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Difficulty: MediumOptimizing Business Processes through FinOps and Cloud Cost Management

A global multiplayer game studio runs its backend infrastructure on Google Cloud across multiple projects bound to a central Cloud Billing account. The infrastructure features two main workloads: a stateless, containerized matchmaking service that experiences unpredictable traffic bursts during special in-game events, and a centralized BigQuery telemetry warehouse used for real-time player analytics. The studio wants to establish FinOps governance and cost optimization without compromising service performance or increasing management overhead. Which TWO recommendations should the lead Cloud Architect make?

  1. Run the stateless matchmaking service on Cloud Run and leverage Flexible Committed Use Discounts (CUDs) at the billing account level to cover baseline compute spending across projects.Answer
  2. Configure BigQuery edition reservations with baseline slot capacity for predictable analytics and enable slot autoscaling to handle intermittent query spikes.Answer
  3. C
    Deploy the stateless matchmaking service onto a dedicated Google Kubernetes Engine (GKE) cluster with fixed node pools to maintain constant baseline infrastructure.
  4. D
    Purchase 3-year resource-based Committed Use Discounts (CUDs) scaled to cover the maximum predicted peak demand of the stateless matchmaking service.
  5. E
    Grant Project Owner primitive IAM roles to senior backend developers so they can directly modify resource sizing and optimize billing settings in real time.

Answer

The studio should deploy the stateless matchmaking service on Cloud Run using Flexible Committed Use Discounts for baseline spending, and implement BigQuery edition reservations with autoscaling slots for telemetry analytics.
Combining serverless execution via Cloud Run with Flexible Committed Use Discounts guarantees that baseline spend is discounted while burst traffic scales dynamically without idle cluster costs. Meanwhile, BigQuery edition reservations paired with autoscaling slots optimize analytics expenses by providing dedicated slots for base queries and dynamic capacity for peak analysis.

Step-by-Step Solution

1
Analyze compute workload requirements and select an optimal cost-effective platform.
Cloud Run efficiently handles containerized stateless workloads with zero-to-N autoscaling, avoiding baseline cluster maintenance costs.
Serverless container execution fits unpredictable traffic spikes while eliminating idle compute infrastructure overhead.
2
Apply appropriate FinOps commitment models for compute expenditure.
Flexible CUDs apply hourly spend commitments across Cloud Run and Compute Engine across all projects, offering flexibility without locking into specific machine types.
Bursty workloads require spending-based flexibility rather than rigid 3-year static node reservations scaled for peak usage.
3
Optimize analytical database queries and slot capacity governance.
Combining BigQuery edition reservations for predictable queries with slot autoscaling handles peak analytics demands cost-efficiently.
Autoscaling slots ensure performance during heavy analytical bursts without paying for idle capacity continuously.

Key Concept

FinOps Cost Optimization and Compute Governance
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Topics

Optimizing Business Processes through FinOps and Cloud Cost Management