobileEdge App

MobileEdge App

NFVi VirtualizationInfrastructure

MobileEdge

Platform

Mobile Edge Host

Mobile Edge System Level Management

MobileEdge Host

Management

Control and User Plane Separation ofEPC Nodes (CUPS)Control and user-plane separation is commonplacein traditional communication network architectures(e.g. SS7) and is essential for many proposed edgecomputing services and applications. The evolvedpacket core (EPC) in 4G networks introduced somecontrol/user plane separation. This came with theintroduction of the Mobility Management Entity(MME) to manage control plane functions relatingto mobility, and S-GW and G-GW platforms to

manage user plane and the remaining control planefunctions. CUPS was introduced in 3GPP-Release 14and further separates the control and user planeswithin the S-GWs and P-GWs. It also introducescomparable control/user plane separation in thetraffic detection functions (TDF), which wereintroduced in 3GPP-Release 11.

When CUPS is used with MEC it allows independentuser-plane and control plane designs. For example,a user-plane might have highly distributedtermination points, with a centralized control plane.

https://www.openapis.org/

http://www.3gpp.org/release-14



Notable use cases for CUPS include the following:

● High bandwidth video optimization, withcentralized service and highly distributed contentmanagement.

● IoT services that have significantly moresignaling relative to payload traffic.

● Low latency services, with centralized control ofedged terminated data traffic, and;

● Business and mission critical services, withcentralized control to manage critical edgenetwork traffic.

CUPS also enables independent user and controlplane upgrades, and valuable disaggregation of

Software Defined Networking (SDN) functionality.With this functionality, MEC capabilities can bedelivered more effectively and in a complementarymanner relative to other 3GPP features such asFlexible Mobile Steering Services (FMSS). FMSS wasalso introduced in 3GPP-Release 14.

CUPS deployments involve much more thaninfrastructure technology upgrades. In particular,when network operators deploy CUPS in their nextgeneration network environments, they mustcontend with legacy environments, the need forre-architected network boundaries, and changes torelated operational activities.

Exhibit 7: CUPS implementation in a mobile network environment

Connected Devices MEC Mobile Core InternetMulti-Access

MobileEdge App

MobileEdge App

NFVi VirtualizationInfrastructure

MobileEdge

Platform

Mobile Edge Host

Mobile Edge System Level Management

MobileEdge Host

Management

SGW-C PGW-C

SGW-U PGW-U

CUPS enables SDN/NFVarchitectures for MEC



Central Office Re-architected as aDatacenter (Open CORD)Tolaga estimates that telecom operators have over430 thousand central offices deployed globally.Since these central offices are geographicallydistributed across network footprints, they havebeen targeted for edge compute deploymentsusing Open CORD. CORD was initially spearheadedby AT&T in the United States and is seeingwidespread support from the telecom operatorcommunity.

The value proposition for CORD is relativelystraightforward. For example, AT&T has 4700 COs

which contain over 300 unique hardwareapplications. With CORD, AT&T aims to:

● Drive cloud-based data center design principlesto COs.

● Virtualize and cloudify existing services, and;

● Create design blueprints for residential (RCORD),enterprise (ECORD) and mobile-centric (MCORD)solutions.

Exhibit 8 shows a functional diagram for mobile-CORD (MCORD), with support for MEC and CUPs,and a variety of service functions including resourcescheduling, network slicing, analytics and servicechaining.

Exhibit 8: Open-CORD functional diagram with MCORD to support MEC, CUPS and enable resourcescheduling, network slicing, analytics and service chaining

Operator ApplicationsResource

SchedulingNetworkSlicing Analytics Service

Chaining

MCORD Platform

Commodity Switching (Leaf and Spine Fabric)

Commodity Servers in Central Offices and Data Centers(compute, storage, networking)

vBBUvBBUvBBU

vCacheDNSMEC

vSGWvPGW

MMEPCRF

vPGW

Mobile Edge Mobile Core

Radio EdgeServices

DistributedEPC

EPC ControlFunctions

CentralizedEPC

https://opencord.org/



Legacy and vested interestsdefine edge compute designs - atleast for nowThe “edge” can reside anywhere outside of a datacenter, extending to end-point devices. As thecommunication industry drives initiatives thatgenerally prioritize the placement of edgecomputing within communication networkenvironments, other players are taking differentapproaches. Webscale providers like Amazon andMicrosoft have “server-less” initiatives that prioritizeedge compute placement in enterprise on-premiselocations. In contrast, device and embedded systemmanufacturers place edge compute functionalitydirectly in end-point devices. For many of theseplayers including Honeywell and Rockwell, edgecomputing provides connectivity and a means forcloud enabling operational technology (OT)devices, such as industrial PCs and programmablelogic controllers (PLC).

The diversity of edge compute initiatives istremendous. Within the broader edge computinglandscape, there are several notable initiatives,which include Open Edge Computing, Open Fogand the Edge-X Foundry, and the Edge ComputingConsortium, which is spearheaded in China. Inaddition a variety of proprietary (“server-less”)architectures are being pursued by webscalecompanies like Amazon and Microsoft, andindustrial companies like General Electric,Honeywell and Siemens.

Open Edge Computing InitiativeThe Open Edge Computing Initiative (OEC) is avendor neutral standardization effort spearheadedby Carnegie Mellon University to extend cloudcapabilities to edge infrastructure. The initiative wasestablished to tackle management difficulties forvirtual machines in distributed cloud (edgecompute) environments. This culminated in novelextensions to OpenStack (i.e. OpenStack++) withso-called “cloudlets” that enable agile VM imagemanagement in edge computing platforms. OECuses existing cloud based architectural designprinciples for compute storage and networking, andOpenStack with extensions for rapid VMprovisioning, handoff across cloudlets and efficientcloudlet discovery.

The underlying principle of the cloudlet architectureis to capitalize on functional commonality acrossVMs. When new VMs are provisioned, the base VMfunctionality is already pre-provisioned, and acompressed “overlay-VM” can be rapidlyprovisioned using the base and launch VMfunctionality, see Exhibit 10. The solution alsoleverages standard SDN and NFV basedenvironments.

In addition to providing an open sourcedevelopment environment for it’s OpenStack++cloudlet solution, the OEC launched its ‘Living EdgeLab’ in 2017, for OEC members and otherecosystem players to cooperate with each otherand the academic community to develop and trialinnovative edge computing solutions.


http://openedgecomputing.org/lel.pdf

http://openedgecomputing.org/lel.pdf



Exhibit 9: Open Edge Compute Initiatives introduces OpenStack++ to accelerate cloud functionality tosuit edge computing environments

OpenStack++

* By creating Overlay VMs and prepopulating Cloudlets with BaseVMs, Cloudlets can be rapidly populated with new VMs

Launch VM ImageBinary DeltaDiff Disk | Diff Memory

Base VMDisk | Memory

Launch VM ImageBinary DeltaDiff Disk | Diff Memory

Base VMDisk | Memory

Create an Overlay IMPopulate CloudletVM using Overlay*

Overlay VMOverlay Disk |

Overlay MemoryCompress Decompress

OpenFog Delivers a Hierarchal EdgeArchitecture for IoTThe OpenFog initiative was originally spearheadedby Cisco and is now under the stewardship of theOpenFog Consortium. Fog emphasizes themigration of cloud functionality to the edge. It hasa primary focus towards IoT solutions and acollaborative partnership with ETSI-MEC, which wasestablished in 2017. The Fog frameworkarchitecture is generally relevant to most edgecomputing initiatives because it emphasizes theimportance of hierarchal edge designs, see Exhibit10. As the OpenFog develops, its Working Groupsare focused on seven parallel capabilities to enableits hierarchal architecture, which include, security,scalability, open platforms, automation, remoteaccess systems, and agility and programmability.

Fog hierarchies vary depending on the servicesbeing supported. For example, the edge computinghierarchy required for smart-manufacturing isdifferent to cloud RAN implementations. Moregenerally, edge compute hierarchies depend on a

variety of technical and commercial factors,regulatory and security considerations, and theimpact of legacy operating environments. Theseinclude the following:

● Technical design factors such as service latency,bandwidth demands, the need for local servicecontrol, service continuity, device energyefficiencies, and operations in constrainedenvironments.

● Commercial factors, which are generally dictatedby dominant ecosystem players and payingcustomers and include factors such as thearchitecture of incumbent systems, availabilityand suitability of real estate to host the edgecomputing infrastructure, and strategies tominimize the cost and complexity of edgecompute installations.

● Regulatory and security factors, such as thoserelating to information privacy and systemsecurity.




● Legacy implementation and operatingenvironments. Commonly edge computing isintroduced to existing systems and operatingenvironments and must be implementedaccordingly. This drives varied edge computingdefinitions. For example, a telecom operator willtypically define edge computing in the context ofwhere it lies within its network, such as at anaccess node, or at transmission pre-aggregationand aggregation layers. For the embeddedsystem community, edge computing brings cloud

connectivity and intelligence to end-pointdevices. For IT organizations, edge computingbrings functionality back from the cloud andextends the scope of enterprise networks.

The hierarchal framework architecture adopted byFog addresses the diversity of solutions beingtargeted for edge computing, it also supportsecosystem fragmentation, which is characteristic ofdistributed systems. This is in contrast to theconsolidation that has occurred in centralized cloudenvironments.

Exhibit 10: OpenFog Consortium defines a hierarchical edge computing architecture

C S N

C S N

C S N

C S N

C S NC S N

C S N

Internet CloudServers (Global)

Core Network(Regional)

Access Edge(Neighborhood)

GatewayCPE (Local)

End-Point Things

C S N

Fog Node

Compute | Storage | Network

The Edge-X FoundryThe Edge-X Foundry is a Linux Foundation initiativelaunched in April 2017 that aims to create astandardized framework for edge computing inIndustrial-IoT environments. It adopts cloud-nativeprinciples and supports both IP and non-IP basedprotocols. It also uses service managementstandards that anticipate the diversity of edgedevices in industry IoT environments. The referencearchitecture for Edge-X is illustrated in Exhibit 11,and has the following key attributes:

● Device services with support for a range ofprotocols typically used in industrial IoT

environments, and SDKs for proprietary protocolsupport.

● Core services which are required for the Edge-Xarchitecture and include core data, command,metadata and registry and configurationfunctionality.

● Replaceable reference services includingsecurity services, device and systemmanagement, and support and export services,and;

● Protocol compatibility with northboundinfrastructure and applications.




Exhibit 11: Edge-X Foundry reference architectureAD

DITIO

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SERVIC

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ANAG

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MODBUSADD'LDEVICESERVICES

REST OPC-UA BACNET ULE BLE MQTT ENOCEAN VIRTUAL

DEVICE SERVICES (ANY COMBINATION OF STANDARD OR PROPRIETARY PROTOCOLS VIA SDKS)

CORE DATA COMMAND METADATA REGISTRYAND CONFIG

SDKs

CORE SERVICES

SEC

UR

ITY

SER

VIC

ES

SUPPORTING SERVICES

RULES ENGINESCHEDULING ALERTS ANDNOTIFICATIONS LOGGING ADDITIONAL

SERVICES

EXPORTING SERVICES

CLIENT REGISTRATION DISTRIBUTION ADDITIONAL SERVCES

CHOICE OF PROTOCOL

LOOSELY COUPLED MICROSERVICES FRAMEWORKNORTHBOUND INFRASTRUCTURE AND APPLICATIONS

REQUIRED INTEROPERABILITY FOUNDATIONREPLACEABLE REFERENCE SERVICES

Edge Computing Consortium(China)The Edge Computing Consortium (ECC) wasestablished in 2016 and is an initiative spearheadedby Huawei in China, with the support of Intel andARM, and other industry stakeholders andacademics. ECC is focused towards coordinatedInformation Technology (IT) and OperationalTechnology (OT) based solutions and provides aframework for achieving this coordination withvertical industry solutions, see Exhibit 12. Like otheredge compute initiatives, the ECC aims to drivecross industry cooperation for ecosystemdevelopment and to reduce operational friction. Itprovides a structured framework that isunderpinned by vertical industry use-cases and

aligned with standardized operations, governanceand service lifestyle management. Currently the ECCis particularly focused towards key industry verticalsincluding energy, transportation, manufacturingand smart cities.

Although ECC currently lacks the recognition ofother edge computing initiatives, it benefits fromthe monumental scale of China and is oftremendous strategic importance to Chinesecompanies like Huawei, and other domesticequipment manufacturers, and end edgecomputing solution providers. In the same way thatChina has incubated its own web-scale providerslike Alibaba with cloud computing services, weexpect that edge computing in China will followsuit.

https://www.alibaba.com/



Exhibit 12: Edge Computing Consortium Reference Architecture

ProductionAdministration Learning ResearchGuided by industrypolicies, creating an

industrial environment

Upstream and downstreamcooperation along the

industry chain; commercialuse of industries

Cutting-edgetechnologies interworkwith academic thought

Research on theindustry's engineeringapplication promotes

standardization

1Requirement

Scenario

2Reference

Architecture

3Test Bed

4Demonstrationand Promotion

5Industry

Cooperation

OperationalMain Line

OICT

Application

TransportationEnergy Manufacturing Smart City

Proprietary Solutions AccelerateEdge ComputingThe edge computing market is fragmented andsupported by numerous proprietary solutions thataddress specific vertical market implementations.This is particularly the case for the smart-devicesand appliances associated with industrial andconsumer services.

Since edge computing ecosystems are fragmented,they normally require vertical integration, orsystems integration, or both. Vertical integration iscommonplace for consumer and industrialsolutions. Smart residential devices, such as smart-appliances and entertainment solutions arevertically integrated with proprietary solutions bycompanies like Amazon, Google, Haier, Samsungand Whirlpool.

In industrial environments, vertically integratedsolutions are commonplace and have a wide rangeof edge computing platform designs. For example,Siemens has its Industrial Edge product line, whichis vertically integrated with its cloud services.Honeywell has its Mobility Edge solution as anextension of enterprise IT environments andGeneral Electric has its Predix platform whichprovides an end-to-end, edge-to-cloudarchitecture. While proprietary solutions like thesewill prevail for the foreseeable future, particularlyfor business and mission critical solutions, there isgrowing interest in standards-based solutions and“horizontal” platforms provided by companies likeAmazon and Microsoft. For example, in April 2018,ABB, HPE and Rittal launched a Secure Edge DataCenter solution that is targeted for industrial andtelecommunication environments and is pre-integrated with Microsoft’s Azure platform.

https://www.siemens.com/global/en/home/products/automation/topic-areas/industrial-edge.html


https://www.ge.com/digital/predix-platform-foundation-digital-industrial-applications

https://new.abb.com/news/detail/4365/abb-hpe-and-rittal-unveil-secure-edge-data-center-to-drive-digitization-of-industrial-plants





New pastures for Amazon withGreengrass and Microsoft withAzure IoTAmazon Green-Grass is server-less edge computingsolution designed as an extension of Amazon’sAWS-IoT platform, (see Exhibit 13). The solution canbe implemented on standard edge computeequipment with programmable AWS lambdacontainers and local analytics and machine learningcapabilities. Greengrass benefits from an intuitiveinterface that is characteristic of Amazon WebServices (AWS). Greengrass was initially architectedto support the MQTT protocol and enables localMQTT messaging amongst devices as required. It is

notable that currently the Greengrass core onlysupports MQTT QoS level 0 (i.e. always only sendmessage once), which we believe illustrates its initialtarget towards non-mission critical sensor networks.

By providing local IoT gateway functionality,Greengrass supports local connectivity with servicecontinuity even with intermittent IoT cloudconnectivity. The Greengrass platform has beenstandardized for a variety of systems includingRaspberry Pi, x86, and the AnnaPurLabs platform-on-a-chip solution (an Amazon subsidiarycompany). Greengrass use cases range from homegateways to small, medium and large enterprises,and are currently targeted towards enterpriseapplications.

Exhibit 13: Amazon Green Grass Functional Architecture

Devices Access Networks

MQTTGreenGrass CoreLocal Execution of

AWS Lambdas

AmazonEC2MQTT

The Microsoft Azure IoT solution competes directlywith Greengrass and enables persistent connectivityamongst heterogeneous devices and the Azure IoTback-end, see Exhibit 14. Azure IoT-Hubs (EdgeCompute Platform) in conjunction with fieldgateways support devices with a variety ofconnectivity types, includingIP/HTTPS,MQTT,AMQP. Microsoft has added itsAzure Stream Analytics to the suite of services that

can be offered in its edge devices. The IoT Hubsconnect to the Azure IoT backend to support:

● event-based management,

● device-to-cloud ingestion,

● reliable cloud-to-device messaging,

● per-device authentication, and

● secure connectivity.


http://www.annapurnalabs.com/




Devices Access Networks

IoT Hub IoT BackendEvent basedDevice-to-cloud ingestionReliable cloud-to-devicemessagingPer-device authenticationand secure connectivity

MicrosoftAzure

IP CapableDevices

Existing IoTDevices

Low PowerDevices

IoT FieldGateways

IoT ProtocolGateways

Exhibit 14: Microsoft Azure IoT

ConclusionEdge computing is capturing broad industryinterest, buoyed by a variety of factors, includingthriving markets for digitization and the widespreadadoption of cloud computing. Other than enablingdistributed computing, there is no specificarchitecture that can be associated with edgecompute. Edge computing infrastructure can resideanywhere outside of data centers all the way toend-point devices. While the demand drivers foredge computing are clear, they don’t necessarilydefine the edge computing architecture either.Instead the edge compute architectures are heavilyinfluenced by the players involved.

The communications industry generally favors thecollocation of edge compute equipment with theirnetwork infrastructure, whether in access networknodes, and pre-aggregation and aggregationlocations in transport networks. Having seen thevalue of their networks erode with the proliferationof cloud computing, the communications industry isseeking opportunities for edge computing to bringvalue back to their networks. Edge computinginitiatives being spearheaded by thecommunications industry include MEC and CUPS,

which are both network centric initiatives, andCORD, which is applying data center technologiesin telecom central office environments. Whileoperators are well positioned to develop networkcentric edge computing capabilities, we believe thata focus towards network-centricity will potentiallyundermine opportunities to address broaderconsumer and enterprise edge computingdemands. Particularly for use-cases that are bestsuited for on-premise edge compute platforms.

Web scale providers like Amazon, Facebook,Google and Microsoft have benefited from theproliferation of cloud computing by essentiallydriving value away from communication networksand to cloud applications and end-point devices.Amazon and Microsoft have large cloud computingrevenues which they are eager to protect as theedge computing market takes hold. This hasculminated in Amazon developing its Greengrassand Microsoft its IoT Azure edge computeplatforms. Both platforms target IoT services andare architecturally designed to anchor on-premiseor end-point device edge compute functionalitywith their respective cloud platforms. Bothcompanies are taking pragmatic approachestowards edge computing, but potentially run the



risk of being disintermediated by competitors thatare not wedded to cloud-centric approachestowards edge computing.

Industrial companies such as General Electric,Honeywell, and Siemens, and consumer electronicsproviders like Amazon, Google, Haier, Samsung andWhirlpool have proprietary and vertically integratededge computing solutions. These players benefitfrom their customer relationships, service offerings,and market reputations, but cannot necessarily relyon vertically integrated approaches prevailingindefinitely. Already players like ABB are partneringwith web-scale providers, to deliver end-to-endsolutions, which we anticipate will become morecommon, particularly for enterprise services.

Although many edge computing solutions areproprietary today, there is a broad industryrecognition that standards are particularlyimportant as the market scales. This has culminatedin a variety of industry initiatives beyond those

being spearheaded by the communicationsindustry. Notable examples include, the Open EdgeComputing Initiative, the Edge-X Foundry, OpenFogand the Edge Computing Consortium (China). Weexpect that the number of initiatives will increaseover the next 12-24 months, but ultimatelyconsolidate as the edge compute market maturesand its salient demands become better understood.

While there are strong market drivers for edgecomputing, it is not without its challenges.Incumbent services that use centralized cloudplatforms create strong incentives for status quo tobe maintained. The added costs and complexitiesassociated with distributed edge computingarchitectures must be justified. Rather thandisrupting conventional cloud services, edgecomputing will be complementary. However,industry players cannot afford to ignore edgecomputing and the role it will play in transformingfuture business opportunities.





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