A digital entertainment platform operates dozens of Google Cloud projects managed by separate game development studios. The workload architecture consists of a steady, predictable baseline of continuous backend telemetry processing, alongside unpredictable, short-lived spikes during live gaming events. The central platform team needs to implement a FinOps governance framework that enforces cost guardrails per studio while optimizing overall compute spend across all projects. Which architecture and cost management strategy should you recommend?
- Leverage Cloud Billing account-level Committed Use Discounts (CUDs) to cover the aggregate steady baseline compute load, deploy Spot VMs in Managed Instance Groups (MIGs) for transient spike workloads, and configure Pub/Sub-driven Cloud Functions on billing budget alerts to execute automated resource governance.Cevap
- BPurchase 3-year Standard Committed Use Discounts (CUDs) individually within each project scaled to match peak anticipated traffic for live gaming events.
- CProvision dedicated Google Kubernetes Engine (GKE) clusters with reserved zonal node pools in every project to handle baseline and spike capacity identically.
- DGrant primitive IAM Owner roles to studio lead engineers so they can manually increase regional quotas and create high-priority compute instances during live event spikes.
Cevap
The optimal strategy leverages Cloud Billing account-level Committed Use Discounts (CUDs) to cover aggregate baseline compute across projects, uses Spot VMs for transient burst workloads, and implements automated budget alert notifications via Pub/Sub and Cloud Functions.
Combining billing account-level CUDs for shared baseline workloads with Spot VMs for transient spikes delivers maximum cost savings. Programmatic billing alerts connected to Cloud Functions provide automated guardrails without over-granting security permissions.
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Anahtar Kavram
FinOps Compute Optimization and Automated Governance