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Difficulty: HardSelecting and Designing Compute and Application Platforms

A financial services organization is modernizing its transaction reconciliation architecture on Google Cloud. The architecture comprises two core workloads: (1) a stateless, containerized HTTP REST microservice that processes customer requests with highly variable traffic spikes and must scale down to zero instances when idle, and (2) a containerized batch job that processes end-of-day financial reconciliation files, running continuously for approximately 6 hours each night. The organization requires a solution that minimizes operational management overhead and eliminates compute infrastructure costs during idle periods. Which TWO compute platform selections should you recommend to meet these requirements?

  1. Deploy the stateless HTTP REST microservice to Cloud Run.Answer
  2. Execute the 6-hour financial reconciliation batch job using Cloud Run Jobs.Answer
  3. C
    Deploy the stateless HTTP REST microservice on a Google Kubernetes Engine (GKE) Autopilot cluster using Horizontal Pod Autoscaling (HPA).
  4. D
    Provision a Compute Engine Managed Instance Group (MIG) scaled via CPU utilization metrics to host the stateless microservice.
  5. E
    Deploy the financial reconciliation batch script to App Engine Standard Environment using background threads.

Answer

Deploy the stateless HTTP REST microservice to Cloud Run, and execute the 6-hour financial reconciliation batch job using Cloud Run Jobs.
Cloud Run is the optimal platform for stateless containerized microservices requiring low management overhead and scale-to-zero capabilities. For containerized batch processes that run to completion over multiple hours (up to 24 hours), Cloud Run Jobs provides a fully managed execution environment that consumes resources only during active execution.

Step-by-Step Solution

1
Analyze the operational and scaling requirements of the stateless microservice
Requirement identified for automatic scaling to zero, low management overhead, and HTTP request handling.
Cloud Run provides a fully managed serverless environment that hosts containerized HTTP microservices with zero cluster setup and automatic scale-to-zero capabilities.
2
Analyze the duration and infrastructure lifecycle requirements of the reconciliation task
Requirement identified for a containerized batch job running to completion over a 6-hour window without standing cluster costs.
Cloud Run Jobs executes task containers that run up to 24 hours and automatically cleans up resources upon completion, avoiding persistent compute billing.
3
Evaluate alternative platform choices against operational overhead and cost constraints
Rejected GKE, Compute Engine MIGs, and App Engine due to added management complexity, static baseline infrastructure costs, or execution duration limits.
GKE and Compute Engine introduce unnecessary operational maintenance, while App Engine Standard is tailored for web request handling rather than multi-hour batch runs.

Key Concept

Selecting serverless compute platforms (Cloud Run services for stateless web APIs and Cloud Run Jobs for long-running batch tasks) to minimize operational overhead and idle costs.
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Topics

Selecting and Designing Compute and Application Platforms