Series brief: Connecting the dots on fully-managed edge computing
The Computer Weekly Developer Network (CWDN) now starts its fully managed edge computing analysis series.
This sector is projected to grow from roughly £500 billion in 2026 to nearly £1.4 trillion by 2031 at a 23.2% compound annual growth rate (CAGR), largely driven by a combination of factors including AI inference, industrial automation and demands related to data sovereignty.
As TechTarget’s Stephen J. Bigelow reminds us, “In simplest terms, edge computing moves some portion of storage and compute resources out of the central data center and closer to the source of the data itself.”
Rather than transmitting raw data to a central datacentre for processing and analysis, that work is instead performed where the data is actually generated — whether that’s a retail store, a factory floor, a national utility or across a smart city.
Key trends
Key trends we are looking to analyse and discuss include zero-touch orchestration & lifecycle management in order to uncover how modern enterprise platforms deploy, update, and secure thousands of geographically distributed nodes without local IT personnel.
Here, we also need to know more about the use of centralised control planes such as , Kubernetes-at-the-edge – a technology which we can define as processes designed to run lightweight, container-orchestration clusters directly on distributed, resource-constrained sites (such as in factories, retail stores etc. and also in vehicles) to enable local processing with robust offline resilience and centralised fleet-wide management.
A key part of this story is the Training-to-Inference shift (also known as edge AI), so we need to hear from specialists that understand how fully managed solutions support localised, power-efficient AI/ML inference (such as processing computer vision or LLMs at the endpoint and not the cloud datacentre) with data resiliency.
What business model fits?
As well as zero-trust security, observability, infrastructure-as-code and remote fleet management, we need to hear about the business models in use here i.e. should organisations be using public cloud edge, so-called telecom edge, colocation edge, on-premises managed edge, hybrid cloud or and multi-cloud approaches.
NOTE: Telecom edge is distributed computing infrastructure within telecommunications networks that processes data near users, reducing latency while supporting 5G, AI, IoT, and real-time applications.
This area will need analysis of consumption pricing, managed services and partner ecosystems.
The discussion in this space may touch on “store-and-forward” architectures – systems that temporarily store data before transmitting it to another system, thereby improving reliability, handling intermittent connectivity, and enabling asynchronous communication between distributed applications.
Agree with the analysts?
Do firms in this space agree with the analysts when they hear them saying things like this?
“Edge computing has gone mainstream,” says Dave McCarthy, research vice president, cloud and edge infrastructure services at IDC. “The ability to distribute applications and data to field locations is a key element of most digital transformation initiatives.”
McCarthy further states that edge computing will now “play a pivotal role in the deployment of AI applications” and that to meet scalability and performance requirements, businesses must embrace the distributed approach to architecture that edge computing provides.
In edge-native security & DevSecOps, we need to examine “immutable operating systems”, zero-trust network access (ZTNA), and physical tampering defence mechanisms integrated directly into the managed service layer.
This series is now open and we invite guest blogs between 500-800 words (1000 max) for submission.
