Migrate To Gcp

How to Architect a Phased, Risk-Minimized Migration to Google Cloud Platform

The urgency isn’t to migrate fast—it’s to migrate without collateral damage. Premature cutover can create production outages or worse: silent data corruption.

The Case Against “Big Bang” Migration

Production downtime, user-facing errors, and data inconsistencies—each one alone can derail a cloud migration. Yet they're routine in “big bang” moves, where every workload shifts at once. Rollback? Frequently impossible at scale.

Instead, a phased migration reduces blast radius, isolates root causes, and enables controlled rollback.

Common symptoms of poorly staged migration:

• Traffic spikes trigger autoscale failures (bad IAM or misconfigured VPC peering)
• Partial data shows up in new systems due to incomplete replication
• Gaps in monitoring lead to untraceable 500s and timeout errors

Incremental phasing enables:

• Live validation of network, identity, and latency before user exposure
• Point-in-time snapshots for fine-grained rollback
• Parallel “smoke test” environments for each batch

Step 1: Inventory and Risk Assess Every Workload

Before Terraform scripts or VPC subnets, audit your stack. List all running workloads, their dependencies, and availability requirements. Include:

• Application tier (version, language, scaling factor)
• Dependent services (databases, caches, third-party APIs)
• Current RPO/RTO metrics (actual values, not just SLA promises)

Practical categorization:

Note: Asset metadata is often stale; cross-check against actual instance count using something like gcloud compute instances list or infrastructure-as-code state files.

Step 2: Build a Secure GCP Landing Zone

Critical mistake: deploying production workloads into a default VPC. Instead:

• Define VPCs mirroring on-prem CIDR/segmentation (usually /24 or /28 subnets for tiering).
• Implement Shared VPCs for multi-team access control.
• Configure firewall egress/ingress rules denylisting by default; open explicit service ports only (tcp:5432, tcp:443).
• Set up Identity-Aware Proxy (IAP) for administrative endpoints.
• Establish Cloud DNS private zones to avoid split-brain network resolution.

Terraform landing zone snippet:

resource "google_compute_network" "core_vpc" {
  name                    = "prod-core-vpc"
  auto_create_subnetworks = false
}
resource "google_compute_subnetwork" "web" {
  name          = "web-01"
  ip_cidr_range = "10.10.16.0/24"
  region        = "us-central1"
  network       = google_compute_network.core_vpc.self_link
}

Gotcha: Mixing UDRs (User-Defined Routes) for on-prem VPN connectivity can conflict with Google Cloud defaults—test with traceroute before production cutover.

Step 3: Match Tool to Data and Compute Type

GCP’s migration tooling is ecosystem-specific.

Example: Migrating a Postgres database
Configure Database Migration Service:

• Source: On-prem PostgreSQL 12.10

• Target: Cloud SQL, PostgreSQL 15

• Replication setup: Continuous, with initial snapshot

• Cutover command: Use gcloud beta sql for quick switchover with transaction replay

  # Example DMS error to expect if source firewall not opened:
  ERROR: connect ECONNREFUSED 192.0.2.58:5432
  

Step 4: Continuous Data Replication & Sync

Never switch production before proving real-time data sync in parallel.

• Relational DBs: Use logical replication; keep replica <5s behind primary.
• Files/objects: Schedule gsutil rsync -r -d every 30 minutes.
• Message brokers: Forward Kafka topics with dual-producer, then cut over.

Non-obvious tip: For large datasets, “snap+sync” beats full-live replication (bulk copy snapshot, then apply binary log deltas). This lowers initial sync duration; draft a runbook with “last-received offset” by topic/partition.

Step 5: Pilot Migration—Break Something Safely

Pick a low-risk workload. Provision infra, apply configs, mirror real production artifacts. After data sync:

• Shift 1-5% of user traffic using Cloud Load Balancing with backend buckets or split DNS.
• Run synthetic load tests (e.g., k6 run smoke.js) with same auth/headers as production.
• Validate:
  • No 5xx/4xx spike in Stackdriver (Cloud Monitoring)
  • Resource utilization per node (gcloud compute instances describe)
  • ACL pass/fail logs against expected egress/ingress policies

Rollback: Maintain dual-write for at least one migration window. If SLIs degrade, swing traffic back via DNS TTL (set to 120s or less during tests).

Step 6: Gradual Traffic Shift, Then Legacy Decom

For critical services, ratchet traffic in small increments (10%, 25%, 50%, 100%) via load balancer weights or DNS. At each phase:

• Monitor latency, error rate, and capacity.
• Check for problems in session stickiness, multi-region failover.
• Cut over only after at least 2x normal peak load is sustained for 24h without major incidents.

Known issue: Some legacy software (e.g., unfinished HTTP/1.0 apps) misbehave behind HTTP(S) load balancing—test at layer 7 before raising Global LB weights.

Final decom checklist:

• Remove old IAM/service accounts; verify audit logs
• Power off legacy VMs (but retain snapshots for 30 days; legal requirement in some industries)
• Archive legacy config/state for compliance

Productionization: Automation & Documentation

• Automate with both Terraform and GCP Deployment Manager—prefer one, but mixed use is common in brownfield.
• Document every migration runbook in Confluence or internal Git. Include cutover steps, rollback paths, and “fast-fail” criteria.
• Stackdriver (now ‘Cloud Operations Suite’): Set up custom metrics/alerts like custom.googleapis.com/legacy-lag-seconds for continuous cutover readiness.

Practical Example:
A recent migration of a healthcare workload required using gcloud compute project-info add-metadata --metadata enable-oslogin=TRUE to satisfy both Google- and on-prem-fed authentication. Missed this, and IAM integration failed silently.

Summary

A successful GCP migration isn’t defined by speed. It is measured by absence of critical incidents, seamless user experience, and the ability to roll back at any phase. Few phased migrations are perfect, but risk drops dramatically with up-to-date documentation, correct tool choice, and phased, tested execution. As always, verify your assumptions on non-production environments first—and remember: temporary duplication is cheaper than permanent loss.

Keywords: Google Cloud Platform migration, controlled cutover, phased deployment, workload categorization, automation, GCP networking, migration runbooks