What is Intelligent Edge Computing?

You’ve heard of edge computing. You may have even built an edge architecture using 5G or platforms like Kubernetes. But do you have a pure edge architecture or an intelligent edge architecture? As the idea of ​​intelligent edge computing emerges more and more, this will be one of the questions organizations ask themselves.

Here’s a primer on what intelligent edge computing means and why it’s important today.

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Defining Intelligent Edge Computing

Simply put, intelligent edge computing is the application of edge computing architecture to workloads involving data analytics, machine learning, or artificial intelligence.

Generally speaking, edge architecture is one that places data or applications at the edge of the network, where users can access them with less latency or reliability delays caused by the network.

Edge architecture can be used for any type of workload. For example, a retailer could use edge infrastructure to process purchases at a local store, which would prevent purchases from being delayed or interrupted due to problems reaching a central server in the cloud. Or, a business could store its backup data in a data center located in the same city so that data can be downloaded faster when a recovery needs to be performed.

These two examples are edge computing use cases. But they are not forms of intelligent edge computing because the workloads they bring do not involve intelligent data processing or analysis.

Intelligent Edge Computing Example

To deploy an intelligent edge computing workload, you need an application that analyzes data in some way.

One example is internet-connected cameras that monitor a home and then use facial recognition AI to figure out who is home. If they detect the presence of an unknown individual without a homeowner on site, they can flag it as a security incident.

In this case, the ability to process data at the edge, rather than having to move it to the cloud, where it is processed and then returned with results, would enable faster decision making – a potentially critical factor in such a security-sensitive use case.

Cars that use sensors to analyze their physical environment are another example of an intelligent edge use case. Cars can generate 25GB of data in an hour. If they had to transfer all that data to the cloud and then apply AI to it, the results might not make sense for vehicles that need to make instant decisions based on the data.

Is it really different from Edge?

You could argue that the concept of the intelligent edge is really just a buzzword that doesn’t add much value to the existing understanding of edge computing. To some extent, that would be a fair assessment. It is a category of edge computing, or a set of potential use cases for edge architecture, rather than just a basic paradigm in and of itself.

However, in a world where data has little value unless it can be processed quickly and automatically, it’s easy to see how the intelligent edge could become the dominant form of massive edge computing. There will always be other edge use cases, but the value of edge architecture may be nowhere more valuable than in situations where there is a lot of data that needs to be analyzed quickly, without having to wait for it to move across the network.

Therefore, expect to hear more and more about intelligent edge computing as businesses take advantage of edge environments to process data more efficiently.