An enterprise is planning to migrate a three-tier web application from an on-premises data center to Google Cloud. Based on workload assessments, the architecture team has chosen a mixed migration approach: Rehosting (lift and shift) the legacy batch processing backend onto Compute Engine virtual machines. Replatforming (lift and optimize) the self-hosted relational database to Cloud SQL for MySQL. Refactoring (move and improve) the stateless application frontend into containerized microservices hosted on Cloud Run. You need to establish a testing and validation protocol to verify the application's functional integrity, scalability, and performance before executing the final production cutover. Which validation strategy should you implement?

Capture source performance baselines, execute functional parity tests with identical input/output scenarios across all tiers, perform incremental load testing on Cloud Run and Cloud SQL, and conduct an end-to-end migration dry run.: ### What a Comprehensive Workload Validation Protocol Is A structured **validation and testing protocol** is an essential phase in cloud migration that systematically assesses migrated workloads across multiple tiers and strategies. It ensures that workloads rehosted to **Compute Engine**, replatformed to **Cloud SQL**, and refactored onto **Cloud Run** satisfy functional specifications, performance Service Level Objectives (SLOs), and operational readiness criteria prior to cutting over production traffic. ### How It Meets the Migration Requirements - **Functional Integrity**: Running functional test cases with varied inputs and evaluating expected outputs across both the source and target environments confirms that application logic and data handling remain identical after containerization and platform modernization. - **Performance and Sizing Validation**: Establishing **baseline benchmarks** on the source systems (such as transaction durations, query latency, execution times, and resource utilization) provides reference metrics to measure target performance and prevent post-migration regressions. - **Scalability Under Load**: Performing gradual **load testing** verifies that autoscaling configurations in **Cloud Run** and capacity allocations (such as vCPUs, memory, and IOPS) in **Cloud SQL** operate reliably under realistic production traffic patterns. - **Cutover and Plan Verification**: Conducting an end-to-end **migration dry run** validates data synchronization, tests failover/fallback procedures, and calibrates the migration timeline without impacting live users. ### Key Advantages and Capabilities - **Objective Comparison**: Leverages quantifiable baseline data to uncover hidden configuration bottlenecks or network latency issues between tiers. - **Risk Mitigation**: Identifies scaling bottlenecks and query execution plan discrepancies before exposing modernized services to customer traffic. - **Operational Confidence**: Validates runbooks, replication tools, and rollback mechanisms through full-scale staging simulation. - **Iterative Refinement**: Enables sizing adjustments for Compute Engine instances and Cloud SQL tiers before committing to production sizing. ### Why This Is Optimal Combining baseline benchmarking, end-to-end functional testing, staged load testing, and migration dry runs directly addresses the challenges associated with combining rehosting, replatforming, and refactoring strategies. It ensures that every tier is rigorously verified for stability and performance.

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Evaluating Migration Strategies Workload Validation Testing

Section: Using migration methodologies, workload testing, network planning, and dependency planning

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An enterprise is planning to migrate a three-tier web application from an on-premises data center to Google Cloud. Based on workload assessments, the architecture team has chosen a mixed migration approach:

Rehosting (lift and shift) the legacy batch processing backend onto Compute Engine virtual machines.
Replatforming (lift and optimize) the self-hosted relational database to Cloud SQL for MySQL.
Refactoring (move and improve) the stateless application frontend into containerized microservices hosted on Cloud Run.

You need to establish a testing and validation protocol to verify the application's functional integrity, scalability, and performance before executing the final production cutover.

Which validation strategy should you implement?

Capture source performance baselines, execute functional parity tests with identical input/output scenarios across all tiers, perform incremental load testing on Cloud Run and Cloud SQL, and conduct an end-to-end migration dry run.

Refactor the unit test suites to mock all database dependencies, perform synthetic browser UI tests against Cloud Run endpoints, and execute a single big-bang cutover during a scheduled maintenance window.

Conduct static code security analysis on container images in Artifact Registry, enable binary authorization, and perform read-only shadow queries against the target database during non-business hours.

Execute the production cutover first, monitor the four golden signals using Google Cloud Observability in production, and dynamically rightsize Cloud SQL and Compute Engine based on live error rates.

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Evaluating Migration Strategies Workload Validation Testing

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