Stabilizing RKE2 Clusters in a Secure DoD Environment
In any modern enterprise, keeping Kubernetes clusters secure and stable is already a challenge. In a secure DoD environment, where applications directly support mission-critical operations, that challenge is magnified tenfold. When I was tasked with stabilizing a fleet of Rancher-managed RKE2 clusters, the stakes were high: outdated infrastructure and long patch cycles had already led to application outages that could not be tolerated in an environment where uptime is directly tied to operational readiness.
The core issue came down to technical debt and process friction. Kubernetes and Rancher release updates at a rapid pace, often to patch critical vulnerabilities. But with a mix of dispersed teams, strict security approval processes, and no automated way of tracking or deploying updates, these environments often lagged behind by months. The risk was clear: every day running outdated versions left clusters exposed to unpatched CVEs, performance degradation, and compatibility issues with container runtimes or security tooling. On top of that, making changes in production wasn’t simple—every action had to be deliberate, approved, and executed without impacting the 99.9% uptime required for applications running on the clusters.
To break the cycle, I focused on automation as the foundation of stability. First, I built an alerting mechanism to notify teams the moment new Rancher or RKE2 versions were released, ensuring we never lost visibility into patch availability. From there, we integrated GitLab pipelines to standardize and accelerate the deployment of new releases, reducing the manual burden of upgrades. To address the most disruptive part of cluster maintenance—node replacement and patching—we introduced Ansible playbooks that automatically cordon and drain nodes in a safe and consistent manner. These workflows not only reduced human error but also ensured that workloads were gracefully migrated, protecting application availability during updates.
The results were immediate and measurable. What used to be a stressful, drawn-out process prone to outages became a predictable, repeatable system. Application downtime dropped significantly, even as the frequency of patching and upgrades increased. Most importantly, the organization gained confidence that its clusters were both secure against emerging threats and resilient enough to support mission needs without interruption.
In the end, the lesson was clear: in a high-stakes environment like the DoD, stability isn’t achieved by avoiding change—it’s achieved by embracing it, automating it, and executing it with precision.
