Worldwide spending on edge computing is expected to reach US$176 billion in 2022, an increase of 14.8 per cent over last year, according to new figures from analyst firm IDC.
Edge computing started out rather modest in its use cases but has quickly expanded across industries and scope. Five years ago an edge network was a few mid-range servers in a ruggedised container. Now Nvidia and Lenovo are deploying GPU-based AI systems.
“If we have learned anything over the last two years, the ability to quickly adapt to rapidly changing conditions is critical to business success. Organisations investing in edge computing combined with AI and modern application design will have an advantage when it comes to tackling whatever challenge comes next,” according to Dave McCarthy, research vice president for Cloud and Edge Infrastructure Services at IDC.
All told, IDC has identified more than 150 use cases for edge computing across various industries and domains. The two edge use cases that will see the largest investments in 2022 are content delivery networks and virtual network functions while the two industries making large investments in edge are manufacturing and retail, McCarthy said via email.
For manufacturing, many edge use cases revolve around process optimisation and safety, McCarthy said. These companies began investing in IoT years ago only to discover a gap between collecting data from equipment and using it to improve business. Edge computing is adding intelligence to that data and enabling operations teams to more quickly identify issues and automate remediation.
The benefits include improving the quality of products and reducing wasted materials. And in manufacturing, milliseconds matter. “The ability to detect a safety issue and take immediate action is incredibly important,” McCarthy said.
“These are use cases where the latency inherent in cloud-based computing becomes prohibitive. That is why you see cloud companies making significant investments in edge solutions that extend their cloud platforms on to the factory floor,” he said, a reference to the Industrial Internet of Things (IIOT).
Retailers are using edge computing to improve both operations and the customer experience. Modern retail stores already have a lot of technology: point-of-sales terminals, digital signage, inventory tracking, security systems, and more. Each one has its own management software, which is typically running in the cloud.
“That creates a problem for retailers that need to make quick, local decisions based on data from multiple systems. Edge computing can act as an aggregation point for this in-store data and combine multiple datasets for a more informed view of operations. It also provides a layer of resiliency if the cloud or network becomes unavailable, ensuring business continuity,” said McCarthy.
Many of these use cases are enabled by AI. For example, video cameras are becoming a universal sensor, detecting everything from hardware defects to theft to whether someone isn’t wearing the appropriate safety equipment. As these use cases get more sophisticated, analysing larger datasets and detecting more conditions, the edge infrastructure required is also changing. Hence the arrival of the GPU at the edge.
Some these use cases are displacing older systems and some are net-new, said McCarthy. In either case, the current wave of edge computing is leveraging concepts developed in the cloud, such as cloud-native applications that are built on containers.
“For some, this means replacing standalone servers and storage with a hyperconverged system that is more flexible and easier to mange remotely. It creates the perfect infrastructure for modern applications and enables organisations to be more agile in how they develop and deploy new capabilities,” he said.
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This UrIoTNews article is syndicated fromGoogle News