workloads from remote clusters As Kubernetes continues to gain traction in the cloud-native ecosystem, the need for robust, scalable, and highly available cluster deployments has become more noticeable. While a Kubernetes cluster can easily expand via additional nodes, the downside of such an approach is that you might have to spend a lot of time troubleshooting the underlying networking or managing and updating resources between clusters.

In the rapidly evolving landscape of IT infrastructure, enterprises are increasingly moving away from traditional virtualization platforms due to rising licensing costs and the limitations these older systems impose on modern cloud-native application needs. The shift towards Kubernetes, which can manage diverse workloads such as containers, virtual machines (VMs), and bare metal environments, accelerates the migration from traditional virtualization platforms.

A common challenge with multi-cluster connectivity is that networking solutions have a prerequisite of pod-to-pod connectivity enabled between the clusters that are to be meshed. This typically requires the user to take care of the underlying network infrastructure in terms of routing.

Kubernetes is used everywhere, from test environments to the most critical production foundations that we use daily, making it undoubtedly a de facto in cloud computing. While this is great news for everyone who works with, administers, and expands Kubernetes, the downside is that it makes Kubernetes a favorable target for malicious actors. Malicious actors typically exploit flaws in the system to gain access to a portion of the environment.

System observability is an essential part of identifying performance issues within your environment because it provides a comprehensive view of how your systems are operating at a glance. Typically, observability is achieved through the collection and analysis of metrics. These metrics, generated by your applications, are deliberately incorporated by developers into the source code to offer insights into the application’s internal processes.

For DevOps and platform teams working with containers and Kubernetes, reducing downtime and improving security posture is crucial. A clear understanding of network topology, service interactions, and workload dependencies is required in cloud-native applications. This is essential for securing and optimizing the Kubernetes deployment and minimizing response time in the event of failure.

Whether an enterprise is migrating its legacy application to a cloud-native architecture or deploying a new cloud-native application, it will face the challenge of integrating with security tools such as firewalls that rely on a stable network identity for security configuration. This is due to the fact that cloud-native workloads aren’t guaranteed to have a fixed network identity.

Calico 3.28 now has enabled VXLAN checksum offload by default for environments with the kernel version of 5.8 or above. In the past, offloading was disabled due to kernel bugs. Please keep in mind, if you are upgrading to 3.28 this change will take effect after node restarts.

When setting up a Kubernetes environment with Amazon Elastic Kubernetes Service (EKS), it is crucial to understand your available networking options. EKS offers a range of networking choices that allow you to build a highly available and scalable cloud environment for your workloads. In this blog post, we will explore the networking and policy enforcement options provided by AWS for Amazon EKS.

Virtualization is a powerful technology that allows businesses to run multiple operating systems and applications on a single physical server. Kubevirt is an open-source tool that makes it easy to deploy and manage virtual machines (VMs) on Kubernetes and Calico is an open-source networking and security tool that provides easy yet robust networking and security for Kubernetes workloads.