Operations | Monitoring | ITSM | DevOps | Cloud

In the last few months I've had the privilege to implement and witness new and exciting Cycle platform workflows emerging. Some of these come from the evolution of our own internal work, while others I've learned from our users. Reflecting on this, I thought it would be beneficial to the community to share! In this article, I'll walk through a handful of the neatest patterns we've seen in action. So I hope you'll join me for a no fluff, real, and practical look at how to get more out of Cycle.

If you’ve ever needed to check how much CPU or memory a Docker container is using, docker stats is the command for the job. It provides real-time resource usage metrics, helping you monitor and troubleshoot containers efficiently. This guide covers everything you need to know about docker stats: how to use it, what each metric means, and how to integrate it into a larger monitoring setup.

The Calico eBPF data plane is your choice if latency is your primary concern. It was very disturbing that some benchmarking brought to our attention that eBPF had higher tail latency than iptables. The 99+% percentiles were higher by as much as a few hundred milliseconds. We did a whole bunch of experiments and we could not crack the nut until we observed that there are some occasional and unexpected TCP reset (RST) packets, but no connections were reset.

Consistent with the words of Darwin, there is a "survival of the fittest" mentality in the DevOps and container orchestration ecosystem. Back in 2019, when Cycle came to market, Kubernetes was among the popular choices for managing containers and remains one of the most prominent players in the space today.

Java remains one of the most widely used programming languages today, especially in enterprise backend systems—and for many good reasons. With each new release, Java’s robust runtime offers additional improvements in performance, security, scalability, and developer productivity. The portability of its code has proven increasingly relevant and useful as the industry embraces ARM64, making Java one of the go-to languages for modern workloads.

Kubernetes has revolutionized how organizations deploy, scale, and manage containerized applications, offering unprecedented efficiency and flexibility. However, the very characteristics that make Kubernetes so powerful—its dynamic, distributed, and ephemeral nature—also create significant challenges for monitoring. Without robust monitoring capabilities, organizations struggle to identify and resolve performance bottlenecks, optimize resource utilization, and maintain security.

With the growing complexity of cloud-native applications, DevOps teams often face challenges when setting up and maintaining Kubernetes observability. AIOps (artificial intelligence for IT operations) makes the process more manageable using AI and machine learning for monitoring, troubleshooting, and performance optimization. In this article, you’ll learn about the common challenges in Kubernetes observability and how AIOps can provide proactive and effective solutions.