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Edge/MEC

Edge computing and multi-access edge computing (MEC) place processing close to where data is generated — at the network edge rather than in distant centralized clouds — to cut latency and reduce backhaul. For applications that demand fast, local responses, such as industrial automation, computer vision, AR, and autonomous systems, the edge is often what makes them viable. Edge is tightly linked to 5G standalone, private networks, and AI inference, and is a key area where operators, hyperscalers, and enterprises both compete and partner. For decision-makers, the questions are where edge genuinely beats centralized cloud and how to balance on-premises, network-edge, and public-cloud processing. This channel covers edge and MEC across operator, hyperscaler, and enterprise deployments — architectures, partnerships, and use cases — with analysis of where moving compute to the edge actually pays off.

Quantum Utility, with IBM’s Imed Othmani | Episode 33

Quantum Utility, with IBM's Imed Othmani | Episode 33
In this week’s episode Patrick Kelly is joined by IBM Quantum Industry Partner, Imed Othmani, in a discussion on progress and obstacles in Quantum Computing. In particular, what and when will be the first practical applications of this technology. It’s a fascinating insight into the path from pure research to business value, and IBM’s approach to finding it.

KPN Launches Private 5G Solutions for Dutch Enterprises

KPN Launches Private 5G Solutions for Dutch Enterprises
KPN launches KPN Campus, a cutting-edge private 5G network designed for large Dutch enterprises. Targeting industries such as industrial, logistics, and healthcare, KPN Campus merges private and hybrid cellular solutions with on-premise computing for enhanced security and reliability.

Ericsson Texas Smart Factory: Sustainable and Energy-Efficient

Ericsson Texas Smart Factory
Ericsson's smart factory based in Texas, US builts 5G and advanced antenna systems radios. The smart factory is 25% more energy-efficient, produces 17% of required power on-site via solar panels, uses 40,000-gallon tanks to collect & reuse rainwater, and reduces shipping distance up to 5 times.

Harnessing Edge Intelligence: Driving Industrial Digitalization

Harnessing Edge Intelligence - Driving Industrial Digitalization
In today's rapidly evolving digital landscape, industrial enterprises are facing unprecedented challenges and opportunities. Disruptions caused by the pandemic, supply chain issues, and intense competition have made it imperative for CIOs to prioritize digital transformation initiatives that seamlessly converge and integrate operational technology (OT) and information technology (IT) domains.

Telecom: A Software Engineering Perspective | Episode 31

Appledore Podcast
What do software leaders within telcos think about the tremendous rate of change and technological innovation taking place? How do they view topics like SaaS, GenAI, or edge computing? How do they consider the vendor landscape? What principles are most important in the era of cloud and continous delivery? In this thoughtful episode, Robert and John talk with Rob Bennett, SVP of Software Engineering at Echostar - the new name for the combined Dish Wireless and Echostar assets that were re-merged in early 2024.

Ensuring Data Privacy in Cloud-Enabled DevOps Environments

Ensuring Data Privacy in Cloud-Enabled DevOps Environments
In the evolving landscape of technology, the fusion of cloud computing and DevOps represents a significant shift, emphasizing the need for stringent data privacy measures. As organizations navigate this integration, addressing data protection becomes paramount, involving challenges such as access controls, encryption, and vulnerability monitoring. This article delves into the complexities of safeguarding sensitive information amidst the dynamic synergy of cloud services and DevOps methodologies, offering insights on developing robust security frameworks to prioritize data privacy.

EnterpriseWeb and Fortinet Demonstrate Adaptive Security for 5G Edge Networks

EnterpriseWeb and Fortinet Demonstrate Adaptive Security for 5G Edge Networks
The rise of edge networking and distributed applications exponentially increases the attack surface for both the enterprise and Telecom infrastructure. EnterpriseWeb and Fortinet have partnered on award-winning solutions that continuously observe, manage and secure 5G multi-access edge computing (MEC) end-to-end. Together, EnterpriseWeb and Fortinet are providing an intelligent SASE solution that provides end-to-end protection spanning network infrastructure (control plane), network traffic (user plane), and application security. To see the advanced capabilities in action, watch the replay of their latest demo in collaboration with Intel, Microsoft and KX - “Secure Dev-centric Networking with CAMARA APIs”.

59. Saving Telco from Itself with Peter Bernard

5G Guys Podcast
This episode of the 5G Guys podcast tackles the existential challenges facing the telecommunications industry, emphasizing how sometimes, telcos are their own biggest adversaries due to their inherently conservative nature. Hosts Dan McVaugh and Wayne Smith welcome repeat guest Pete Bernard to discuss his new venture, Edgecelsior, and explore how the telco industry can evolve amidst the rapid technological changes spurred by new technologies like AI, 5G, and edge computing. Bernard shares insights from his career, the strategic pivot towards Edge and IoT technologies at Microsoft, and his decision to start Edgecelsior, focusing on industry analytics, content publishing, and strategic work for other companies.

Verizon Advances in Private 5G and Enterprise Connectivity

Verizon Advances in Private 5G and Enterprise Connectivity
In the dynamic field of telecommunications, Verizon Business, steered by Jennifer Artley and Arvin Singh, emerges as a frontrunner in the domain of private 5G networks and enterprise solutions. This initiative is set to redefine how businesses utilize private networks, incorporating cutting-edge IoT and edge computing to unlock new possibilities. The article provides an insightful analysis of Verizon's strategic endeavors in bolstering enterprise connectivity, facing challenges head-on, and setting new benchmarks for the industry.

Vodafone Clinches Awards in Business, Sustainability, and PR

Vodafone Clinches Awards in Business, Sustainability, and PR
Vodafone shines in the limelight, securing prestigious accolades in business, sustainability, and PR at recent award events. These honors underscore the telecom giant's commitment to utilizing technology for societal benefit, highlighting its initiatives in digital inclusivity and business service enhancement. A week to remember for Vodafone, as it bags awards at the edie Awards, Mobile News Awards, and European Sponsorship Association (ESA) Awards, reflecting its pledge to positive change and digital empowerment.

SK Telecom’s AI Journey: Reinventing Telecommunications

SK Telecom's AI Journey - Reinventing Telecommunications
In an era marked by rapid technological evolution, SK Telecom (SKT) stands at the forefront, leveraging artificial intelligence (AI) to reinvent telecommunications. This transformative journey aims not only to enhance connectivity solutions but also to redefine customer experiences, illustrating the boundless potential of AI integration in setting new industry benchmarks.

Frequently Asked Questions

What’s the difference between ‘the cloud’ and ‘the edge’ in telecom?
Cloud computing typically runs in a relatively small number of large, centralized data centers, often located far from any individual user, which is efficient for many workloads but introduces unavoidable physical distance, and therefore latency, between where data is generated and where it’s processed. Edge computing, specifically MEC, places computing resources much closer to where data actually originates, at cell towers, base stations, or local facilities, cutting the round-trip delay for applications where that distance meaningfully matters. The tradeoff is that edge sites generally have far less raw computing capacity than a massive centralized data center, so edge deployments tend to handle specific, latency-sensitive workloads locally while still relying on the broader cloud for less time-critical processing and coordination.
Is MEC mainly a telecom-specific concept, or does it apply more broadly?
It started as a mobile-network-specific concept, originally called Mobile Edge Computing when ETSI introduced it in the mid-2010s, focused on placing computing resources within mobile radio access network infrastructure. ETSI broadened the concept to Multi-access Edge Computing in 2017 specifically to extend it beyond cellular networks to also cover fixed-line broadband and Wi-Fi access, recognizing that the underlying need, computing resources close to the point of data generation, applies regardless of access technology. Current standards work is extending the concept further still, with ETSI’s MEC group releasing Phase 4 specifications in late 2025 focused on developer-friendly APIs for vertical industries and explicit alignment with emerging 6G requirements.
What applications actually benefit from edge computing?
The clearest use cases are ones where milliseconds genuinely matter, or where large amounts of locally generated data would otherwise need to travel back to a distant data center unnecessarily. Autonomous vehicles need to process sensor data and make navigation decisions in near real time, where even modest added latency could be meaningful for safety. Industrial automation and predictive maintenance benefit from edge processing of sensor data from factory equipment. AR and VR applications need responsive, low-latency rendering support. Smart city video analytics, like traffic monitoring, generates enormous volumes of video data far more efficient to process locally. Increasingly, running AI inference closer to users for real-time applications is becoming one of the most significant edge use cases of all.
Why are telecom operators excited about edge computing as a revenue source?
Beyond reducing backhaul costs, edge sites give telecom operators something cloud hyperscalers don’t have by default: physical proximity and direct integration with the radio network across thousands of locations nationwide. This positions operators uniquely to offer latency-sensitive computing services that a centralized cloud data center simply can’t match on responsiveness, regardless of raw computing power. Operators are increasingly positioning these edge locations specifically as AI inference points, sometimes described as compact ‘AI factories,’ capable of running real-time AI workloads close to users. This opens a genuinely new monetization path beyond selling connectivity itself, letting operators compete in the broader computing and AI infrastructure market using distributed physical infrastructure cloud-only providers would need years to replicate.
How mature is MEC deployment in 2026?
By 2026, MEC has moved well past the concept or early-pilot stage into active, expanding commercial deployment. ETSI’s MEC group has produced more than 50 technical specifications covering reference architectures, service enablers, and deployment guidelines, and released its Phase 4 work in late 2025, focused on developer-friendly APIs and explicit alignment with open-source projects and 6G preparation. Telecom operators worldwide are actively pairing MEC deployments with private 5G networks, AI workloads, and Open RAN integration in live commercial deployments rather than isolated trials. The technology continues to mature rather than being fully settled; convergence between MEC and Open RAN architectures remains an active area of development.
How does edge computing relate to private 5G networks?
Edge computing and private 5G networks are frequently deployed together because they solve complementary problems for the same enterprise use cases. A private 5G network provides dedicated, reliable, high-performance wireless connectivity across a facility like a factory or port, while edge computing provides the local processing power needed to actually act on the data that connectivity carries, without sending everything back to a distant cloud data center. A manufacturing facility, for example, might use private 5G to connect cameras and sensors across the factory floor, with an edge deployment at that same facility processing video analytics or controlling automated machinery in near real time. This pairing is one of the most common patterns in enterprise digital transformation projects today.
What’s the difference between edge computing and Open RAN’s ‘Cloud RAN’ concept?
Edge computing and Cloud RAN address related but distinct parts of the network. Cloud RAN refers specifically to running radio access network functions, the software controlling how a cell site transmits and receives wireless signals, on cloud-based, software-defined infrastructure rather than dedicated radio hardware. Edge computing, particularly MEC, refers more broadly to running general-purpose application workloads, not just radio network functions, close to the network edge, things like video analytics, AI inference, or industrial automation software. In practice, the two concepts increasingly converge physically, since the same edge infrastructure supporting Cloud RAN’s virtualized radio functions can often also host MEC application workloads on shared hardware.
What are the biggest technical challenges in deploying edge computing at scale?
Deploying edge computing at scale introduces several persistent technical challenges. Managing and orchestrating computing resources across potentially thousands of geographically distributed edge sites is meaningfully more complex than managing a small number of centralized data centers, since each edge location has limited physical space, power, and cooling capacity. Ensuring consistent security across so many distributed locations, each a potential point of vulnerability, requires more extensive security architecture than securing a handful of centralized facilities. There’s also a workload placement challenge: deciding which tasks genuinely benefit from edge processing versus which are better handled centrally, since over-provisioning edge capacity for workloads that don’t truly require it can be an inefficient use of limited, expensive infrastructure.
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