Operations | Monitoring | ITSM | DevOps | Cloud

Our goal has always been to automate and standardize the management of infrastructure and deployment of applications across user infrastructure. Today, we are excited to announce that we've launched support for Google Cloud (GCP), expanding our natively supported providers list and further expanding the choice, flexibility, and reach the platform offers to current and new users. With this new integration, users can deploy, manage, and scale their containerized workloads to GCP compute infrastructure.

More than anything else, Kubernetes troubleshooting relies on the ability to quickly contextualize the problem with what’s happening in the rest of the cluster. As complicated as this may sound, SPEED is really the name of the game. After all, more often than not, you will be conducting your investigation under the glow of fires burning bright in production. Getting relevant context quickly and seeing things holistically is exactly what Komodor was created for.

The lockc project provides mandatory access controls (MAC) for container workloads. Its goal is to improve the current state of container/host isolation. The lockc team believes that container engines and runtimes do not provide enough isolation from the host, which I describe later in the “Why do we need it?” Section. In this blog post, I’ll provide an introduction to lockc, discuss why you need it and show you how to try it out for yourself.

Monitoring is a critical part of any computer system that has been brought in to a production-ready state. No IT system exists in true isolation, and even the simplest systems interact in interesting ways with the systems “surrounding” them. Since compute time, memory, and long-term storage are all finite, it’s necessary at the very least to understand how these things are being allocated.

While the global spend on artificial intelligence (AI) and machine learning (ML) was $50 billion in 2020 and is expected to increase to $110 billion by 2024 per an IDC report, AI/ML success has been hard to come by—and often slow to arrive when it does. There are four main impediments to successful adoption of AI/ML in the cloud-native enterprise.

As a software engineer, leveraging GitHub is one of those tools that transcend your personal and professional development activities. Bringing open source to the masses, GitHub is a familiar platform for many. A newer addition to the GitHub Platform is GitHub Actions which was originally a workflow engine, now expanding into CI/CD.