Migrate To Google Cloud

Seamless Migration of Legacy Applications to Google Cloud: Tactics for Minimal Downtime

Downtime during legacy application migration carries real business risk—lost transactions, support calls, angry users. Yet, large-scale moves to Google Cloud, if executed with engineering discipline, often yield downtime windows measured in minutes.

Stop Assuming Migration Requires a Rewrite

It's common to inherit an aging Java or .NET monolith, tightly coupled to an on-prem Oracle database. Full rewrites rarely deliver on time. Instead, prioritize migration approaches that keep critical systems operational, deferring modernization until after cloud migration is stable.

Inventory: Detailed Mapping Comes First

Migration failure often roots in incomplete system knowledge.

• Component discovery: Use nmap, lsof, or GCP’s Application Discovery tools to enumerate all running services and open ports.
• Dependency tracing: Map out internal and external integrations. Check for hard-coded IP addresses, deprecated APIs, and encrypted file mounts.
• Peak usage profiling: Extract traffic patterns from nginx or ELB logs; record batch window overlaps.
• Data lineage: Track not only customer transactions, but also scheduled exports, CSV-based partner interfaces, and any nightly ETL pipelines.

Document everything. Surprises are expensive after cutover.

Migration Strategy—Pick Your Poison

Three practical options, each with its own trade-offs:

Note: Critical workloads (finance, healthcare) typically start with rehost. Large stateful workloads may see side effects (e.g., clock skew during VM import; watch for drift in systemd logs).

Environment Prep—Not Just About Spinning Up VMs

1. Network Topology:

   • Implement shared VPCs for multi-project environments.
   • Strict firewalling via IAM conditions—curb lateral movement.

2. Connectivity:

   • VPN or Cloud Interconnect.
     Use dynamic routing (BGP) to avoid static route headaches.

3. Resource Parity:

   • Match machine types (n2-standard-8, not just "8 vCPU") and disk IOPS.
   • Set up test clusters using gcloud beta compute instances create before production cut.

4. Monitoring:

   • Stackdriver (now Cloud Operations Suite): pre-integrate log sinks for ERROR, CRITICAL events.
   • Set alerting on key KPIs: disk latency, DB CPU, API error rates.

5. Database Prep:

   • Stand up Cloud SQL, Spanner, or Memorystore—choose based on scale, not just migration ease.

Data Replication: Close the Consistency Gap

This is the trap zone for many teams.

• Transactional databases: Use native replication (Oracle Data Guard, SQL Server Always On) when possible. For database engines without cloud parity, investigate third-party solutions like SharePlex, or build a Dataflow streaming pipeline.
• Batch workloads: Preserve consistency. Schedule down-time for large table imports using gsutil, then enable ongoing CDC (Change Data Capture).

Example—Oracle to PostgreSQL via DMS (Database Migration Service):

gcloud beta sql migration jobs create --source=<oracle-conn> --destination=<cloudsql-conn> --type=cdc

Expect minor datatype mismatches; test all critical procedures (e.g., stored procedures importing invoice batches).

Gotcha: Replication lag up to several minutes is common before tuning. Mitigate by freezing writes just before final cutover.

Blue-Green Cutover: The Only Sensible Option for Stateful Systems

• Deploy “green” (new) environment in parallel—isolate ingress to internal team for smoke testing.
• Run dual-write validation for idempotent operations (if possible) to surface integration drift.
• Switch routing via Cloud Load Balancer or change DNS TTL to low values (<60s) hours in advance.
• Monitor metrics side-by-side for at least 24 hours before full traffic shift.

Rollback is non-negotiable: always keep on-prem infra hot until at least two full business cycles are complete post-migration.

Test—Beyond Unit and Integration

• End-to-end workflows in production-like staging.
  • Run synthetic transactions (e.g., simulated purchases) through the “green” stack.
• Performance against worst-known batch job:
  • Schedule largest file imports and most expensive DB queries.
• Failure simulation:
  • Inject network faults (tc netem on VM or GKE node pool) to confirm resilience.

Execution: Minute-by-Minute Checklist

1. Announce freeze to stakeholders.
2. Finalize delta data sync (verify lag with custom scripts, e.g. check last primary key, not just record count).
3. Update DNS or Load Balancer configuration.
4. Actively monitor error logs and synthetic user flows.
5. Roll back instantly if error rates spike—do not attempt quick, piecemeal fixes mid-cutover.

Field Example: Retail B2B Platform Migration (2023)

• Source: On-prem EBS-backed VMs running .NET Core 2.1, SQL Server 2014, Windows Server 2016.
• Approach:
  • Used Migrate for Compute Engine for initial rehost, with backup “plan B” images kept on old infrastructure for nine days.
  • Set up non-default Cloud Interconnect with redundant on-prem routers due to past provider instability.
  • Continuous SQL transaction replication with AWS Data Migration Service, customized for cross-cloud DB migration.
  • Cutover during scheduled maintenance—slightly delayed by Windows Activation Key mismatch (0x8007232B).
• Aftermath:
  • Logged 3 minutes of intermittent API 500 errors due to missed connection string swap in a sidecar service.
  • Full rollback unnecessary; remediated via hotfix and redeploy.
• Tip: Test under high concurrency beforehand—file handle exhaustion errors can cause cascade failures that rarely surface in lab conditions.

Final Note

Legacy migration is a discipline of constraint: minimal downtime, measurable risk, controlled blast radius. Google Cloud tooling (particularly GKE and Migrate for Compute Engine) shorthands much of the manual labor, but only if inputs—complete inventories, accurate testing, rollback plans—are in place. The “one click migration” is fiction. Methodical, informed, engineering-driven execution isn't.

References:

• Google Cloud Migration Center
• GKE Release Notes
• Known issue: GCP Identity-Aware Proxy does not always propagate original client IP through HTTP headers—affects IP-based audit logging post-move.

Not perfect, improves over time. That’s migration.