Edge Computing

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Edge Computing

by James G. Barr

Docid: 00021078

Publication Date: 2209

Report Type: TUTORIAL


As the term implies, edge computing is computing at the network edge.
According to Gartner, “Edge computing describes a computing topology in
which information processing and content collection and delivery are
placed closer to the sources of this information.”1

Report Contents:

Executive Summary

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As the term implies, edge computing is computing at the network edge.

The Internet of Things Tutorial
The Internet of Things
Market Trends Market
The Software of the
Internet of Things Tutorial
Smart Machines Tutorial

Edge computing involves the positioning of compute, storage, and
networking resources proximate to the end users they serve and the various
devices these end users employ. An emerging field, especially given the
exponential growth of the Internet of Things (IoT) and the resultant
proliferation of edge devices like smartphones and smart sensors, edge
computing is demanding the attention of enterprise operations and security
departments, just as cloud computing did a decade ago.

If the edge concept sounds familiar, it should. Modern computing has
followed a curious, but predictable, cycle, from centralization of
computing resources to decentralization, and back again. For example, the
centralized mainframe era gave way to the decentralized client/server era.
About two decades later, the decentralized client/server era gave way to
the centralized cloud computing era. Today, the centralized cloud
computing era is giving way to the decentralized edge computing era.

These transitions do not represent any clean breaks in computing
philosophy. Mainframes still exist, so do client/server systems, and cloud
computing is still expanding. Edge computing, like the previous computing
models, represents a collective effort to create a more diverse, and more
nuanced, set of computing options – each designed to satisfy a more
diverse and nuanced set of enterprise requirements.

The impetus behind edge computing was the realization that some data,
like data generated by autonomous automobile sensors, must be processed
immediately. It cannot be shuttled to a cloud repository, processed by a
backend analytics package, and returned to an automotive steering system,
at least not in time to prevent an accident. The data must be processed on
the spot, or “at the network edge.” The situation is analogous to a
paramedic attending an accident victim. The paramedic can relay the
patient’s vital signs to the hospital, but must act immediately to stop
any major bleeding.

Perhaps a more timely metaphor for edge computing can be seen in the sudden
proliferation of home offices owing to the COVID-19 pandemic. To prevent
disease transmission, most office or knowledge workers were denied access
(at least, temporarily) to their headquarters building (their office cloud).
In response, they retreated to their individual home offices (which exist at
the edge of their office network).

A big factor in favor of edge computing is, of course, lower costs. As
analyst Teresa Tung remarks, “Processing at the edge makes cloud upload
and storage cheaper. Why pay for full-fidelity data when a summarized view
or key insights might be all you need?”2

According to Accenture, “The prime advantage of edge computing is
[that the] user experience improves because relevance increases with
[Edge] unlocks valuable data to shape new opportunities
and innovation for the future. More sensors generate more data, and there
is more processing at the location where the data is created – which is
faster, more reliable and safer. Integrated with knowledge from the cloud,
the system yields better predictions and more relevant information,
repeating in a cycle of continuous improvement.”3

Fog Computing

Closely related to edge computing is the concept of fog computing. Fog
computing generally refers to the network connections between edge devices
and the cloud. In some respects, fog computing represents the connective
tissue between edge computing and cloud computing.4

According to the US National Institute of Standards and Technology
(NIST), fog computing is a horizontal, physical, or virtual resource
paradigm that resides between smart end-devices, i.e., edge
devices, and traditional cloud or data centers. This paradigm supports
vertically-isolated, latency-sensitive applications by providing
ubiquitous, scalable, layered, federated, and distributed computing,
storage, and network connectivity.5

Figure 1 illustrates the relationship between cloud computing (top
layer), fog computing (middle layer), and edge computing (bottom layer).

Figure 1. Fog computing supporting a cloud-based ecosystem
for edge devices

Figure 1. Fog computing supporting a cloud-based ecosystem for edge devices

Source: NIST6

Edge Use Cases

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The Internet of Things

Edge computing is a principal enabling technology of the “Internet of
Things” (IoT). The IoT effectively extends the dominion of the Internet
from cyber space to the physical world, creating a network of smart
devices that form the digital/mechanical equivalent of the body’s central
nervous system. The IoT reflects a vision of the future in which all
objects – from simple sensors to large-scale physical systems – are
rendered intelligent, with the ability to:

  • Sample their immediate environment,
  • Observe and report their status,
  • Receive and execute real-time instructions,
  • Exert control over other objects (as directed), and/or
  • Operate autonomously.

Edge computing permits IoT data to be processed close to its source
rather than having it transmitted to a central data center or the cloud.
From a more technical perspective, IDC defines edge computing as a “mesh
network of micro data centers that process or store critical data locally
and push all received data to a central data center or cloud storage

While all data may eventually be pushed to a central repository for
machine learning or other analytical purposes, edge computing, in a
metaphor advanced by analyst Brandon Butler, “triages the data … so some
of it is processed locally, reducing the backhaul traffic to the central
repository. Typically, this is done by the [IoT] devices transferring the
data to a local device that includes compute, storage and network
connectivity in a small form factor. Data is processed at the edge, and
all or a portion of it is sent to the central processing or storage
repository in a corporate data center, co-location facility or IaaS

IIoT and Industry 4.0

Raising the stakes even higher for edge computing is the vital role the
technology plays in advancing the Industrial Internet of Things (IIoT),
which is “accelerating the journey towards [Industry] 4.0 adoption.”8

As described by the World Economic Forum, “Industry 4.0 refers to the
‘smart’ and connected production systems that are designed to sense,
predict, and interact with the physical world, so as to make decisions
that support production in real-time. In manufacturing, it can increase
productivity, energy efficiency, and sustainability. It increases
productivity by reducing downtime and maintenance costs.”9

Portable Information Devices

Even before the Internet of Things, the universe of edge devices was vast
and ever-expanding. We refer, of course, to portable information devices,

  • Smartphones
  • Tablets
  • Laptops
  • Handheld activators
  • Remote controls

Since these devices often house personally identifiable information (PII) –
as well as other confidential data subject to digital and physical theft and
manipulation – edge computing demands robust and reliable edge security.10

Emerging Applications

Edge computing is still an emerging field. According to a 2019 Heavy
Reading survey, the enterprise “applications most likely to drive initial
edge deployments” are:

  • Industrial or factory automation – 55 percent
  • Augmented reality or virtual reality – 27 percent
  • Safe cities – 27 percent
  • Smart cities with enhanced/localized experiences – 41 percent
  • Autonomous vehicles – 18 percent
  • Radio Access Network (RAN)-aware video optimization – 5 percent
  • Gaming – 9 percent
  • Enhanced retail experience and immersion – 23 percent
  • Other – 5 percent11

Importantly, edge computing augments, not replaces, cloud computing. Data
processed at the edge may – indeed, probably will – be forwarded to a
cloud for the purposes of collecting historical data, detecting
operational trends, and refining operational processes.

Where Edge Thrives

Edge computing is well adapted to environments featuring:

  • Poor or Intermittent Connectivity or Low Bandwidth
    Edge computing can compensate for poor communications – circumstances in
    which data cannot be efficiently transferred from IoT devices to a
    designated central processing center.
  • High Latency – Edge computing can compensate for high
    latency – the often extended time required to transfer data to a central
    repository, have the data processed, and then have the processed data
    returned to the edge device. Latency can be a critical factor in
    situations involving autonomous vehicles and industrial robots where
    split-second actions are required.

Small-to-Medium-Sized Enterprises

Research conducted by MarketsandMarkets suggests that
small-to-medium-sized enterprises (SMEs) are embracing edge computing
solutions “due to ease of operations and enhanced scalability.”12

Helping facilitate SME use are commercial offerings from firms such as:

  • Amazon Web Services – “AWS edge services deliver data
    processing, analysis, and storage close to [client] endpoints, allowing
    [clients] to deploy APIs and tools to locations outside AWS data
  • TIBCO Software – “TIBCO Software enables application
    logic to be deployed close to or even on a device, ensuring data is
    accurately collected, transformed, and analyzed.”

Potential Business Operations

The Edge Computing Consortium predicts edge computing will contribute to
a wide range of business operations, such as:

Predictive Maintenance – Take elevators, for example.
“Edge computing can help elevator vendors upgrade from the traditional
preventive maintenance to next-generation real-time predictive

Energy Efficiency Management – “Edge computing can
improve energy and efficiency management as follows:

  • Lower energy consumption – Real-time energy and
    efficiency control can reduce buildings’ energy consumption and costs.
  • Lower maintenance costs – Automatic energy
    information collection [can reduce] manual collection costs and
    maintenance costs.
  • Higher reliability – Control planning and policy
    synchronization [can be] stored at the network edge, which ensures
    correct operations and management in case of cloud faults. At the same
    time, the edge can monitor the status of devices such as streetlights,
    switches, and air conditioners, perform predictive maintenance, and
    adjust policies in real time if device faults occur.”13

Edge Data Security

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As with any technology – especially an emerging technology – security is
a serious concern.

As analyst Brandon Butler observes, “There are two sides of the edge
computing security coin. Some argue that security is theoretically better
in an edge computing environment because data is not traveling over a
network, and it’s staying closer to where it was created. The less data in
a corporate data center or cloud environment, the less data there is to be
vulnerable if one of those environments is comprised.

“The flip side of that is some believe edge computing is inherently less
secure because the edge devices themselves can be more vulnerable. In
designing any edge … computing deployment, therefore, security must be
… paramount.”14

Since, historically, computer security has been an afterthought,
enterprise officials must learn from past failures and design security
into edge computing systems. After all, the consequences of compromising
an autonomous vehicle or industrial robot can be catastrophic, more
damaging certainly than a typical consumer data breach. Edge computing
incidents can potentially cost lives.

When evaluating edge security (and privacy), it helps to remember that
all enterprises – indeed, all citizens – are invested in edge computing.
Smartphones, desktops, laptops, tablets, and game consoles all qualify as
edge devices since they can collect and process data locally while also
transmitting data over a network.

Edge Security Strategy

As viewed by analyst Paul Mazzucco, securing an edge infrastructure
involves three basic commitments:

  • Extending the enterprise security perimeter to include edge devices.
  • Employing automatic protection mechanisms, like endpoint security
    software. (In most cases, manual intervention is impractical.)
  • Encrypting sensitive data (to decrease the cyber “attack surface”).15

Importantly, “edge computing software can dramatically improve
performance by achieving the next level of security, such as:

  • “Edge-based threat detection,
  • “Data minimization, and
  • “Decentralized infrastructure.”16

Edge Vs. Cloud

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Some proponents of edge computing suggest that edge will eventually
replace cloud computing. Peter Levine, managing partner at venture firm
Andreessen Horowitz, predicted in 2016 that new edge apps “will obviate
cloud computing as we know it.”17

The truth is that edge computing is merely the next logical development
in a long-standing contest between centralized and distributed computing.
Utilizing a rough timeline:

  • Centralized computing went mainstream in the 1960s with the
    introduction of large-scale mainframe computers.
  • Distributed computing began to assert itself in the 1990s with the
    deployment of client/server systems – miniature mainframes (servers)
    serving smart clients (PCs).
  • Centralized computing rebounded in the 2000s with the advent of cloud
  • Today, distributed computing is again on the rise, with the
    establishment of edge computing.

The situation is, of course, more complicated since early iterations of
distributed technology, e.g., content delivery networks (CDNs),
smartphones, and PCs, can be classified in hindsight as first generation
edge computing systems.

As far as edge vs. cloud, the two computing paradigms (one distributed,
the other centralized) are actually complementary. In industrial
environments, for example, where rapid decision-making is crucial, edge
systems can capture, process, and act upon relevant data. They might then
forward that data to a central data center or the cloud for subsequent
analysis – analysis aimed at improving edge operations.

Edge computing helps relieve connectivity, latency, bandwidth, and other
issues that plague centralized computing. At the same time, centralized
computing can be employed to store and mine massive amounts of data,
enabling traditional “backend” operations, and feeding machine learning
algorithms to promote process improvement.

Future of Edge

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As predicted by MarketsandMarkets, the edge computing market should
expand from $36.5 billion in 2021 to $87.3 billion by 2026, at an
impressive compound annual growth rate (CAGR) of 19.0 percent during the
forecast period.

Spurring the growth are a variety of factors:

  • The rapid adoption of IoT, and especially IIoT, technology;
  • Exponentially-increasing data volumes and network traffic; and
  • Automated decision-making solutions that render edge systems practical
    (“edge works”) and productive (no need to automatically shuffle data to
    the cloud).18

As with cybersecurity initiatives, one of the primary obstacles to edge
computing may be the absence of in-house cloud talent. As Accenture
explains, those individuals who “understand what belongs at the edge, why,
and when. Edge isn’t about retooling, especially for companies that are
already leveraging the cloud. It’s about extending those capabilities out
to the edge.”19

Analyst Nati Shalom believes that “we will see an explosion in [edge
computing] as more and more end-user devices use it to improve
performance, functionality, and battery life. Where once edge devices were
limited to smartphones, tablets, laptops, PCs, and game consoles, we are
now seeing it employed in virtual reality headsets, autonomous vehicles,
drones, wearable tech, augmented reality devices, and more.”20

In addition to becoming more numerous, edge devices will become more
autonomous. First-generation devices, like smartphones and laptops, were
designed to facilitate human-computer interactions. Today’s edge devices,
with their sensors, controllers, and actuators, are heavily integrated
into industrial processes, eliminating the human element in an effort to
increase productivity, reliability, and security.

Critically, as analyst Reinhardt Krause reminds us, “Some technologies
associated with edge computing – including artificial intelligence,
augmented reality, 5G wireless, autonomous vehicles and the industrial
Internet of Things – are still nascent.”21 As these related
technologies evolve, expect a commensurate evolution in edge computing
technology and market growth.

According to Mordor Intelligence:

  • “The financial and banking industry is expected to show a significant
    adoption of edge computing solutions, owing to the increasing adoption
    of digital and mobile banking initiatives.
  • “Widespread adoption of blockchain has considerable potential to drive
    the consumption of edge computing services.”22


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About the Author

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James G. Barr is a leading business continuity analyst
and business writer with more than 40 years’ IT experience. A member of
“Who’s Who in Finance and Industry,” Mr. Barr has designed, developed, and
deployed business continuity plans for a number of Fortune 500 firms. He
is the author of several books, including How to Succeed in Business
BY Really Trying
, a member of Faulkner’s Advisory Panel, and a
senior editor for Faulkner’s Security Management Practices.
Mr. Barr can be reached via e-mail at jgbarr@faulkner.com.

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