Global spending on edge computing to grow nearly 15% in 2022

Global Perspective
Global spending on edge computing to grow nearly 15% in 2022
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Global spending on edge computing in 2022 is expected to increase 14.8% over 2021 spending to reach $176 billion (£129.5bn).

Strong growth is forecast to continue well into 2025, when spending will reach roughly $274 billion (£201.5bn) according to leading market research firm International Data Corporation (IDC).

IDC’s latest ‘Worldwide Edge Spending Guide‘ expects enterprise adoption of edge computing to facilitate the bulk of global growth.

Edge hardware in particular will take up most of this year’s spending (85%), with key use cases lying in content delivery networks (CDNs), virtual network functions (VNFs), and edge analytics.

CDNs and VNFs are among the use cases that will see the largest investments, owing to how capital intensive network service provision can be. However, an increasing amount of research and development is being directed towards developing more flexible edge platforms.

Enterprise spending on edge computing will be largest in sectors such as manufacturing, production asset management, smart grids, public safety, and transportation systems.

Whilst hardware spending will dominate 2022, the market for professional services in the edge industry will develop over time with a five-year CAGR of 19.6%. By 2025, professional services will account for 50% of edge spending.

In an earlier report on global infrastructure trends, the IDC identified four areas that are emerging as core edge use cases. These workloads are business intelligence, networking and security, content delivery, and text and image analytics.

Max Pepper, senior research analyst at IDC, said: “The rapid deployment of edge computing is significantly shaping workload evolution.

“As edge technology continues to expand in usage in a variety of workplace environments, we are seeing growing interest in expected concurrent workload growth in areas such as business intelligence and analytics, AI/ML-related workloads, and content workloads.”

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