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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:
Which compute provisioning strategy should you implement to optimize resource allocation and cost?
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.
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.
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.
StudyCerts
StudyCertsn2-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.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.
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.