Cloud Migration: VM to Containers to Kubernetes.

The workload is an AEM-adjacent API or background service currently coupled to a VM. Azure supplies the cloud context; no particular managed Kubernetes product or completed migration is implied.

Packaging the process may carry forward writable local directories, scheduled tasks, host-specific certificates and fixed network addresses. Those assumptions become failure modes when replicas move between nodes or run concurrently.

Inventory runtime libraries, configuration, authentication, outbound endpoints and persistent data. Keep stateless request handling separate from durable storage and identify which dependencies stay external during the transition rather than moving everything at once.

Build a reproducible Docker image and externalize environment inputs. Replace local assumptions with explicit service contracts, define startup and shutdown behavior, and test process replacement before introducing multiple replicas.

• Locate local writes, uploads, caches and scheduled jobs; assign each a durability and concurrency requirement.
• Map DNS, TLS, firewall and egress dependencies between Azure networks and external systems.
• Declare resource requests and limits from observed workload behavior, then validate them under representative load.

First compare VM and container behavior, then validate the container behind Kubernetes routing. Introduce automated delivery after the workload contract is understood, and keep a documented return path until cutover acceptance is complete.

A persistent volume is not a backup strategy, and storage behavior depends on the chosen provisioner and access mode. Validate restore procedures, ownership and failure recovery; shared writes or scheduled work may require coordination outside the service process.

A container image improves packaging even when the eventual runtime is simpler than Kubernetes. Cluster adoption adds operational responsibilities; choose it only when orchestration, workload isolation and delivery requirements justify those costs.

Continue with the complementary design in Azure and Kubernetes: Readiness for AEM-Adjacent Container Workloads.

The expected technical gains are reproducible deployment inputs and clearer runtime dependencies. Compare old and candidate behavior before describing the migration as successful.

• Replay representative API requests and background tasks against VM and container baselines.
• Replace Pods and test node disruption without losing durable data or duplicating non-idempotent work.
• Validate DNS, certificates, outbound restrictions and external authentication from the target network.
• Rehearse rollback with changed state, queued work and active connections; verify restore evidence separately.

The hard part is discovering which behavior belonged to the application and which belonged to the machine. Once that boundary is explicit, containerization and automated delivery become reviewable engineering steps.

These sources document product behavior. The design and validation approach above are engineering proposals, not claims made by the vendors.

• Kubernetes: persistent volumes
• Kubernetes: startup, readiness and liveness probes
• Kubernetes: Deployments