Your organization is deploying containerized database workloads (AlloyDB Omni) on Google Compute Engine virtual machines in production. Performance analysis and sizing assessments define the following technical and operational requirements: The database engine operates optimally with 8 vCPUs and requires 64 GB of system RAM to keep its active working set cached in memory (an 8 GB RAM per vCPU ratio). Allocating additional vCPUs beyond 8 yields no performance gains and incurs unnecessary licensing and compute overhead. The database must run continuously in production without sudden preemption or unexpected terminations. Which compute provisioning strategy should you implement to optimize resource allocation and cost?

Provision N2 virtual machines using custom machine types configured with exactly 8 vCPUs and 64 GB of RAM.: ### What Custom Machine Types Are **Custom machine types** in Google Compute Engine allow you to customize the exact number of **vCPUs** and amount of **memory (RAM)** for a virtual machine instance, rather than selecting from predefined machine types. ### How It Meets the Requirements - **Targeted Resource Ratio**: The database workload requires an exact ratio of **8 GB of RAM per vCPU** (8 vCPUs and 64 GB RAM). Custom machine types allow provisioning precisely 64 GB of RAM for 8 vCPUs without forcing the adoption of extra compute cores. - **Avoiding Over-Provisioning**: Predefined standard machine types (like `n2-standard-16`) provide 4 GB per vCPU, meaning 64 GB of RAM would mandate purchasing 16 vCPUs—doubling vCPU costs for compute capacity that provides no performance benefit. - **Production Reliability**: Standard on-demand custom VMs run continuously without the risk of sudden preemption. ### Key Advantages - **Tailored Resource Allocation**: Prevents spending on underutilized vCPUs or RAM. - **Optimized Memory Ratios**: Perfectly aligns with memory-heavy database working sets and buffer pool requirements. - **Predictable Performance**: Ensures steady-state baseline performance on the **N2 machine family**. - **Cost Efficiency**: Minimizes overall cloud expenditure by billing only for the exact resources provisioned. ### Why This Is Optimal Using custom machine types satisfies the specific 8:1 memory-to-vCPU ratio needed by the database while preventing the waste associated with predefined machine types that force higher core counts.

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Alloydb Omni Compute Resource Optimization Ratio

Section: Compute resource provisioning

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Your organization is deploying containerized database workloads (AlloyDB Omni) on Google Compute Engine virtual machines in production.

Performance analysis and sizing assessments define the following technical and operational requirements:

The database engine operates optimally with 8 vCPUs and requires 64 GB of system RAM to keep its active working set cached in memory (an 8 GB RAM per vCPU ratio).
Allocating additional vCPUs beyond 8 yields no performance gains and incurs unnecessary licensing and compute overhead.
The database must run continuously in production without sudden preemption or unexpected terminations.

Which compute provisioning strategy should you implement to optimize resource allocation and cost?

Deploy the database workloads as Spot VMs using predefined n2-highcpu-16 instances to lower hourly compute rates.

Configure Compute Engine rightsizing recommendations to dynamically upscale and downscale the VM instance resources in real time.

Provision predefined n2-standard-16 instances to satisfy the 64 GB memory requirement and disable idle cores at the operating system level.

Provision N2 virtual machines using custom machine types configured with exactly 8 vCPUs and 64 GB of RAM.

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Alloydb Omni Compute Resource Optimization Ratio

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