Operations | Monitoring | ITSM | DevOps | Cloud

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.

Just like countless other business functions, cloud delivery has now shifted entirely to remote working for now and perhaps longer term. Canonical has been a remote-first company for some time, so we thought we’d take this opportunity to share some of the best practices we’ve learned along the way. Communication Effective communication between customers, engineers, and project managers is the most critical element of successful cloud delivery.

In classic Puppet deployment architecture, compile masters are widely used when the number of managed nodes goes up. Multiple compile masters sit behind a load balancer to take care of the additional workloads. It is not rare to see Puppet adopters launching the compile masters in the public cloud, such as Amazon Web Service (AWS) and Google Cloud Platform.

I’ve been writing about continuous cloud optimization for a while now, and recently, I’ve spoken with several organizations to understand any challenges they’re currently facing in their automation journey. Their insights would help us understand how we can improve our technology to better support them. I discovered two fundamental themes behind their challenges.

From smart thermostats and grocery store checkouts to public utility infrastructures and industrial manufacturing lines, the Internet of Things (IoT) is all around us—and growing larger every day. But with this rapid growth comes a number of operational challenges: IoT devices collect a large amount of data, and are often distributed across harsh, ever-changing environments.

Kubernetes has a lot of features and deployment options for running containers. One of these is the StatefulSet. In this blog post, we’ll discuss what a StatefulSet is, what it can be used for, and how to create and update them.