Introduction to Edge Computing in IIoTAn Industrial Internet Consortium White Paper
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Edge Computing Task GroupIntroduction to Edge Computing in IIoT
Almost every use case and every connected device focused on by the Industrial Internet
Consortium (IIC) requires some sort of compute capability at its source, at the edge. Multiple
sources define edge computing as “cloud computing systems that perform data processing at theedge of the network, near the source of the data”. While this is certainly true, it only scratches
the surface of the immense power and remarkable capabilities that edge computing applications
and architectures can provide to solve industrial internet users’ toughest challenges. But, as is
typical with any powerful technology, innovative architectures and new terminology are needed
to facilitate implementation, bringing increased complexity with it.

Purpose and Audience
This white paper provides practical guidance on edge computing, architectures and the building
blocks necessary for an edge computing implementation. It defines edge computing architecturalfunctions and highlights key use case considerations
Consequently, there is a need to identify:
 where the edge is, its defining characteristics,
 key drivers for implementing edge computing and why compute capabilities should be deployed at the edge in Industrial Internet of Things(IIoT) systems.
It also informs architecture and testbed teams through: identifying and evaluating standards, practices and characteristics best suited for
addressing edge computing holistically, and highlighting gaps where needed, identifying deployment models and crosscutting functions that address patterns andcharacteristics for edge computing deployment and exploring and identifying extensions to the current edge computing model that expand
and enhance the functionality of edge computing devices.
Several detailed use cases are highlighted, edge computing for industrial analytics is exploredand security challenges for edge computing implementations are also considered. (Detailed
security information from the IIC can be found in the Industrial Internet Security Framework.1)

1“The Industrial Internet of Things; Volume G4: Security Framework,” Industrial Internet Consortium (IIC),
2016: www.iiconsortium.org/IISF

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Introduction to Edge Computing in IIoT

Viewpoints and Crosscutting Concerns
This whitepaper targets the technical community and focuses on the functional andimplementation viewpoints from the IIC’s Industrial Internet Reference Architecture2 (IIRA):
The functional viewpoint focuses on the functional components in an IIoT system, their structure
and interrelation, the interfaces and interactions between them and the relation and interactionsof the system with external elements in the environment to support the usages and activities ofthe overall system. These concerns are of interest to system and component architects,
developers and integrators (see IIRA Chapter 6).
The implementation viewpoint deals with the technologies needed to implement functional
components (functional viewpoint), their communication schemes and their lifecycle
procedures. These elements are coordinated by activities (usage viewpoint) and supportive of
the system capabilities (business viewpoint). These concerns are of interest to system andcomponent architects, developers and integrators and system operators (see IIRA Chapter 7).
It identifies the attributes of edge computing related to the Edge, Platform and Enterprise Tiers
described in the IIRA as well as the crosscutting functionality such as data management,
connectivity, orchestration, analytics and security.
This white paper has a technical focus to identify deployment models and edge-computing
implementation patterns across the several crosscutting functions. Figure 1 shows a multi-layeredge computing architecture with crosscutting functions useful in deploying edge-computing
architectures as defined by the IIRA.
We fully recognize that the software driving edge computing is an essential component of edge
computing implementations and architectures, but the sheer volume of operating system andplatform choices options are beyond the scope of this white paper.

2“The Industrial Internet of Things; Volume G1: Reference Architecture” Industrial Internet Consortium(IIC), 2018. www.iiconsortium.org/IIRA

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Introduction to Edge Computing in IIoT

Figure 1: IIC Crosscutting Functions Across Edge Computing Architectures

Where is the Edge?
The edge is a logical layer rather than a specific physical divide, so it is open to individual opinion
and interpretation of “where” the edge is. The business and usage viewpoints provide clues,
while the functional and implementation viewpoints deal with the technical aspects.
From the business perspective, the location of the edge depends on the business problem or “keyobjectives” to be addressed.

Quote“Key objectives are quantifiable high-level technical and ultimately businessoutcomes expected of the resultant system..." and “Fundamental capabilities referto high-level specifications of the essential ability of the system to complete specificmajor business tasks”. (Ref IIRA).

There is a continuum of fundamental capabilities for an IIoT solution and “the edge” moves along
this continuum based on the requirements of the problem at hand, as shown by the following
examples found in typical industrial operations.

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Figure 2: Edge continuum for a typical industrial environment

EXAMPLE 1: PROTECT EQUIPMENT FROM DAMAGE BY OVERHEATING
In this scenario, a “dumb” thermocouple measures temperature on a pump. A pump with edge
computing capability can perform basic analytics to determine if a defined threshold is exceeded
and shut the pump down in milliseconds. There is no decision latency and no need forconnectivity to perform this function. Connectivity is not necessary, but it may be used for
notification. The time value of the temperature information decays rapidly as delayed response
can result in equipment damage. In this case the edge is at the device level as it can achieve thekey objective, even if connectivity to higher-level systems and networks are interrupted.

EXAMPLE 2: MONITOR THE PERFORMANCE OF PLANT AREAS OR PRODUCTION LINES
The performance of equipment and production lines are often expressed through performanceindicators like Overall Equipment Effectiveness (OEE). Near real-time analytics on multiple data
points from sensors in the plant area can be processed on a local gateway and provide OEE trends
and alerts to operational systems or personnel. In this case, the fundamental capability requires
information from multiple equipment sources to perform simple analytics. The time value of
information is high as response delays waiting for decisions from the cloud can cause significant
losses. This business problem suggests that the edge is at the plant area level.

EXAMPLE 3: OPTIMIZE SUPPLY CHAIN FOR A LOCATION OR FACTORY TWICE DAILY
Optimizing supply chain processes for a local facility, factory or an oil field requires data frommultiple sources at short intervals to apply optimization algorithms and analytics that will adapt
supply-chain plans in business systems such as SCM or ERP. The fundamental capability requires
local or factory-level connectivity with decisions made in hours. Additional information outside
the perimeter of the factory may be useful, but not mandatory for effective optimization. In thisinstance, the edge is at the perimeter of the factory, plant or local facility.
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EXAMPLE 4: PREDICT EQUIPMENT FAILURE AND SCHEDULE PROACTIVE RESPONSE
Machine learning models to predict Electric Submersible Pump (ESP) failures require data frommultiple offshore platforms. The analytics models are complex and a large amount of data isneeded to train and re-train the models. It also requires regular data feeds from operating ESPs
to determine each unit’s remaining useful life. The data from individual ESPs need to be analyzed
regularly but information decay is much slower than in the other scenarios and decisions can betaken daily or weekly. Computation is typically performed at the enterprise level using a public
or private cloud and is at the top end of the edge continuum.
The edge can be anywhere along the time-value graph (see Figure 3) as these examples illustrate.
It is “where” data for sensors is used to achieve a specific key objective or address a specificbusiness problem.

Why Compute at the Edge
Edge computing is a decentralized computing infrastructure in which computing resources andapplication services can be distributed along the communication path from the data source tothe cloud. That is, computational needs can be satisfied “at the edge,” where the data iscollected, or where the user performs certain actions. The benefits are:

improved performance,compliance, data privacy and data security concerns are satisfied andreduced operational cost.

Figure 3: Time-Value curves for Edge information

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Introduction to Edge Computing in IIoT
We examine each in turn.

IMPROVE PERFORMANCEThe edge is not merely a way to collect data for transmission to the cloud, it also processes,
analyzes and acts on the collected data at the edge within milliseconds and is therefore essential
for optimizing industrial data at every aspect of an operation.
In a windfarm, for example, if wind speed or direction changes, the edge software onsite cananalyze this data in real-time and adjust individual turbines to optimize overall wind farm
production. Only aggregated data is sent to the cloud, reducing communication bandwidth
requirements and improving data transfer time.
In addition, the turbines generate terabytes of data. Sending this data to a cloud platform to run
advanced analytics may be technologically achievable, but cost prohibitive to do daily. Throughedge computing, the end user can capture streaming data from a turbine and use it in real-time
to prevent unplanned downtime and extend the life of the equipment while reducing the data
set to a more manageable size for transmission to the cloud.
The challenge of transmitting large quantities of data in real-time cost-effectively from remoteindustrial sites can be mitigated by adding intelligence to devices at the edge of the network, inthe plant or field. Edge computing on the device brings analytics capabilities closer to themachine and provides a less expensive option for optimizing asset performance.

Figure 4: Benefits of Edge Computing

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Introduction to Edge Computing in IIoTCOMPLIANCE, DATA PRIVACY AND DATA SECURITY
Public cloud creates a long list of privacy, regulatory and compliance issues related to classified
or sensitive data. Today, service providers can guarantee private access and control but at theprice of being cumbersome, costly, inelastic and difficult to manage.
Edge computing allows enterprises to operate independently using a public/private cloud by
using local computing based in that area, region, domain or the required local security
boundaries.

REDUCE OPERATIONAL COST
Connectivity, data migration, bandwidth and latency features of cloud computing are expensive.
Edge computing addresses these by reducing bandwidth requirements and latency.
If an oil and gas company drilling in Nigeria, for example, requires computing to predict oil-well
production-decline rate, the alternatives are to build their own data centers (with the associatedcost and scale limitations) or to use a cloud provider (where the nearest datacenter can be 5,000
miles away) with significant costs and unreliable service. With edge computing, the end user canprocess data in real time locally at a fraction of the cost of the public cloud, while still maintaining
the flexibility that a cloud infrastructure provides.
Edge computing creates a valuable continuum from the device to the cloud to handle the massive
amounts of data generated from IIoT. Processing data closer to where it is produced and at the
response times required by the local applications addresses the challenges of rapidly increasingdata volume. Edge computing decreases response time to events by eliminating a round trip tothe cloud for analysis. It avoids costly bandwidth additions by eliminating the need to transmit
gigabytes of data to the cloud. It also protects sensitive IIoT data by analyzing it locally within aprivate network.
Consequently, enterprises using edge computing may improve and optimize operational
performance, and address compliance and security concerns while efficiently managing costs.

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Introduction to Edge Computing in IIoTDIAGRAM OF EDGE COMPUTING EXAMPLES

Figure 5: Edge Computing Topology

Figure 5 illustrates multiple examples of edge computing implementations based on differingbusiness points of view. The examples progress from left to right as the edge layer becomes more
complex and aggregates multiple system functions below. The computing layer moves up thearchitecture stack, aggregating processing capabilities, information and data from below.
The multitude of choices means there is a layered edge-cloud synergy rather edge versus cloud.
Where possible, digitalization is always going to use edge and cloud synergistically in which fastand localized compute take place at the edge while global compute, model development,
management and security can benefit from the “wisdom of the cloud”.
Starting with an example of a simple temperature controller (see “A” on the left in Figure 5), theproblem to be solved is temperature monitoring and control of a specific device or zone. In thiscase, the edge devices would be the thermocouple sending temperature data and the element
providing the heating or cooling, and the edge computing device would be the temperature
controller running the control algorithm and making the adjustments.
If the objective is to orchestrate temperature across several devices or areas, then the edge
becomes the temperature controllers themselves (whether individual components or standalone
systems) and the edge-computing layer becomes the system coordinating the control, typically a
PLC or SCADA system (“B” in Figure 5).
If the business objective is to monitor and manage multiple geographically dispersed facilities,
then the edge is each individual facility reporting its status to a compute layer in the cloud (“C”
in Figure 5).
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Characteristics of the IIoT Edge Computing Model
Edge computing exists vertically within the full stack from device to cloud and horizontally across
IIoT subsystems. The new computing model is fully distributed and can support a wide range ofinteractions and communication paradigms including: peer-to-peer networking, for example security cameras communicating about objects
within their scope, edge-device collaboration such as self-organizing vehicles that travel together or acommunity of wind turbines in remote locations, distributed queries across data stored in devices, in the cloud and anywhere in between, distributed data management, defining where and what data is to be stored, and for how
long and data governance including quality, discovery, usability, privacy and security aspects ofdata.

Key Drivers: Cloud to Edge Computing
IIoT disrupts the cloud-computing model with new usage scenarios leading to theserequirements:
Time sensitive: Often decisions need to be made within milliseconds while a round trip to thecloud introduces undesirable latency. Reliability and critical-path control management make it
too risky to rely solely on remote logic. A good example is autonomous guided vehicles; althoughan anti-collision algorithm can execute in the cloud, it is best to run the algorithms at the edge.
Communication: Mobile network infrastructure tends to follow the pattern of deploying to highly
populated urban areas, before trickling down to rural or remote locations. For assets that are
truly remote, satellite connectivity may be the only option. This creates a paradigm where IIoTuse cases for industries such as mining, oil & gas, chemicals and shipping are not well served byrobust affordable communication.
Data boundary: In some applications, the data produced and consumed by devices is required byother devices only within the local area. This local data can be acquired and served with low
latency by the edge to the users in the local area. Depending upon the use case, the radius of thelocal area can vary from a few centimeters from the device to an entire neighborhood or city. In
augmented-reality scenarios, for example in smart cities, local edge infrastructures can store
information about points of interest of a neighborhood. Since most of the access to the data (orconsumption of the data) will be made in the same local area, there is no need to store all
information in the cloud. As a truck transitions from private to public network and acrosssovereign boundaries both enterprise policies and local data regulation will determine what canbe stored locally and what can be sent to the cloud.

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Introduction to Edge Computing in IIoT
Data volume: The amount of data generated by sensors can be huge. For example, hundreds ofhigh-resolution cameras creating video streams at 30 frames per second could clog
communication channels. Edge computing allows data to be processed and stored locally with
only preprocessed data being transferred to the cloud.
IT/OT convergence: Historically the operational technologies (OT) that are used to manage andautomate industrial equipment exist at the edge of the network while information technologies(IT) have been more centralized. Though these systems have been treated separately, there isvalue in having an integrated IT/OT strategy that offers:business data needed for interpreting or contextualizing IoT data for decision making,availability of both existing and new business outcomes, business models that leverage
integrated data andstandard processes to drive outcomes.

Data governance deals with quality, discovery, usability, privacy and security aspects of data.
Insufficient data governance can leave a company vulnerable to major business disruptions. On
the other hand, extreme data governance can stifle innovation. Edge computing helps simplify
data governance by: reducing data clutter: high volume time-series data can be analyzed at the edge,
 refining data usability: edge computing allows data to be contextualized resulting in
better usability, improving data privacy: security policy at the edge allows only relevant data sharedwith the systems up in the hierarchy and lowering the impact of security breach: since edge computing allows for the data
storage and analysis to be federated, impact of a security breach can be contained.

Use Cases
This section describes use cases that illustrate the benefits of edge computing. Figure 6 shows
logical entities residing on either the cloud or the edge, connected through WANs. When clouds
were first introduced, the trend was to “shift everything into the cloud”, but, due to network
latency and the cost to transmit a large amount of data, more logical tasks remained at the edge.
With the improvement of the processing power and capability, the amount of tasks performedon the edge will continue to grow.

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Introduction to Edge Computing in IIoT

Figure 6 Logical Architecture Diagram for Edge Computing

To facilitate discussions on the boundaries and the necessary means to enable edge computing,there are “Key Requirements”, “Edge Boundary” and “Edge Devices” clauses added to each usecase. “Key Requirements” are not intended to be a standard so they are not normative. The “Edge
Boundary” is one view from the contributor to the uses case and is not intended to be definitive
as typically, the boundary of each service or application varies as drawn by the system designers.
Similarly, the term “Boundary Devices” portrayed in the use cases by the contributors is not an
exhaustive list.
Through the examination of all the use cases described here, we inferred general requirements
for edge computing that may be common to all use cases:
Communications: Edge devices must continue to function even though data communications may
be temporarily interrupted.
Edge device capability: The edge devices need to support edge-computing capabilities:
communication, local computing and local storage.

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Edge device functionality: The edge devices can be customized with features to fit various verticalindustries, such as compact size, low power consumption, anti-vibration, electromagnetic
shielding, waterproof and dust proof.

CREW SAFETY MANAGEMENT
Objective: Use wearable multi-gas detectors to monitor employees’ exposure to harmful gases
during a shift. Create real-time exposure profiles using data from the sensors and adjust the work
schedule or work flow to prevent health issues.
Description of the use case: Safety in a hazardous or life-threatening operating environment, suchas a mine, is a top issue. Other issues are production related, such as the status of the tools used,
the status of the conveyer or vehicles used to carry out the ore, and the amount of ore produced
in a measured period.
Key Requirements:
1. (Environment monitoring) poisonous gas detection, ambient temperature detection andcontrol and lighting control need to be implemented.
2. (Personal monitoring) the personal vital-sign monitoring system must be installed locallyand the data sent out to a central monitoring station in the operation center.
Edge Boundary: Operation site (e.g. at the mine operation office or operation center).
Edge Devices: Personal vital sign monitor (body temperature, heart rate and blood pressure, CO2
level), personal tracker, environment monitors (ambient temperature, CO level, hazardous gas
detection, lighting), tools, trucks and unmanned vehicles, conveyer belts and weighting devices.

FLEET TRACKING AND PLATOONING
Objective: Combine real-time GPS data with vehicle usage data from sensors to monitor andoptimize the location and status of the fleet.
Description of the use case: A trucking company must operate its vehicles safely and efficiently.
In addition to optimal routing in the delivery routes, the mechanical status of each vehicle cancontribute to timely maintenance to improve the operation efficiency and safety. The operationis originally designed for individual trucks but it can be extended to several trucks to form a“platoon” of unmanned trucks to increase operational efficiency.
Platooning is typically used for self-driving vehicles. The lead vehicle detects the lane and traffic
condition to decide the optimal speed to maneuver. This information is then communicatedthrough the following vehicles in the platoon. Each car has vehicle-to-vehicle (V2V)
communication capabilities and controls for driving safety. The lead vehicle gathers theinformation, such as the location of the fleet and the operating condition of each vehicle andreports to the office.

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Introduction to Edge Computing in IIoT
Key Requirements:
1. GPS based vehicle tracking supported by a local map.
2. The vehicle status monitoring system is installed locally and the data shall be sent out toa central monitoring station in the operation center.
3. A connection to a central location providing traffic updates
Edge Boundary: The lead vehicle of the platoon.
Edge Devices: Vehicle status monitor (engine speed, temperature, break pad thickness andhydraulic subsystem, transmission subsystem, weight and shock status, tire pressure, fuel level,etc.), GPS tracker, environment monitors (ambient temperature, lane detection, vicinity vehicle
detection), container status monitor (for refrigerating) and V2V driving safety devices.

PREDICTIVE MAINTENANCE–CONNECTED ELEVATORS
Objective: With edge applications installed on connected elevators, operators and technicians
are able to perform predictive maintenance of the elevators based on the data they provide at
the edge.
Description of the use case: Operators of connected elevators rely on edge functions to achieve
predictive maintenance of their systems for the systems to become more reliable and reduce
system downtime. The operational cost of these systems is greatly reduced since the efficiencyof the system can be significantly improved.
A connected elevator uses many sensors for gathering data on noise, vibration, temperature, etc.
The operational status of the elevator can then be derived from analyzing the sensed data. Withelevators connected to edge computing devices, and the sensed data uploaded to the cloud,elevator operators can obtain the running status of all of their elevators. Elevator technicians arethen able to perform predictive maintenance using edge computing data, and data in the cloud,to check and maintain those elevators selectively that are more likely to fail based upon analytics.
Predictive maintenance increases the operational efficiency of the equipment while reducing themaintenance expense through targeted failure prevention and avoidance of unplanned
downtime.
Key Requirements:
1. The edge devices offer containers, open APIs that allow third parties to develop
applications to be installed on the edge devices.
2. To support 7 x 24 monitoring, the edge devices support runtime update of its softwareand firmware.
Edge Boundary: The elevator operations center or the elevator itself
Edge Devices: Infrared sensors, weight sensors, smoke detectors, vibration inductors, noise
sensors, cameras and operator interfaces.
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Introduction to Edge Computing in IIoTPRODUCT TRACEABILITY
Objective: Regulations in the food industry (e.g., EC 128/2002) require manufacturers to establish
systems that enable traceability of food products across all stages of production, processing anddistribution. While this use case focuses on the food industry, product traceability is important
across multiple industries.
Description of the use case: Pieces of plastic in chocolate bars, bacterial contamination of cream
cheese, falsely declared ingredients in pasta-based ready meals—a food product may be recalled
for any number of reasons. Time is of the essence when it comes to product recalls. As well as
damaging the manufacturer’s reputation, these situations can be expensive, with costs rising as
the whereabouts of the end products become less clear.
Bar codes, 2D codes, or electronic transponders are used to identify objects depending onwhether they are individual items, primary and secondary packaging, pallets, trucks orcontainers. When it comes to deciding which technology to use, financial factors and the objects
and processes involved must be taken into account. For example, a bar code can be printed onto
an egg, while the cartons holding six or twelve eggs can be labeled with 2D codes and additional
plain text such as the best-before date. A transponder, on the other hand, can be added toshipping cartons, pallets and other aggregated containers. The various methods of productidentification described above ensure that the flow of materials across the supply chain arelabeled, identified and tracked.
Industrial machinery, automated guided vehicles (AGVs) and collaborative robots or “cobots” areincreasingly prevalent on the factory floor. The most targeted applications are packaging andpalletizing, pick and place, machine tending and assembly and quality inspection. As issues may
occur at any step in the supply chain including the quality or handling of materials, contamination
introduced by people or machines or faulty processes, product quality and product traceability
require orchestrating, recording and verifying the people, processes and machines involved.
Sensors with edge-computing capabilities allow these product identification methods to bechecked against stored data for verification to ensure the flow of goods, people, process andmachines. The right product goes into the right package and onto the shelf with criticalinformation appearing correctly on the package and full genealogy available in the cloud.
Key Requirements:
1. Consider ambient conditions when selecting edge devices within the food industry, suchas humidity, cold storage and outdoors.
2. Location and tracking of items across all stages of production, processing and distribution
is important.
3. Sensors and computer vision systems identify particulates or contaminants in food.
4. Sensors and edge computing to orchestrate, record and verify the people, processes andmachines involved.
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Edge Boundary: machine on which the sensor is located.
Edge Devices: bar code readers for 1D or 2D codes, vision sensor for recording images, RFID tags
and readers.

Edge Computing and Industrial Analytics
Analytics is broadly defined as a discipline transforming data into information and business value
through systematic analysis. Industrial analytics is the use of analytics in IIoT systems.
Advanced analytics is at the core of this next-generation level of transformation and, whenapplied to machine, process and grid data, provides new insights and intelligence to optimize
decision-making and enable intelligent operations leading to transformational businessoutcomes and social value. These new insights and intelligence can be applied across any level of
any industry if the appropriate data can be collected and analytics applied correctly. Some say
data is the new oil. If that’s the case, then data analytics is the new engine that propels the IIoTtransformation.
Analytics can be classified in a number of different ways depending on where they are performed,
the window of time for a relevant and meaningful response, and what functionality they aretrying to achieve. A single analytical flow could involve edge analytics for initial distillation of dataand immediate actuation, analytics in the cloud comingling the latest news from the edge withhistoric big data stores and back to the edge for further actuation.

TECHNOLOGY AND THE EVOLUTION OF INDUSTRIAL ANALYTICS
Advances in IT and OT capabilities such as compute capacity, communication bandwidth, lowlatency, software capability and sensor technology have removed technological constraints andallowed analytics to be deployed through an entire IoT system. For instance, looking at the edgetier of a system, the processing capability available at the edge in conjunction with low-latency
communication have enabled algorithms to be run in real time supporting models that generate
insights and real-time control for the system. Similarly, looking at the cloud tier, what was once
impractical, performing streaming analytics on enormous data sets, is now possible thanks to big
data compute capabilities and high-bandwidth communications. These same advances have alsoenabled the distribution of analytics so that they need not be centralized and can be
implemented across the IIoT ecosystem.

WHERE SHOULD THE ANALYTICS BE PERFORMED?
Most industrial analytics deployments use a hybrid approach where analytics run at all tiers fromedge to cloud, with analytics at a particular tier addressing a specific business objective.
Cost benefits stem from reducing the amount of data being sent to and stored in the cloud. Edge
analytics mitigates the cost of storing and processing low-value and oft-repeated data. Analytical
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models are not helped by data noise. Instead of creating an unnecessary noisy big data problem,edge analytics can distill data prior to sending it on to the cloud.

Security Considerations for Edge Computing
Security is an important consideration for edge computing. More components andcommunication channels create a greater potential for attack vectors. Innovations are required
to monitor, manage and secure globally distributed systems and contain inevitable breaches. The
IISF documents a generalized end-to-end security framework. In edge computing
implementations: security must be built-in to each device and at every level of the architecture, computing and networking endpoints must be monitored and managed,
 latest patches must be applied, attacks must be isolated and quarantined and affected components must be able to be healed.

Orchestration
The centralized nature of cloud computing enables access to a scalable and elastic pool ofshareable physical or virtual resources. As computing is distributed to the edge, resources canstill be shareable but elasticity is challenged because: compute resources could be in separate islands where they cannot communicate tocoordinate computation,
 locations of compute resources may be difficult or costly to access, compute in ruggedized enclosures might not be expandable and technicians to perform the work may not be easily available at the edge.
With these limitations, the approach is inverted. Understanding both the “as-built” computetarget and net-available resources is critical to deploying the right software to the right location.
Once deployed, tools to manage, monitor and secure the entire lifecycle are required. Software
may need to be throttled or redeployed, memory usage restricted, databases and logs truncatedif resource thresholds are challenged. We also need to predict usage trends to address issues
before they occur.
The challenge for developers and administrators is to understand not only the physicalrequirements of their applications (computing inputs, outputs, connectivity, etc.), but also thesecurity and processing requirements and how those requirements translate to different CPU
and OS types. Industry standard calculations and metrics may be required.
The two main activities essential to deliver an orchestration solution are:

infrastructure management to handle the lifecycle of devices at the edge including thecommissioning and provisioning of resources and

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Introduction to Edge Computing in IIoTorchestration to manage the lifecycle of services and applications and the dependencies
between them.

Various standardization efforts share this understanding of orchestration, such as the ETSI MultiAccess Edge Computing (MEC) initiative, the ETSI NFV Management and Orchestration (MANO).
Orchestration and infrastructure management at the edge poses challenges not faced in thecloud mainly due to:
Heterogeneity of devices and application domains: At the edge, there are no expectations on
homogeneity regarding devices, or the hardware and software platforms. An infrastructure
management system needs to be flexible enough to manage a plethora of devices to consume
their resources seamlessly. To provide some level of homogeneity at the edge, both virtualizationand containerization technologies may be employed. Also, devices can behave differently basedon the application domain. An orchestration solution in a smart factory environment where thenodes are static and the network reliable will have a different behavior from those orchestrating
a logistics or smart cities domain where vehicles are consistently mobile and subjected to variable
connection quality.
Different connectivity and communication technologies: IIoT gateways must handle multiple
connectivity solutions using different protocols. The orchestrator must be aware of the available
solutions to guarantee communication between deployed functions and applications.
Differences in capabilities, requirements and constraints: The higher level of homogeneity andthe virtually infinite availability of resources in the cloud ease the orchestration process.
Conversely, a broader range of service requirements, device capabilities and constraints areobserved at the edge. For example, devices at the edge have different sensors, actuators, realtime operating system or networks, some nodes can provide accelerators, others will not. The
orchestrator must be aware of the capabilities found in the infrastructure. Also, the constraints
on these nodes need to be known beforehand (e.g. bandwidth, battery, CPU power, memory).
At orchestration time, a service must be able to describe its requirements, and the requirements
will be checked against the available capabilities and constraints found in the infrastructure.
With that in mind, orchestrators can operate both vertically and horizontally. Verticalorchestrators handle services in a specific domain, while horizontal orchestrators manage
services across different domains providing integration among them. An example would be asmart factory that relies on a logistics company, and each has its own orchestrator. A horizontal
orchestrator composes services that span the different domains (e.g. a service that adjusts
throughput of a production line based on the current location of the necessary supplies).
Orchestration is an important aspect of edge computing to provide a platform to support both IT
and OT activities in IIoT. The ability to coordinate the deployment of new services andapplications gives the edge the capacity to be programmable and deliver the services required

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Introduction to Edge Computing in IIoT
by its consumers. While trying to ensure the quality of the service required, its presence in edge
solutions needs to be enforced.

Conclusion
Edge computing has been implemented in a variety of IIoT deployments; however, the need tomodernize edge architectures became apparent with the emergence of cloud computing. The
rapid decline in processor and memory cost enables more advanced decision-logic closer towhere the data is created, at the edge. The industry has learned that a “one-size-fits-all”
approach has never been adequate for IIoT. It is also true that the IIoT system designers always
know where the edge boundary is, and what devices in the system can be categorized as edge
devices. System designers are challenged to implement an architecture that is managed,
orchestrated, trustworthy and secure. The next phase of the work will be to address theseconcerns in the Technical Report.
While we have tried to lay out the significance of edge computing of future IIoT systems, we know
it is a never-ending task as new IIoT applications and new considerations appear every day. We
intend this paper to trigger more in-depth conversations and invite your participation.
This is not the end; rather, a beginning.

Authors and Legal Notice
Copyright © 2018 Industrial Internet Consortium, a program of Object Management Group, Inc.
(“OMG”).
This document is a work product of the Industrial Internet Consortium Edge Computing Task
Group, co-chaired by Mitch Tseng (Huawei), Todd Edmunds (Cisco) and Lalit Canaran (SAP SE).
Authors: The following persons have written substantial portions of material content in thisdocument: Mitch Tseng (Huawei), Todd Edmunds (Cisco) and Lalit Canaran (SAP SE).
Contributors: The following persons have contributed valuable ideas and feedback thatsignificantly improve the content and quality of this document: Pieter van Schalkwyk (XMPro),
Cliff Whitehead (Rockwell Automation), Mike McBride (Huawei), Mingui Zhang (Huawei), Prof.
Thomas Magedanz (Fraunhofer Fokus), Mathias Santos de Brito (Fraunhofer Fokus), Eddie Lee(Moxa), Michael Thomas (SAS), Jill Oertel (SICK), Shi-Wan Lin (Thingswise), Will Sobel (VIMANA),
Wael Diab (Huawei), Eric Harper (ABB), Vijay Ujjain (PricewaterhouseCoopers) and Mark
Crawford (SAP SE).
Technical Editor: Stephen Mellor (IIC staff) oversaw the process of organizing the contributionsof the above Authors and Contributors into an integrated document.

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