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Difficulty: HardPlanning and Assessing Cloud Compute Solutions

A financial analytics firm is designing a cost-effective Google Cloud compute environment for two distinct workloads:

1. A nightly batch calculation task that is fully fault-tolerant, can resume from checkpoints if interrupted, requires specific custom OS kernel modules, and runs for 6 hours.
2. An event-driven microservice that receives unpredictable HTTP webhooks throughout the day and requires scaling down to zero instances during idle periods with minimal management overhead.

Which TWO compute configurations should the engineering team select to satisfy these operational and architectural requirements?

  1. Deploy Compute Engine Spot VM instances configured with a custom VM image for the nightly batch calculation task.Answer
  2. Deploy Cloud Run to host the event-driven microservice receiving unpredictable HTTP webhooks.Answer
  3. C
    Deploy GKE Autopilot clusters to run the nightly batch calculation task with custom kernel modules.
  4. D
    Deploy Cloud Functions (1st gen) to execute the 6-hour nightly batch calculation task.
  5. E
    Provision standard On-Demand Compute Engine instances attached to a 3-year Committed Use Discount for the nightly batch workload.

Answer

Select Compute Engine Spot VMs with custom images for the fault-tolerant batch task requiring custom kernel modules, and select Cloud Run for the HTTP webhook microservice that needs automatic scale-to-zero capabilities.
Compute Engine Spot VMs offer up to 90% cost reduction for fault-tolerant jobs while permitting full OS kernel customization. Cloud Run provides zero-management HTTP container execution with automatic scale-to-zero capabilities.

Step-by-Step Solution

1
Analyze the workload requirements for the nightly batch calculation.
The task runs for 6 hours, is fault-tolerant/checkpointable, and requires custom OS kernel modules.
Fault-tolerant batch processing aligns with Spot VMs for maximum cost savings, and OS kernel customization requires Compute Engine Virtual Machines where full OS access is permitted.
2
Analyze the requirements for the event-driven microservice.
The service handles unpredictable HTTP webhooks, requires scaling down to zero when idle, and demands low operational overhead.
Cloud Run natively supports containerized HTTP requests, scales down to zero automatically during idle windows, and abstracts server management.
3
Evaluate why alternative compute options fail the specified constraints.
GKE Autopilot restricts OS kernel customization; Cloud Functions cannot run 6-hour jobs due to timeout limits; 3-year Committed Use Discounts incur unnecessary continuous costs for intermittent 6-hour daily jobs.
Architectural trade-offs dictate selecting service boundaries matched strictly to statefulness, execution duration, and OS customization level.

Key Concept

Selecting GCP compute services based on OS access requirements, workload fault tolerance, execution time limits, and autoscaling characteristics.
Estimated Time:2m 30s
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Topics

Planning and Assessing Cloud Compute Solutions

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