Cluster lifecycle management

What cluster lifecycle management means in OpenShift

Cluster lifecycle management in OpenShift is about everything that happens to a cluster after the initial installation and before final decommissioning:

• Creating new clusters in a consistent, repeatable way
• Applying day-2 configuration and infrastructure changes
• Upgrading OpenShift versions and Operators
• Managing node capacity and hardware changes
• Backing up critical cluster state and planning for disaster recovery
• Decommissioning clusters safely

In OpenShift, lifecycle is managed differently depending on whether you use:

• Installer-Provisioned Infrastructure (IPI)
• User-Provisioned Infrastructure (UPI)
• A managed OpenShift service (e.g., ROSA, ARO, OSD)

Declarative cluster definition and Git-based workflows

For self-managed OpenShift (IPI/UPI), the cluster is defined largely by:

• Installation configuration (install-config.yaml)
• Infrastructure-as-Code (IaC) (Terraform/Ansible/cloud-native tools)
• Cluster configuration manifests and Operators

A common pattern is to:

1. Store cluster definitions in Git (install config, infra code, base YAML).
2. Use pipelines or GitOps tools to:
• Create new clusters from these definitions
• Apply controlled changes to existing clusters
3. Treat clusters as disposable/replaceable:
• Prefer re-creating clusters when possible for large changes
• Use automation to ensure identical environments (dev/test/prod)

This enables consistent, repeatable lifecycle operations across many clusters.

Day-1 vs Day-2 operations

Cluster lifecycle is often split into:

• Day-1 operations: Install and bring the cluster to a baseline:
• Cluster creation
• Initial networking and storage setup
• Initial identity provider configuration
• Day-2 operations: Ongoing changes and maintenance:
• Upgrades (cluster and Operators)
• Node additions/removals
• Infra changes (storage classes, networks)
• Policy, security and configuration drift management

Lifecycle management focuses mostly on Day-2, since Day-1 is covered by the deployment model chapters.

Cluster upgrades

Upgrades are central to lifecycle management: they deliver security fixes, new features, and API changes.

Types of upgrades

• Minor version upgrades: e.g., 4.13 → 4.14
  Typically require careful planning, maintenance windows, and compatibility checks.
• Patch upgrades: e.g., 4.14.0 → 4.14.7
  Usually smaller, focused on bug and security fixes, but still follow the same basic process.

Skipping multiple minor versions is generally unsupported; you move through supported upgrade paths.

OpenShift Update Service (OUS)

OpenShift includes an update service that:

• Advertises available and supported upgrade targets
• Encodes upgrade edges (which versions can upgrade to which)
• Provides information about known issues and blocked upgrades

Clusters connect (directly or via proxies/mirrors) to this service to discover safe upgrade paths.

Cluster Version Operator (CVO)

The Cluster Version Operator is the core component managing upgrades:

• Continuously reconciles the cluster to the desired OpenShift version
• Applies and monitors rollout of updated components
• Pauses or rolls back when issues are detected (within limits)
• Exposes status via:
• Web console “Cluster Settings”
• oc commands such as oc get clusterversion and oc describe clusterversion

From a lifecycle perspective, you:

• Set or change the desired version
• Monitor upgrade progress and health
• Keep the cluster in a “reconciled” state over time

Upgrade strategies and policies

Key lifecycle decisions around upgrades:

• Cadence:
• Security-focused patch upgrades (frequent)
• Feature-focused minor upgrades (less frequent, more testing)
• Channels:
• Different update channels (e.g., stable, candidate, etc.) influence which upgrades the cluster sees
• Windows:
• Plan maintenance windows that match business needs and SLOs
• For 24/7 environments, design around workload disruption (e.g., aggressive pod anti-affinity, PDB tuning)
• Pre-upgrade checks:
• API deprecation reports
• Operator compatibility and third-party integrations
• Available capacity for rolling restarts

For managed OpenShift, the provider often performs or orchestrates upgrades, but you still manage:

• Windows and blackout periods
• Application readiness and testing
• Communication and risk management

Day-2 configuration and Operators

OpenShift uses Operators extensively for day-2 management of platform capabilities:

• Cluster Operators manage core components (ingress, network, storage, authentication, etc.).
• Add-on Operators (via Operator Lifecycle Manager) manage platform services (databases, logging, monitoring add-ons, etc.).

From a lifecycle perspective:

• Many cluster-level configurations (e.g., ingress controllers, network policies, storage defaults) are applied once and then evolve slowly.
• Changes are often represented as custom resources (CRs) managed by Operators.
• Operator upgrades (and sometimes CR schema changes) are part of your lifecycle plan.

Typical lifecycle tasks include:

• Reviewing and updating cluster-wide configurations as requirements change
• Version-aligning Operators with the OpenShift version
• Cleaning up unused Operators and CRs to reduce drift and complexity

Node lifecycle and capacity management

Nodes are the physical or virtual machines that back your cluster. Managing node lifecycle is a continuous task.

Node pools and machine management

In IPI and many managed models, the Machine API and MachineSets provide:

• Declarative node groups (similar to auto-scaling groups)
• Automated provisioning, replacement, and scaling

Lifecycle operations:

• Scale node counts up/down for worker pools
• Introduce new instance types or hardware classes by creating new MachineSets
• Drain and remove old MachineSets to retire hardware or cut over to new types

In UPI or specialized environments (e.g., on-prem bare metal without Machine API), similar management is done via:

• External tooling (Ansible, Terraform)
• Manual node join/drain operations
• Integration with external autoscalers where applicable

Node maintenance

Node maintenance is a recurring part of lifecycle management:

• Kernel/OS patching
• Firmware upgrades
• Hardware replacement
• Hypervisor or cloud host maintenance

OpenShift patterns for safe maintenance:

• Cordoning: Mark node unschedulable to stop new Pods landing there.
• Draining: Evict Pods and respect PodDisruptionBudgets.
• Reboot and validation: Wait for node to rejoin Ready status and workloads to stabilize.

For tightly controlled environments, maintenance is often rolled across nodes in batches with:

• Capacity buffers to avoid overcommitment
• Pre- and post-check validation (health checks, synthetic tests)
• Scheduling integration to avoid impacting critical jobs

Autoscaling

Lifecycle plans often include both:

• Cluster autoscaler: Adjusts node pool sizes based on pending Pods.
• Horizontal Pod Autoscaler (application-level, covered elsewhere).

From a lifecycle view, you:

• Define which node pools can scale and within what min/max bounds
• Ensure underlying cloud quotas and hardware pools can support the peak
• Periodically review scaling rules as workload patterns evolve

Cluster configuration drift and policy management

Over time, many small changes can cause configuration drift between clusters or from the original design.

Lifecycle management aims to:

• Keep critical cluster configuration declarative and version-controlled
• Use GitOps or similar workflows to:
• Store “golden” cluster baselines
• Automatically reconcile clusters to those baselines
• Apply policies at scale (for multi-cluster fleets) using:
• Policy engines (e.g., Gatekeeper/Kyverno, ACM policies)
• Organization-wide standards for:
• Security and RBAC
• Network and ingress defaults
• Storage and backup policies

Drift detection and remediation are essential, especially when:

• Multiple teams have cluster-admin capabilities
• Clusters live for many years
• Regulatory or compliance requirements exist

Backup, recovery, and disaster readiness

Cluster lifecycle is tightly linked to how you protect critical cluster state.

While a separate chapter can cover techniques and tools in detail, from a lifecycle point of view you must decide:

• What needs to be backed up:
• Cluster configuration (API resources, etcd)
• Operator and platform configuration CRs
• Application-level data (PVCs, databases)
• How often and where backups are stored:
• On-cluster vs off-cluster
• Cross-zone/region replication
• How restores fit into lifecycle strategies:
• In-place restore vs create-new-cluster-and-restore
• Use of “cluster rebuild from Git” vs recovering etcd state

Mature lifecycle processes include regular restore drills to validate that:

• Backups are usable
• RPO/RTO targets are realistic
• Documentation and runbooks are accurate

Multi-cluster and fleet lifecycle management

As environments grow, you rarely manage a single cluster in isolation. Lifecycle scales to:

• Development, staging, and production clusters
• Regional clusters for latency or regulatory reasons
• Specialized clusters (GPU/HPC, edge, secure enclaves)

Key aspects of fleet-level lifecycle:

• Standardization:
• Base cluster configurations derived from a common template
• Shared operator sets and versions where possible
• Centralized management:
• Tools like Red Hat Advanced Cluster Management (ACM) or similar
• Unified views for health, policy, and upgrade status
• Staggered upgrades:
• Canary clusters upgraded first
• Gradual rollouts across clusters with rollback plans
• Lifecycle alignment with environments:
• Short-lived ephemeral clusters for testing
• Long-lived stable clusters for regulated workloads

Fleet lifecycle thinking helps avoid one-off “snowflake” clusters that become hard to maintain.

Decommissioning and end-of-life (EOL)

End-of-life planning is part of the lifecycle, not an afterthought. Typical reasons:

• Replacing clusters with new major versions or architectures
• Consolidating workload across fewer clusters
• Decommissioning data centers or cloud accounts

A controlled cluster retirement includes:

1. Quiescing workloads:
• Stop new deployments
• Move traffic and data to successor clusters
2. Data migration:
• Move application state (PVCs, databases) if needed
• Validate cutover and data integrity
3. Policy and secrets cleanup:
• Remove external credentials, keys, and integrations
4. Final shutdown:
• Back up any remaining state required by policy
• Delete cluster resources and underlying infrastructure
5. Documentation and review:
• Capture lessons learned
• Update templates and lifecycle processes for future clusters

Treating decommissioning as a defined phase avoids orphaned infrastructure, lingering security risk, and unexpected costs.

Operational maturity and lifecycle phases

Putting it all together, a typical OpenShift cluster passes through recognizable lifecycle phases:

1. Design and bootstrapping
2. Initial rollout and stabilization
3. Steady-state operations
4. Growth and optimization
5. Migration or consolidation
6. Decommissioning

Cluster lifecycle management is about:

• Defining what each phase means in your organization
• Codifying processes (runbooks, automations, policies)
• Continuously improving based on incidents, audits, and new capabilities in OpenShift

A well-managed lifecycle lets you run OpenShift clusters for years with predictable behavior, controlled risk, and minimal surprises—even as requirements, user loads, and platform versions evolve.