Kubernetes is a powerful orchestration system, however, it can be really hard to configure its deployment process. Specific apps can help you manage multiple independent resources like pods, services, deployments, and replica sets. Yet, each must be described in the YAML manifest file.

As the Kubernetes ecosystem grows, new technologies are being developed that enable a wider range of applications and use cases. The growth of edge computing has driven a need for some of these technologies to enable the deployment of Kubernetes to low-resource infrastructure to the network edge. In this blog post, we are going to introduce you to one method of deploying k3OS to the edge. You can use this method to automatically register your edge machine to a Rancher instance as a control plane.

Get Better Observability With Machine Learning Anomaly Detection. DevOps teams today are challenged with the rapid growth and complexity of infrastructure. Managing those environments only through static thresholds becomes insufficient, so to address this issue, modern DevOps teams rely on advanced ML/AI algorithms.

The Cloud Is a No-Brainer… Most companies by now have a “to the cloud” initiative that’s at one stage or another of maturity. It’s a great way to make better use of all your resources—people, time, and money. And it gives you more flexibility. You don’t have to size your entire application architecture for important but short-lived peaks, such as Black Friday for retailers or year-end ERP closing for large manufacturers, to name just a couple examples.

MicroK8s was first released in late 2018 and has seen significant adoption rates from developers and enterprises alike ever since. Taking increasing demand and curiosity around the topic, we have already given an introduction to MicroK8s as well as covered how to deploy MicroK8s locally in previous blog posts. This time, we’ll take a look at MicroK8s’ applied value, by examining common MicroK8s use cases. Among others, this includes AI/ML workflows.

Reliable systems are vital to meeting customer expectations. Downtime not only hurts a company’s bottom line but can be detrimental to reputation. Our goal at Gremlin is to help enterprises build more reliable systems using Chaos Engineering. Whether your infrastructure is deployed on bare metal in a corporate-owned data center or as Kubernetes-orchestrated microservices in a public cloud, chaos experiments can help you find system weaknesses early, before they affect customers.