A financial technology enterprise operates an online payment processing service on Google Cloud. The infrastructure consists of a steady-state backend API running continuously 24/7 on Compute Engine instances, alongside an unpredictable nightly batch data reconciliation process that runs for 2 hours. The finance team requires a cost optimization governance strategy that lowers compute expenditure for baseline workloads while executing short-lived batch jobs cost-effectively without incurring commitments for unused capacity. Which strategy should the Cloud Architect recommend?
- Purchase Committed Use Discounts for the steady-state baseline capacity and use Spot VMs for the nightly batch reconciliation jobs.Cevap
- BPurchase 3-year standard Committed Use Discounts covering 100% of peak resource usage, including the maximum anticipated capacity of the nightly batch jobs.
- CMigrate the nightly batch reconciliation jobs to a dedicated, high-availability Google Kubernetes Engine cluster with fixed node pools.
- DAssign the primitive Owner IAM role to the central FinOps automated service account so it can delete non-baseline virtual machines after batch execution.
Cevap
Purchasing Committed Use Discounts for the steady-state baseline capacity and leveraging Spot VMs for unpredictable nightly batch processing provides optimal cost savings while maintaining financial flexibility.
The solution properly aligns cloud pricing mechanisms with workload patterns: 24/7 steady-state API backends benefit from Committed Use Discounts, while transient, fault-tolerant batch workloads capitalize on the steep discounts of Spot VMs without long-term commitment risks.
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Anahtar Kavram
Workload-driven pricing model selection combining Committed Use Discounts (CUDs) for baseline resources and Spot VMs for transient batch workloads.