An online automotive auction enterprise operates a transaction backend on Google Cloud with a steady baseline compute footprint running 24/7 on Compute Engine instances, alongside highly variable, fault-tolerant batch workloads for image rendering and valuation reports. The cloud architecture team needs to implement a FinOps strategy to maximize cost optimization without compromising workload performance or flexibility. Which approach should the Cloud Architect recommend?
- Purchase Flexible Committed Use Discounts (CUDs) to cover the predictable baseline compute footprint, and leverage Spot VMs for the fault-tolerant batch processing workloads.Answer
- BPurchase 3-year standard Committed Use Discounts (CUDs) sized to cover the combined peak capacity of both the steady baseline footprint and the variable batch processing workloads.
- CMigrate the variable image rendering and valuation batch jobs to an always-on Google Kubernetes Engine (GKE) cluster with static node pools to guarantee instant resource availability.
- DGrant the primitive Owner IAM role to the automated cost-management service account so it can dynamically scale and modify Compute Engine instances across all projects.
Answer
Combining Flexible Committed Use Discounts (CUDs) for baseline compute capacity with Spot VMs for fault-tolerant, variable batch workloads optimizes cost while maintaining operational flexibility.
The correct strategy combines Flexible Committed Use Discounts (CUDs) for predictable 24/7 baseline compute needs with Spot VMs for stateless, fault-tolerant batch processing. This minimizes spending on steady infrastructure while capitalizing on deep discounts for interruptible workloads without incurring financial waste from over-committing.
Step-by-Step Solution
Key Concept
Optimizing Business Processes through FinOps and Cloud Cost Management
Estimated Time:1m 30s