Bringing Intelligenceto the Edge:
Exploring Edge Computing

Edge Computing:
The old and the new
Edge Computing is not a new concept. As a subset of
distributed computing, it has been talked about for
many years as a mechanism to move latency sensitive
applications closer to their source of data.
However, over recent years, several factors have combined toreally drive the adoption in this space moving Edge out of thehype cycle and into a modern platform choice to sit alongsideits counterparts in cloud and the data center. These factors
include the rapid increase in the capability of networking
technologies such as 5G, the rise of data derived from IoT, theAI revolution and the power of microservice based applications.
Further, with the ever-increasing number of available device
types, Edge Computing is further converging the worlds ofInformation Technology (IT) and Operational Technology (OT).

Customers in many vertical industries are not only using Edge
solutions, they are also dependent on them to run a data driven
business that can make real time changes. As customers startdriving their technological reach out to the Edge, strategic
consideration is required as deploying solutions successfully
can be both nuanced and bespoke.
We know Edge is now a central pillar of a hybrid platform
strategy, and the build out of further distributed applications todrive AI and other initiatives will be part of many organisations’strategies. What will keep CTOs awake at night is how to ensure
the solution efficiently and securely delivers its expectedbusiness value as Edge devices, Edge networks and local data
storage proliferate in the coming years.

Customers in many
vertical industries are
not only using Edge
solutions, they are alsodependent on themto run a data-driven
business that can makereal-time changes.

2The Fourth Industrial Revolution
Industry 4.0 is a term to describe the integration of intelligent digital technologies into industrial processes by encompassing Edge-based technologies such as IoT networks,AI, Big Data, robotics, and automation. We are already seeing how the implementation of these solutions are revolutionising vertical industries with uses cases such as:

Public Sector

Smart Cities

Healthcare

Providing secure border control by processing
biometric data such as facial recognition via localcameras and sensors at border checkpoints.

Managing urban infrastructure by optimisingtraffic lights, waste management, and energy
grids.

Edge devices such as wearables and medical
sensors securely collect patient data and transmit
it to healthcare providers.

Defence

RetailFinance

Soldiers can be digitally enabled with devices
such as wearable sensors, smart helmets, andcommunication gear, collecting data from various
sources, including GPS, biometrics, environmental
sensors, and weapon systems.

Through the deployment of sensors and cameras
in stores, real-time data can be gathered on
customer behaviour, such as foot traffic patterns,
dwell times, and product interactions.

Insurers can assess risk and make underwriting
decisions in real-time by monitoring data from
IoT sensors in homes. Understanding risks suchas water leaks or fire hazards enables insurers toprice services more accurately.

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Finding the Edge:
the 6 core areas toconsider
The use cases for Edge, clearly aligned to businessoutcomes, will affect target architectures. Oncethese have been created, successful Edge adoption
requires the knowledge and service adjacency
across several key areas to successfully deploy,
maintain and evolve solutions.
Being able to bring infrastructure, applications,data, security, and networking together to meet thespecific requirements of the use cases, in addition
to any operating model changes required, is key toreducing the risk and accelerating the delivery ofEdge solutions.

1. Data
Data at the Edge is no longer bound within the confinesof a secure data center or cloud platform which createsboth opportunity and challenge. AI is a key use case forEdge, and Edge is a key enabler for AI to meet some of themany use cases. As AI is a data-heavy, compute-intensive
technology it becomes a perfect candidate for Edge asmodels and processing can be driven at source withminimal network latency. This is one of the reasons Edgeis driving data creation at an unprecedented scale, somuch so that it’s predicted that:

Edge Data Management is evolving as a focussed
discipline for storing, managing, and processing datafrom the nearest source to reduce latency, mitigate shortdelays and enable high throughput. The application
and use case will determine if data is sent back to acentralised server or cloud platform or is processed
locally. Clearly, the further the data travels the moreconsideration needs to be given to security andfactors such as encryption in transit, but other key
considerations are:•	By 2025, more than 50% of enterprise-managed data
will be created and processed outside the data centeror Cloud.1• Backup and recovery• Performance and reliability• Data storage both locally and remotely•	Data partitioning where huge amounts of data arebeing collected (i.e. from sensors)• How to use AI services on Edge data•	By 2027, 20% of large Enterprises will have deployed
an Edge management and orchestration solution,compared to less than 1% in 2023.1•	The number of IoT devices will triple from 2020 to 2030,
and the data they produce will grow even faster.1

1. 2023 Gartner IT Infrastructure, Operations & Cloud Strategies Conference42. Infrastructure
Today there is a proliferation of Edge in various form factors.
They may be IoT sensors, cameras, mobile devices, drones,
and as many other connected devices as you can imagine
from the Internet of Things. The hyperscalers now providelocalised ‘zones’ to run cloud services in metro areas or supply
hardware devices that can run certain services decoupled fromthe network. These include AWS Outposts, AzureStack HCI andGoogle Distributed Cloud.

As reliability is crucial for Edge applications, the infrastructure
must be flexible and efficient emphasising automation andvisibility in management platforms to simplify decision-making.
By failing to consider these aspects organisations are in dangerof repeating the mistakes made 10-15 years ago when IoT
struggled to gain traction due to a lack of standardisation andsupport in device management.

This is where we bring in the more granular concept of what
Gartner have termed ‘Cloud-Out versus Edge-In’. Edge-in is
about building out CSP independent solutions and connecting
them back to the Cloud. Such as devices in stores or wearables.
Cloud-Out is about extending what you already do in Cloud tothe Edge. Such as a HCI device from a hyperscaler.

Mapping Edge-In and Cloud-Out Services and Locations

The challenges of managing these devices, outside of thetraditional perimeters of the data cener or cloud platform
is twofold. Firstly, how do you perform standard operational
services on these devices, such as monitoring, alerting,patching and support? Secondly, a different type ofmanagement service is required. At the Edge, you could havethousands of locations and all sorts of diversity in the field.
One location will be cold, the other one will be hot. One location
will have a mobile cellular network available another may
be Wi-Fi.

IoT Edge

ROBO Edge

Cloud-Out

Mobile Edge Compute (MEC)CDN Edge

Centralised Cloud/Global Data Center
Regional (or Far) DC

Hyperscaler/Cloud
Providers

Telco
Providers

Local (or Micro DC) Edge
Compute (or Local) Edge

Telco
Providers

IndependentSolution
Providers

Gateway (or Near) Edge
Device Edge
Edge-In

Figure 3: Mapping Edge-In and Cloud-Out Services and Locations, gartner.com

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3. Applications
To successfully meet the use cases required applications must be
deployed to run at the Edge and be executable across various device
types. These applications need to be built with key requirements inmind around low latency, local data access and eventual consistency
with a centralised platform. APIs are used to abstract the underlyingedge infrastructure allowing an application to interact with the edge
infrastructure without needing to understand the underlying device
technology in depth. The APIs at the edge can also support applications
working offline (asynchronously) and working synchronously to connect
back to a central platform (such as the cloud).
Microservices and Container solutions have emerged as a common
method of running services or executing software in smaller form devices
and utilising these API layers. Containers are an especially good fit foredge applications providing modularity, segregation, and immutability.
Applications can be deployed on many different edge tiers, each with theirown unique resource characteristics with the containers representing
them able to scale up or down depending on changing resources andother conditions.
This is further supported by orchestration tools, like those provided by
Kubernetes, that can recognise edge locations as they would data centre
or cloud platforms and being able to manage them from a centralised
container control plane, such as RedHat OpenShift or the related services
delivered by hyperscalers.

4. Networking
There are two things that will shape the network requirements for your Edge Computing solution;• Location• ApplicationThese are derived from use cases, combined with a data flow mapping exercise. As examples:
If the use case means locations are remote, this will inform available connectivity options, and if theapplication demands a certain level of bandwidth the options will be refined. Options for connecting to aremote location may be reduced to Public 5G, LTE or Low Earth Orbit Satellite Internet and then the bandwidth
demands of the application and subsequent data transfer may require SD-WAN to maximise performance.
Conversely the use case may be defined by a lack of stable bandwidth to a location and so Edge Computing
is the solution, but it will still have its own compute, gateway, and device edge connectivity requirements.
The location may be small so minimising the footprint of the Edge solution means using SmartNICs tooffload network functions to reduce load on the Compute platform or even to deploy a software version of a
hardware network appliance. The location environment may be harsh either in terms of exposure to weather,or permanent high or low temperatures, which will require specialised equipment to solve. The use case mayalso dictate the type of network technology required, for example the connectivity for a large factory flooror warehouse with a high density of connected devices may be better provided by Private 5G rather than
traditional Wi-Fi.
Finally, the distribution of Edge locations and their requirements to connect back to the mothership,particularly if that is Cloud, may be better served by a modern connectivity platform that uses an Internet on
ramp to a private, high-speed backbone, that has proximity to the hyperscalers. Understanding the use caseand its location and application requirements is fundamental to how you will architect your network.

65. Security

6. The importance of platform thinking

Security good practices will apply to Edge as much as they do to any other part of thebusiness, from the user to infrastructure and networking, through to shifting left towards
the application, but there are some specific considerations. For example:

Although we have talked about technology domains individually here, we are notsaying that they need to be architected individually. A focus on maximising theavailability of modern, consolidated, multi-domain platforms will be key to Edge
success, as this will accelerate deployment, streamline operations, and ultimately
provide a stronger security posture.

•	Zero Trust principles should be applied to Edge. Identity driven least privilege is criticallyimportant as it is anywhere else.
•	Secure Access Service Edge (SASE), particularly through Zero Trust Network Access(ZTNA), will ensure user access to Edge workloads is secured and reduce the attack
surface of those workloads.

Platform engineering will be a requirement to address any vendor platform gaps
to ensure maximum efficiencies in the final solution and ultimately maximise thebusiness value of Edge.

•	Securing IoT/OT edge devices will require dynamic segmentation both from each otherand other parts of the network.
•	If a location is remote an extra focus on physical security may be required as well as
encryption at rest and in flight.
•	SmartNIC technology comes into play again to minimise the effects that securitycontrols, such as firewalls and IDS/IPS, have on the footprint of the solution.
The challenge is to provide consistent policy and enforcement while dealing with thenuances of the use cases and diversity of the components of Edge solutions.

7How Computacenter
can help
As more of our customers begin to drive their technology tothe Edge, we have a defined set of service areas designed
to deliver the assessment, architecture, management, andtransformation our customers need. Computacenter havededicated business units and specialists across each of thecore consideration areas, therefore placing us in a uniqueposition to help our customers.
Computacenter's Advisory & Assessment Services can advise
you on the best technology choices, designing the solution
end-to-end, including integration into existing environments
and the Cloud. We can build workload optimised platforms for
artificial intelligence, databases and IoT solutions.
Using the breadth of our portfolio expertise and technology
partners, we Engineer & Implement the most appropriate
Edge platforms for our customers to achieve their digital andtransformational requirements.
And finally, to deliver operational efficiencies, we offer services
to help Adopt & Operate platforms. From training and day 2
support through to ongoing optimisation, scalability planningand lifecycle management, we can help customers to get themost value from their Edge solutions.

Let Computacenter take you on your journey to the Edge!

Advise & AssessPlatform

DataNetworkSecurity

Engineer & Implement

Adopt & Operate• Requirements gathering• Deployment services• Training and support• Business case creation• Integration to existing systems• Scalability planning• P latform recommendations/
Battle Days

• Networking and connectivity• P erformance optimisation• Security implementation• Lifecycle management• Compliance and regulation• Circular Services• Architecture design• Data assessment andclassification

8The Office of the CTO
Computacenter’s Office of the CTO (OCTO) team leads in the exploration
and application of technology products and delivery methodology toaid the digital transformation of our customers. As a team of crossfunctional technologists with extensive industry and IT experience
we deliver thought leadership, advice, and real-world implementation
experience to help our customers achieve their goals.