vm-to-container-migration

name	vm-to-container-migration
enabled	true
description	Use when performing vm to container migration — provides a structured approach for migrating VM-based workloads to containers, covering application analysis, Dockerfile creation, orchestration setup, storage and networking adaptation, and progressive rollout strategies. Targets Kubernetes or similar container platforms.
required_connections	[{"prefix":"container-registry","label":"Container Registry"},{"prefix":"k8s","label":"Kubernetes Cluster"}]
config_fields	[{"key":"source_vm_os","label":"Source VM Operating System","required":true,"placeholder":"e.g., Ubuntu 22.04, RHEL 8"},{"key":"target_platform","label":"Target Container Platform","required":true,"placeholder":"e.g., EKS, GKE, AKS, OpenShift"},{"key":"application_type","label":"Application Type","required":false,"placeholder":"e.g., Java Spring Boot, Node.js, Python Django"}]
features	["CLOUD_MIGRATION","CONTAINERS","KUBERNETES"]

VM to Container Migration Plan

Phase 1: Application Analysis

1. 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
2. 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

1. Write Dockerfile based on application requirements
2. Separate build-time and runtime dependencies
3. Implement multi-stage builds to minimize image size
4. Configure non-root user for security
5. Add health check instructions
6. Build and scan image for vulnerabilities

Phase 3: Kubernetes Manifest Design

1. Create Deployment or StatefulSet manifests
2. Define resource requests and limits
3. Configure ConfigMaps and Secrets
4. Set up PersistentVolumeClaims for stateful data
5. Define Services and Ingress rules
6. Configure horizontal pod autoscaling
7. Implement pod disruption budgets

Phase 4: Storage & Networking Adaptation

1. Migrate persistent data to cloud-native storage
2. Replace VM networking with Kubernetes Services
3. Configure service mesh if needed (Istio, Linkerd)
4. Set up network policies for pod-to-pod communication
5. Implement external DNS and certificate management

Phase 5: Testing & Validation

1. Deploy containerized workload to staging
2. Run functional tests against containerized version
3. Compare performance metrics (latency, throughput, resource usage)
4. Validate persistent storage behavior (writes, reads, failover)
5. Test scaling behavior under load
6. Verify logging and monitoring integration

Phase 6: Progressive Rollout

1. Deploy to production alongside existing VM workload
2. Route a percentage of traffic to containers
3. Monitor error rates and latency
4. Gradually increase traffic to containers
5. 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