High availability in Kubernetes ensures your cluster stays operational even during failures. This guide explains how to design and deploy a fault-tolerant Kubernetes cluster, covering essential components, redundancy strategies, and configuration steps.

Key Takeaways:

• Why High Availability Matters: Prevent downtime caused by hardware failures, network issues, or maintenance.
• Core Strategies:
  • Use multiple control plane nodes to eliminate single points of failure.
  • Distribute worker nodes across zones or regions for resilience.
  • Implement load balancers to manage traffic and ensure smooth failovers.
• Critical Components:
  • API server, etcd database, scheduler, and controller managers need redundancy.
  • Choose between stacked or external etcd topologies based on your setup’s complexity and scale.
• Deployment Steps:
  • Use kubeadm to set up the cluster.
  • Configure load balancers, health checks, and worker nodes.
  • Test failovers and backup processes regularly.

High availability requires careful planning, robust infrastructure, and ongoing testing to ensure consistent performance and uptime.

[ Kube 1.5 ] Set up highly available Kubernetes cluster step by step | Keepalived & Haproxy

Planning Your High Availability Kubernetes Cluster

When building a high availability (HA) Kubernetes cluster, it’s crucial to align your design with clear business and technical goals. Without thoughtful planning, you might end up with a system that’s either over-complicated or too fragile to meet your availability needs. Below, we’ll explore the core considerations and architectural decisions to help you strike the right balance.

Assessing Business and Technical Requirements

Start by defining your tolerance for downtime and data loss. These parameters will shape every technical choice you make for your cluster.

• Recovery Time Objective (RTO): This measures how quickly your systems need to recover after a failure. For example, if your business demands systems to be operational within 5 minutes, you’ll need automated failover processes and pre-configured standby resources. On the other hand, if longer recovery times are acceptable, you might opt for simpler, more cost-effective solutions that involve manual intervention.
• Recovery Point Objective (RPO): This determines how much data loss is acceptable. For instance, a financial trading platform might require zero data loss, necessitating synchronous data replication. Meanwhile, an e-commerce platform might tolerate a small gap in data to reduce system complexity.

You’ll also need to define your availability target. For reference:

• 99.9% uptime permits about 8.77 hours of downtime annually.
• 99.99% uptime reduces that to roughly 52.6 minutes.

In addition, consider your application’s traffic patterns and scaling needs. Predictable traffic spikes require different strategies compared to applications that experience sudden, unpredictable surges. Resource-intensive workloads may call for specialized node pools with tailored hardware setups, which will influence how you distribute workloads across zones.

These metrics form the foundation of your cluster architecture, balancing technical efficiency with business demands. The next step is to determine how geographic distribution affects your design.

Choosing Regional vs. Zonal Architectures

The way you distribute your cluster geographically plays a big role in its resilience. Both zonal and regional architectures offer distinct advantages depending on your needs.

• Zonal Architectures: These deploy resources across multiple availability zones within a single region. They protect against individual data center failures while maintaining low latency between components. This setup is well-suited for handling localized issues like power outages or network failures within a specific zone.
• Regional Architectures: These distribute resources across multiple geographic regions, offering protection against large-scale disasters like natural events or regional network outages. However, this approach often introduces higher latency, which can impact the performance of components like etcd and overall cluster responsiveness.

Regional deployments work best for applications with global user bases or when regulations require data to be stored in specific countries. They’re also ideal for organizations with strict disaster recovery needs.

For most HA setups, a multi-zone control plane offers a balanced approach. By placing control plane nodes across three availability zones within a single region, you ensure that etcd can maintain quorum even if one zone fails. This approach delivers fault tolerance without the latency drawbacks of cross-region communication.

Worker nodes can follow similar distribution patterns, but there’s more flexibility here. Stateless applications can run on any node, while stateful workloads may require careful placement to ensure data remains accessible and performance stays consistent.

Networking and Redundancy Requirements

A robust networking strategy is key to supporting both north-south traffic (client-to-cluster) and east-west traffic (communication between cluster components). Redundancy at multiple layers is non-negotiable.

• Use multiple load balancers with /healthz checks distributed across zones. Each load balancer should be capable of handling the full traffic load to eliminate single points of failure.
• Ensure network path diversity to guard against connectivity issues. Traffic between zones should have multiple physical routes, and your cloud provider or data center must offer redundant network infrastructure.
• For DNS and service discovery, deploy multiple DNS servers with appropriate TTL configurations for cluster endpoints. While DNS-based load balancing adds redundancy, be aware that client-side DNS caching can delay failover detection.

When working with persistent volumes, ensure that storage remains accessible during zone failures. This might involve cross-zone replication or distributed storage systems. Also, plan for sufficient network bandwidth to handle data synchronization during recovery events, especially for large datasets.

If you’re considering Serverion’s infrastructure, their global data center locations offer strong support for both zonal and regional architectures. Their VPS and dedicated server options provide a solid compute foundation for your cluster nodes, while their colocation services enable hybrid deployments that combine the flexibility of cloud with the control of on-premises setups. Plus, their redundant network infrastructure is built to handle the connectivity demands of high availability clusters, ensuring your Kubernetes deployment stays resilient and reliable.

Core Components and Topologies for High Availability

Creating a highly available Kubernetes cluster means understanding the essential components that keep your system running and deciding how to arrange them. These decisions directly affect your cluster’s reliability, performance, and complexity.

Key Kubernetes Components for HA

The control plane is the backbone of your Kubernetes cluster. It includes the API server, scheduler, controller managers, and etcd, all of which play critical roles in maintaining operations.

• API Server: The API server is the central hub, processing requests from kubectl, worker nodes, and other internal components. Running multiple API servers across zones ensures that losing one server doesn’t disrupt the cluster.
• Scheduler: The scheduler assigns pods to nodes based on available resources and defined constraints. While you can deploy multiple schedulers for redundancy, only one actively makes decisions at a time. If the active scheduler fails, another steps in.
• Controller Managers: These continuously monitor the cluster’s state, ensuring resources align with the desired configuration. They use leader election, so only one instance actively manages resources, while backups stand ready to take over if needed.
• etcd: This distributed key-value store holds configuration data, secrets, and state information. It uses a consensus algorithm, requiring a majority of nodes (quorum) to function. For instance, a three-node etcd cluster can handle losing one node without losing functionality.
• Kubelet: Running on each worker node, the kubelet communicates with the API server to receive pod specs and report node status. While kubelets themselves aren’t clustered for high availability, having multiple worker nodes ensures workloads continue even if some nodes fail.

Once you understand these components, the next step is to choose a topology that best fits your needs.

HA Topologies: Stacked vs. External etcd

When organizing control plane components, you have two main options, each with its own trade-offs in terms of reliability and complexity.

• Stacked etcd Topology: Here, etcd instances are co-located with control plane components on the same nodes. This setup is simpler to deploy and requires fewer servers. However, it introduces a risk: if a control plane node fails, both the control plane services and an etcd member are lost.
• External etcd Topology: In this approach, etcd runs on dedicated nodes separate from the control plane. This separation provides better isolation and allows independent scaling of resources, making it a good choice for larger or more demanding environments.

Feature	Stacked etcd	External etcd
Setup Complexity	Easier to deploy and manage	Requires more nodes and management
Resource Isolation	Shared resources with control plane	Dedicated resources for etcd
Failure Impact	Both etcd and control plane affected	Failures managed independently
Scalability	Limited by shared resources	Independent scaling possible

For smaller deployments, a stacked topology offers a simpler starting point with sufficient redundancy. On the other hand, larger clusters or those with strict uptime needs may benefit from the added resilience of an external etcd setup.

With your topology chosen, the next step is configuring load balancers to ensure smooth operations.

Load Balancer Configuration

Load balancers play a key role in distributing API requests across multiple API servers and managing failovers when servers go down. Without one, clients would need to track individual API server endpoints, complicating the process.

A properly configured load balancer should:

• Perform health checks on the /healthz endpoint of each API server. An HTTP 200 response indicates readiness, while an HTTP 500 signals a problem. Health checks should run every 10–15 seconds with a 5-second timeout to ensure quick detection of issues.
• Distribute requests evenly, as Kubernetes API servers are stateless. Session affinity isn’t typically required, allowing traffic to flow smoothly even during server failures.
• Handle SSL termination. You can offload TLS processing at the load balancer to reduce the API servers’ workload or pass encrypted traffic through for end-to-end encryption if compliance demands it.

For added redundancy, deploy multiple load balancers across different zones. DNS-based load balancing can provide another layer of failover, but keep in mind that DNS caching may cause delays during transitions.

If you’re using Serverion’s infrastructure, their dedicated servers provide robust control plane performance, while VPS options are ideal for smaller setups. With data centers worldwide, Serverion supports multi-zone configurations and offers load balancing tools to handle traffic distribution effectively, even in challenging network conditions.

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Step-by-Step Guide: Deploying HA Kubernetes with kubeadm

Now that you’re familiar with the components and topologies, it’s time to build your highly available Kubernetes cluster. We’ll use kubeadm for this guide – it simplifies deployment while still letting you control the configuration.

Infrastructure Setup and Prerequisites

Start by preparing your infrastructure to handle production workloads.

You’ll need at least three control plane nodes (minimum: 2 CPU cores and 4 GB RAM; recommended: 4 cores and 8 GB RAM) and two or more worker nodes (minimum: 1 core and 2 GB RAM). Install a supported Linux distribution, such as Ubuntu 20.04/22.04, CentOS 8, or Rocky Linux 9, on all nodes. Ensure each node has a unique hostname and can communicate with the others over the network.

Disable swap on all nodes since Kubernetes doesn’t support it. Run sudo swapoff -a and comment out any swap entries in /etc/fstab to make the change permanent. Open the necessary ports: 6443 (API server), 2379-2380 (etcd), 10250 (kubelet), and 10251-10252 (scheduler/controller-manager).

Install a container runtime on each node. Most users opt for containerd, which is well-supported. Configure it to use systemd as the cgroup driver to align with Kubernetes’ default settings. Then install kubeadm, kubelet, and kubectl on all nodes, ensuring they all run the same Kubernetes version to avoid compatibility issues.

Set up a load balancer before initializing the cluster. The load balancer can be hardware-based, part of a cloud provider’s offerings, or a software solution like HAProxy. It should listen on port 6443 and forward traffic to the API servers on your control plane nodes.

For a globally fault-tolerant setup, consider using dedicated servers for control plane nodes and VPS instances for worker nodes.

Setting Up Control Plane Nodes

The first control plane node is the foundation of your cluster. Instead of using command-line flags, create a kubeadm configuration file to define your HA settings.

Create a file named kubeadm-config.yaml and include your cluster configuration. Set the controlPlaneEndpoint to the address and port of your load balancer. For a stacked etcd topology, kubeadm will configure etcd on the control plane nodes automatically. If you’re using external etcd, specify the endpoints in this file.

Initialize the first control plane node with the following command:
sudo kubeadm init --config=kubeadm-config.yaml --upload-certs
The --upload-certs flag simplifies the process of distributing certificates to other control plane nodes. This step takes a few minutes and will output join commands for adding additional nodes.

Store these join commands securely – they contain sensitive tokens. Next, configure kubectl on the first control plane node:
mkdir -p $HOME/.kube && sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config && sudo chown $(id -u):$(id -g) $HOME/.kube/config

Before adding more nodes, install a CNI plugin suitable for your environment.

Use the join command from the initialization output to add the remaining control plane nodes:
sudo kubeadm join load-balancer-ip:6443 --token <token> --discovery-token-ca-cert-hash sha256:<hash> --control-plane --certificate-key <certificate-key>
Run this command on each additional control plane node.

Verify that all control plane nodes are operational by running:
kubectl get nodes
You should see all nodes listed with a "Ready" status.

Configuring etcd and Load Balancers

Fine-tune your etcd and load balancer settings to complete the HA setup.

If you’re using a stacked etcd topology, kubeadm configures it automatically. For external etcd clusters, you’ll need to set up etcd on dedicated nodes, generate secure communication certificates, and configure each etcd member to recognize the others. Always use an odd number of etcd members (e.g., 3, 5, or 7) to maintain quorum during failures.

Check etcd health by running:
sudo kubectl exec -n kube-system etcd-<node-name> -- etcdctl --endpoints=https://127.0.0.1:2379 --cacert=/etc/kubernetes/pki/etcd/ca.crt --cert=/etc/kubernetes/pki/etcd/server.crt --key=/etc/kubernetes/pki/etcd/server.key endpoint health
All endpoints should report as healthy.

For load balancers, configure health checks to monitor the /healthz endpoint on port 6443 of each API server. Set the interval to 10 seconds with a 5-second timeout, and ensure unhealthy servers are automatically removed and re-added when they recover.

To test the load balancer, stop the API server on one control plane node (sudo systemctl stop kubelet) and verify that kubectl commands still work. Restart the service and ensure the node rejoins the cluster.

If you’re using multiple load balancers, configure them in an active-passive setup or use DNS round-robin for initial load distribution. Document failover procedures to guide your team in handling load balancer issues.

Adding Worker Nodes and Testing Cluster Health

Worker nodes are the backbone of your cluster, providing the compute power for your applications. Adding them is straightforward, but testing ensures the cluster is resilient.

Use the worker node join command provided during the initial kubeadm setup:
sudo kubeadm join load-balancer-ip:6443 --token <token> --discovery-token-ca-cert-hash sha256:<hash>
If the token has expired, you can generate a new one.

Check that worker nodes have joined successfully by running:
kubectl get nodes
All nodes should show a "Ready" status. If a node remains in "NotReady", inspect the kubelet logs with:
sudo journalctl -u kubelet -f

Deploy a test application to confirm the cluster’s health. For example, create an nginx deployment with multiple replicas:
kubectl create deployment nginx-test --image=nginx --replicas=5
Then check pod distribution across nodes:
kubectl get pods -o wide

Simulate failures to test HA functionality. For control plane nodes, stop the kubelet service on one node and confirm kubectl commands still work. If you have more than three control plane nodes, try stopping two nodes simultaneously – the cluster should remain operational as long as a majority of nodes are healthy.

For worker nodes, simulate a failure by cordoning and draining a node:
kubectl cordon <node-name> && kubectl drain <node-name> --ignore-daemonsets --delete-emptydir-data
Observe as Kubernetes reschedules pods to other nodes.

Monitor the cluster’s components with:
kubectl get componentstatuses and kubectl get pods -n kube-system
All system pods should be running, and components should report as healthy. For ongoing monitoring, use tools like Prometheus to track metrics over time.

Don’t forget to set up etcd and certificate backups. Regularly test your backup and restore procedures in a non-production environment to ensure they’re effective.

With your highly available Kubernetes cluster operational and tested, you’re ready to support continuous operations and perform routine maintenance with confidence.

Best Practices for HA Kubernetes Operations

Setting up a highly available Kubernetes cluster is just the first step. To keep it running efficiently and reliably, you’ll need to focus on ongoing monitoring, testing, and operational best practices. These steps will help you maintain performance, avoid downtime, and ensure your cluster stays resilient.

Monitoring and Maintenance

Effective monitoring is the backbone of high availability (HA). Use tools like Prometheus and Grafana to track key metrics such as CPU usage, memory consumption, network latency, and the performance of etcd. Pay close attention to etcd health by monitoring metrics like leader elections, proposal failures, and disk I/O latency. Set up alerts for critical thresholds – for instance, if CPU usage exceeds 80% across multiple nodes or if etcd latency goes beyond 100ms, immediate action is required. Regularly use the etcdctl endpoint status command to ensure all etcd members are synchronized and functioning properly.

Keep your Kubernetes components up to date with a structured schedule. Plan quarterly updates for minor releases and apply security patches as soon as they’re available. Always test updates in a staging environment before deploying them to production. When updating, handle etcd and Kubernetes separately to minimize risks – never update both at the same time.

Certificate management is another critical area. Kubernetes certificates typically expire after one year, making automated renewal a must. Use tools like kubeadm or cert-manager to handle renewals, and monitor expiration dates closely. Test your renewal processes monthly to avoid unexpected downtime caused by expired certificates.

Centralize log aggregation with tools like Fluentd or Fluent Bit. This makes it easier to correlate events across nodes and components during incident response. By implementing these monitoring and maintenance practices, you’ll catch potential issues early, helping to safeguard your cluster’s availability.

Testing Failover and Backup Procedures

Monitoring alone isn’t enough – you also need to rigorously test your failover and backup processes. Conduct monthly fault injection tests to simulate real-world failures. For example, shut down control plane nodes, create network partitions, or overload worker nodes to see how your system responds. Track recovery times for each scenario and work toward reducing them.

Regularly test etcd backup and restore procedures to ensure data integrity. Perform these tests in a separate environment to verify accuracy and measure the time it takes to restore. If your restore process exceeds your Recovery Time Objective (RTO), consider faster storage solutions or streamlining your procedures. Automate etcd backups every six hours and store them in distributed locations for added security.

Application-level failover testing is equally important. Use tools like Chaos Monkey or Litmus to randomly terminate pods or nodes during business hours. This helps identify whether your applications can handle failures without impacting users.

Create detailed runbooks for common failure scenarios. These should include step-by-step recovery instructions, escalation contacts, and decision trees for different types of incidents. Update these documents after every incident and test them with various team members to ensure clarity and usability.

Backup verification goes beyond simply creating backups. Regularly restore your cluster state in isolated environments and confirm that applications function as expected. Test full cluster restores as well as individual namespace recoveries to prepare for a range of disaster scenarios.

Designing Applications for HA

For applications to thrive in an HA environment, they need to be designed with availability in mind. Pod Disruption Budgets (PDBs) help ensure that a minimum number of replicas remain available during maintenance or scaling. For critical services, set minAvailable to a specific number of replicas rather than a percentage.

Use anti-affinity rules to prevent single points of failure. With podAntiAffinity, you can spread replicas across different nodes or availability zones. For stateful applications like databases, combine anti-affinity with topology spread constraints to evenly distribute workloads.

Configure resource requests and limits based on actual usage data. This ensures the Kubernetes scheduler can make smarter placement decisions and avoids resource contention. Review and adjust these values quarterly based on your monitoring data.

Health checks play a vital role in maintaining application readiness. Use liveness probes to detect unresponsive processes and readiness probes to manage traffic routing. Fine-tune timeout values to strike a balance – overly aggressive settings can cause unnecessary restarts, while lenient ones may allow failed pods to keep receiving traffic.

Whenever possible, design applications to be stateless. Store session data in external systems like Redis or databases instead of in-memory. This allows pods to restart or scale without affecting user sessions. For applications that require state, use StatefulSets with persistent volumes and ensure data is replicated across zones. These strategies, paired with resilient infrastructure, help ensure your applications remain available.

Using Serverion‘s Infrastructure for HA Kubernetes

Serverion’s global data center network simplifies geographic distribution, a key component of high availability. Deploy control plane nodes across multiple regions to achieve true redundancy. Their dedicated servers provide the consistent performance needed for etcd clusters, while VPS instances offer cost-effective scalability for worker nodes.

Dedicated servers from Serverion are ideal for control plane nodes because they eliminate the "noisy neighbor" effect, ensuring predictable performance. For organizations with compliance requirements or existing hardware investments, Serverion’s colocation services enable hybrid architectures. This setup allows you to combine on-premises infrastructure with their data centers, supported by high-bandwidth connections for real-time data replication and seamless failover.

Serverion’s multiple data center locations also make disaster recovery more robust. Set up standby clusters in different regions and use tools like Velero for application-level backups that can be restored across clusters. Their DNS hosting services enable automated failover by updating DNS records when a primary site goes offline.

Additionally, Serverion offers infrastructure-level protection and SSL certificate services to secure both external and internal traffic. Their server management services handle hardware monitoring, OS updates, and basic security tasks, allowing your team to focus on Kubernetes-specific operations. This combination of features provides a strong foundation for maintaining HA Kubernetes clusters.

Conclusion

Every design choice and operational step contributes to creating a reliable Kubernetes cluster. Building a highly available Kubernetes setup takes thoughtful planning, solid execution, and ongoing upkeep to maintain both its resilience and performance.

Selecting the right topology and setting up a dependable load balancer ensures uninterrupted API access. For many organizations, the stacked control plane model strikes a good balance between simplicity and dependability. Tools like kubeadm make deployment easier and help manage certificates effectively.

Operational success hinges on proactive monitoring, regular failover drills, and designing applications with features like Pod Disruption Budgets and anti-affinity rules. These measures help workloads stay steady during infrastructure hiccups, ensuring reliable performance.

Serverion’s global infrastructure adds another layer of reliability to this strategy. By offering geographic diversity and strong disaster recovery options, paired with dedicated servers, they help maintain consistent control plane performance across multiple data centers.

FAQs

What’s the difference between stacked and external etcd setups in Kubernetes, and how do I choose the best one for my cluster?

The key distinction between stacked and external etcd configurations lies in where the etcd database operates and how it’s managed. In a stacked setup, etcd runs on the same nodes as the Kubernetes control plane components. This method is easier to implement and less expensive, but it comes with a trade-off: a node failure can impact both the control plane and etcd, potentially causing significant disruptions.

In contrast, an external etcd topology places etcd on separate, dedicated machines. This approach enhances resilience and performance, especially for larger or production-grade clusters. However, it also involves greater complexity in terms of configuration and ongoing maintenance.

For smaller or less critical Kubernetes environments, a stacked setup typically meets the needs. But when it comes to large-scale or high-availability production clusters, external etcd is the preferred option to maintain reliability and stability.

What are the best practices for monitoring and maintaining a highly available Kubernetes cluster to meet uptime goals?

To keep your Kubernetes cluster running smoothly and meeting uptime expectations, you need to monitor three critical layers: infrastructure, platform, and applications. Tools like Prometheus can help you track essential metrics, while Grafana makes it easy to visualize the data. Pay close attention to metrics like CPU usage, memory consumption, pod restarts, and error rates. Setting up alerts ensures you can quickly spot and address any issues before they escalate.

When setting up your cluster, stick to best practices. Enable role-based access control (RBAC) to manage permissions effectively, organize resources into namespaces for better structure, and deploy multiple control plane nodes with load balancers to enhance fault tolerance. Regularly updating to the latest Kubernetes version and scheduling proactive maintenance are equally important. These measures not only reduce downtime but also ensure your cluster can scale to meet your business needs.

How can I design my applications for high availability in a Kubernetes cluster?

To keep your applications running smoothly in a Kubernetes cluster, start by setting up multiple replicas of your application through Kubernetes Deployments. This spreads the workload and ensures your app can handle pod failures without interruptions.

Another helpful tool is the Pod Disruption Budget. This feature helps maintain a minimum number of active pods during updates or maintenance, reducing downtime. For even greater reliability, deploy your cluster across multiple zones or regions. This setup safeguards your applications against localized outages and boosts redundancy.

Using these methods, your Kubernetes setup will be more resilient, ensuring steady performance even when disruptions occur.

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