cloudthinker-ai VM to Container Migration Plan
Phase 1: Application Analysis
- Profile the VM workload
- Identify all running processes and services
- Map filesystem dependencies and mount points
- Document network ports and protocols
- Identify persistent storage requirements
- Catalog environment variables and configuration files
- List cron jobs and scheduled tasks
- Assess containerization readiness
Readiness Checklist
| Criterion | Status | Notes |
|---|---|---|
| Stateless or state externalized | [ ] | |
| Single process per container viable | [ ] | |
| Logs written to stdout/stderr | [ ] | |
| Configuration via env vars possible | [ ] | |
| No dependency on host-specific paths | [ ] | |
| Health check endpoint available | [ ] | |
| Graceful shutdown handling | [ ] |
Phase 2: Container Image Creation
- Write Dockerfile based on application requirements
- Separate build-time and runtime dependencies
- Implement multi-stage builds to minimize image size
- Configure non-root user for security
- Add health check instructions
- Build and scan image for vulnerabilities
Phase 3: Kubernetes Manifest Design
- Create Deployment or StatefulSet manifests
- Define resource requests and limits
- Configure ConfigMaps and Secrets
- Set up PersistentVolumeClaims for stateful data
- Define Services and Ingress rules
- Configure horizontal pod autoscaling
- Implement pod disruption budgets
Phase 4: Storage & Networking Adaptation
- Migrate persistent data to cloud-native storage
- Replace VM networking with Kubernetes Services
- Configure service mesh if needed (Istio, Linkerd)
- Set up network policies for pod-to-pod communication
- Implement external DNS and certificate management
Phase 5: Testing & Validation
- Deploy containerized workload to staging
- Run functional tests against containerized version
- Compare performance metrics (latency, throughput, resource usage)
- Validate persistent storage behavior (writes, reads, failover)
- Test scaling behavior under load
- Verify logging and monitoring integration
Phase 6: Progressive Rollout
- Deploy to production alongside existing VM workload
- Route a percentage of traffic to containers
- Monitor error rates and latency
- Gradually increase traffic to containers
- Decommission VM workload after stabilization
Counter-Rationalizations
| Shortcut | Counter | Why |
|---|---|---|
| "We can skip some steps for this case" | Adapt the workflow steps, don't skip them | Skipped steps are where incidents and oversights originate |
| "The user seems to already know what to do" | Complete all workflow phases with the user | The workflow catches blind spots that experience alone misses |
| "This is a minor case, full process is overkill" | Scale the process down, don't turn it off | Minor cases become major when unstructured; the process scales, not disappears |
| "I'll fill in the details later" | Complete each section before moving on | Deferred details are forgotten; real-time capture is more accurate |
| "The template output isn't necessary" | Always produce the structured output format | Structured output enables comparison, audit trails, and handoff to other teams |
Output Format
- Application Profile: Dependencies, ports, storage, config summary
- Dockerfile and Kubernetes Manifests: Production-ready artifacts
- Migration Runbook: Step-by-step guide with validation checks
- Performance Comparison: VM vs. container metrics
- Rollback Procedure: Steps to revert traffic to VM
Action Items
- Complete application profiling on source VM
- Create and test Dockerfile in development
- Write Kubernetes manifests and validate in staging
- Set up CI/CD pipeline for container builds
- Execute progressive rollout to production
- Monitor containerized workload for 14 days
- Decommission source VM after sign-off