A media streaming company is using the Google Cloud Pricing Calculator to estimate monthly compute expenses for a video transcribing pipeline. The system requires 10 Compute Engine virtual machines running 24 hours a day, 7 days a week for steady-state ingestion, alongside a temporary pool of 30 virtual machines that run for only 3 hours each night to process video rendering jobs. Which configuration approach in the Google Cloud Pricing Calculator provides the most accurate and cost-effective monthly cost estimate for this workload?
- Apply Committed Use Discounts (CUDs) to the 10 continuous ingestion virtual machines and configure Spot VMs or hourly usage for the 3-hour nightly rendering pool.Answer
- BApply Committed Use Discounts (CUDs) to all 40 virtual machines in the calculator for a 1-year or 3-year term to maximize total discount percentages.
- CConfigure Spot VMs for the 10 steady-state ingestion virtual machines in the calculator to obtain the lowest continuous hourly rate.
- DModel the 3-hour nightly rendering batch jobs as lightweight Cloud Functions in the pricing calculator without evaluating execution runtime limits.
Answer
Apply Committed Use Discounts (CUDs) to the 10 continuous ingestion virtual machines and configure Spot VMs or hourly usage for the 3-hour nightly rendering pool.
The correct option properly separates the continuous baseline workload from the transient batch workload. Baseline workloads running 24/7 benefit from Committed Use Discounts (CUDs) in the Pricing Calculator, while short-lived fault-tolerant nightly jobs are accurately modeled using Spot VMs or actual hourly usage.
Step-by-Step Solution
Key Concept
Estimating Google Cloud Compute Engine expenses by properly matching Committed Use Discounts to baseline workloads and Spot/On-Demand pricing to transient batch workloads in the Pricing Calculator.