Edge Adoption Accelerates as Enterprises Seek Lower Latency

The enterprise technology sector faces mounting pressure to process unprecedented volumes of data while enabling real-time applications and maintaining system reliability. As organisations deploy more Internet of Things (IoT) devices and data-intensive applications, edge computing has emerged as an architectural approach to managing these demands by moving computation closer to data sources.

Marking a departure from centralised cloud computing models, edge computing represents a distributed architecture that places compute resources at the network periphery, enabling real-time processing for applications ranging from augmented reality to industrial automation.

“Edge combines some of the best elements of cloud and on-premise deployments,” says Mark Toman, Client Director at BT Wholesale. “By bringing compute power closer to end use cases through 5G, Edge supports the ultra-low latency connectivity required for new innovations such as VR/AR and AI.”

The technology addresses limitations in current infrastructure. While cloud computing provides scalable resources, the physical distance between users and data centres creates latency that impedes time-sensitive applications. Edge computing reduces these delays by processing data locally, enabling new services that require immediate responsiveness.

“Edge Computing is a modern method of data processing that occurs at the 'edge' of a network, closer to where the data is generated, rather than being sent to a distant central server or cloud for processing,” explains Mark Cunningham, Head of Public Sector and Solution Sales at TalkTalk Business.

Overcoming infrastructure challenges

The implementation of edge computing presents technical and operational hurdles. Organisations face significant costs in deploying distributed nodes, while managing security across multiple data access points adds complexity.

“Edge workloads are often highly data-intensive, processing massive amounts of real-time data from sources like IoT devices,” Mark Cunningham says. “Managing such large volumes of data, particularly in real-time, can strain network resources and lead to bandwidth issues.”

Nokia, the telecommunications equipment manufacturer, addresses these challenges through appliance-based solutions. Erez Sverdlov, Vice President of Cloud and Network Services, Europe at Nokia, notes that its 5G User Plane Function (UPF) provides “the same robust, secure software as our cloud-based 5G UPF, however, in a much smaller form-factor with extremely high throughput – 400Gbps in a two-server 96 cores configuration using HP Gen11 and AMD’s Zen 4 chipsets.”

Interoperability between platforms presents additional complications. As a result, Erez recommends partnerships with cloud providers and the use of container orchestration platforms like Kubernetes for resource automation and scaling. He emphasises the importance of adherence to open standards to enhance interoperability across systems.

Critical infrastructure and public services

Edge computing enables organisations to extend services to areas with limited infrastructure. The technology supports remote operations, monitoring systems, and IoT-enabled industrial applications by processing data locally rather than relying on distant data centres.

“Critical National Infrastructure can benefit greatly from enhanced data-intensive security and monitoring technologies supported by a combination of Edge computing, safeguarding operations, and citizens,” Mark Toman says.

In urban environments, edge computing enhances public services through smart city applications. Erez explains that edge computing “supports the rapid deployment of smart city technologies while protecting against service outages due to backhaul failures or congestion, or computational overload of a centralised system.”

These deployments enable real-time data processing for traffic management, public safety, and environmental monitoring. Nokia demonstrates practical applications through partnerships, combining its Core Software as a Service with NTT’s and DoCoMo’s In-Network Service Acceleration Platform to enable efficient processing of AI and video data at the network edge.

Performance improvements

The technology enables organisations to enhance service quality through localised data processing and analysis. “Edge Computing allows providers to deliver hyper-personalised services by providing real-time data analysis locally,” Mark Cunningham says. “It also allows for faster response times for services, which is especially important for real-time applications like online gaming, media streaming, and share trading.”

AI-powered capabilities at the edge enable predictive maintenance and personalised services, reducing disruptions and addressing operational needs in real time. This proactive approach represents a shift from reactive support to preventative maintenance.

Security considerations

Security remains a priority as edge computing deployment expands. TalkTalk’s Mark emphasises the importance of protecting sensitive data: “We need to prioritise data security and develop robust encryption and compliance processes to ensure that sensitive data is protected at every stage.”

Organisations implement various solutions to address security concerns. “One way to tackle these challenges is through effective software-defined network and device management,” he says. “For example, a cloud-managed system like the Cisco Meraki dashboard can provide centralised control over all connected IoT devices, giving businesses real-time visibility into their network access and usage.”

Future developments

The enterprise technology sector anticipates significant changes in edge computing implementation. Many organisations plan to adopt multi-cloud strategies, offering flexibility and scalability for edge infrastructure.

“The ultra-fast connectivity and low latency that Edge can deliver will enable pioneering use cases such as VR headsets which allow builders and surveyors to analyse building sites without physically being there,” Mark Toman says. “VR/AR can also be used for inventory management and warehousing operations to reduce errors and speed up the picking process.”

The integration of edge computing with AI creates opportunities for new services. Organisations anticipate developments in autonomous systems, industrial IoT applications and augmented reality services.

“From now to 2030, technology will revolutionise industry,” Mark Cunningham says. “At the forefront of this is Edge Computing. This will open more options for AI to be adopted and deployed on any application, any device and anywhere.”

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