You run a nightly genomics pipeline that needs about 16 vCPUs for three hours to finish before the next business day. The workflow engine tolerates worker failures and can restart individual tasks on any healthy VM. The job currently runs on eight on-demand e2-standard-8 instances and uses no autoscaling. Finance has asked you to cut the compute bill for this workload by at least 70 % without lengthening the runtime. What should you do?
Migrate the workload to eight on-demand c2-standard-8 instances to finish faster and receive committed-use discounts.
Replace the existing VMs with eight e2-standard-8 Spot VMs and keep autoscaling disabled.
Containerize the pipeline and deploy it to Cloud Run with a maximum of eight instances.
Create a managed instance group that uses Spot VMs with a custom machine type of 4 vCPUs and 8 GB RAM, and configure the autoscaler to maintain 16 vCPUs at all times.
Spot VMs are billed at discounts of up to 91 % compared with on-demand pricing, but they can be preempted at any time. Using a managed instance group of Spot VMs lets Compute Engine automatically recreate any VM that is preempted, so capacity is quickly restored. By defining a custom machine type with 4 vCPUs and 8 GB RAM and an autoscaler that maintains an aggregate 16 vCPUs, you preserve the required total compute while roughly halving per-VM cost in addition to the Spot discount-easily exceeding the 70 % savings target. Keeping the original e2-standard-8 size on Spot VMs would save money but risks throughput loss during preemption because capacity is not automatically recovered. Cloud Run's maximum request timeout is 60 minutes, so it cannot handle a three-hour HPC batch job, and switching to on-demand C2 instances would increase costs instead of reducing them.
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GCP Associate Cloud Engineer
Planning and implementing a cloud solution
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