Implementing Self-Managed Kubernetes Clusters

In the realm of modern cloud-native application development, the ability to have applications auto-scaled to handle varying traffic is nothing short of a blessing. Scaling resources according to usage becomes a fundamental aspect of ensuring seamless user experiences.

While setting up and managing Kubernetes clusters is relatively straightforward with managed services from cloud providers like AWS (EKS), GCP (GKE), Azure (AKS), and DigitalOcean Kubernetes Cluster, the landscape changes when considering on-premises data centers. This blog post delves into the challenges and opportunities associated with implementing and maintaining self-managed Kubernetes clusters, covering distribution options, configuration techniques, best practices, essential add-ons, and potential challenges.

So when I started using and suggesting Kubernetes for production, I initially leaned towards managed services, mostly EKS and GKE. However, for deepening my understanding of Kubernetes, I delved into self-managed options through labs and pursued my CKA certification. Later on, after honing my skills, implementing self-managed Kubernetes clusters for production proved to be a valuable experience, offering numerous advantages over its challenges.

When to Use Self-Managed Kubernetes Clusters:

Self-managed Kubernetes clusters become a compelling choice when:

Advanced Orchestration Requirements: Applications requiring advanced orchestrations such as autoscaling, rolling updates, and rollbacks benefit from the flexibility and control of self-managed clusters.

Microservices Architecture: For architectures following a microservices or multiservice pattern, where services need to scale independently and communicate efficiently.

On-Premises Constraints: Organizational policies or legal constraints may limit the use of public clouds or managed clusters, making self-management the preferred option. Some organizations simply can’t use the public cloud, as they are bound by stringent regulations related to compliance and data privacy issues

Dedicated Team: Having a dedicated team for engineering and maintenance ensures proper care and understanding of the entire system.

Control Over Management Layer: Self-managed clusters provide control over the management layer, unlike fully managed Kubernetes services in the cloud that limit configuration access to the cluster master.

Multi-tenant applications: Applications designed to serve multiple customers from a single app instance benefit from Kubernetes’ ability to provide a secure isolated environment for each tenant.

Cost: Cost is probably the most important reason to run Kubernetes on-premises. Running all of your applications in the public cloud can get expensive at scale. Specifically, if your applications rely on ingesting and processing large amounts of data, a public cloud can get extremely expensive. If you have existing data centers on-premises or in a co-location-hosted facility, running Kubernetes on-premises can be an effective way to reduce your operational costs.

Options for Kubernetes Distribution:

Several options are available, including:

Kubeadm: A popular choice for bootstrapping Kubernetes clusters, offering simplicity and flexibility. Kubeadm Documentation

RKE/RKE2: Known for ease of use and reliability, suitable for both small and large-scale deployments. RKE2 Documentation

K3s: A lightweight distribution designed for resource-constrained environments, suitable for edge computing. K3s Documentation

Kubespray: A community project deploying production-ready Kubernetes clusters using Ansible. Kubespray Documentation

EKS Anywhere, VMware Tanzu, and OpenShift: Solutions from major vendors offering robust features and enterprise support. EKS Anywhere Documentation | VMware Tanzu | OpenShift Documentation

Custom Distributions: Tailoring Kubernetes clusters to specific needs, especially in scenarios where existing distributions don’t meet specific requirements.

Understanding the architecture of Kubernetes control plane through initiatives like Kelsey Hightower’s “Kubernetes the Hard Way” can enhance the expertise of your team.

Configuration and Automation:

Configuration details for each distribution are available on their official sites. Utilizing Infrastructure as Code (IaC) tools like Terraform can automate the provisioning of virtual machines, while configuration management tools like Ansible can handle packages, dependencies, and services on both master and worker nodes.

Integrating this process into a continuous integration/continuous deployment (CI/CD) pipeline using tools like Jenkins or GitHub Actions streamlines the entire setup, ensuring consistency and reproducibility. Consider implementing GitOps principles, where the entire cluster’s configuration is stored in a Git repository for version control and easy rollback of changes.

Best Practices:

Maintaining self-managed Kubernetes clusters involves several best practices, including:

Resource Planning: Understand the resource requirements of your applications and scale the cluster accordingly.

Regular Updates: Stay current with Kubernetes releases and regularly update your cluster to benefit from the latest features, enhancements, and security patches.

Backup and Disaster Recovery: Implement robust backup and disaster recovery strategies to protect your cluster data and configurations.

Monitoring and Logging: Utilize monitoring tools like Prometheus and Grafana, and implement logging mechanisms such as Elasticsearch and Fluentd to troubleshoot issues proactively and understand the cluster’s performance.

Horizontal Pod Autoscaling (HPA): Leverage Kubernetes HPA to automatically adjust the number of running pods in response to changing demand.

Custom Resource Definitions (CRDs): Use CRDs to extend Kubernetes and define custom resources specific to your applications.

Required Tools, Add-ons, and Plugins:

Enhance your Kubernetes cluster with essential tools, add-ons and plugins:

Dashboard: A web interface for Kubernetes. Dashboard Documentation

Ingress Controller: Manages external access to services within a cluster, providing routing and load balancing. Ingress Nginx Documentation

Metric Server: Collects and provides resource usage metrics. Metric Server Documentation

Cert Manager: Automates the management and issuance of TLS certificates. Cert Manager Documentation

Prometheus and Grafana: Monitoring and observability tools for tracking and visualizing performance metrics. Kube Prometheus Stack Documentation

Istio: Implements a service mesh for enhanced observability, security, and traffic management. Istio Documentation

Knative: For building and managing serverless applications on Kubernetes. Knative Documentation

KEDA: Event-Driven autoscaling for Kubernetes. KEDA Documentation

Velero: A tool for safely backing up, restoring, performing disaster recovery, and migrating Kubernetes cluster resources and persistent volumes. Velero Documentation

KubeVirt: Run virtual machines on Kubernetes. KubeVirt Documentation

ClusterMan: Autoscale and manage your compute clusters. ClusterMan Documentation

Lens: IDE for KubernetesLens

Kubectl Snapshot: Snapshot Cluster. Kubectl Snapshot Documentation

Node Problem Detector: Aims to make various node problems visible to the upstream layers in the cluster management stack. Node Problem Detector Documentation

ArgoCD: A declarative, GitOps continuous delivery tool for Kubernetes. ArgoCD Documentation

Challenges:

Identifying and addressing challenges associated with implementing and maintaining self-managed Kubernetes clusters is crucial. Common challenges include:

Resource Constraints: Managing resources efficiently, especially in on-premises environments with limited hardware.

Compatibility Issues: Ensuring compatibility between different Kubernetes components, plugins, and add-ons.

Auto-scaling: Auto-scaling based on workload needs can help save resources. This is difficult to achieve for bself managed Kubernetes clusters unless you are using IaC like Terraform with event based trigger to provision Virtual Machines as mentioned above or a bare metal automation platform such as open-source Ironic or Platform9’s Managed Bare Metal.

Troubleshooting Complexity: Diagnosing and resolving issues can be complex, necessitating a skilled and experienced team.

Scale and Performance: Ensuring the cluster can scale and perform optimally as workloads increase.

Upgrades and Rollbacks: Managing upgrades smoothly and safely, including rollback procedures in case of issues.

Security Patching: Timely application of security patches to protect against vulnerabilities.

also published at medium on the author's blog.