Exploring the edge: a must for 5G success

Learn to capture the unique opportunities of edge computing - greater performance, reliability, data sovereignty plus reduced cost and bandwidth for the transport network.

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Edge computing

Edge computing is all about bringing things closer together - a shift which is essential to meet the networking and computing demands of a connected 5G world. Learn what it is, and how it can give you an edge over the competition.

Edge computing is a distributed framework in which compute capabilities (such as the processing, analysis and storage of data for an application) are moved to the 'edge' of a network, geographically closer to where the data is being generated or consumed.

This means shorter distances for data to travel, as well as less data being sent back and forth between devices and centralized data centers and congesting the network. As a result, edge computing delivers benefits such as low latency and high bandwidth, plus more control over data sovereignty and handling.

Edge computing in practice

Imagine moving the processing of security footage from a centralized headquarters data center to a private network edge close to the camera locations. All the raw video data would no longer need to be sent to headquarters for processing.

Instead, it could be done nearby, enabling the footage to be analyzed and acted upon much more quickly, while saving costs and resources. Plus, all potentially sensitive data could be kept locally - a crucial factor for complying with data protection regulations.

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What is the edge?

When trying to understand edge computing, the 'edge' can seem like a vague and undefined place. But the site of an 'edge' is actually very specific - it just depends on the particular use case and its needs and characteristics. The real question then should be: Where is the edge?

Edge computing infrastructure can be deployed in four main locations, each offering different capabilities - the extended public edge, the network edge, the private edge and the gateway edge.

Private edge

In a private edge, computing and storage resources are often deployed at an enterprise premises such as a factory. Edge computing resources can be standalone or integrated with a private network and software-defined wide-area network (SD-WAN). These often have third party applications running on an enterprise proprietary stack or third-party private cloud.

Network edge

In a network edge, computing and storage resources are distributed across communication service provider (CSP) premises, between national, regional and local access sites. These can be standalone or integrated with the mobile cloud (running both telecom and third-party workloads). Edge compute can be seen as an extension of the CSPs existing network capabilities.

Public edge

In an extended public edge, computing and storage resources are distributed from central cloud sites located outside the CSP's premises, for example at a co-location site or a hyperscale cloud provider (HCP) data center. Site and cloud infrastructure is owned by HCPs or third-party cloud providers and others such as information or operational technology (IT or OT) players.

Gateway edge

Gateway edge is where small computing and storage resources are deployed at an enterprise or consumer-facing premises, or within mobile physical objects, for example trains, ambulances or private vehicles. Gateway edge also includes wireless WAN router-based edge solutions primarily used in enterprise sites, vehicles, Internet of Things (IoT) applications and more.

Edge computing vs cloud computing

Edge computing focuses on bringing computing resources closer to where data is generated. It is best for situations where low latency or real-time processing are required, or where large volumes of data are being unnecessarily transmitted to a central location.

Cloud computing relies on centralized data centers accessed over a network, making it better suited for applications that don't require real-time processing. However, it can be the same as edge computing if the cloud compute capabilities are distributed and placed close to where the data is generated.

It's important to remember that the choice of compute location depends entirely on the application data and how it will be used. An enterprise will therefore leverage both distributed and centralized cloud environments for different use cases and purposes, like in a hybrid cloud environment.

Higher performance and efficiency

With data being processed nearby, there is less latency than with centralized data centers, as well as increased performance and bandwidth for the transport network.

Data sovereignty

Comply fully with jurisdictional data regulations and sovereignty laws by allowing data to be processed locally, or within a particular geographical region.

Improved Quality of Experience (QoE)

Elevate QoE and enable innovative solutions that use real-time analytics for video processing, low latency for remote equipment control or offloading computational heavy functions to enable slim, lightweight devices such as extended reality (XR) glasses.

Advanced automation

Real-time data analysis and insights provide a platform for next-level automation, using machine learning (ML) and artificial intelligence (AI) technologies for time-critical decision-making.

Enhanced security and privacy

On premise edge computing helps insulate those using private networks from cyberattacks and threats, while also enjoying less risk of data being intercepted in transit, improving security and privacy.

5G and edge technology delivering two-way value

Many of the advantages of edge computing, including lower latency and data sovereignty, can only be achieved if the distributed compute and storage resources are closely paired with connectivity. Partnered with the power of 5G, edge computing can be leveraged to its full potential.

With the service differentiation and traffic routing capabilities of 5G standalone and network slicing, edge computing will push telecom capabilities and revenue opportunities further than ever. And in return, edge will enable exciting new and future use cases.

Demand driver	Edge capability in 5G
Application latency	With the app closer to the user and 5G radio, the latency can be reduced, supporting new use cases.
Application exposure	The new 5G core will also offer application exposure for edge deployments.
Transport offload	5G bandwidths may increase traffic further. Service delivery from the edge will minimize the backhaul traffic.
Processing offload	Application processing at the edge will offload devices at central datacenters while preserving user experience.

Manufacturing, healthcare and gaming and entertainment are three of the top vertical industries with enormous potential when it comes to edge computing. However, the early maturity level and long time-to-market common in the manufacturing and healthcare industries means the reality looks slightly different.

Gaming and entertainment use cases stand out among the most advanced and market-ready to date, together with real-time analytics, autonomous and connected vehicles and video optimization.

Gaming and entertainment

The opportunities of edge for gaming (and cloud or 5G gaming in particular) are clear, with low latency from edge and 5G SA capabilities like network slicing offering CSPs unique monetization opportunities. Not to mention giving gamers the chance to finally banish their archnemesis - lag.

Vehicles

Connectivity is a key factor for vehicle manufacturers and fleet managers alike - with constant, reliable connections and real-time data processing a must for connected vehicles. But edge computing can also be leveraged inside the vehicle - wherever the road takes it. Cradlepoint's connected vehicle solutions provide seamless connectivity and services for applications including critical public safety vehicles such as ambulances and fire trucks.

Virtual, augmented and mixed reality

Immersive technologies and extended reality (XR) are transforming how we experience and engage with the world around us, from concerts and sporting events to work and even shopping. There's plenty of 5G opportunity in the world of XR - and with it comes demanding data processing requirements that have a lot to gain from offloading compute capabilities to the edge.

Manufacturing and robotics

Much like vehicles, when it comes to advanced manufacturing technologies like autonomous things and smart factories, high performing, reliable connectivity is crucial to avoid costly downtime. And with Industry 4.0 and the Internet of Things (IoT) relying on advancements in automation, robotics, artificial intelligence, machine learning and digital twins, it's clear that edge computing has a significant role to play - in many different ways.

Use cases (non-exhaustive)	General overview	Private edge	Network edge
CDN-related cases	Latency: 100ms-1s+. Bandwidth: high
Video processing	Latency: 100-200ms. Bandwidth: high. Sofety and regulations
Manufacturing. heavy industry plants applications	Latency: 1ms-1s. Bandwidth: variable. Reliability, regulations, data privacy
Business park and city offices, retail shops	Latency: 30ms-1s. Bandwidth: variable. Reliability, safety
Cloud gaming services	Latency: 30ms-1s. Bandwidth: variable. Reliability, safety
Data collection and processing (including AI/ML)	Latency: 100ms-1s. Bandwidth high. Data processing distributed
Vehicles	Latency: 10-100ms. Bandwidth: mid/high. From private cars to AGVs
XR(AR/VR/MR)	Latency: 10-50ms. Bandwidth: high. Availability and complex processing

Application placement location, short- to mid-term:

Higher probability/deployment ratio Lower probability/deployment ratio Very low probability/deployment ratio

The challenges and opportunities of edge

Today’s communication service providers (CSPs) are ideally positioned to deliver intelligent traffic routing from the mobile network to the optimal location of the application, as well as having people on the ground and expert knowledge of network topology and efficiency, device management and more.

The evolution to cloud-native network functions and distributed cloud computing enables CSPs to move beyond traditional connectivity-service models and opens new doors to adjacent industries. But edge also requires a transformative shift in how those CSPs approach and invest in their business, technologies and the ecosystem.

They need to compare the costs and advantages of compute resources in different locations and build a strong business case for new services and use cases. It's an approach that can be challenging without the right guidance.

There are five interdependent key areas that have been identified by the standards, CSPs and analysists to be the most significant methods of defining and deploying an edge computing solution.

Infrastructure

A footprint-flexible, efficient, automated infrastructure provides high reliability. For this to happen, the edge infrastructure needs to be deployed either on-premise at enterprises or in the CSP network, hosting both telco workloads and 3rd party/over the top applications with a limited local management system.

Orchestration

Orchestration should provide smart 3rd party/over the top workload placement and topology discovery, controlling which sites the 3rd party/over the top applications are deployed on, and how they’re configured.

Exposure

To simplify the application developers’ interaction with the telecom network, the exposure solution should expose the APIs for the edge, as well as at the edge for telco applications, for consumption by 3rd party/over the top applications. By exposing information like user equipment and device location, use cases can be improved.

Traffic routing

Routing data to the nearest edge location where the application is hosted helps meet the demand of the application – delivering a better customer experience. This process should be a simplified mechanism, such as distributed anchor or multiple sessions, aligned with standardized approach.

User plane

To enable termination at distributed sites, the user plane is critical. Through the deployment of a 3GPP-compliant, low footprint Packet Core user plane functions, including local LCM support, the solutioan baecomes easy to install and manage.

Strategic guidance for your journey

Which edge strategy is right for you?

This playbook of edge deployment strategies brings together our most valuable lessons and takeaways from years of experience and industry collaboration. Learn how to choose the right go-to-market model, technologies, partnerships and more.

Download the playbook

Scaling up edge computing in the real world

With Carlos Bravo, Director of Cloud Strategy at Ericsson and Mark Thiele, Co-founder & CEO of Edgevana.

Edge creates massive opportunities for telecom operators

With Erik Ekudden, CTO at Ericsson and Randeep Sekhon, CTO at Bharti Airtel.

The next generation network is a true game changer

With Erik Ekudden, CTO at Ericsson and Ronnie Vasishta, SVP Telecom at NVIDIA.

The use cases of the future are all about collaboration

With Erik Ekudden, CTO, Ericsson and Bikash Koley, VP of Global Networking at Google Cloud.

Collaborate and win in the edge ecosystem

Compute capabilities and users aren't the only things that need to be brought together for the full potential of edge to be realized. Building a strong ecosystem today is key for success in the future.

Edge computing is still in an early phase, and an industry standard is not yet agreed on that covers all aspects. But standardization and industry harmonization will be vital to avoid fragmentation in standards, technology and interfaces.

Stakeholders across the telecom industry need to work closely together to form ecosystem partnerships around potential use cases. They also need to actively engage in industry initiatives to align on edge computing, APIs and exposure - and help protect competition, openness and innovation.

Motor City Wash Works put a shine on their industrial automation and retail systems with wireless LAN and WAN for edge connectivity.

Telstra break new ground, offering customers edge computing in virtual and hybrid 5G private network environments

Far EasTone pave the way for enterprise edge use cases in a world-first 5G network slicing trial using Local Packet Gateway.

Network compute fabric

The network compute fabric ensures we have a flexible and programmable execution environment for data-intensive and latency-sensitive industrial use cases. Learn more about how we work with network compute fabric in Ericsson Research.

Edge Research