On-Prem VMs to GKE with Migrate to Containers Blueprint
Skip VM rehosting and modernize on-prem VMs straight into containers on GKE with Migrate to Containers. A fit assessment screens candidates, the tool generates images and manifests, and twelve-factor refinement plus externalized state make them production-ready.
What and Why
This blueprint takes on-premises VMs and modernizes them straight into containers on Google Kubernetes Engine (GKE) using Migrate to Containers, rather than first rehosting them as cloud VMs. The tool extracts an application's filesystem and runtime from a source VM and produces container artifacts (Dockerfile, image, and Kubernetes manifests) that run on GKE, compressing a two-step VM-then-containerize journey into one.
Phases
Assessment. Inventory candidate VMs: OS, processes, ports, data, and dependencies. Migrate to Containers suits stateless Linux web and application servers best; databases and complex stateful systems are poor fits.
Fit analysis. Run the Migrate to Containers fit assessment against each VM. It flags blockers (kernel modules, GUI dependencies, heavy local state) and rates containerization suitability. Reserve unsuitable VMs for managed services or traditional rehosting.
Cluster build. Provision GKE (Standard or Autopilot) with a landing zone, VPC-native networking, ingress, CSI storage, Workload Identity, and a GitOps controller such as Argo CD.
Migrate to Containers. For each suitable VM, run the migration to generate image and manifests, externalize state to Cloud SQL or persistent volumes, refine the generated artifacts toward twelve-factor (config in env, logs to stdout), and deploy to GKE.
Operations. Establish autoscaling, resource requests and limits, monitoring, and a registry-based image lifecycle. Decommission source VMs after validation.
Key Risks and Mitigations
- Fit mismatch. Trust the fit assessment; do not force stateful or kernel-dependent VMs through the tool.
- Generated artifacts need hardening. Treat tool output as a starting point; harden images and apply twelve-factor cleanup.
- Stateful data. Externalize to Cloud SQL or reliable persistent volumes before cutover.
- Skills gap on GKE. Use Autopilot and managed defaults where possible.
Recommended Tooling
Migrate to Containers, GKE with Helm, Argo CD for GitOps, Cloud SQL for externalized data, Artifact Registry, and Google Cloud Managed Service for Prometheus.
Success Metrics
Track resource utilization gains versus the VM baseline, deployment frequency, cost reduction, and mean time to recovery.
Prerequisites
VM access for the migration agent, a GKE cluster and landing zone, a container registry, externalized data targets, and a fit assessment per candidate VM.