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Edge computing to face a paradigm shift as worldwide spend is set to exceed $232 billion

Edge computing to face a paradigm shift as worldwide spend is set to exceed $232 billion

Worldwide spending on edge computing is expected to be $232 billion in 2024, an increase of 15.4% over 2023, according to a new forecast from the International Data Corporation (IDC) Worldwide Edge Spending Guide.

The future of the edge computing market appears promising, poised for exponential growth and transformative impact across various industries. As we delve into what lies ahead, and based on IDC’s latest prediction, it is becoming apparent that several key factors will shape this burgeoning market.

“Edge computing will play a pivotal role in the deployment of AI applications,” says Dave McCarthy, research vice president, cloud and edge services at IDC.

“To meet scalability and performance requirements, organizations will need to adopt the distributed approach to architecture that edge computing provides. OEMs, ISVs, and service providers are taking advantage of this market opportunity by extending feature sets to enable AI in edge locations.”

The integration of artificial intelligence (AI) and machine learning (ML) at the edge will unlock new possibilities for innovation. By embedding intelligence directly into edge devices, organizations can extract actionable insights from data in real-time, enabling autonomous decision-making and personalized experiences. This paradigm shift towards distributed AI empowers edge devices to adapt, learn, and evolve without relying solely on centralized cloud resources.

What about edge deployments?

With advancements in IoT, 5G, AI, and security, edge computing will revolutionize how data is processed, analyzed, and utilized at the edge of networks.

Tilly Gilbert, director, consulting at STL Partners exclusively shares her insight on expected trends the edge computing market will see this year.

“In 2024, I expect to see the trend continuing of consolidation in the edge data center market (e.g. as seen with recent European deals like nLighten and Portus data centers). This should make seamless deployments in many sites across a region more straightforward. I also expect to see increased investment in high-density compute infrastructure at the edge to support some types of AI workloads, like real-time inferencing,” she adds.

“Finally, I believe we will see even more of a focus on data sovereignty and bandwidth reduction as a driver of edge deployments, above and beyond requirements for low latency.”

According to IDC, examples of emerging edge use cases that are forecast to have the fastest spending growth over the 2022-2027 period include autonomous mining operations, site design and management (construction), pipeline inspection (utilities), augmented training (multiple industries), and expert shopping advisors & product recommendations (retail).

“Enterprise investments have continued to shift the past 24 months toward infrastructure expansion and greenfield deployments. Companies are acting on plans to build more robust local computing infrastructure capabilities. And through it all, customer-facing new services and products and enabling new business processes are top enterprise drivers,” adds Marcus Torchia, research vice president, data & analytics at IDC.

“Over the next two years, the share of planned investments moderately favor MEC offerings. Yet in the balance, enterprises are looking to rationalize total service provider outlays. This sets up a dynamic market of capex and opex based edge offerings competing for investment dollars through 2027.”

According to IDC, the largest investment share will continue to be led by hardware, at close to 40% of total spending, to build out edge capabilities especially driven by service provider infrastructure.

Restructuring of edge computing

Formerly seen as a technical term confined to IT circles, edge computing is now synonymous with efficiency and agility in the digital landscape. Companies are reshaping their messaging to highlight its transformative potential across diverse sectors, emphasizing speed, reliability, and reduced latency.

Jabez Tan, head of research at Structure Research tells EdgeIR that the edge computing segment will likely go through a branding pivot given the recent focus on generative AI applications, similar to how many hosting companies in the past started to rebrand as cloud computing providers as the industry reached an inflection point roughly 10 years ago.

“Many edge computing companies will likely be re-positioning themselves as AI companies or more specifically providers that cater for AI inference workloads that are thought of to be more connectivity-centric that will translate into more location sensitive deployments and infrastructure closer to certain subsets of end users,” he adds.

Echoing Tan’s sentiment, Daniel Beazer, senior analyst at Analysys Mason equally believes that there is going to be a “long overdue” pivot in edge compute in 2024.

“Four or five years ago we were talking about mobile use cases like autonomous vehicles, where workloads would follow the end user for latency reasons, moving from one network or cloud to another. This kind of ‘edge-in’ application required a central control plane: a common orchestration layer for networks and edge computing assets, and different layers of management across the stack,” he adds.

“To respond to the application’s mobility, this ‘edge-in’ orchestrator needed a routing capability in order to move applications and transfer or synchronize application data from edge to edge. However, the use cases that are emerging now are ‘cloud-out’ rather than ‘edge-in’. The edge problem to be solved is not how to support an autonomous vehicle across multiple geographical areas and networks, but the more mundane one of how to stop a supermarket’s twenty check-out tills each with more than thirty tenant applications apiece from crashing as often as they do. Autonomous vehicles will come but not as quickly as we thought and the market needs to pivot to these more mundane but more realistic use cases.”

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