What is intelligent edge computing?

You have heard of edge computing. You may even have built an edge architecture using platforms such as 5G or 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 that organizations ask themselves.

This is an introductory knowledge about the meaning of intelligent edge computing and why it has become important today.

Define intelligent edge computing

In short, intelligent edge computing is the application of edge computing architecture in workloads involving data analysis, machine learning, or artificial intelligence.

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

Edge architecture can be used for any type of workload. For example, retailers can use edge infrastructure to process purchase transactions in local stores, which will prevent delays or interruptions in purchases due to problems reaching a central server in the cloud. Or, companies can store their backup data in a data center located in the same city to download the data faster when it needs to perform a restore.

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

Examples of Intelligent Edge Computing 

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

One example is to monitor the home’s networked cameras and then use facial recognition AI to find out who is at home. If they detect the presence of an unknown individual without a homeowner at the scene, they can mark 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, process the data in the cloud, and then return the results, will enable faster decision-making-in such a security-sensitive use case, This is a potentially critical factor.

Cars that use sensors to analyze the physical environment are another example of a smart edge use case. The car can generate 25GB of data in one hour. If they have to transfer all data to the cloud and then apply artificial intelligence to the cloud, the results may be meaningless for vehicles that need to make immediate decisions based on the data.

Is it really different from the edge? 

You might say that the concept of intelligent edge is actually just a fashionable word, and the understanding of existing edge computing does not add much value. To some extent, this will be a fair assessment. It is a category of edge computing, or a set of potential use cases for edge architecture, not just a basic example of itself.

However, in a world where data is of little value unless it can be processed automatically and quickly, it is easy to see how the intelligent edge has become the main form of huge edge computing. There will always be other edge use cases, but the value of edge architecture may be nowhere more valuable than when there is a large amount of data that needs to be analyzed quickly without waiting for the network to move.

Therefore, as companies use the edge environment to process data more efficiently, it is expected that more and more information about intelligent edge computing will be heard.