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Difficulty: MediumDesigning Infrastructure for Business Requirements and Cost Optimization

A telemetry organization ingests vehicle diagnostic logs into Cloud Storage. Once a week, an intensive batch processing job analyzes the aggregated files. The processing run requires substantial compute capacity for approximately 6 hours, can tolerate individual node interruptions, and must complete within a 12-hour SLA window. The business objective is to minimize total compute costs while avoiding unnecessary baseline infrastructure expenditure. Which architecture should you recommend?

  1. Deploy a Compute Engine Managed Instance Group (MIG) using Spot VMs with custom instance templates and queue-based autoscaling.Answer
  2. B
    Provision a dedicated Google Kubernetes Engine (GKE) cluster with continuously running standard On-Demand nodes to guarantee zero disruption.
  3. C
    Purchase 3-year Committed Use Discounts (CUDs) for a dedicated pool of standard Compute Engine instances sized for peak weekly processing requirements.
  4. D
    Store the telemetry data in Cloud Spanner and execute analytical processing directly against the database cluster using SQL queries.

Answer

Deploying a Compute Engine Managed Instance Group (MIG) using Spot VMs with queue-based autoscaling is the optimal choice to minimize compute costs for fault-tolerant, short-duration weekly batch processing.
Using a Compute Engine Managed Instance Group (MIG) configured with Spot VMs allows the system to scale compute resources dynamically for the 6-hour processing run at up to an 80% discount compared to standard rates. Because the batch job can tolerate node interruptions and has a 12-hour completion window, Spot VMs fulfill all business and SLA constraints at minimal cost.

Step-by-Step Solution

1
Analyze workload characteristics and operational constraints
The batch job runs for 6 hours once per week, tolerates node termination, and has a 12-hour SLA window.
Fault tolerance and short operational duration make the workload ideal for pre-emptible, discounted infrastructure.
2
Evaluate GCP pricing models against usage patterns
Spot VMs offer up to 80% discount for interruptible compute, whereas CUDs and continuous On-Demand instances incur heavy costs for idle time during the remaining 162 hours of the week.
Paying continuous baseline pricing for intermittent periodic processing is financially inefficient.
3
Select compute deployment architecture
A Compute Engine MIG configured with Spot VMs scales up during the batch run and scales down to zero when idle.
This satisfies the requirement to minimize cost while meeting SLA bounds.

Key Concept

Cost optimization for fault-tolerant batch processing using Spot VMs and dynamic scaling.
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Topics

Designing Infrastructure for Business Requirements and Cost Optimization