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This article mainly explains "how Kubernetes supports the edge computing platform". The content of the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn how to support the edge computing platform with Kubernetes.

How Kubernetes helps edge computing platforms

Some observers believe that Kubernetes is ready to host applications in marginal environments. For example, Canonical's Ammar Naqvi wrote that Kubernetes is "a key component of edge computing," and the Cloud Native Foundation (Cloud Native computing Foundation) promotes Kubernetes as an edge solution. It also sponsors KubeEdge, a complete open source platform dedicated to edge deployment based on Kubernetes, which became an incubator for CNCF last November.

Of course, these organizations are a little biased. Canonical and CNCF have made deep investments in Kubernetes, and they will benefit a lot if Kubernetes is popular as a solution for building edge computing platforms.

Still, they offered some good advice on why Kubernetes was born to fit on the edge. As of this writing, Kubernetes provides a common control plane that can be used with any type of underlying edge infrastructure, which means that it will simplify the deployment and management of workloads across a variety of edge environments. Kubernetes is also good at balancing traffic and minimizing latency, which is a priority for edge workloads. Moreover, by acting as a deployment environment for DevOps CI / CD pipelines, Kubernetes will make it easy for developers to continuously update edge applications.

In addition to vendors and developers who have a clear interest in promoting Kubernetes, there is evidence that more neutral groups also see Kubernetes as an important part of future edge computing platforms. In a report based on interviews with more than a dozen thought leaders and vendors in the cloud computing and data center industries, Omdia found that more than half of them now see Kubernetes deployed for edge workloads.

According to the Omdia report, "Kubernetes will become a technology that can manage workloads from the cloud to the edge, providing a general application management platform."

In short, we have every reason to believe that Kubernetes will become an increasingly important part of edge computing platforms in the coming years.

Kubernetes Edge Challenge

However, for Kubernetes to become a truly seamless solution for managing applications deployed at the edge, developers need to solve several challenges.

Perhaps the biggest problem is ensuring low-latency data transmission between the central data center and the edge location. In many ways, moving data quickly is the biggest challenge for edge computing; application orchestration is a secondary issue. Kubernetes itself does not optimize data transmission. This is true of the data structure, but Kubernetes does not integrate with it in a specific way.

To ensure low-latency data movement in an Kubernetes-based edge environment, developers building edge computing platforms will need to make it easier to deploy a combination of Kubernetes and data structure solutions. They may also need to improve the way Kubernetes handles internal data movement, for example, by making it easier for Kubernetes to know which internal traffic needs to be prioritized. Currently, Kubernetes does a good job of balancing incoming traffic from external endpoints, but is not very good at managing internal traffic counted per millisecond. Similarly, Kubernetes will benefit from stronger workload scheduling control. Kubernetes enables administrators to assign applications to individual nodes, which works well when all nodes are running in a single data center.

But what if the nodes are distributed in multiple edge locations and may be in a central data center? In this case, you may want to be able to manage applications hosted by independent edge locations and balance traffic between them. This is very complex in today's Kubernetes unless you have only one node at each edge, which is impossible. What Kubernetes needs, then, is a feature that allows administrators to define how workloads are placed and prioritized based on node pools in different geographic locations, not just individual nodes running in the same physical location.

Multi-cluster management is still a challenge. Initially, Kubernetes was designed to assume more or less that each organization would run a cluster and might be divided into multiple namespaces. Things have changed over the years, and most Kubernetes vendors now support multi-cluster management. But they don't do well, and managing workloads across multiple clusters is still a secondary consideration for Kubernetes developers. This is a problem for edge computing platforms, where organizations may choose to run separate clusters at each edge to isolate workloads and simplify the management of truly large-scale environments.

Conclusion

Kubernetes has many advantages that can be used as a basis for edge computing platforms, but there are also some obvious shortcomings in this respect. So some architects think that Kubernetes is not suitable for certain workloads that may be deployed at the edge, such as containerized telecommunications applications.

On the other hand, with enough development efforts, the challenges faced by Kubernetes as the foundation of edge computing platform can be solved. The real question we should ask may not be whether Kubernetes can work at the edge (for now, the answer is just as an edge solution), but whether developers are willing to put in the effort needed to make it a mature edge computing platform.

Thank you for your reading, the above is the content of "how Kubernetes supports the edge computing platform". After the study of this article, I believe you have a deeper understanding of how Kubernetes supports the edge computing platform, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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